Artemisinin reduces COVID-19 risk: real-time meta-analysis of 3 studies

@CovidAnalysis, October 2026, Version 1V1
Abstract
Significantly lower risk is seen for recovery. One study shows significant benefit.
Meta-analysis using the most serious outcome reported shows 34% [11‑51%] lower risk. Results are similar for peer-reviewed studies and better for Randomized Controlled Trials. Results are consistent with early treatment being more effective than late treatment.
Currently there is limited data, with only 217 patients and only 9 control events for the most serious outcome in trials to date. Studies to date are from only 3 different groups.
2 RCTs with 1,366 patients have not reported results (up to 3 years late)1,2.
No treatment is 100% effective. Protocols combine safe and effective options with individual risk/benefit analysis and monitoring. Other treatments are more effective. All data and sources to reproduce this analysis are in the appendix.
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Evolution of COVID-19 clinical evidence Meta-analysis results over time Artemisinin p=0.0066 Acetaminophen p=0.00000021 2020 2021 2022 2023 2024 2025 2026 Lowerrisk Higherrisk c19early.org October 2026 50% 0% -50%
Artemisinin for COVID-19 — Highlights
Artemisinin reduces risk with very high confidence for recovery and in pooled analysis, and very low confidence for hospitalization.
46th treatment shown effective in November 2022, now with p = 0.0066 from 3 studies, recognized in 13 countries.
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.
c19early.org
October 2026

Artemisinin COVID-19 studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Trieu (RCT)52%0.48 · 0.22–1.06no recov.8/399/21ARTI-19 Chandiwana (RCT)−152%2.52 · 0.11–60.46hosp.1/750/39CT1
Early treatment47%0.53 · 0.24–1.149/1149/6047% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.1
Lin31%0.69 · 0.50–0.95recov. time18 (n)25 (n)
Late treatment31%0.69 · 0.50–0.9518 (n)25 (n)31% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.024
All studies34%0.66 · 0.49–0.899/1329/8534% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.006600.511.52+
1 CT: study uses combined treatment
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← Artemisinin
reduces riskArtemisinin
increases risk →
B
-100% -50% 0% 50% 100% Timeline of COVID-19 artemisinin studies (pooled effects) 2020 2021 2022 Favorsartemisinin Favorscontrol c19early.org October 2026 November 2022: efficacy (pooled outcomes)
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 artemisinin studies. The marked date indicates the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes.
Introduction
Fig. 2. SARS-CoV-2 spike protein fibrin binding leads to thromboinflammation and neuropathology, from3.
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 injury4-20 and cognitive deficits7,12, cardiovascular complications21-27, DNA damage28-31, organ failure, and death. Even mild untreated infections may result in persistent cognitive deficits32—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,33-40, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 12,000 compounds may reduce COVID-19 risk41, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.
Analysis
We analyze all significant controlled studies of artemisinin 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, and Randomized Controlled Trials (RCTs).
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.
Prophylaxis pre-exposure post-exposure Early treatment Late treatment Treatment delay viral load disease severity exposed symptom onset hospitalized ICU Expected benefit of antiviral therapy −2 0 2 4 6 8 10 Time relative to symptom onset (days) trajectories vary widely immediately or soon after symptoms after disease progression
Fig. 3. Treatment stages.
Evaluation Notes
Preclinical Research
5 in silico studies support the efficacy of artemisinin49-53.
3 in vitro studies support the efficacy of artemisinin49,52,54.
An in vivo animal study supports the efficacy of artemisinin52.
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, 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, and 10 show forest plots for random-effects meta-analysis of all studies with pooled effects, hospitalization, progression, recovery, viral clearance, and peer reviewed studies.
Table 1. Random-effects meta-analysis for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, and for specific outcomes. Results show the relative risk with treatment and the 95% confidence interval. * p<0.05.
Relative Risk Studies Patients
All studies0.66 [0.49‑0.89]**3217
Peer-reviewedPeer-reviewed0.70 [0.50‑0.96]*2157
RCTsRCTs0.53 [0.24‑1.14]2174
HospitalizationHosp.0.92 [0.80‑1.05]2157
Recovery0.68 [0.54‑0.86]**3181
Viral0.88 [0.63‑1.24]2119
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.
Early treatment Late treatment
All studies0.53 [0.24‑1.14]0.53
[0.24‑1.14]		0.69 [0.50‑0.95]*0.69*
[0.50‑0.95]
Peer-reviewedPeer-reviewed2.52 [0.11‑60.46]2.52
[0.11‑60.46]		0.69 [0.50‑0.95]*0.69*
[0.50‑0.95]
RCTsRCTs0.53 [0.24‑1.14]0.53
[0.24‑1.14]
HospitalizationHosp.2.52 [0.11‑60.46]2.52
[0.11‑60.46]		0.92 [0.80‑1.05]0.92
[0.80‑1.05]
Recovery0.68 [0.49‑0.95]*0.68*
[0.49‑0.95]		0.69 [0.50‑0.95]*0.69*
[0.50‑0.95]
Viral1.01 [0.95‑1.08]1.01
[0.95‑1.08]		0.71 [0.50‑1.01]0.71
[0.50‑1.01]
0 0.25 0.5 0.75 1 1.25 1.5+ All studies Late treatment Early treatment Efficacy in COVID-19 artemisinin studies (pooled effects) Favors artemisinin Favors control c19early.org October 2026
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

Artemisinin COVID-19 studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Trieu (RCT)52%0.48 · 0.22–1.06no recov.8/399/21ARTI-19 Chandiwana (RCT)−152%2.52 · 0.11–60.46hosp.1/750/39CT1
Early treatment47%0.53 · 0.24–1.149/1149/6047% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.1
Lin31%0.69 · 0.50–0.95recov. time18 (n)25 (n)
Late treatment31%0.69 · 0.50–0.9518 (n)25 (n)31% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.024
All studies34%0.66 · 0.49–0.899/1329/8534% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.006600.511.52+
1 CT: study uses combined treatment
Rotate screen for more detailsIncrease width for more details
← Artemisinin
reduces riskArtemisinin
increases risk →
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.
c19early.org
October 2026

Artemisinin COVID-19 hospitalization results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Chandiwana (RCT)−152%2.52 · 0.11–60.46hosp.1/750/39CT1
Early treatment−152%2.52 · 0.11–60.461/750/39152% higher risk
Tau² = 0.00, I² = 0.0%, p = 0.58
Lin8%0.92 · 0.80–1.05hosp. time18 (n)25 (n)
Late treatment8%0.92 · 0.80–1.0518 (n)25 (n)8% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.22
All studies8%0.92 · 0.80–1.051/930/648% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.2300.511.52+
1 CT: study uses combined treatment
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← Artemisinin
reduces riskArtemisinin
increases risk →
Fig. 6. Random-effects meta-analysis for hospitalization.
c19early.org
October 2026

Artemisinin COVID-19 progression results

StudyImprovementRR · 95% CITreatmentControlRelative Risk
Chandiwana (RCT)29%0.71 · 0.17–3.0439 (n)39 (n)CT1
Early treatment29%0.71 · 0.17–3.0439 (n)39 (n)29% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.66
All studies29%0.71 · 0.17–3.0439 (n)39 (n)29% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.6600.511.52+
1 CT: study uses combined treatment
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← Artemisinin
reduces riskArtemisinin
increases risk →
Fig. 7. Random-effects meta-analysis for progression.
c19early.org
October 2026

Artemisinin COVID-19 recovery results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Trieu (RCT)52%0.48 · 0.22–1.06no recov.8/399/21ARTI-19 Chandiwana (RCT)27%0.73 · 0.51–1.06recov. time39 (n)39 (n)CT1
Early treatment32%0.68 · 0.49–0.958/789/6032% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.023
Lin31%0.69 · 0.50–0.95recov. time18 (n)25 (n)
Late treatment31%0.69 · 0.50–0.9518 (n)25 (n)31% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.024
All studies32%0.68 · 0.54–0.868/969/8532% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.001400.511.52+
1 CT: study uses combined treatment
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← Artemisinin
reduces riskArtemisinin
increases risk →
Fig. 8. Random-effects meta-analysis for recovery.
c19early.org
October 2026

Artemisinin COVID-19 viral clearance results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Chandiwana (RCT)−1%1.01 · 0.95–1.08viral load39 (n)37 (n)CT1
Early treatment−1%1.01 · 0.95–1.0839 (n)37 (n)1% higher risk
Tau² = 0.00, I² = 0.0%, p = 0.76
Lin29%0.71 · 0.50–1.01viral time18 (n)25 (n)
Late treatment29%0.71 · 0.50–1.0118 (n)25 (n)29% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.054
All studies12%0.88 · 0.63–1.2457 (n)62 (n)12% lower risk
Tau² = 0.05, I² = 73.7%, p = 0.4800.511.52+
1 CT: study uses combined treatment
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← Artemisinin
reduces riskArtemisinin
increases risk →
Fig. 9. Random-effects meta-analysis for viral clearance.
c19early.org
October 2026

Artemisinin COVID-19 peer reviewed studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Chandiwana (RCT)−152%2.52 · 0.11–60.46hosp.1/750/39CT1
Early treatment−152%2.52 · 0.11–60.461/750/39152% higher risk
Tau² = 0.00, I² = 0.0%, p = 0.58
Lin31%0.69 · 0.50–0.95recov. time18 (n)25 (n)
Late treatment31%0.69 · 0.50–0.9518 (n)25 (n)31% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.024
All studies30%0.70 · 0.50–0.961/930/6430% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.02800.511.52+
1 CT: study uses combined treatment
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← Artemisinin
reduces riskArtemisinin
increases risk →
Fig. 10. 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.
Randomized Controlled Trials (RCTs)
Fig. 11 shows a comparison of results for RCTs and observational studies. Random-effects meta-analysis of RCTs shows 47% improvement, compared to 31% for other studies. Fig. 12 shows a forest plot for random-effects meta-analysis of all Randomized Controlled Trials. RCT results are included in Table 1 and Table 2.
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Fig. 11. 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 biases57, and analysis of double-blind RCTs has identified extreme levels of bias58. 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.
Fig. 13. For COVID-19, observational study results do not systematically differ from RCTs, RR 0.97 [0.91‑1.03] across 227 treatments62.
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]46. 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 see66,67.
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.
c19early.org
October 2026

Artemisinin COVID-19 Randomized Controlled Trials

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk
Trieu (RCT)52%0.48 · 0.22–1.06no recov.8/399/21ARTI-19 Chandiwana (RCT)−152%2.52 · 0.11–60.46hosp.1/750/39CT1
Early treatment47%0.53 · 0.24–1.149/1149/6047% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.1
All studies47%0.53 · 0.24–1.149/1149/6047% lower risk
Tau² = 0.00, I² = 0.0%, p = 0.100.511.52+
1 CT: study uses combined treatment
Rotate screen for more detailsIncrease width for more details
← Artemisinin
reduces riskArtemisinin
increases risk →
Fig. 12. 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.
Unreported RCTs
2 artemisinin RCTs have not reported results1,2. The trials report report an estimated total of 1,366 patients. The results are delayed from 2 years to over 3 years.
Media Censorship
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."42.
We analyze media coverage for the 227 treatments we cover using Altmetric68, which reports the number of ~12,000 tracked news outlets that covered each study69. Studies are considered to have received significant media coverage if they were covered by at least 0.5% of the tracked news outlets. Fig. 14, 15, and 16 show the bias toward negative results for low-cost treatments, in contrast to the opposite bias for high-profit treatments. This 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, and high-profit treatments were also more difficult to access, especially for earlier treatment which improves efficacy and minimizes community transmission.
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Fig. 14. Media was more likely to cover negative or inconclusive results for low-cost treatments, and positive results for high-profit treatments.
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Fig. 15. Mainstream media was biased against positive results for low-cost treatments.
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Fig. 16. In contrast to the results for low-cost treatments, mainstream media was biased towards positive results for high-cost treatments.
A combination of factors may have led to the media's suppression 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 banned on Twitter and YouTube.
•
FDA requires "no adequate, approved, and available alternatives"
in order to grant an EUA for novel high-profit interventions, creating a strong 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. Relatively few journalists have a strong ability to analyze clinical trials and are outnumbered by the rest.
•
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, fear, and anxieties. A negative bias may improve ratings and revenue, increasing motivation to continue watching coverage. A combination of low-cost treatments greatly reducing risk conflicts with the negative narrative.
Authority Delay
25 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 hours70,71. 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 delayResult
Post-exposure prophylaxis86% fewer cases72
<24 hours-33 hours symptoms73
24-48 hours-13 hours symptoms73
Inpatients-2.5 hours to improvement74
Fig. 17 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.
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Fig. 17. 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 variants76, for example the Gamma variant shows significantly different characteristics77-80. 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 variants81,82.
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.
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 synergistic54,85-108, 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
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. 18 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.0000000001). Similarly, Fig. 19 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. 20 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.
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Fig. 18. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Fig. 19. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Fig. 18. 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. 21 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Fig. 21. 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
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 results110-113. For artemisinin, there is currently not enough data to evaluate publication bias with high confidence.
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. 22 shows a scatter plot of results for prospective and retrospective studies. 100% of retrospective studies report positive effects, compared to 50% of prospective studies, consistent with a bias toward publishing positive results.
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Fig. 22. Prospective vs. retrospective studies. The diamonds show the results of random-effects meta-analysis.
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. Artemisinin for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 artemisinin 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 artemisinin 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 alone54,85-108. 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 3 studies combine treatments. The results of artemisinin alone may differ. 1 of 2 RCTs use combined treatment.
Reviews
Multiple reviews cover artemisinin for COVID-19, presenting additional background on mechanisms and related results, including114-118.
Perspective
Results compared with other treatments
SARS-CoV-2 infection and replication involves a complex interplay of 500+ host and viral proteins and other factors33-40, providing many therapeutic targets. Over 12,000 compounds have been predicted to reduce COVID-19 risk41, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Fig. 23 shows an overview of the results for artemisinin in the context of multiple COVID-19 treatments, and Fig. 24 shows a plot of efficacy vs. cost for COVID-19 treatments.
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Fig. 23. 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 efficacy119.
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Fig. 24. Efficacy vs. cost for COVID-19 treatments.
Conclusion
Studies to date show that artemisinin is an effective treatment for COVID-19. Significantly lower risk is seen for recovery. One study shows significant benefit. Meta-analysis using the most serious outcome reported shows 34% [11‑51%] lower risk. Results are similar for peer-reviewed studies and better for Randomized Controlled Trials. Results are consistent with early treatment being more effective than late treatment.
Currently there is limited data, with only 217 patients and only 9 control events for the most serious outcome in trials to date. Studies to date are from only 3 different groups.
 
Contact.
Contact us on X at @CovidAnalysis.
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 228,000 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/artmeta.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
Early treatmentRCT · 114 patients · South Africa · Sep 2020 – Aug 2021
Artemisinin for COVID-19
Chandiwana et al. eBioMedicine · November 2022 · NCT04532931
Faster recovery with treatment
Not statistically significant · p = 0.099
High COI low-risk patient RCT in South Africa, showing no significant differences with artesunate-amodiaquine or pyronaridine-artesunate. Submit Corrections or Updates.
Estimated 966 patient artemisinin late treatment RCT with results not reported over 2 years after estimated completion. Submit Corrections or Updates.
Estimated 400 patient artemisinin late treatment RCT with results not reported over 3 years after estimated completion. Submit Corrections or Updates.
Late treatmentRetrospective · 43 patients · China
Artemisinin for COVID-19
Lin et al. China Critical Care Medicine · April 2020
Faster recovery with artemisinin
p = 0.024
Prospective study of 43 hospitalized COVID-19 patients in China showing faster symptom improvement and shorter hospitalization with artesunate. Artesunate 60 mg twice daily for 10 days. Submit Corrections or Updates.
RCT 60 mild-moderate COVID-19 patients showing faster recovery with artemisinin. Submit Corrections or Updates.
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 artemisinin 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 artemisinin for COVID-19 that report a comparison with a control group are included in the main analysis. 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. 25. 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 reduction120. 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 PythonMeta121 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 1125. 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 (3.14.8) with scipy (1.18.1), pythonmeta (1.26), numpy (2.5.3), 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 reliability133. 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 issues135. 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 bias131,132 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 effective70,71.
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/artmeta.html.
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.
Chandiwana, 11/1/2022, Randomized Controlled Trial, South Africa, peer-reviewed, mean age 34.9, 16 authors, study period 3 September, 2020 - 23 August, 2021, this trial uses multiple treatments in the treatment arm (combined with amodiaquine or pyronaridine) - results of individual treatments may vary, trial NCT04532931 (history).
Submit Corrections or Updates.
 risk of hospitalization, 152.0% higher, RR 2.52, p = 1.00, treatment 1 of 75 (1.3%), control 0 of 39 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm).
risk of progression, 28.7% lower, RR 0.71, p = 0.66, treatment 39, control 39, adjusted per study, both artesunate arms.
risk of progression, 31.0% higher, OR 1.31, p = 0.73, treatment 39, control 39, adjusted per study, ASAQ, day 28, Table S9, RR approximated with OR.
risk of progression, 71.0% lower, OR 0.29, p = 0.22, treatment 36, control 39, adjusted per study, PA, day 28, Table S9, RR approximated with OR.
time to WHO zero score, 26.8% lower, HR 0.73, p = 0.10, treatment 39, control 39, both artesunate arms.
time to WHO zero score, 22.5% lower, HR 0.78, p = 0.33, treatment 39, control 39, inverted to make HR<1 favor treatment, ASAQ, Cox proportional hazards, Table S10.
time to WHO zero score, 31.0% lower, HR 0.69, p = 0.17, treatment 36, control 39, inverted to make HR<1 favor treatment, PA, Cox proportional hazards, Table S10.
viral load, 1.1% higher, relative load 1.01, p = 0.76, treatment 39, control 37, both artesunate arms.
viral load, 4.6% higher, relative load 1.05, p = 0.02, treatment mean 3.25 (±0.26) n=39, control mean 3.4 (±0.27) n=37, ASAQ, day 14, Table S7.
viral load, 2.3% lower, relative load 0.98, p = 0.23, treatment mean 3.48 (±0.29) n=36, control mean 3.4 (±0.27) n=37, PA, day 14, Table S7.
risk of no viral clearance, 34.3% higher, RR 1.34, p = 0.18, treatment 39, control 38, adjusted per study, both artesunate arms.
risk of no viral clearance, 25.0% higher, RR 1.25, p = 0.48, treatment 24 of 39 (61.5%), control 25 of 38 (65.8%), adjusted per study, inverted to make RR<1 favor treatment, ASAQ.
risk of no viral clearance, 44.9% higher, RR 1.45, p = 0.25, treatment 23 of 33 (69.7%), control 25 of 38 (65.8%), adjusted per study, inverted to make RR<1 favor treatment, PA.
Trieu, 2/1/2021, Randomized Controlled Trial, India, preprint, median age 44.5, 7 authors, study period 8 October, 2020 - 21 November, 2020, trial CTRI/2020/09/028044 (ARTI-19).
Submit Corrections or Updates.
 risk of no recovery, 52.1% lower, RR 0.48, p = 0.08, treatment 8 of 39 (20.5%), control 9 of 21 (42.9%), NNT 4.5, day 5.
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.
Fowler, 5/18/2024, Randomized Controlled Trial, Canada, trial NCT04330690 (history) (CATCO).
Submit Corrections or Updates.
 Estimated 966 patient RCT with results unknown and over 2 years late.
Gyanwali, 12/1/2022, Randomized Controlled Trial, Nepal, trial NCT05273242 (history).
Submit Corrections or Updates.
 Estimated 400 patient RCT with results unknown and over 3 years late.
Lin, 4/28/2020, retrospective, China, peer-reviewed, 9 authors.
Submit Corrections or Updates.
 recovery time, 31.2% lower, relative time 0.69, p = 0.02, treatment mean 3.33 (±1.91) n=18, control mean 4.84 (±2.19) n=25.
recovery time, 27.9% lower, relative time 0.72, p = 0.04, treatment mean 5.39 (±2.36) n=18, control mean 7.48 (±3.78) n=25, lung lesion absorption start.
recovery time, 17.2% lower, relative time 0.83, p = 0.03, treatment mean 14.11 (±4.16) n=18, control mean 17.04 (±4.42) n=25, lung lesion absorption greater than 70%.
hospitalization time, 8.2% lower, relative time 0.92, p = 0.22, treatment mean 16.56 (±3.71) n=18, control mean 18.04 (±3.97) n=25.
time to viral-, 29.3% lower, relative time 0.71, p = 0.05, treatment mean 4.72 (±2.16) n=18, control mean 6.68 (±3.76) n=25.
Footnotes
a.
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.
b.
Monoclonal antibodies were previously included. Other treatments such as dexamethasone, tocilizumab, and baricitinib were recommended for late stage hospitalized patients.
c.
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.
d.
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 environment127,128. Research also suggests potential cardioprotective effects at lower doses, but cardiotoxicity with excessive dosage129. Bobrowski et al. also indicate negative effects if HCQ and remdesivir are combined.
e.
Peters 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.
References