Probiotics reduce COVID-19 risk: real-time meta-analysis of 29 studies

@CovidAnalysis, October 2026, Version 31V31 (V1 2021)

Outcome (studies)

Improvement

Relative risk · 95% CI

All studies (29)27%

Mortality (10)59%

Ventilation (3)38%

ICU admission (5)21%

Hospitalization (5)13%

Recovery (14)19%

Cases (9)36%

Viral clearance (4)8%

RCTs (17)30%

RCT mortality (6)41%

Prophylaxis (10)37%

Early (7)34%

Late (12)18%

after exclusions

00.511.5+

← Probiotics
reduce riskIncreased
risk →

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Abstract

Significantly lower risk is seen for mortality, hospitalization, progression, recovery, and cases. 13 studies from 12 independent teams in 9 countries show significant benefit.

Meta-analysis using the most serious outcome reported shows 27% [18‑36%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies. Better results are seen with early treatment.

Results are very robust—in worst case exclusion sensitivity analysis 26 of 29 studies must be excluded before statistical significance is lost.

Outcome (studies)

Improvement

Relative risk · 95% CI

All studies (29)27%

Mortality (10)59%

Ventilation (3)38%

ICU admission (5)21%

Hospitalization (5)13%

Recovery (14)19%

Cases (9)36%

Viral clearance (4)8%

RCTs (17)30%

RCT mortality (6)41%

Prophylaxis (10)37%

Early (7)34%

Late (12)18%

after exclusions

00.511.5+

← Probiotics
reduce riskIncreased
risk →

Probiotic efficacy depends on the specific strains used. Specific microbes may decrease or increase COVID-19 risk1.

No treatment is 100% effective. Protocols combine safe and effective options with individual risk/benefit analysis and monitoring. Other treatments are more effective. Dietary sources may be preferred. The quality of non-prescription supplements varies widely2-4. Many probiotic supplements may not include labeled ingredients5. All data and sources to reproduce this analysis are in the appendix.

Other meta-analyses show significant improvements with probiotics for hospitalization6 and recovery6,7.

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Probiotics for COVID-19

Highlights

Probiotics reduce risk with very high confidence for mortality, hospitalization, recovery, and in pooled analysis, high confidence for cases, and low confidence for progression.
Probiotic efficacy depends on the specific strains used.
Early treatment and prophylaxis are more effective than late treatment.
19th treatment shown effective in March 2021, now with p = 0.00000044 from 29 studies.
Real-time updates and corrections with a consistent protocol for 227 treatments. Outcome specific analysis and combined evidence from all studies including treatment delay, a primary confounding factor.

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c19early.org

October 2026

Probiotics COVID-19 studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Haran (RCT)67%0.33 · 0.01–8.16death0/1741/176 Gutiérre.. (DB RCT)35%0.65 · 0.53–0.80no recov.69/147105/146 Veterini29%0.71 · 0.41–1.22viral time15 (n)15 (n) Navarro-Ló.. (RCT)33%0.67 · 0.45–1.00no recov.14/2413/15 Hassan (RCT)80%0.20 · 0.02–1.65hosp.1/505/50 Kolesnyk (DB RCT)60%0.40 · 0.18–0.90no recov.6/3416/36 Yamamoto (DB RCT)26%0.74 · 0.51–1.07no recov.45 (n)46 (n)PLATEAU

Early treatment34%0.66 · 0.57–0.7690/489140/48434% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

d'Ettorre87%0.13 · 0.01–2.33death0/284/42 Ceccarelli64%0.36 · 0.18–0.68death10/8834/112 Shah (RCT)11%0.89 · 0.75–1.06recov. time30 (n)30 (n)CT¹ Li−12%1.12 · 0.74–1.69no disch.30/12341/188 Zhang14%0.86 · 0.77–0.96hosp. time150 (n)150 (n) Ceccarelli70%0.30 · 0.01–7.02death0/401/29 Ivashkin (RCT)−2%1.02 · 0.26–3.97death4/994/101 Zhang65%0.35 · 0.02–8.30ventilation0/251/30 Saviano (RCT)67%0.33 · 0.01–7.95death0/401/40 Trinchieri78%0.22 · 0.03–1.93death1/213/14 Di Pierro (RCT)62%0.38 · 0.11–1.25death3/258/25 Giancola (DB RCT)15%0.85 · 0.06–12.87death1/271/23

Late treatment18%0.82 · 0.69–0.9849/69698/78418% lower risk

Tau² = 0.02, I² = 26.0%, p = 0.027

Louca8%0.92 · 0.85–0.99casespopulation-based cohort Di Pierro (RCT)98%0.02 · 0.00–0.33cases0/6424/64 Holt30%0.70 · 0.45–1.10cases20/909426/14,318COVIDENCE UK Ahanchian (DB RCT)73%0.27 · 0.03–2.25symp. case1/294/31 Fernánde.. (DB RCT)−2%1.02 · 0.06–16.09death1/981/100 Wischme.. (DB RCT)33%0.67 · 0.38–1.17m/s case16/9124/91PROTECT-EHC Rodrigue.. (DB RCT)9%0.91 · 0.12–6.70cases2/1272/128 Catinean40%0.60 · 0.41–0.88recovery60 (n)60 (n) Di Pierro78%0.22 · 0.07–0.67cases186 (n)101 (n) Sarlin (DB RCT)33%0.67 · 0.11–3.98cases2/4133/414

Prophylaxis37%0.63 · 0.45–0.8842/1,977484/15,30737% lower risk

Tau² = 0.10, I² = 57.7%, p = 0.0063

All studies27%0.73 · 0.64–0.82181/3,162722/16,57527% lower risk

Tau² = 0.02, I² = 45.3%, p < 0.000100.511.52+

¹ CT: study uses combined treatment

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Fig. 1. A. Random-effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix. B. Timeline of results in probiotics studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes, one or more specific outcome, and pooled outcomes in RCTs. Efficacy based on RCTs only was delayed by 9.3 months, compared to using all studies.

Introduction

Fig. 2. SARS-CoV-2 spike protein fibrin binding leads to thromboinflammation and neuropathology, from8.

Immediate treatment recommended

SARS-CoV-2 infection primarily begins in the upper respiratory tract and may progress to the lower respiratory tract, other tissues, and the nervous and cardiovascular systems, which may lead to cytokine storm, pneumonia, ARDS, neurological injury9-25 and cognitive deficits12,17, cardiovascular complications26-32, DNA damage33-36, organ failure, and death. Even mild untreated infections may result in persistent cognitive deficits37—the spike protein binds to fibrin leading to fibrinolysis-resistant blood clots, thromboinflammation, and neuropathology. Minimizing replication as early as possible is recommended.

Many treatments are expected to modulate infection

SARS-CoV-2 infection and replication involves the complex interplay of 500+ host and viral proteins and other factorsA,38-45, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 12,000 compounds may reduce COVID-19 risk46, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.

Other infections

Studies have shown efficacy with probiotics for upper respiratory infections47, respiratory tract infections48, and the common cold49.

Analysis

We analyze all significant controlled studies of Probiotics for COVID-19. Search methods, inclusion criteria, effect extraction criteria (more serious outcomes have priority), all individual study data, PRISMA answers, and statistical methods are detailed in Appendix 1. We present random-effects meta-analysis results for all studies, studies within each treatment stage, individual outcomes, peer-reviewed studies, Randomized Controlled Trials (RCTs), and higher quality studies.

Treatment timing

Fig. 3 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early treatment refers to treatment immediately or soon after symptoms appear, while late treatment refers to more delayed treatment.

Fig. 3. Treatment stages.

Evaluation Notes

Analyzing the data from first principles is always important, and may be especially important for COVID-19. Bias in the design, analysis, and reporting of clinical studies has long been reported, and may be increased for COVID-19 due to politicization. For example, Scott Alexander noted that "if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele."50 Social pressure to report politically acceptable results was very strong. There may therefore be unusually strong bias in design, analysis, and reporting. For example, Prof. Greenland reports that Hayward et al. applied an unjustified and undisclosed extremely tight, null-centered Bayesian prior that biases towards more politically acceptable results52. While the politicization focused on certain treatments, it was not limited to them. For example, media coverage was highly biased against positive results for low-cost treatments53 across the 227 treatments we cover. Studies should be evaluated from first principles, incorporating the treatment delay, treatment regimen, patient population, and other confounding factors. For COVID-19, there is no significant difference in the results of RCTs compared to observational studies, RR 0.97 [0.91‑1.03]54—in both cases bias varies from minimal to extreme, and all studies must be evaluated individually.

US authorities claim only three high-profit drugs from companies with strong US lobbying are beneficial for early treatment (2 repurposed drugs - remdesivir and molnupiravir, and one novel drug - nirmatrelvir)B. COVID-19 involves the interplay of many viral and host proteins and factors, providing over 500 therapeutic targets55. Given the 12,000+ proposed treatments46, existing results for many treatments with overlapping secondary complications and therapeutic targets, and the combination of known safety and pharmocokinetics for certain treatments along with strong mechanistic evidence, the probability of only 3 high-profit drugs from top lobbying companies being beneficial is extremely low. This likely reflects a bias toward evaluation of drugs with strong financial resources and efforts devoted to passing the US approval process, and very limited or non-existing evaluation of low-cost treatments. Indeed, official recommendations varied widely between countries56. Some countries evaluated limited low-cost treatments early and 26 low-cost treatments were approved in one or more countries56.

Contrary to claims found online, we analyze both all studies and higher-quality studies (with evaluation focusing on known issues and bias, and the potential impact on outcomes, rather than just the risk of bias), we analyze specific outcomes and pooled outcomes (with extensive analysis and validation of pooled outcomes), and we do not include preclinical studies or retracted studies in meta-analysis. Pooled outcomes can detect efficacy earlier but do not bias towards efficacy—the inclusion of less appropriate designs such as very late treatment may hide efficacy for appopriate use.

Preclinical Research

2 in vitro studies support the efficacy of probioticss57,58.

Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.

Results

Table 1 summarizes the results for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Fig. 4 plots individual results by treatment stage. Fig. 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, and 15 show forest plots for random-effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, peer reviewed studies, and long COVID.

Table 1. Random-effects meta-analysis for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes. Results show the relative risk with treatment and the 95% confidence interval. * p<0.05 ** p<0.01 *** p<0.001 **** p<0.0001.
	Relative Risk	Studies	Patients

All studies	0.73 [0.64‑0.82]****	29	10K
After exclusions	0.73 [0.64‑0.83]****	26	4,430
Peer-reviewedPeer-reviewed	0.73 [0.65‑0.83]****	26	10K
RCTsRCTs	0.70 [0.60‑0.82]****	17	3,033

Mortality	0.41 [0.26‑0.65]***	10	1,302
VentilationVent.	0.62 [0.21‑1.87]	3	325
ICU admissionICU	0.79 [0.52‑1.20]	5	599
HospitalizationHosp.	0.87 [0.79‑0.95]**	5	890
Recovery	0.81 [0.73‑0.89]****	14	2,166
Cases	0.64 [0.45‑0.93]*	9	10K
Viral	0.92 [0.62‑1.35]	4	732

RCT mortality	0.59 [0.27‑1.27]	6	928
RCT hospitalizationRCT hosp.	0.87 [0.75‑1.02]	4	590

Table 2. Random-effects meta-analysis results by treatment stage. Results show the relative risk with treatment and the 95% confidence interval.treatment and the 95% confidence interval. * p<0.05 ** p<0.01 *** p<0.001 **** p<0.0001.
	Early treatment	Late treatment	Prophylaxis

All studies	0.66 [0.57‑0.76]**0.66** [0.57‑0.76]	0.82 [0.69‑0.98]0.82 [0.69‑0.98]	0.63 [0.45‑0.88]0.63 [0.45‑0.88]
After exclusions	0.65 [0.56‑0.76]**0.65** [0.56‑0.76]	0.84 [0.71‑0.99]0.84 [0.71‑0.99]	0.59 [0.39‑0.89]0.59 [0.39‑0.89]
Peer-reviewedPeer-reviewed	0.66 [0.57‑0.77]**0.66** [0.57‑0.77]	0.82 [0.69‑0.98]0.82 [0.69‑0.98]	0.61 [0.41‑0.90]0.61 [0.41‑0.90]
RCTsRCTs	0.65 [0.56‑0.76]**0.65** [0.56‑0.76]	0.88 [0.74‑1.04]0.88 [0.74‑1.04]	0.51 [0.23‑1.10]0.51 [0.23‑1.10]

Mortality	0.33 [0.01‑8.16]0.33 [0.01‑8.16]	0.40 [0.25‑0.65]*0.40* [0.25‑0.65]	1.02 [0.06‑16.09]1.02 [0.06‑16.09]
VentilationVent.		0.62 [0.21‑1.87]0.62 [0.21‑1.87]
ICU admissionICU		0.79 [0.52‑1.20]0.79 [0.52‑1.20]
HospitalizationHosp.	0.31 [0.09‑1.13]0.31 [0.09‑1.13]	0.87 [0.80‑0.95]0.87 [0.80‑0.95]
Recovery	0.70 [0.62‑0.80]**0.70** [0.62‑0.80]	0.91 [0.84‑0.98]0.91 [0.84‑0.98]	0.68 [0.48‑0.98]0.68 [0.48‑0.98]
Cases			0.64 [0.45‑0.93]0.64 [0.45‑0.93]
Viral	0.66 [0.40‑1.10]0.66 [0.40‑1.10]	1.06 [0.66‑1.70]1.06 [0.66‑1.70]

RCT mortality	0.33 [0.01‑8.16]0.33 [0.01‑8.16]	0.58 [0.25‑1.33]0.58 [0.25‑1.33]	1.02 [0.06‑16.09]1.02 [0.06‑16.09]
RCT hospitalizationRCT hosp.	0.31 [0.09‑1.13]0.31 [0.09‑1.13]	0.89 [0.76‑1.04]0.89 [0.76‑1.04]

Fig. 4. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random-effects meta-analysis.

c19early.org

October 2026

Probiotics COVID-19 studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Early treatment34%0.66 · 0.57–0.7690/489140/48434% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

Late treatment18%0.82 · 0.69–0.9849/69698/78418% lower risk

Tau² = 0.02, I² = 26.0%, p = 0.027

Prophylaxis37%0.63 · 0.45–0.8842/1,977484/15,30737% lower risk

Tau² = 0.10, I² = 57.7%, p = 0.0063

All studies27%0.73 · 0.64–0.82181/3,162722/16,57527% lower risk

Tau² = 0.02, I² = 45.3%, p < 0.000100.511.52+

¹ CT: study uses combined treatment

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Fig. 5. Random-effects meta-analysis for all studies. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.

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October 2026

Probiotics COVID-19 mortality results

StudyImprovementRR · 95% CITreatmentControlRelative Risk

Haran (RCT)67%0.33 · 0.01–8.160/1741/176

Early treatment67%0.33 · 0.01–8.160/1741/17667% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.51

d'Ettorre87%0.13 · 0.01–2.330/284/42 Ceccarelli64%0.36 · 0.18–0.6810/8834/112 Ceccarelli70%0.30 · 0.01–7.020/401/29 Ivashkin (RCT)−2%1.02 · 0.26–3.974/994/101 Saviano (RCT)67%0.33 · 0.01–7.950/401/40 Trinchieri78%0.22 · 0.03–1.931/213/14 Di Pierro (RCT)62%0.38 · 0.11–1.253/258/25 Giancola (DB RCT)15%0.85 · 0.06–12.871/271/23

Late treatment60%0.40 · 0.25–0.6519/36856/38660% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.00022

Fernánde.. (DB RCT)−2%1.02 · 0.06–16.091/981/100

Prophylaxis−2%1.02 · 0.06–16.091/981/1002% higher risk

Tau² = 0.00, I² = 0.0%, p = 0.99

All studies59%0.41 · 0.26–0.6520/64058/66259% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.0002200.511.52+

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Fig. 6. Random-effects meta-analysis for mortality results.

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October 2026

Probiotics COVID-19 mechanical ventilation results

StudyImprovementRR · 95% CITreatmentControlRelative Risk

d'Ettorre77%0.23 · 0.01–4.630/282/42 Ivashkin (RCT)18%0.82 · 0.23–2.954/995/101 Zhang65%0.35 · 0.02–8.300/251/30

Late treatment38%0.62 · 0.21–1.874/1528/17338% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.4

All studies38%0.62 · 0.21–1.874/1528/17338% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.400.511.52+

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Fig. 7. Random-effects meta-analysis for ventilation.

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October 2026

Probiotics COVID-19 ICU results

StudyImprovementRR · 95% CITreatmentControlRelative Risk

Ceccarelli15%0.85 · 0.48–1.5016/8824/112 Ceccarelli82%0.18 · 0.02–1.541/404/29 Ivashkin (RCT)27%0.73 · 0.24–2.225/997/101 Saviano (RCT)86%0.14 · 0.01–2.680/403/40 Di Pierro (RCT)0%1.00 · 0.45–2.248/258/25

Late treatment21%0.79 · 0.52–1.2030/29246/30721% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.27

All studies21%0.79 · 0.52–1.2030/29246/30721% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.2700.511.52+

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Fig. 8. Random-effects meta-analysis for ICU admission.

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October 2026

Probiotics COVID-19 hospitalization results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Haran (RCT)60%0.40 · 0.08–2.06hosp.2/1745/176 Hassan (RCT)80%0.20 · 0.02–1.65hosp.1/505/50

Early treatment69%0.31 · 0.09–1.133/22410/22669% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.075

Shah (RCT)11%0.89 · 0.76–1.05hosp. time30 (n)30 (n)CT¹ Zhang14%0.86 · 0.77–0.96hosp. time150 (n)150 (n) Saviano (RCT)26%0.74 · 0.30–1.83hosp. time40 (n)40 (n)

Late treatment13%0.87 · 0.80–0.95220 (n)220 (n)13% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.003

All studies13%0.87 · 0.79–0.953/44410/44613% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.00200.511.52+

¹ CT: study uses combined treatment

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Fig. 9. Random-effects meta-analysis for hospitalization.

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October 2026

Probiotics COVID-19 progression results

StudyImprovementRR · 95% CITreatmentControlRelative Risk

d'Ettorre88%0.12 · 0.02–0.6128 (n)42 (n)

Late treatment88%0.12 · 0.02–0.6128 (n)42 (n)88% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.011

All studies88%0.12 · 0.02–0.6128 (n)42 (n)88% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.01100.511.52+

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Fig. 10. Random-effects meta-analysis for progression.

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October 2026

Probiotics COVID-19 recovery results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Haran (RCT)20%0.80 · 0.61–1.04recov. time169 (n)172 (n) Gutiérre.. (DB RCT)35%0.65 · 0.53–0.80no recov.69/147105/146 Navarro-Ló.. (RCT)33%0.67 · 0.45–1.00no recov.14/2413/15 Hassan (RCT)18%0.82 · 0.56–1.21no recov.23/5028/50 Kolesnyk (DB RCT)60%0.40 · 0.18–0.90no recov.6/3416/36 Yamamoto (DB RCT)26%0.74 · 0.51–1.07no recov.45 (n)46 (n)PLATEAU

Early treatment30%0.70 · 0.62–0.80112/469162/46530% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

Shah (RCT)11%0.89 · 0.75–1.06recov. time30 (n)30 (n)CT¹ Li−12%1.12 · 0.74–1.69no disch.30/12341/188 Zhang14%0.86 · 0.75–0.98recov. time150 (n)150 (n) Ivashkin (RCT)5%0.95 · 0.84–1.08recov. time99 (n)101 (n) Giancola (DB RCT)33%0.67 · 0.30–1.48no recov.22 (n)20 (n)

Late treatment9%0.91 · 0.84–0.9830/42441/4899% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.015

Fernánde.. (DB RCT)−38%1.38 · 0.48–3.97recov. time10 (n)7 (n) Wischme.. (DB RCT)27%0.73 · 0.37–1.45recov. time91 (n)91 (n)PROTECT-EHC Catinean40%0.60 · 0.41–0.88recovery60 (n)60 (n)

Prophylaxis32%0.68 · 0.48–0.98161 (n)158 (n)32% lower risk

Tau² = 0.01, I² = 9.4%, p = 0.035

All studies19%0.81 · 0.73–0.89142/1,054203/1,11219% lower risk

Tau² = 0.01, I² = 39.9%, p < 0.000100.511.52+

¹ CT: study uses combined treatment

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Fig. 11. Random-effects meta-analysis for recovery.

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October 2026

Probiotics COVID-19 case results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Louca8%0.92 · 0.85–0.99casespopulation-based cohort Di Pierro (RCT)98%0.02 · 0.00–0.33cases0/6424/64 Holt30%0.70 · 0.45–1.10cases20/909426/14,318COVIDENCE UK Ahanchian (DB RCT)73%0.27 · 0.03–2.25symp. case1/294/31 Fernánde.. (DB RCT)−2%1.02 · 0.37–2.80symp. case7/987/100 Wischme.. (DB RCT)38%0.62 · 0.41–0.93symp. case24/9139/91PROTECT-EHC Rodrigue.. (DB RCT)9%0.91 · 0.12–6.70cases2/1272/128 Di Pierro78%0.22 · 0.07–0.67cases186 (n)101 (n) Sarlin (DB RCT)33%0.67 · 0.11–3.98cases2/4133/414

Prophylaxis36%0.64 · 0.45–0.9356/1,917505/15,24736% lower risk

Tau² = 0.11, I² = 58.4%, p = 0.017

All studies36%0.64 · 0.45–0.9356/1,917505/15,24736% lower risk

Tau² = 0.11, I² = 58.4%, p = 0.01700.511.52+

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Fig. 12. Random-effects meta-analysis for cases.

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Probiotics COVID-19 viral clearance results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Veterini29%0.71 · 0.41–1.22viral time15 (n)15 (n) Yamamoto (DB RCT)62%0.38 · 0.09–1.73viral+45 (n)46 (n)PLATEAU

Early treatment34%0.66 · 0.40–1.1060 (n)61 (n)34% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.11

Li−35%1.35 · 1.13–1.62viral time123 (n)188 (n) Zhang17%0.83 · 0.75–0.93viral time150 (n)150 (n)

Late treatment−6%1.06 · 0.66–1.70273 (n)338 (n)6% higher risk

Tau² = 0.11, I² = 95.1%, p = 0.83

All studies8%0.92 · 0.62–1.35333 (n)399 (n)8% lower risk

Tau² = 0.10, I² = 86.9%, p = 0.6600.511.52+

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Fig. 13. Random-effects meta-analysis for viral clearance.

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Probiotics COVID-19 peer reviewed studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Gutiérre.. (DB RCT)35%0.65 · 0.53–0.80no recov.69/147105/146 Veterini29%0.71 · 0.41–1.22viral time15 (n)15 (n) Navarro-Ló.. (RCT)33%0.67 · 0.45–1.00no recov.14/2413/15 Kolesnyk (DB RCT)60%0.40 · 0.18–0.90no recov.6/3416/36 Yamamoto (DB RCT)26%0.74 · 0.51–1.07no recov.45 (n)46 (n)PLATEAU

Early treatment34%0.66 · 0.57–0.7789/265134/25834% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

Late treatment18%0.82 · 0.69–0.9849/69698/78418% lower risk

Tau² = 0.02, I² = 26.0%, p = 0.027

Louca8%0.92 · 0.85–0.99casespopulation-based cohort Di Pierro (RCT)98%0.02 · 0.00–0.33cases0/6424/64 Holt30%0.70 · 0.45–1.10cases20/909426/14,318COVIDENCE UK Ahanchian (DB RCT)73%0.27 · 0.03–2.25symp. case1/294/31 Fernánde.. (DB RCT)−2%1.02 · 0.06–16.09death1/981/100 Rodrigue.. (DB RCT)9%0.91 · 0.12–6.70cases2/1272/128 Catinean40%0.60 · 0.41–0.88recovery60 (n)60 (n) Di Pierro78%0.22 · 0.07–0.67cases186 (n)101 (n) Sarlin (DB RCT)33%0.67 · 0.11–3.98cases2/4133/414

Prophylaxis39%0.61 · 0.41–0.9026/1,886460/15,21639% lower risk

Tau² = 0.12, I² = 60.6%, p = 0.012

All studies27%0.73 · 0.65–0.83164/2,847692/16,25827% lower risk

Tau² = 0.03, I² = 48.4%, p < 0.000100.511.52+

¹ CT: study uses combined treatment

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Fig. 14. Random-effects meta-analysis for peer reviewed studies. Zeraatkar et al. analyze 356 COVID-19 trials, finding no significant evidence that preprint results are inconsistent with peer-reviewed studies. They also show extremely long peer-review delays, with a median of 6 months to journal publication. A six month delay was equivalent to around 1.5 million deaths during the first two years of the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Davidson et al. also showed no important difference between meta-analysis results of preprints and peer-reviewed publications for COVID-19, based on 37 meta-analyses including 114 trials. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details. Analysis validating pooled outcomes for COVID-19 can be found below.

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Probiotics COVID-19 long COVID results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Kolesnyk (DB RCT)68%0.32 · 0.13–0.78PASC5/3416/35

Early treatment68%0.32 · 0.13–0.785/3416/3568% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.012

All studies68%0.32 · 0.13–0.785/3416/3568% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.01200.511.52+

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Fig. 15. Random-effects meta-analysis for long COVID. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details. Analysis validating pooled outcomes for COVID-19 can be found below.

Randomized Controlled Trials (RCTs)

Fig. 16 shows a comparison of results for RCTs and observational studies. Random-effects meta-analysis of RCTs shows 30% improvement, compared to 24% for other studies. Fig. 17, 18, and 19 show forest plots for random-effects meta-analysis of all Randomized Controlled Trials, RCT mortality results, and RCT hospitalization results. RCT results are included in Table 1 and Table 2.

Fig. 16. Results for RCTs and observational studies.

RCTs have many potential biases

RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases61, and analysis of double-blind RCTs has identified extreme levels of bias62. For COVID-19, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, reporting, and the potential for fraud. All of these biases have been observed with COVID-19 RCTs. There is no guarantee that a specific RCT provides a higher level of evidence.

Conflicts of interest for COVID-19 RCTs

RCTs are expensive and many RCTs are funded by pharmaceutical companies or other organizations with conflicts of interest, for example governments that previously denied treatment with the study drug. For COVID-19, this creates an incentive to show efficacy for patented commercial products, and an incentive to show a lack of efficacy for inexpensive treatments. The bias is expected to be significant, for example Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that trials funded by for-profit organizations were 5 times more likely to recommend the experimental drug compared with those funded by nonprofit organizations. Bekelman et al. and Lundh et al. show that industry-sponsored studies are more likely to be favorable. For COVID-19, some major philanthropic organizations are largely funded by investments with extreme conflicts of interest for and against specific COVID-19 interventions.

RCTs for novel acute diseases requiring rapid treatment

High quality RCTs for novel acute diseases are more challenging, with increased ethical issues due to the urgency of treatment, increased risk due to enrollment delays, and more difficult design with a rapidly evolving evidence base. For COVID-19, the most common site of initial infection is the upper respiratory tract. Immediate treatment is likely to be most successful and may prevent or slow progression to other parts of the body. For a non-prophylaxis RCT, it makes sense to provide treatment in advance and instruct patients to use it immediately on symptoms, just as some governments have done by providing medication kits in advance. Unfortunately, no RCTs have been done in this way. Every treatment RCT to date involves delayed treatment. Among the 227 treatments we have analyzed, 67% of RCTs involve very late treatment 5+ days after onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of early treatments. They may more accurately represent results for treatments that require visiting a medical facility, e.g., those requiring intravenous administration.

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COVID-19 RCT vs. observational results from 6,000+ studies

RR · CI

Low-cost0.98 · 0.90–1.06 High-profit0.93 · 0.85–1.02

All treatments0.97 · 0.91–1.03

00.511.52

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Fig. 20. For COVID-19, observational study results do not systematically differ from RCTs, RR 0.97 [0.91‑1.03] across 227 treatments66.

Observational studies have been shown to be reliable

Evidence shows that observational studies can also provide reliable results. Concato et al. found that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. Anglemyer et al. analyzed reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates.

We performed a similar analysis across the 227 treatments we cover, showing no significant difference in the results of RCTs compared to observational studies, RR 0.97 [0.91‑1.03]54. Similar results are found for all low-cost treatments, RR 0.98 [0.90‑1.06]. High-cost treatments show a non-significant trend towards RCTs showing greater efficacy, RR 0.93 [0.85‑1.02]. Details can be found in the supplementary data.

Lee et al. showed that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or remote survey bias may have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see70,71.

RCTs may be less reliable

Concato et al. report a paradoxical finding—RCT results had higher variability, and only RCTs were found to sometimes report significant results the opposite of the overall result. The same trend is seen for the most popular (most politicized) COVID-19 treatments—considering all statistically significant results reported in studies, RCTs are slightly more likely to report a result in the opposite direction. In other words, for these COVID-19 treatments and for the topics covered by Concato et al., assuming causality from a single study is more likely to result in an incorrect conclusion for RCTs.

Increased risk of inconsistent results for RCTs suggests higher prevalence of bias, which may arise due to many issues including design bias, conflicts of interest, treatment differences by physicians aware of allocation, attrition bias, ascertainment bias, randomization failures, errors, or fraud.

Using all studies identifies efficacy 8+ months faster (9+ months for low-cost treatments)

Currently, 59 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 54% have been confirmed in RCTs, with a mean delay of 7.8 months (62% with 8.7 months delay for low-cost treatments). The remaining treatments either have no RCTs, or the point estimate is consistent.

All studies must be carefully analyzed

Neither observational studies nor RCTs prove causation—any study can be flawed or fraudulent. We need much more, for example a combination of results from many independent teams, detailed understanding of each study, knowledge of conflicts/team reliability, dose-response relationships, delay-response relationships, logical results across outcomes, or details consistent with preclinical expectations.

All studies must be evaluated individually. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For off-patent medications, very high conflict of interest trials may be more likely to be RCTs, and more likely to be large trials that dominate meta-analyses.

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Probiotics COVID-19 Randomized Controlled Trials

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Haran (RCT)67%0.33 · 0.01–8.16death0/1741/176 Gutiérre.. (DB RCT)35%0.65 · 0.53–0.80no recov.69/147105/146 Navarro-Ló.. (RCT)33%0.67 · 0.45–1.00no recov.14/2413/15 Hassan (RCT)80%0.20 · 0.02–1.65hosp.1/505/50 Kolesnyk (DB RCT)60%0.40 · 0.18–0.90no recov.6/3416/36 Yamamoto (DB RCT)26%0.74 · 0.51–1.07no recov.45 (n)46 (n)PLATEAU

Early treatment35%0.65 · 0.56–0.7690/474140/46935% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

Shah (RCT)11%0.89 · 0.75–1.06recov. time30 (n)30 (n)CT¹ Ivashkin (RCT)−2%1.02 · 0.26–3.97death4/994/101 Saviano (RCT)67%0.33 · 0.01–7.95death0/401/40 Di Pierro (RCT)62%0.38 · 0.11–1.25death3/258/25 Giancola (DB RCT)15%0.85 · 0.06–12.87death1/271/23

Late treatment12%0.88 · 0.74–1.048/22114/21912% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.12

Di Pierro (RCT)98%0.02 · 0.00–0.33cases0/6424/64 Ahanchian (DB RCT)73%0.27 · 0.03–2.25symp. case1/294/31 Fernánde.. (DB RCT)−2%1.02 · 0.06–16.09death1/981/100 Wischme.. (DB RCT)33%0.67 · 0.38–1.17m/s case16/9124/91PROTECT-EHC Rodrigue.. (DB RCT)9%0.91 · 0.12–6.70cases2/1272/128 Sarlin (DB RCT)33%0.67 · 0.11–3.98cases2/4133/414

Prophylaxis49%0.51 · 0.23–1.1022/82258/82849% lower risk

Tau² = 0.25, I² = 25.4%, p = 0.086

All studies30%0.70 · 0.60–0.82120/1,517212/1,51630% lower risk

Tau² = 0.02, I² = 17.6%, p < 0.000100.511.52+

¹ CT: study uses combined treatment

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Fig. 17. Random-effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.

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Probiotics COVID-19 RCT mortality results

StudyImprovementRR · 95% CITreatmentControlRelative Risk

Haran (RCT)67%0.33 · 0.01–8.160/1741/176

Early treatment67%0.33 · 0.01–8.160/1741/17667% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.51

Ivashkin (RCT)−2%1.02 · 0.26–3.974/994/101 Saviano (RCT)67%0.33 · 0.01–7.950/401/40 Di Pierro (RCT)62%0.38 · 0.11–1.253/258/25 Giancola (DB RCT)15%0.85 · 0.06–12.871/271/23

Late treatment42%0.58 · 0.25–1.338/19114/18942% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.2

Fernánde.. (DB RCT)−2%1.02 · 0.06–16.091/981/100

Prophylaxis−2%1.02 · 0.06–16.091/981/1002% higher risk

Tau² = 0.00, I² = 0.0%, p = 0.99

All studies41%0.59 · 0.27–1.279/46316/46541% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.1800.511.52+

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Fig. 18. Random-effects meta-analysis for RCT mortality results.

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Probiotics COVID-19 RCT hospitalization results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Haran (RCT)60%0.40 · 0.08–2.06hosp.2/1745/176 Hassan (RCT)80%0.20 · 0.02–1.65hosp.1/505/50

Early treatment69%0.31 · 0.09–1.133/22410/22669% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.075

Shah (RCT)11%0.89 · 0.76–1.05hosp. time30 (n)30 (n)CT¹ Saviano (RCT)26%0.74 · 0.30–1.83hosp. time40 (n)40 (n)

Late treatment11%0.89 · 0.76–1.0470 (n)70 (n)11% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.15

All studies13%0.87 · 0.75–1.023/29410/29613% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.09700.511.52+

¹ CT: study uses combined treatment

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Fig. 19. Random-effects meta-analysis for RCT hospitalization results.

Exclusions

To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which can be easily influenced by potential bias, may ignore or underemphasize serious issues not captured in the checklists, and may overemphasize issues unlikely to alter outcomes in specific cases (for example certain specifics of randomization with a very large effect size and well-matched baseline characteristics).

The studies excluded are as below. Fig. 21 shows a forest plot for random-effects meta-analysis of all studies after exclusions.

Di Pierro, unadjusted differences between groups.

Holt, significant unadjusted confounding possible.

Veterini, the observered difference in duration could be caused by the baseline difference in Ct values.

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Probiotics COVID-19 studies after exclusions

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Early treatment35%0.65 · 0.56–0.7690/474140/46935% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

d'Ettorre87%0.13 · 0.01–2.33death0/284/42 Ceccarelli64%0.36 · 0.18–0.68death10/8834/112 Shah (RCT)11%0.89 · 0.75–1.06recov. time30 (n)30 (n)CT¹ Li−12%1.12 · 0.74–1.69no disch.30/12341/188 Zhang14%0.86 · 0.77–0.96hosp. time150 (n)150 (n) Ceccarelli70%0.30 · 0.01–7.02death0/401/29 Ivashkin (RCT)−2%1.02 · 0.26–3.97death4/994/101 Zhang65%0.35 · 0.02–8.30ventilation0/251/30 Saviano (RCT)67%0.33 · 0.01–7.95death0/401/40 Trinchieri78%0.22 · 0.03–1.93death1/213/14 Giancola (DB RCT)15%0.85 · 0.06–12.87death1/271/23

Late treatment16%0.84 · 0.71–0.9946/67190/75916% lower risk

Tau² = 0.01, I² = 23.4%, p = 0.038

Louca8%0.92 · 0.85–0.99casespopulation-based cohort Di Pierro (RCT)98%0.02 · 0.00–0.33cases0/6424/64 Ahanchian (DB RCT)73%0.27 · 0.03–2.25symp. case1/294/31 Fernánde.. (DB RCT)−2%1.02 · 0.06–16.09death1/981/100 Wischme.. (DB RCT)33%0.67 · 0.38–1.17m/s case16/9124/91PROTECT-EHC Rodrigue.. (DB RCT)9%0.91 · 0.12–6.70cases2/1272/128 Catinean40%0.60 · 0.41–0.88recovery60 (n)60 (n) Di Pierro78%0.22 · 0.07–0.67cases186 (n)101 (n) Sarlin (DB RCT)33%0.67 · 0.11–3.98cases2/4133/414

Prophylaxis41%0.59 · 0.39–0.8922/1,06858/98941% lower risk

Tau² = 0.14, I² = 60.4%, p = 0.013

All studies27%0.73 · 0.64–0.83158/2,213288/2,21727% lower risk

Tau² = 0.03, I² = 48.3%, p < 0.000100.511.52+

¹ CT: study uses combined treatment

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Fig. 21. Random-effects meta-analysis for all studies after exclusions. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.

Media Bias

Low-cost treatments were subject to bias and censorship during the pandemic. Scientific bias is seen in the design, analysis, presentation, and selective reporting of studies, which often favored negative results. A similar bias is seen in the media coverage for low-cost treatments. While broadly seen, bias was particularly notable for ivermectin and hydroxychloroquine, e.g., Scott Alexander noted that "if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele."50.

We analyze media coverage for the 227 treatments we cover using Altmetric75 and PlumX Metrics76. Altmetric covers ~14,000 news outlets77 and PlumX covers ~55,000 news sources78. The two services are complementary with coverage of different news sources and different methods for identifying articles not containing a DOI. We consider studies to have received significant media coverage if they were covered by at least 100 articles in either Altmetric of PlumX78,79. The most widespread study was covered by 1,047 outlets. Studies covered by major syndicated sources shows hundreds of news articles, while studies covered by few outlets typically appear in niche medical news sources or press releases. Fig. 22 and 23 show bias towards negative results for low-cost treatments, in contrast to the opposite bias for high-profit treatments.

The media bias may result in widespread incorrect perceptions on the relative efficacy of high-profit and low-cost treatments. The impact is significant—increased cost limits the use of high-profit treatments and treatment equity. High-profit treatments were also more difficult to access, especially for earlier treatment which improves efficacy and minimizes community transmission.

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Media coverage of low-cost COVID‑19 treatments

Media covered only 0.8% of studies showing significantly lower COVID-19 risk

Only 5 positive studies were covered

2020 2021 2022 2023

0.8%

one each: budesonide, cannabidiol, fluvoxamine, HCQ, vitamin D

34 negative studies were covered

2020 2021 2022 2023

HCQ 12, ivermectin 6, lopinavir/ritonavir 4, azithromycin 3, vitamin D 3, fluvoxamine 2, one each: ibuprofen, metformin, vitamin C, zinc

5.4×

Negative bias for low-cost treatments

negative studies were 5.4 times more likely to be covered

Fig. 22. Mainstream media coverage was biased against positive results for low-cost treatments. When restricting to RCTs, the bias was even larger, with negative RCTs 6.9× more likely to be covered.

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Media coverage of high-profit COVID‑19 treatments

Media was 3.8× more likely to cover positive studies

19 positive studies were covered

2020 2021 2022 2023

11%

paxlovid 5, remdesivir 3, casirivimab/imdevimab 2, convalescent plasma 2, molnupiravir 2, one each: antiandrogens, peginterferon lambda, sargramostim, tixagevimab/cilgavimab, tocilizumab

Only 6 negative studies were covered

2020 2021 2022 2023

remdesivir 3, one each: bamlanivimab/etesevimab, bebtelovimab, sotrovimab

3.8×

Positive bias for high-profit treatments

positive studies were 3.8 times more likely to be covered

Fig. 23. For high-profit treatments, mainstream media coverage was biased towards positive results. When restricting to RCTs, the bias was even larger, with positive RCTs 12.0× more likely to be covered.

A combination of factors may have led to the very low media coverage of low-cost treatments:

•

Politicization

led to a media environment where coverage was often framed to support a political narrative rather than to provide objective scientific information. As Scott Alexander said:

"if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele."

There was strong social pressure to discredit low-cost treatments.

•

Censorship

of information conflicting with selected authorities. For example, individuals and organizations presenting conflicting science were often shadowbanned or completely banned on Twitter and YouTube if the science they were reporting conflicted with the opinions of specific authorities selected by those companies, including when the authorities had not even reviewed the scientific results in question.

•

FDA requires "no adequate, approved, and available alternatives"

in order to grant an EUA for novel high-profit interventions, creating an incentive for authorities to ignore or downplay existing low-cost treatments.

•

Regulatory capture

biases authorities towards high-profit interventions.

•

Authorities ignored most evidence for low-cost treatments

, for example the NIH references only 2% of studies in delayed, rarely-updated, biased commentaries with no quantitive analysis.

•

Media coverage of science is often not very accurate

, e.g., misunderstanding confounding issues. For example the media widely considered the RECOVERY HCQ RCT to be conclusive on efficacy, but very late treatment of late-stage patients (mostly on oxygen already) with an excessive toxic dose (shown dangerous in a dose comparison RCT) provides no information on the recommended early/prophylactic treatment. With difficulting in understanding basic confounders like treatment delay and dose, the media may favor deferring to authorities. Many studies for low-cost treatments require greater expertise to analyze.

•

Substantial funding from pharmaceutical advertising

biases editorial decisions towards high-profit interventions.

•

PR power

- companies/teams with strong PR presence are favored in the media, which correlates with high-profit and high conflict of interest studies.

•

The media was very negative in general

, inflating risk, and elevating fear and anxiety. A negative bias may improve ratings and revenue, increasing motivation to continue watching coverage.

Authority Delay

26 low-cost treatments were approved in one or more countries, yet many countries approved no low-cost treatments. The countries that did adopt low-cost treatments analyzed the evidence early and made timely approvals. With few exceptions, authorities did not change their initial views, regardless of how much evidence accumulated showing either efficacy or harm. Why?

The harms of smoking here hidden for 25 yearsC. Authorities did not analyze the data in real-time, failing to act when harm was known. Widespread acknowledgement of harm came only after attempts by two new surgeon generals, along with pressure from health advocates and a new president, and a review of 7,000 studies.

Similarly for COVID-19, most authorities and experts did not proactively analyze data in real-time. This guarantees delayed recognition of efficacy or harm, by which time moral, legal, career, and reputational liabilities strongly disincentivize any admission of error. Claims of no efficacy (for effective treatments) or safety (for harmful treatments) were often made prior to strong data being available. Correction would require admitting to errors that increased mortality, which is unlikely with the same generation of officials.

Analysis of potential treatments was rarely done, and when done these were typically minimal efforts. For example, NIH reviews were highly delayed, cover only a tiny fraction of treatments, reference only 2% of studies for the treatments covered, and include no quantitative analysis. They appear as rarely updated side projects from external panels implicitly tasked with justifying prior failures. As with smoking, the thousands of studies could (and should) have been analyzed and acted on in real-time.

A key structural improvement, applicable to all current and future diseases, is for authorities to implement real-time proactive analysis of clinical evidence. This does not remove all bias, but does make it possible to act on evidence, whereas delayed action may be unlikely due to moral, legal, career, and reputational liabilities.

c19early.org

Delayed public health acknowledgments

Official acknowledgment of efficacy or harm is often delayed—legal, career, and status risks disincentivize admission of error.

	Evidence	Official Acknowledgment	Approx. Delay
Citrus Fruit (vitamin C) for Scurvy (effectiveness)	1747: James Lind conducted one of the first-ever controlled clinical trials, proving that oranges and lemons cured scurvy in sailors.	1795: The British Royal Navy finally made a daily ration of lemon juice a standard issue for all its sailors, effectively eliminating the disease.	48 years
Handwashing (lower mortality)	1847: Dr. Ignaz Semmelweis provided conclusive proof that having doctors wash their hands with a chlorine solution before delivering babies reduced maternal mortality rates from over 18% to around 1%.	~1870s: Semmelweis's findings were rejected and he was ridiculed. His work was only validated decades later (after his death).	~20+ years
Helicobacter pylori (bacteria causes ulcers)	1982-1984: Marshall and Warren discovered that Helicobacter pylori bacteria causes ulcers, confirmed via direct exposure. Officials maintained that ulcers were caused by stress and spicy food.	1994: The US NIH released a consensus statement officially recommending antibiotics as the standard treatment for peptic ulcers, overturning decades of acid-suppression therapy.	~12 years
Asbestos (causes asbestosis & cancer)	1924: The British Medical Journal published the first case study of a death from "asbestosis." By 1918, U.S. insurance companies had stopped selling life insurance to asbestos workers.	1971 (US): The Occupational Safety and Health Administration (OSHA) was formed and began regulating asbestos as a carcinogen, setting the first federal workplace safety standards for it.	~47 years
Leaded Gasoline (neurotoxicity)	~1924: Dangers of low-level lead exposure were known. Experts like Alice Hamilton warned the U.S. Surgeon General that adding lead to gasoline would cause widespread public poisoning.	1973 (US): The Environmental Protection Agency (EPA) ordered the first phasedown of lead in gasoline, following the Clean Air Act of 1970. A full ban for on-road vehicles took effect in 1996.	~49 years
Harms of Smoking (causes lung cancer)	1939: Franz Müller (Germany) published the first case-control epidemiological study strongly linking tobacco smoking to lung cancer. This was followed by major U.S. & U.K. studies in the 1950s.	1964 (US): The U.S. Surgeon General's report, "Smoking and Health," was released. It was the first U.S. government report to definitively link smoking to lung cancer and heart disease.	25 years

Heterogeneity

Heterogeneity in COVID-19 studies arises from many factors including:

Treatment delay

The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours80,81. Baloxavir marboxil studies for influenza also show that treatment delay is critical — Ikematsu et al. report an 86% reduction in cases for post-exposure prophylaxis, Hayden et al. show a 33 hour reduction in the time to alleviation of symptoms for treatment within 24 hours and a reduction of 13 hours for treatment within 24-48 hours, and Kumar et al. report only 2.5 hours improvement for inpatient treatment.

Table 3. Studies of baloxavir marboxil for influenza show that early treatment is more effective.
Treatment delay	Result
Post-exposure prophylaxis	86% fewer cases82
<24 hours	-33 hours symptoms83
24-48 hours	-13 hours symptoms83
Inpatients	-2.5 hours to improvement84

Fig. 24 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 227 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.

Fig. 24. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 227 treatments.

Patient demographics

Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results, for example as in López-Medina et al.

SARS-CoV-2 variants

Efficacy may depend critically on the distribution of SARS-CoV-2 variants encountered by patients. Risk varies significantly across variants86, for example the Gamma variant shows significantly different characteristics87-90. Different mechanisms of action may be more or less effective depending on variants, for example the degree to which TMPRSS2 contributes to viral entry can differ across variants91,92.

Treatment regimen

Effectiveness may depend strongly on the dosage and treatment regimen.

Medication quality

The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. Williams et al. analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. Xu et al. analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer. Non-prescription supplements may show very wide variations in quality2,3.

Other treatments

The use of other treatments may significantly affect outcomes, including supplements, other medications, or other interventions such as prone positioning. Treatments may be synergistic95-119, therefore efficacy may depend strongly on combined treatments.

Effect measured

Across all studies there is a strong association between different outcomes, for example improved recovery is strongly associated with lower mortality. However, efficacy may differ depending on the effect measured, for example a treatment may be more effective against secondary complications and have minimal effect on viral clearance.

Meta-analysis

The distribution of studies will alter the outcome of a meta-analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though early treatment is very effective. All meta-analyses combine heterogeneous studies, varying in population, variants, and potentially all factors above, and therefore may obscure efficacy by including studies where treatment is less effective. Generally, we expect the estimated effect size from meta-analysis to be less than that for the optimal case. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations. While we present results for all studies, we also present treatment time and individual outcome analyses, which may be more informative for specific use cases.

Pooled Effects

Pooled effects are no longer required to show efficacy as of March 2021

This section validates the use of pooled effects for COVID-19, which enables earlier detection of efficacy, however pooled effects are no longer required for probiotics as of March 2021. Efficacy is now known based on specific outcomes.

Combining studies is required

For COVID-19, delay in clinical results translates into additional death and morbidity, as well as additional economic and societal damage. Combining the results of studies reporting different outcomes is required. There may be no mortality in a trial with low-risk patients, however a reduction in severity or improved viral clearance may translate into lower mortality in a high-risk population. Different studies may report lower severity, improved recovery, and lower mortality, and the significance may be very high when combining the results. "The studies reported different outcomes" is not a good reason for disregarding results. Pooling the results of studies reporting different outcomes allows us to use more of the available information. Logically we should, and do, use additional information when evaluating treatments—for example dose-response and treatment delay-response relationships provide additional evidence of efficacy that is considered when reviewing the evidence for a treatment.

Specific outcome and pooled analyses

We present both specific outcome and pooled analyses. In order to combine the results of studies reporting different outcomes we use the most serious outcome reported in each study, based on the thesis that improvement in the most serious outcome provides comparable measures of efficacy for a treatment. A critical advantage of this approach is simplicity and transparency. There are many other ways to combine evidence for different outcomes, along with additional evidence such as dose-response relationships, however these increase complexity.

Ethical and practical issues limit high-risk trials

Trials with high-risk patients may be restricted due to ethics for treatments that are known or expected to be effective, and they increase difficulty for recruiting. Using less severe outcomes as a proxy for more serious outcomes allows faster and safer collection of evidence.

Validating pooled outcome analysis for COVID-19

For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, which follows from a reduction in PCR positivity. We can directly test this for COVID-19.

Analysis of the the association between different outcomes across studies from all 227 treatments we cover confirms the validity of pooled outcome analysis for COVID-19. Fig. 25 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.0000000001). Similarly, Fig. 26 shows that improved recovery is very strongly associated with lower mortality (p < 0.0000000001). Considering the extremes, Singh et al. show an association between viral clearance and hospitalization or death, with p = 0.003 after excluding one large outlier from a mutagenic treatment, and based on 44 RCTs including 52,384 patients. Fig. 27 shows that improved viral clearance is strongly associated with fewer serious outcomes. The association is very similar to Singh et al., with higher confidence due to the larger number of studies. As with Singh et al., the confidence increases when excluding the outlier treatment, from p = 0.0000000074 to p = 0.00000000015.

Fig. 25. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.

Fig. 26. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.

Fig. 25. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.

Pooled outcomes identify efficacy 5 months faster (7 months for RCTs)

Currently, 59 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 85% of these have been confirmed with one or more specific outcomes, with a mean delay of 4.6 months. When restricting to RCTs only, 53% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 7.5 months. Fig. 28 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.

Fig. 28. The time when studies showed that treatments were effective, defined as statistically significant improvement of ≥10% from ≥3 studies. Pooled results typically show efficacy earlier than specific outcome results. Results from all studies often shows efficacy much earlier than when restricting to RCTs. Results reflect conditions as used in trials to date, these depend on the population treated, treatment delay, and treatment regimen.

Limitations

Pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral clearance may show no efficacy if most studies only examine viral clearance. In practice, it is rare for a non-antiviral treatment to report viral clearance and to not report clinical outcomes; and in practice other sources of heterogeneity such as differences in treatment delay are more likely to hide efficacy.

Summary

Analysis validates the use of pooled effects and shows significantly faster detection of efficacy on average. However, as with all meta-analyses, it is important to review the different studies included. We also present individual outcome analyses, which may be more informative for specific use cases.

Discussion

Results for other infections

Studies have also shown efficacy with probiotics for upper respiratory infections47, respiratory tract infections48, and the common cold49.

Publication bias

Publishing is often biased towards positive results, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results121-124.

One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.

Fig. 29 shows a scatter plot of results for prospective and retrospective studies. 70% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 32% of prospective studies, consistent with a bias toward publishing positive results. The median effect size for retrospective studies is 52% improvement, compared to 33% for prospective studies, suggesting a potential bias towards publishing results showing higher efficacy.

Fig. 29. Prospective vs. retrospective studies. The diamonds show the results of random-effects meta-analysis.

Funnel plot analysis

Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Fig. 30 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05125-132. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.

Fig. 30. Example funnel plot analysis for simulated perfect trials.

Conflicts of interest

Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Probiotics for COVID-19 lack this because they are generally inexpensive and widely available. In contrast, most COVID-19 probiotics trials have been run by physicians on the front lines with the primary goal of finding the best methods to save human lives and minimize the collateral damage caused by COVID-19. While pharmaceutical companies are careful to run trials under optimal conditions (for example, restricting patients to those most likely to benefit, only including patients that can be treated soon after onset when necessary, and ensuring accurate dosing), not all probiotics trials represent the optimal conditions for efficacy.

Limitations

Summary statistics from meta-analysis necessarily lose information. As with all meta-analyses, studies are heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses for specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.

Some analyses classify treatment based on early or late administration, as done here, while others distinguish between mild, moderate, and severe cases. Viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.

Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.

Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials with conflicts of interest may use designs better suited to the preferred outcome.

In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.

Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone95-119. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.

This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.

No treatment or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.

Notes

1 of 29 studies combine treatments. The results of probiotics alone may differ. 1 of 17 RCTs use combined treatment. Other meta-analyses show significant improvements with probiotics for hospitalization6 and recovery6,7.

Reviews

Many reviews cover probiotics for COVID-19, presenting additional background on mechanisms and related results, including133-149.

Perspective

Results compared with other treatments

SARS-CoV-2 infection and replication involves a complex interplay of 500+ host and viral proteins and other factors38-45, providing many therapeutic targets. Over 12,000 compounds have been predicted to reduce COVID-19 risk46, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Fig. 31 shows an overview of the results for probiotics in the context of multiple COVID-19 treatments, and Fig. 32 shows a plot of efficacy vs. cost for COVID-19 treatments.

Fig. 31. Scatter plot showing results within the context of multiple COVID-19 treatments. Diamonds shows the results of random-effects meta-analysis. 0.5% of 12,000+ proposed treatments show efficacy150.

Fig. 32. Efficacy vs. cost for COVID-19 treatments.

Conclusion

Significantly lower risk is seen for mortality, hospitalization, progression, recovery, and cases. 13 studies from 12 independent teams in 9 countries show significant benefit. Meta-analysis using the most serious outcome reported shows 27% [18‑36%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies. Better results are seen with early treatment.

Results are very robust—in worst case exclusion sensitivity analysis 26 of 29 studies must be excluded before statistical significance is lost.

Probiotic efficacy depends on the specific strains used. Specific microbes may decrease or increase COVID-19 risk1.

Other meta-analyses show significant improvements with probiotics for hospitalization6 and recovery6,7.

Studies have also shown efficacy with probiotics for upper respiratory infections47, respiratory tract infections48, and the common cold49.

Contact.

Funding.

We have received no funding or compensation in any form, and do not accept donations. This is entirely volunteer work.

Conflicts of interest.

We have no conflicts of interest. We have no affiliation with any pharmaceutical companies, supplement companies, governments, political parties, or advocacy organizations.

AI.

We use AI models (Gemini, Grok, Claude, and ChatGPT) tasked with functioning as additional peer-reviewers to check for errors, suggest improvements, and review spelling and grammar. Any corrections are manually verified. Our preference for em dashes is independent of AI.

Updates.

Our COVID-19 meta-analyses involve the extraction of over 229,000 datapoints from thousands of papers for 227 treatments. We thank the thousands of scientists, physicians, and other contributors that have provided updates, suggestions, feedback, and corrections. These are all welcome and can be submitted at https://c19early.org/kmeta.html.

Dedication.

This work is dedicated to top evidence-based physicians that worked tirelessly to analyze evidence and greatly reduce mortality and morbidity during the pandemic. In alphabetical order: Dr. Thomas J. Borody, Dr. Mary Talley Bowden, Dr. Flavio Cadegiani, Dr. Shankara Chetty, Dr. Ryan Cole, Dr. George Fareed, Dr. Sabine Hazan, Dr. Pierre Kory, Dr. Tess Lawrie, Dr. Robert Malone, Dr. Paul Marik, Dr. Peter McCullough, Dr. Didier Raoult, Dr. Harvey Risch, Dr. Jackie Stone, Dr. Brian Tyson, Dr. Joseph Varon, and Dr. Vladimir Zelenko.

Public domain.

This is a public domain work distributed in accordance with the Creative Commons CC0 1.0 Universal license, which dedicates the work to the public domain by waiving all rights worldwide under copyright law. You can distribute, remix, adapt, and build upon this work in any medium or format, including for commercial purposes, without asking permission. Referenced material and third-party images retain any original copyrights or restrictions. See: https://creativecommons.org/publicdomain/zero/1.0/.

Study Notes

Ahanchian et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Fewer symptomatic cases and cases

Not statistically significant · p = 0.35 (Fewer symptomatic cases) and p = 0.24 (cases)

Small RCT 60 healthcare workers in Iran, showing lower cases with treatment but without statistical significance. Once daily oral synbiotic capsule (Lactocare®) containing 1 billion CFU L. (Lactobacillus) casei, L. rhamnosus, Streptococcus thermophilus, Bifidobacterium breve, L. acidophilus, Bifidobacterium infantis, L. bulgaricus, and Fructooligosacharide. Submit Corrections or Updates.

Catinean et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Improved recovery with probiotics

p = 0.0082

Retrospective 60 patients in Romania taking probiotics and 60 matched controls, showing faster symptom resolution with the use of probiotics. Spore-based probiotic containing five strains of Bacillus. Submit Corrections or Updates.

Ceccarelli et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Lower mortality and ICU admission

Not statistically significant · p = 0.42 (mortality) and p = 0.15 (ICU admission)

Prospective analysis of 69 severe COVID-19 patients requiring non-invasive oxygen therapy, 40 treated with probiotic formulation SLAB51, showing lower oxygen requirements and higher blood levels of pO2, O2Hb and SaO2 with treatment. Authors suggest that enzymes in SLAB51 could reduce oxygen requirements in intestinal cells, resulting in more oxygen available for other organs. Submit Corrections or Updates.

Ceccarelli et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Lower mortality with probiotics

p = 0.003

Retrospective 200 severe condition hospitalized patients in Italy, 88 treated with probiotic Sivomixx, showing lower mortality with treatment. Submit Corrections or Updates.

d'Ettorre et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Lower progression with probiotics

p = 0.011

Retrospective 70 hospitalized patients in Italy, 28 treated with probiotic Sivomixx, showing lower risk of respiratory failure and faster recovery with treatment. Submit Corrections or Updates.

Di Pierro et al.

Outcome

Improvement

Relative Risk · 95% CI

Case

78%0.22 · 0.07–0.67

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Fewer cases with probiotics

p = 0.0074

Retrospective study of 287 nursery school children in Italy, 186 treated with S. salivarius K12 probiotic. The probiotic group had significantly lower rates of COVID-19, bronchitis, sinusitis, and laryngitis as well as lower antibiotic use. The study was registered retrospectively and details of COVID-19 diagnosis are not provided. Parents that administer the treatment may also use other treatments or take other actions that reduce risk for their children. Submit Corrections or Updates.

Di Pierro et al.

Outcome

Improvement

Relative Risk · 95% CI

Case

98%0.02 · 0.00–0.33

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Fewer cases with probiotics

p < 0.000001

Interim report on an RCT for prophylactic treatment with S. salivarius K12, showing significantly lower cases with treatment. Only patients with symptoms or known positive contacts were tested. Trial identification/registration details are not provided. Submit Corrections or Updates.

Di Pierro et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Lower mortality with probiotics

Not statistically significant · p = 0.17

RCT 50 hospitalized patients in Pakistan, 25 treated with S. salivarius K12, showing lower mortality with treatment, without statistical significance. There were more patients with higher oxygen requirements at baseline in the control group - 18 vs. 6 with O2 ≥ 8 L/min. Submit Corrections or Updates.

Fernández-Ferreiro et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Slower recovery and more cases

Not statistically significant · p = 0.56 (Slower recovery) and p = 0.53 (more cases)

RCT 200 nursing home residents over 60 years old in Spain showing Loigolactobacillus coryniformis K8 probiotic administration enhanced IgG antibody response in subjects previously infected with SARS-CoV-2 and tended to improve IgA antibody response in those over 85 years old not previously infected, in the context of COVID-19 vaccination. There was no significant difference in incidence of COVID-19 infection between the probiotic and placebo groups during the study. The probiotic group had a higher percentage of asymptomatic COVID-19 cases compared to placebo, without statistical significance. Submit Corrections or Updates.

Giancola et al.

Outcome

Improvement

Relative Risk · 95% CI

Mortality

15%0.85 · 0.06 to >9

Moderate/severe symptoms, combined

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Improved recovery with probiotics

Not statistically significant · p = 0.32

RCT 52 acute COVID-19 inpatients in Italy showing a multistrain synbiotic formula prevented a decrease in gut microbiota diversity and prevented decreases in lymphocyte count and hemoglobin levels compared to placebo. The probiotic group also had enrichment of beneficial bacteria and fewer neurological/neurocognitive symptoms at 6 months, although not statistically significant. Authors suggest modulating gut microbiota in acute COVID-19 through probiotics could be a useful supportive strategy. Submit Corrections or Updates.

Gutiérrez-Castrellón et al.

Outcome

Improvement

Relative Risk · 95% CI

Recovery

35%0.65 · 0.53–0.80

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Improved recovery with probiotics

p = 0.000017

RCT 293 outpatients in Mexico, 147 treated with a probiotic composed of three L. plantarum strains (KABP022, KABP023 and KABP033) and one P. acidilacti strain (KABP021), showing improved recovery with treatment. There were no hospitalizations or deaths. Submit Corrections or Updates.

Haran et al.

Outcome

Improvement

Relative Risk · 95% CI

Hospitalization/ER/urgent care

50%0.50 · 0.21–1.20

Time to resolution of symptoms

20%0.80 · 0.61–1.04

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Lower hospitalization and fewer hosp./ER visits

Not statistically significant · p = 0.45 (hospitalization) and p = 0.13 (fewer hosp./ER visits)

RCT 350 COVID+ outpatients in the USA, 174 treated with prebiotic KB109 (a microbiome metabolic therapy candidate), showing lower combined hospitalization, ER, and urgent care visits with treatment. NCT04414124. Submit Corrections or Updates.

Hassan et al.

Outcome

Improvement

Relative Risk · 95% CI

no CI

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Lower hospitalization and improved recovery

Not statistically significant · p = 0.2 (hospitalization) and p = 0.42 (recovery)

RCT 150 patients in Egypt showing no significant difference in outcomes with probiotic lactobacillus acidophilus, although hospitalization was 2% versus 10% for control. SOC included vitamin C, D, and zinc. Submit Corrections or Updates.

Holt et al.

Outcome

Improvement

Relative Risk · 95% CI

Case

30%0.70 · 0.45–1.10

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Fewer cases with probiotics

Not statistically significant · p = 0.11

Prospective survey-based study with 15,227 people in the UK, showing lower risk of COVID-19 cases with vitamin A, vitamin D, zinc, selenium, probiotics, and inhaled corticosteroids; and higher risk with metformin and vitamin C. Statistical significance was not reached for any of these. Except for vitamin D, the results for treatments we follow were only adjusted for age, sex, duration of participation, and test frequency. Submit Corrections or Updates.

Ivashkin et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Trial underpowered for serious outcomes

RCT 200 patients, 99 treated with a probiotic (Lacticaseibacillus rhamnosus PDV 1705, Bifidobacterium bifidum PDV 0903, Bifidobacterium longum subsp. infantis PDV 1911, and Bifidobacterium longum subsp. longum PDV 2301). There was no significant difference in mortality or recovery time, however benefits were seen for diarrhea. NCT04854941. Submit Corrections or Updates.

Kolesnyk et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Improved recovery and lower long COVID

p = 0.021 (recovery) and p = 0.0082 (long COVID)

RCT 73 outpatients with mild COVID-19 showing improved recovery and increased RBD/spike antibody response with 28 days of a multi-strain probiotic (Bifidobacterium (B.) lactis BI040, B. longum BL020, Lactobacillus (L) rhamnosus LR110, L. casei LC130, L. acidophilus LA120, 5 billion CFU total). Submit Corrections or Updates.

Li et al.

Outcome

Improvement

Relative Risk · 95% CI

Time to viral clearance

-35%1.35 · 1.13–1.62

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Slower viral clearance with probiotics

p = 0.001

Retrospective 311 severe condition hospitalized patients in China, 123 treated with probiotics, showing slower viral clearance and recovery with treatment. Authors note that probiotics were able to moderate immunity and decrease the incidence of secondary infections. Submit Corrections or Updates.

Louca et al.

Outcome

Improvement

Relative Risk · 95% CI

Case

8%0.92 · 0.85–0.99

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Fewer cases with probiotics

p = 0.03

Survey analysis of dietary supplements showing probiotic usage associated with lower incidence of COVID-19. These results are for PCR+ cases only, they do not reflect potential benefits for reducing the severity of cases. A number of biases could affect the results, for example users of the app may not be representative of the general population, and people experiencing symptoms may be more likely to install and use the app. Submit Corrections or Updates.

Navarro-López et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Improved recovery with probiotics

Not statistically significant · p = 0.083

RCT with 24 probiotics and 15 control patients in Spain, showing lower overall symptoms and lower digestive symptoms with treatment. Kluyveromyces marxianus B0399 plus lactobacillus rhamnosus CECT 30579. Submit Corrections or Updates.

Rodriguez-Blanque et al.

Outcome

Improvement

Relative Risk · 95% CI

Case

9%0.91 · 0.12–6.70

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Trial underpowered to detect differences

Prophylaxis RCT with 127 probiotics and 128 control healthcare workers in Spain, showing no significant difference in cases. There were only 4 cases. Severity information by arm is not provided. L. coryniformis K8 CECT 5711.

Treatment may help sustain the immune response to vaccination - in the subgroup of subjects for whom more than 81 days had passed since they received the first dose, IgG levels were significantly higher in the treatment group. Patients that started probiotic consumption before the first vaccine dose also reported significantly fewer side effects. Submit Corrections or Updates.

Sarlin et al.

Outcome

Improvement

Relative Risk · 95% CI

Case

33%0.67 · 0.11–3.98

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Trial underpowered to detect differences

RCT 827 children aged 1-6 years in daycare in Finland analyzing the effectiveness of daily Streptococcus salivarius K12 oral probiotic use for 6 months in preventing acute otitis media (AOM). The probiotic group did not have a significantly lower rate of AOM requiring antibiotics compared to placebo. A secondary outcome shows no significant difference in COVID-19, with only 2 and 3 cases in the treatment and placebo groups. Submit Corrections or Updates.

Saviano et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Lower ICU admission and shorter hospitalization

Not statistically significant · p = 0.24 (ICU admission) and p = 0.52 (hospitalization)

RCT 80 COVID-19 interstitial pneumonia patients in Italy, 40 treated with probiotics, showing significantly reduced gut inflammatory markers with treatment, and lower ICU admission and mortality, without statistical significance. Bifidobacterium lactis LA 304, lactobacillus salivarius LA 302, and lactobacillus acidophilus LA 201 bid for 10 days.

Submit Corrections or Updates.

Shah et al.

Outcome

Improvement

Relative Risk · 95% CI

Time to clinical improvement

Clinical improvement (b)

4%0.96 · 0.80–1.16

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Improved recovery with treatment

p = 0.0048

Small RCT 60 patients in India, 30 treated with ImmunoSEB and ProbioSEB CSC3, showing faster recovery with treatment. CTRI/2020/09/027685, CTRI/2020/08/027168. Submit Corrections or Updates.

Trinchieri et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Lower mortality with probiotics

Not statistically significant · p = 0.28

Retrospective COVID-19 patients requiring CPAP, 21 treated with SLAB51 probiotics and 15 control patients, showing improved outcomes with treatment, despite significantly lower blood oxygenation at baseline in the treatment group. Submit Corrections or Updates.

Veterini et al.

Outcome

Improvement

Relative Risk · 95% CI

Time to viral clearance

29%0.71 · 0.41–1.22

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Faster viral clearance with probiotics

Not statistically significant · p = 0.22

Small case control analysis with 15 probiotics patients and 15 contol patients, showing no significant differences. PCR tests were only done weekly. Dosage is unknown. 115/LOE/301.4.2/IX/2020. Submit Corrections or Updates.

Wischmeyer et al.

Outcome

Improvement

Relative Risk · 95% CI

primary

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Fewer symptomatic cases with probiotics

p = 0.02

RCT 182 COVID-19 exposed patients, 91 treated with daily probiotic Lactobacillus rhamnosus GG starting a median of 3 days from exposure, showing lower symptomatic COVID-19 with treatment. There were no hospitalizations or deaths. Submit Corrections or Updates.

Yamamoto et al.

Outcome

Improvement

Relative Risk · 95% CI

Recovery

26%0.74 · 0.51–1.07

Viral clearance, relative decrease in viral load...

62%0.38 · 0.09–1.73

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Improved viral clearance with probiotics

Not statistically significant · p = 0.21

RCT 100 outpatients with mild COVID-19 showing no significant difference in overall symptom severity with Lactococcus lactis strain Plasma (LC-Plasma). However, post hoc analysis revealed a significantly higher proportion of patients without smell and taste disturbances in the LC-Plasma group at later time points (97.8% vs 82.6% on day 13, p=0.030). LC-Plasma showed earlier viral load reduction by day 4 and increased plasmacytoid dendritic cell counts. Authors hypothesize LC-Plasma may suppress neurological damage through pDC activation, facilitating immune response against upper airway symptoms. There were no serious adverse events. Submit Corrections or Updates.

Zhang et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

No significant difference in outcomes

Pilot study of probiotic SIM01 with 25 consecutive COVID-19 patients in Hong Kong and 30 control patients treated by a different team during the same time period, showing improved antibody formation, reduced viral load and pro-inflammatory responses, and improvements for gut dysbiosis. SIM01 contains bifidobacteria strains, galactooligosaccharides, xylooligosaccharide, and resistant dextrin (derived from metagenomic databases of COVID-19 patients and healthy patients). Submit Corrections or Updates.

Zhang et al.

Outcome

Improvement

Relative Risk · 95% CI

Hospitalization time

14%0.86 · 0.77–0.96

Time to clinical improvement

14%0.86 · 0.75–0.98

Time to viral clearance

17%0.83 · 0.75–0.93

00.511.52+

← Probiotics
reduce riskProbiotics
increase risk →

Shorter hospitalization and faster recovery

p = 0.009 (hospitalization) and p = 0.022 (recovery)

Retrospective 375 patients in China, 179 treated with probiotics (Bifidobacterium, Lactobacillus, and Enterococcus), showing improved clinical outcomes with treatment. Submit Corrections or Updates.

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Appendix 1. Methods and Data

Search methods

We perform ongoing searches of PubMed, medRxiv, Europe PMC, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org, which regularly receives notification of studies upon publication. Search terms are probiotics and COVID-19 or SARS-CoV-2. Automated searches are performed twice daily, with all matches reviewed for inclusion. All studies regarding the use of probiotics for COVID-19 that report a comparison with a control group are included in the main analysis. Sensitivity analysis is performed, excluding studies with major issues, epidemiological studies, and studies with minimal available information. Studies with major unexplained data issues, for example major outcome data that is impossible to be correct with no response from the authors, are excluded.

Fig. 33. Mid-recovery results can more accurately reflect efficacy when almost all patients recover. Mateja et al. confirm that intermediate viral load results more accurately reflect hospitalization/death.

Effect extraction

We extracted effect sizes and associated data from all studies. If studies report multiple kinds of effects then the most serious outcome is used in pooled analysis, while other outcomes are included in the outcome-specific analyses. For example, if effects for mortality and cases are reported then they are both used in specific outcome analyses, while mortality is used for pooled analysis. If symptomatic results are reported at multiple times, we use the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days have preference. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms are not used, the next most serious outcome with one or more events is used. For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcomes are considered more important than viral outcomes. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available. After most or all patients have recovered there is little or no room for an effective treatment to do better, however faster recovery is valuable. An IPD meta-analysis confirms that intermediate viral load reduction is more closely associated with hospitalization/death than later viral load reduction151. If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO₂ is more important than cough.

Statistical methods

Forest plots are computed using PythonMeta152 with the DerSimonian and Laird random-effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Results are presented with 95% confidence intervals. Heterogeneity among studies was assessed using the I² statistic. When results provide an odds ratio, we compute the relative risk when possible, or convert to a relative risk according to Zhang et al. Reported confidence intervals and p-values are used when available, and adjusted values are used when provided. If multiple types of adjustments are reported propensity score matching and multivariable regression has preference over propensity score matching or weighting, which has preference over multivariable regression. Adjusted results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed Altman, Altman (B), and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1156. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.15.0) with scipy (1.18.1), pythonmeta (1.26), numpy (2.5.4), statsmodels (0.15.0), and plotly (6.9.0). Mixed-effects meta-regression results are computed with R (4.4.0) using the metafor (4.6-0) and rms (6.8-0) packages, and using the most serious sufficiently powered outcome. For all statistical tests, a p-value less than 0.05 was considered statistically significant. Grobid 0.8.2 is used to parse PDF documents.

When evaluating potential effect modification across groups, we use an interaction test as described by Altman (C) et al. We compared the log-transformed relative risks using a z-test, deriving the standard error of the difference from the 95% confidence intervals. A two-sided interaction p-value of < 0.05 was considered a statistically significant difference in treatment effect between the groups.

Quality evaluation

Cochrane RoB 2/ROBINS-I are often used to evaluate studies, and have the advantage of providing standardized rules that can be applied with minimal understanding of the domain and study. However, the rules do not account for many real-world issues, often overemphasize or underemphasize others, and studies show low inter-rater reliability164. Certain domains are more applicable for these tools, however the time-sensitive nature of a pandemic, with significant mortality for every day of delay in evidence assessment, and the characteristics of COVID-19 make them inappropriate for this domain. This can be demonstrated with examples where expert RoB 2/ROBINS-I ratings do not match reality for COVID-19. Popp et al. use RoB 2 to classify Reis et al. as low risk of bias, however this is the opposite of reality—the trial not only has very high risk of bias, but has very high actual known bias, refusing to release data despite pledging to, reporting multiple impossible numbers, having blinding and randomization failure, and many other issues166. Axfors et al. use RoB 2 to classify Horby et al. as low risk of bias, however this is the opposite of reality—the very late treatment and excessive dosage used produces results with no relevance to recommended usage. HCQ shows poor results with late treatment and excessive dosage, and the combination shows harmD. Hempenius et al. use ROBINS-I to classify 33 studies for HCQ. The two rated as having the lowest risk of bias162,163 are far from the most informative. Both involve very late treatment, providing no information on recommended usage, and ROBINS-I does a very poor job of accounting for the impact of confounding factorsE.

Our quality evaluation focuses on known issues and bias, and the potential impact on outcomes, rather than just the risk of bias. The estimated potential impact of each confounding factor, and the direction of the impact is considered. For example, consider a study that shows significantly lower risk, the value of the study varies significantly if confounding points to an underestimate or an overestimate of efficacy. In one case, the real effect may be null, while the other case provides stronger evidence of efficacy (which may be greater than the study shows). Analysis focusing on the risk of bias, while simpler, may penalize studies for theoretical or technical issues that have no or minimal impact on outcomes. Analysis also depends on the outcome, for example certain issues are less relevant for objective outcomes such as mortality. Inaccurate penalization, and inaccurate high-quality evaluation in the face of known major issues affecting outcomes, increases in significance during a pandemic when immediate recognition of new evidence is critical, and when considering all global studies, as required during a pandemic. Investigators in other countries may have different customs for design, analysis, and reporting, and different English language skills, however they may not be less diligent or have greater bias. Investigators in lower-pharmaceutical-profit countries may have lower bias towards profitable interventions.

Treatment time

We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective80,81.

Living analysis

This is a living analysis and is updated regularly. Submit updates or corrections with the form below. We received no funding, this research is done in our spare time. We have no affiliation with any pharmaceutical companies, supplement companies, governments, political parties, or advocacy organizations.

A summary of study results is below. Please submit updates and corrections at the bottom of this page.

A summary of study results is below. Please submit updates and corrections at https://c19early.org/kmeta.html.

Early treatment

Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.

Gutiérrez-Castrellón, 5/24/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Mexico, peer-reviewed, 9 authors, study period 19 August, 2020 - 2 February, 2021, average treatment delay 4.0 days, trial NCT04517422 (history). Submit Corrections or Updates.	risk of no recovery, 34.7% lower, RR 0.65, p < 0.001, treatment 69 of 147 (46.9%), control 105 of 146 (71.9%), NNT 4.0.
Haran, 3/29/2021, Randomized Controlled Trial, USA, preprint, 6 authors, study period 2 July, 2020 - 23 December, 2020, trial NCT04414124 (history). Submit Corrections or Updates.	risk of death, 66.5% lower, RR 0.33, p = 1.00, treatment 0 of 174 (0.0%), control 1 of 176 (0.6%), NNT 176, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), death two weeks after study withdrawal.
	risk of hospitalization, 59.5% lower, RR 0.40, p = 0.45, treatment 2 of 174 (1.1%), control 5 of 176 (2.8%), NNT 59, including treatment period.
	risk of hospitalization/ER/urgent care, 50.0% lower, RR 0.50, p = 0.13, treatment 7 of 169 (4.1%), control 15 of 181 (8.3%), NNT 24.
	time to resolution of symptoms, 20.3% lower, relative time 0.80, p = 0.10, treatment 169, control 172, inverted to make RR<1 favor treatment.
Hassan, 6/13/2023, Randomized Controlled Trial, Egypt, preprint, 6 authors, study period July 2021 - August 2022. Submit Corrections or Updates.	risk of hospitalization, 80.0% lower, RR 0.20, p = 0.20, treatment 1 of 50 (2.0%), control 5 of 50 (10.0%), NNT 12.
	risk of no recovery, 17.9% lower, RR 0.82, p = 0.42, treatment 23 of 50 (46.0%), control 28 of 50 (56.0%), NNT 10.0.
Kolesnyk, 1/4/2024, Double Blind Randomized Controlled Trial, placebo-controlled, Ukraine, peer-reviewed, 10 authors, study period November 2021 - June 2022, trial NCT04907877 (history). Submit Corrections or Updates.	WHO score >1, 60.3% lower, RR 0.40, p = 0.02, treatment 6 of 34 (17.6%), control 16 of 36 (44.4%), NNT 3.7.
	recovery time, 21.4% lower, relative time 0.79, p = 0.04, treatment 34, control 36.
	PCFS ≥1, 67.8% lower, RR 0.32, p = 0.008, treatment 5 of 34 (14.7%), control 16 of 35 (45.7%), NNT 3.2, long COVID, Supplementary Table 1.
Navarro-López, 8/24/2022, Randomized Controlled Trial, Spain, peer-reviewed, 13 authors, study period December 2020 - February 2021, trial NCT04390477 (history). Submit Corrections or Updates.	risk of no recovery, 32.7% lower, RR 0.67, p = 0.08, treatment 14 of 24 (58.3%), control 13 of 15 (86.7%), NNT 3.5, day 30.
	risk of no recovery, 53.1% lower, RR 0.47, p = 0.10, treatment 6 of 24 (25.0%), control 8 of 15 (53.3%), NNT 3.5, digestive symptoms, day 30.
	relative recovery, 20.0% better, RR 0.80, p = 0.03, treatment 24, control 15, relative symptom improvement, day 30.
	relative recovery, 26.1% better, RR 0.74, p = 0.06, treatment 24, control 15, relative improvement for digestive symptoms, day 30.
Veterini, 6/30/2021, retrospective, Indonesia, peer-reviewed, 6 authors, excluded in exclusion analyses: the observered difference in duration could be caused by the baseline difference in Ct values. Submit Corrections or Updates.	time to viral-, 29.0% lower, relative time 0.71, p = 0.22, treatment 15, control 15.
Yamamoto, 10/25/2025, Double Blind Randomized Controlled Trial, placebo-controlled, Japan, peer-reviewed, 29 authors, study period December 2021 - April 2022, trial jRCTs071210097 (PLATEAU). Submit Corrections or Updates.	risk of no recovery, 26.0% lower, RR 0.74, p < 0.001, treatment mean 0.76 (±0.18) n=45, control mean 1.02 (±0.3) n=46, relative total severity score, day 14.
	risk of no viral clearance, 61.6% lower, RR 0.38, p = 0.21, treatment 27.9 [19.3-36.4] n=45, control 10.7 [-14.6-36.0] n=46, relative decrease in viral load, mid-recovery, day 4.

Late treatment

Ceccarelli, 8/23/2021, prospective, Italy, peer-reviewed, 10 authors. Submit Corrections or Updates.	risk of death, 70.4% lower, RR 0.30, p = 0.42, treatment 0 of 40 (0.0%), control 1 of 29 (3.4%), NNT 29, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
	risk of ICU admission, 81.9% lower, RR 0.18, p = 0.15, treatment 1 of 40 (2.5%), control 4 of 29 (13.8%), NNT 8.9.
Ceccarelli (B), 1/11/2021, retrospective, Italy, peer-reviewed, 14 authors. Submit Corrections or Updates.	risk of death, 64.2% lower, RR 0.36, p = 0.003, treatment 10 of 88 (11.4%), control 34 of 112 (30.4%), NNT 5.3, adjusted per study, odds ratio converted to relative risk.
	risk of ICU admission, 15.2% lower, RR 0.85, p = 0.60, treatment 16 of 88 (18.2%), control 24 of 112 (21.4%), NNT 31.
d'Ettorre, 7/7/2020, retrospective, Italy, peer-reviewed, 17 authors. Submit Corrections or Updates.	risk of death, 87.0% lower, RR 0.13, p = 0.14, treatment 0 of 28 (0.0%), control 4 of 42 (9.5%), NNT 10, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
	risk of mechanical ventilation, 76.9% lower, RR 0.23, p = 0.51, treatment 0 of 28 (0.0%), control 2 of 42 (4.8%), NNT 21, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
	respiratory failure, 88.4% lower, OR 0.12, p = 0.01, treatment 28, control 42, inverted to make OR<1 favor treatment, RR approximated with OR.
Di Pierro, 9/28/2022, Randomized Controlled Trial, Pakistan, peer-reviewed, mean age 48.5, 7 authors, study period 11 August, 2021 - 18 November, 2021, trial NCT05043376 (history), excluded in exclusion analyses: unadjusted differences between groups. Submit Corrections or Updates.	risk of death, 62.5% lower, RR 0.38, p = 0.17, treatment 3 of 25 (12.0%), control 8 of 25 (32.0%), NNT 5.0.
	risk of ICU admission, no change, RR 1.00, p = 1.00, treatment 8 of 25 (32.0%), control 8 of 25 (32.0%).
Giancola, 7/16/2024, Double Blind Randomized Controlled Trial, placebo-controlled, Italy, peer-reviewed, 16 authors, study period 18 January, 2022 - 21 March, 2023. Submit Corrections or Updates.	risk of death, 14.8% lower, RR 0.85, p = 1.00, treatment 1 of 27 (3.7%), control 1 of 23 (4.3%), NNT 155.
	risk of moderate/severe symptoms, 33.2% lower, RR 0.67, p = 0.32, treatment 22, control 20, combined.
	risk of moderate/severe symptoms, 21.2% higher, RR 1.21, p = 1.00, treatment 4 of 22 (18.2%), control 3 of 20 (15.0%), moderate/severe symptoms, day 180, cardio-respiratory symptoms.
	risk of moderate/severe symptoms, 69.7% lower, RR 0.30, p = 0.12, treatment 2 of 22 (9.1%), control 6 of 20 (30.0%), NNT 4.8, moderate/severe symptoms, day 180, digestive symptoms.
	risk of moderate/severe symptoms, 74.0% lower, RR 0.26, p = 0.06, treatment 2 of 22 (9.1%), control 7 of 20 (35.0%), NNT 3.9, moderate/severe symptoms, day 180, neurological/neurocognitive symptoms.
	risk of moderate/severe symptoms, 13.6% higher, RR 1.14, p = 0.76, treatment 10 of 22 (45.5%), control 8 of 20 (40.0%), moderate/severe symptoms, day 180, systemic symptoms.
Ivashkin, 10/13/2021, Randomized Controlled Trial, Russia, peer-reviewed, 11 authors, study period December 2020 - March 2021, average treatment delay 8.0 days, trial NCT04854941 (history). Submit Corrections or Updates.	risk of death, 2.0% higher, RR 1.02, p = 1.00, treatment 4 of 99 (4.0%), control 4 of 101 (4.0%).
	risk of mechanical ventilation, 18.4% lower, RR 0.82, p = 1.00, treatment 4 of 99 (4.0%), control 5 of 101 (5.0%), NNT 110.
	risk of ICU admission, 27.1% lower, RR 0.73, p = 0.77, treatment 5 of 99 (5.1%), control 7 of 101 (6.9%), NNT 53.
	recovery time, 4.8% lower, relative time 0.95, p = 0.47, treatment 99, control 101.
Li (B), 3/5/2021, retrospective, China, peer-reviewed, 7 authors, average treatment delay 13.0 days. Submit Corrections or Updates.	risk of no hospital discharge, 11.8% higher, RR 1.12, p = 0.68, treatment 30 of 123 (24.4%), control 41 of 188 (21.8%).
	time to discharge, 60.0% higher, relative time 1.60, p < 0.001, treatment 123, control 188.
	time to viral-, 35.3% higher, relative time 1.35, p < 0.001, treatment 123, control 188.
Saviano, 6/28/2022, Randomized Controlled Trial, Italy, peer-reviewed, mean age 59.8, 9 authors. Submit Corrections or Updates.	risk of death, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 40 (0.0%), control 1 of 40 (2.5%), NNT 40, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
	risk of ICU admission, 85.7% lower, RR 0.14, p = 0.24, treatment 0 of 40 (0.0%), control 3 of 40 (7.5%), NNT 13, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
	hospitalization time, 26.3% lower, relative time 0.74, p = 0.52, treatment mean 14.0 (±6.0) n=40, control mean 19.0 (±10.0) n=40.
Shah, 2/2/2021, Randomized Controlled Trial, India, peer-reviewed, 3 authors, this trial uses multiple treatments in the treatment arm (combined with multi-enzyme formulation) - results of individual treatments may vary. Submit Corrections or Updates.	time to clinical improvement, 10.8% lower, relative time 0.89, p = 0.19, treatment 30, control 30.
	hospitalization time, 10.6% lower, relative time 0.89, p = 0.18, treatment 30, control 30.
	risk of no clinical improvement, 83.3% lower, RR 0.17, p = 0.005, treatment 2 of 30 (6.7%), control 12 of 30 (40.0%), NNT 3.0, day 10 mid-recovery.
	risk of no clinical improvement, 3.7% lower, RR 0.96, p = 1.00, treatment 26 of 30 (86.7%), control 27 of 30 (90.0%), NNT 30, day 7.
Trinchieri, 8/1/2022, retrospective, Italy, peer-reviewed, 10 authors, study period November 2020 - March 2021. Submit Corrections or Updates.	risk of death, 77.8% lower, RR 0.22, p = 0.28, treatment 1 of 21 (4.8%), control 3 of 14 (21.4%), NNT 6.0.
	risk of miscellaneous, 77.8% lower, RR 0.22, p < 0.001, treatment 4 of 21 (19.0%), control 12 of 14 (85.7%), NNT 1.5, CPAP, day 7.
	risk of miscellaneous, 9.5% lower, RR 0.90, p = 0.51, treatment 19 of 21 (90.5%), control 14 of 14 (100.0%), NNT 10, CPAP, day 3.
Zhang (B), 3/2/2022, retrospective, China, peer-reviewed, 12 authors, trial NCT04581018 (history). Submit Corrections or Updates.	risk of mechanical ventilation, 64.7% lower, RR 0.35, p = 1.00, treatment 0 of 25 (0.0%), control 1 of 30 (3.3%), NNT 30, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
	risk of no antibody formation, 67.3% lower, RR 0.33, p = 0.06, treatment 3 of 25 (12.0%), control 11 of 30 (36.7%), NNT 4.1.
Zhang (C), 8/4/2021, retrospective, China, peer-reviewed, 14 authors. Submit Corrections or Updates.	hospitalization time, 13.6% lower, relative time 0.86, p = 0.009, treatment 150, control 150, PSM.
	time to clinical improvement, 14.3% lower, relative time 0.86, p = 0.02, treatment 150, control 150, PSM.
	time to viral-, 16.7% lower, relative time 0.83, p < 0.001, treatment 150, control 150, PSM.

Prophylaxis

Ahanchian, 5/31/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, 14 authors, study period July 2020 - August 2020, trial IRCT20101020004976N6. Submit Corrections or Updates.	respiratory symptoms, 73.3% lower, RR 0.27, p = 0.35, treatment 1 of 29 (3.4%), control 4 of 31 (12.9%), NNT 11.
	risk of case, 85.3% lower, RR 0.15, p = 0.24, treatment 0 of 29 (0.0%), control 3 of 31 (9.7%), NNT 10, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
Catinean, 1/17/2023, retrospective, Romania, peer-reviewed, 4 authors, study period 15 September, 2020 - 15 February, 2021. Submit Corrections or Updates.	symptom resolution, 40.5% lower, HR 0.60, p = 0.008, treatment 60, control 60, inverted to make HR<1 favor treatment.
	resolution of fever, 37.5% lower, HR 0.62, p = 0.02, treatment 60, control 60, inverted to make HR<1 favor treatment, fever.
Di Pierro (C), 9/30/2023, retrospective, Italy, peer-reviewed, 10 authors, study period January 2022 - March 2022, trial NCT05840926 (history). Submit Corrections or Updates.	risk of case, 77.8% lower, RR 0.22, p = 0.007, treatment mean 0.02 (±0.15) n=186, control mean 0.09 (±0.29) n=101.
Di Pierro (D), 3/12/2021, Randomized Controlled Trial, Italy, peer-reviewed, 2 authors, study period September 2020 - December 2020. Submit Corrections or Updates.	risk of case, 98.0% lower, RR 0.02, p < 0.001, treatment 0 of 64 (0.0%), control 24 of 64 (37.5%), NNT 2.7, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
Fernández-Ferreiro, 1/5/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Spain, peer-reviewed, mean age 83.1, 9 authors, study period January 2021 - April 2021, trial NCT04756466 (history). Submit Corrections or Updates.	risk of death, 2.0% higher, RR 1.02, p = 1.00, treatment 1 of 98 (1.0%), control 1 of 100 (1.0%).
	recovery time, 37.8% higher, relative time 1.38, p = 0.56, treatment mean 8.45 (±9.69) n=10, control mean 6.13 (±4.22) n=7.
	risk of severe case, 27.6% higher, RR 1.28, p = 0.75, treatment 5 of 98 (5.1%), control 4 of 100 (4.0%).
	risk of symptomatic case, 2.0% higher, RR 1.02, p = 1.00, treatment 7 of 98 (7.1%), control 7 of 100 (7.0%).
	risk of case, 35.1% higher, RR 1.35, p = 0.53, treatment 11 of 98 (11.2%), control 8 of 100 (8.0%), adjusted per study.
Holt, 3/30/2021, prospective, United Kingdom, peer-reviewed, 34 authors, study period 1 May, 2020 - 5 February, 2021, trial NCT04330599 (history) (COVIDENCE UK), excluded in exclusion analyses: significant unadjusted confounding possible. Submit Corrections or Updates.	risk of case, 30.4% lower, RR 0.70, p = 0.11, treatment 20 of 909 (2.2%), control 426 of 14,318 (3.0%), NNT 129, adjusted per study, odds ratio converted to relative risk, minimally adjusted, group sizes approximated.
Louca, 11/30/2020, retrospective, United Kingdom, peer-reviewed, 26 authors. Submit Corrections or Updates.	risk of case, 8.5% lower, RR 0.92, p = 0.03, odds ratio converted to relative risk, United Kingdom, all adjustment model.
Rodriguez-Blanque, 8/3/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Spain, peer-reviewed, 7 authors, study period 24 April, 2020 - 20 July, 2020, trial NCT04366180 (history). Submit Corrections or Updates.	risk of case, 9.3% lower, RR 0.91, p = 0.92, treatment 2 of 127 (1.6%), control 2 of 128 (1.6%), adjusted per study, multivariable.
Sarlin, 11/2/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Finland, peer-reviewed, 7 authors, study period 1 August, 2020 - 31 May, 2021. Submit Corrections or Updates.	risk of case, 33.2% lower, RR 0.67, p = 1.00, treatment 2 of 413 (0.5%), control 3 of 414 (0.7%), NNT 416.
Wischmeyer, 1/5/2022, Double Blind Randomized Controlled Trial, USA, preprint, 21 authors, study period 24 June, 2020 - 8 July, 2021, trial NCT04399252 (history) (PROTECT-EHC). Submit Corrections or Updates.	risk of moderate/severe case, 33.3% lower, RR 0.67, p = 0.15, treatment 16 of 91 (17.6%), control 24 of 91 (26.4%), NNT 11.
	risk of symptomatic case, 38.5% lower, RR 0.62, p = 0.02, treatment 24 of 91 (26.4%), control 39 of 91 (42.9%), NNT 6.1, primary outcome.
	recovery time, 27.3% lower, relative time 0.73, p = 0.37, treatment 91, control 91.
	risk of case, 42.9% lower, RR 0.57, p = 0.17, treatment 8 of 91 (8.8%), control 14 of 91 (15.4%), NNT 15.

Supplementary Data

Footnotes

Viral infection and replication involves attachment, entry, uncoating and release, genome replication and transcription, translation and protein processing, assembly and budding, and release. Each step can be disrupted by therapeutics.

Monoclonal antibodies were previously included. Other treatments such as dexamethasone, tocilizumab, and baricitinib were recommended for late stage hospitalized patients.

Smoking was known to cause lung cancer since at least 1939, but this was not widely recognized in the US until 1964, 25 years later. Surgeon general Leroy Burney tried publicizing the danger starting in 1957, with limited success. Surgeon general Luther Terry, appointed in 1961, prompted by President Kennedy in 1962 amid pressure from health advocates, finally got recognition in 1964. The 1964 report reviewed 7,000+ studies, but these could (and should) have been reviewed and acted upon in real-time as they were published. Historians attribute the 25 year delay to an industry campaign to manufacture doubt and controversy, through tactics like funding biased and fraudulent research from "independent" organizations, attacking scientists, and political lobbying. The success of the industry campaign is only possible because officials did not analyze the research in detail. Fraudulent industry research supported prior failures, but would have been called out as fraudulent by officials that analyzed and understood the research in real-time.

When administered late in infection, HCQ may enhance viral egress by further increasing lysosomal pH beyond the effect of ORF3a's water channel activity, thereby promoting lysosomal exocytosis, inactivating degradative enzymes, and facilitating the release of SARS-CoV-2 particles into the extracellular environment158,159. Research also suggests potential cardioprotective effects at lower doses, but cardiotoxicity with excessive dosage160. Bobrowski et al. also indicate negative effects if HCQ and remdesivir are combined.

Peters (B) et al. is subject to confounding by calendar-time (SOC evolved rapidly early in the pandemic, the linear covariate does not reflect non-linear SOC changes and hospital specific effects), hospital type (non-treatment hospitals were tertiary university centers), confounding by indication (4/7 hospitals initiated treatment on deterioration), immortal-time bias for as-treated (exposure assigned after baseline), significant differences for other experimental treatments, potential overadjustment from collider bias (steroid use and indication bias), limited baseline severity information, differences in hospice referral propensity across hospitals, unadjusted difference in time from onset to admission, difference in PCR positivity, and other factors. Mahévas et al. is subject to confounding by hospital (treatment highly dependent on the hospital, different SOC/ICU transfer practices, not included in PS), immortal time (only partly addressed in sensitivity analysis), co-treatment differences, calendar-time (SOC evolved rapidly early in the pandemic), binary coding for age (age ≥65 despite steep age-risk gradient), residual imbalance (variables dropped from PS), a composite outcome dependent on hospital triage/capacity, and other factors.

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