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Favipiravir for COVID-19: real-time meta analysis of 61 studies

Covid Analysis, June 2023
https://c19early.org/ameta.html
 
0 0.5 1 1.5+ All studies 20% 61 29,214 Improvement, Studies, Patients Relative Risk Mortality 15% 32 23,314 Ventilation -1% 9 11,214 ICU admission -28% 17 5,759 Hospitalization -5% 19 6,493 Progression 21% 10 12,134 Recovery 14% 24 5,749 Viral clearance 21% 25 4,897 RCTs 23% 30 5,901 Peer-reviewed 22% 58 28,493 Early 23% 18 11,571 Late 19% 43 17,643 Favipiravir for COVID-19 c19early.org/a Jun 2023 Favorsfavipiravir Favorscontrol after exclusions
Statistically significant improvements are seen for recovery and viral clearance. 29 studies from 29 independent teams in 16 different countries show statistically significant improvements in isolation (14 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 20% [10‑30%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies.
0 0.5 1 1.5+ All studies 20% 61 29,214 Improvement, Studies, Patients Relative Risk Mortality 15% 32 23,314 Ventilation -1% 9 11,214 ICU admission -28% 17 5,759 Hospitalization -5% 19 6,493 Progression 21% 10 12,134 Recovery 14% 24 5,749 Viral clearance 21% 25 4,897 RCTs 23% 30 5,901 Peer-reviewed 22% 58 28,493 Early 23% 18 11,571 Late 19% 43 17,643 Favipiravir for COVID-19 c19early.org/a Jun 2023 Favorsfavipiravir Favorscontrol after exclusions
Studies to date do not show a significant benefit for mortality. Potential risks of the mechanism of action include the creation of dangerous variants, and mutagenicity, carcinogenicity, teratogenicity, and embryotoxicity [Hadj Hassine, Waters, Zhirnov].
No treatment, vaccine, or intervention is 100% effective and available. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments are more effective. Only 8% of favipiravir studies show zero events with treatment.
All data to reproduce this paper and sources are in the appendix. Other meta analyses for favipiravir can be found in [Hung, Lan], showing significant improvements for hospital discharge, improvement, and viral clearance.
Evolution of COVID-19 clinical evidence Favipiravir p=0.00032 Acetaminophen p=0.0000018 2020 2021 2022 2023 Effective Harmful c19early.org June 2023 meta analysis results (pooled effects) 100% 50% 0% -50%
Percentage improvement with favipiravir (more)
All studies Early treatment Late treatment Studies Patients Authors
All studies20% [10‑30%]
***
23% [-13‑48%]19% [7‑28%]
**
61 29,214 1,018
Randomized Controlled TrialsRCTs23% [9‑36%]
**
18% [-22‑45%]24% [7‑38%]
**
30 5,901 639
Mortality15% [-1‑28%]44% [-33‑76%]13% [-4‑27%] 32 23,314 510
Highlights
Favipiravir reduces risk for COVID-19 with very high confidence for viral clearance and in pooled analysis, high confidence for recovery, low confidence for mortality, and very low confidence for progression, however increased risk is seen with high confidence for ICU admission. Potential risks include the creation of dangerous variants, carcinogenicity, and genotoxicity.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 51 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ruzhentsova (RCT) 6% 0.94 [0.78-1.14] hosp. 3/112 2/56 Improvement, RR [CI] Treatment Control Udwadia (RCT) 40% 0.60 [0.38-0.95] recov. time 75 (n) 75 (n) Sawanpanyalert 68% 0.32 [0.15-0.66] progression n/a n/a Holubar (DB RCT) 89% 0.11 [0.01-2.02] hosp. 0/75 4/74 Alattar (PSM) 33% 0.67 [0.28-1.61] death 8/387 12/387 Bosaeed (DB RCT) -619% 7.19 [0.38-138] ICU 3/112 0/119 FLARE Lowe (DB RCT) -202% 3.02 [0.13-72.6] ICU 1/59 0/60 Adhikari (RCT) -40% 1.40 [0.57-3.44] no improv. 10/38 6/32 Tsuzuki 13% 0.87 [0.52-1.46] death 2,532 (n) 5,122 (n) Qadir 97% 0.03 [0.00-0.47] death 0/125 17/125 Usanma Koban 86% 0.14 [0.02-0.70] viral+ 47 (n) 79 (n) Sirijatuphat (RCT) 64% 0.36 [0.20-0.64] improv. 62 (n) 31 (n) McMahon (RCT) -1% 1.01 [0.34-3.03] oxygen 6/99 6/100 PRESECO Golan (DB RCT) 67% 0.33 [0.01-8.12] death 0/599 1/588 Bruminhent -227% 3.27 [1.43-7.50] progression n/a n/a Chandiwana (RCT) -13% 1.13 [0.23-5.46] progression 37 (n) 39 (n) CT​2 Vaezi (DB RCT) -105% 2.05 [0.40-10.6] hosp. 4/38 2/39 PLATCOV Luvira (RCT) -6% 1.06 [0.69-1.63] viral rate 116 (n) 132 (n) Tau​2 = 0.32, I​2 = 62.0%, p = 0.18 Early treatment 23% 0.77 [0.52-1.13] 35/4,513 50/7,058 23% improvement Cai 69% 0.31 [0.10-0.96] pneumonia 35 (n) 45 (n) Improvement, RR [CI] Treatment Control Ivashchenko (RCT) 46% 0.54 [0.33-0.88] viral+ 15/40 14/20 Lou (RCT) -422% 5.22 [0.28-96.2] ICU 2/9 0/10 Pushkar (RCT) 14% 0.86 [0.73-1.00] no recov. 73/100 85/100 Khamis (RCT) 15% 0.85 [0.28-2.59] death 5/44 6/45 OT​1 CT​2 Solaymani.. (RCT) -19% 1.19 [0.70-2.04] death 26/190 21/183 OT​1 Zhao (RCT) 59% 0.41 [0.16-1.03] viral+ 7/36 9/19 Aghajani 26% 0.74 [0.43-1.27] death 40 (n) 951 (n) Alamer -56% 1.56 [0.73-3.36] death 12/233 21/223 Almoosa -42% 1.42 [0.90-2.25] death 33/110 24/116 Shinkai (SB RCT) 37% 0.63 [0.40-0.98] imp. time 107 (n) 49 (n) Assiri (ICU) -79% 1.79 [0.33-8.02] death 11/67 3/51 ICU patients Kulzhanova 88% 0.12 [0.04-0.37] no improv. 3/40 25/40 Chen (RCT) -3% 1.03 [0.15-7.22] ICU 2/116 2/120 OT​1 Alotaibi 57% 0.43 [0.18-1.01] death 244 (n) 193 (n) OT​1 Tabarsi (RCT) 30% 0.70 [0.17-2.88] death 3/32 4/30 OT​1 FIGHT-COVID-19 Atipornwa.. (RCT) 23% 0.77 [0.35-1.67] death 10/100 13/100 OT​1 CT​2 Damayanti 54% 0.46 [0.22-0.92] no recov. 96 (n) 96 (n) Shenoy (DB RCT) -29% 1.29 [0.60-2.77] death 14/175 11/178 Chuah (RCT) -1154% 12.54 [0.76-208] death 5/250 0/250 Finberg (RCT) -200% 3.00 [0.13-70.3] death 1/25 0/25 Al Mutair (ICU) 7% 0.93 [0.77-1.12] death 119/269 128/269 ICU patients OT​1 Kurniyanto 48% 0.52 [0.22-1.25] death 10/325 9/152 Cilli 38% 0.62 [0.24-1.63] death 5/23 8/23 Al-Muhsen -263% 3.63 [1.06-12.4] death 156 (n) 442 (n) Yulia 85% 0.15 [0.02-1.02] death 432 (all patients) Uyaroğlu (PSM) 67% 0.33 [0.01-7.96] death 0/42 1/42 OT​1 AlQahtani (RCT) -196% 2.96 [0.12-71.1] death 1/54 0/52 Shinada 7% 0.93 [0.45-1.89] hosp. 17 (n) 17 (n) Hassaniazad (RCT) 68% 0.32 [0.07-1.48] death 2/32 6/31 OT​1 Hafez -3% 1.03 [0.68-1.56] viral+ 59 (n) 1,446 (n) CT​2 Rahman (DB RCT) 89% 0.11 [0.01-0.75] no improv. 1/19 8/16 Tehrani (RCT) 34% 0.66 [0.34-1.26] hosp. 10/38 16/40 Abdulrahman (ICU) 3% 0.97 [0.81-1.18] death 74/193 593/1,506 ICU patients Acar Sevinc (ICU) 16% 0.84 [0.62-1.12] death 57/85 12/15 ICU patients OT​1 Tawfik 96% 0.04 [0.00-0.26] death 1/103 17/62 Babayigit -184% 2.84 [1.27-6.14] ventilation 47/325 17/977 Behboodikhah 68% 0.32 [0.05-1.83] death 95 (n) 2,079 (n) PIONEER Shah (RCT) 26% 0.74 [0.44-1.23] death 26/251 34/248 Alosaimi (PSM) 80% 0.20 [0.01-4.03] death 0/37 2/37 OT​1 Hartantri 76% 0.24 [0.11-0.54] death n/a n/a Alshamrani (PSM) -14% 1.14 [0.96-1.35] death 326/1,159 316/1,380 Arfijanto 51% 0.49 [0.26-0.94] viral+ 8/37 55/125 Tau​2 = 0.06, I​2 = 69.0%, p = 0.0022 Late treatment 19% 0.81 [0.72-0.93] 909/5,408 1,460/11,803 19% improvement All studies 20% 0.80 [0.70-0.90] 944/9,921 1,510/18,861 20% improvement 61 favipiravir COVID-19 studies c19early.org/a Jun 2023 Tau​2 = 0.07, I​2 = 68.0%, p = 0.00032 Effect extraction pre-specified(most serious outcome, see appendix) 1 OT: comparison with other treatment2 CT: study uses combined treatment Favors favipiravir Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ruzhentsova (RCT) 6% hospitalization Relative Risk [CI] Udwadia (RCT) 40% recovery Sawanpanyalert 68% progression Holubar (DB RCT) 89% hospitalization Alattar (PSM) 33% death Bosaeed (DB RCT) -619% ICU admission FLARE Lowe (DB RCT) -202% ICU admission Adhikari (RCT) -40% improvement Tsuzuki 13% death Qadir 97% death Usanma Koban 86% viral- Sirijatup.. (RCT) 64% improv. McMahon (RCT) -1% oxygen therapy PRESECO Golan (DB RCT) 67% death Bruminhent -227% progression Chandiwana (RCT) -13% progression CT​2 Vaezi (DB RCT) -105% hospitalization PLATCOV Luvira (RCT) -6% viral rate Tau​2 = 0.32, I​2 = 62.0%, p = 0.18 Early treatment 23% 23% improvement Cai 69% pneumonia Ivashchenko (RCT) 46% viral- Lou (RCT) -422% ICU admission Pushkar (RCT) 14% recovery Khamis (RCT) 15% death OT​1 CT​2 Solayman.. (RCT) -19% death OT​1 Zhao (RCT) 59% viral- Aghajani 26% death Alamer -56% death Almoosa -42% death Shinkai (SB RCT) 37% imp. time Assiri (ICU) -79% death ICU patients Kulzhanova 88% improvement Chen (RCT) -3% ICU admission OT​1 Alotaibi 57% death OT​1 Tabarsi (RCT) 30% death OT​1 FIGHT-COVID-19 Atipornw.. (RCT) 23% death OT​1 CT​2 Damayanti 54% recovery Shenoy (DB RCT) -29% death Chuah (RCT) -1154% death Finberg (RCT) -200% death Al Mutair (ICU) 7% death ICU patients OT​1 Kurniyanto 48% death Cilli 38% death Al-Muhsen -263% death Yulia 85% death Uyaroğlu (PSM) 67% death OT​1 AlQahtani (RCT) -196% death Shinada 7% hospitalization Hassaniazad (RCT) 68% death OT​1 Hafez -3% viral- CT​2 Rahman (DB RCT) 89% improvement Tehrani (RCT) 34% hospitalization Abdulrahman (ICU) 3% death ICU patients Acar Sevinc (ICU) 16% death ICU patients OT​1 Tawfik 96% death Babayigit -184% ventilation Behboodikhah 68% death PIONEER Shah (RCT) 26% death Alosaimi (PSM) 80% death OT​1 Hartantri 76% death Alshamrani (PSM) -14% death Arfijanto 51% viral- Tau​2 = 0.06, I​2 = 69.0%, p = 0.0022 Late treatment 19% 19% improvement All studies 20% 20% improvement 61 favipiravir COVID-19 studies c19early.org/a Jun 2023 Tau​2 = 0.07, I​2 = 68.0%, p = 0.00032 Effect extraction pre-specifiedRotate device for footnotes/details Favors favipiravir Favors control
B
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C
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D
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Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. 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. C. Results within the context of multiple COVID-19 treatments. 0.9% of 3,989 proposed treatments show efficacy [c19early.org]. D. Timeline of results in favipiravir 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 0.7 months, compared to using all studies.
We analyze all significant studies concerning the use of favipiravir 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 after exclusions.
Figure 2 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.
Figure 2. Treatment stages.
2 In Vitro studies support the efficacy of favipiravir [Unal, Yildiz Pekoz].
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.
Table 1 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, 9, 10, and 11 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, viral clearance, and peer reviewed studies.
Table 1. Random effects meta-analysis for all stages combined, with different exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  ** p<0.01  **** p<0.0001.
Improvement Studies Patients Authors
All studies20% [10‑30%]
***
61 29,214 1,018
After exclusions23% [10‑33%]
***
50 24,326 861
Peer-reviewed studiesPeer-reviewed22% [11‑32%]
***
58 28,493 978
Randomized Controlled TrialsRCTs23% [9‑36%]
**
30 5,901 639
Mortality15% [-1‑28%]32 23,314 510
VentilationVent.-1% [-56‑35%]9 11,214 308
ICU admissionICU-28% [-57‑-5%]
*
17 5,759 317
HospitalizationHosp.-5% [-29‑15%]19 6,493 342
Recovery14% [2‑25%]
*
24 5,749 482
Viral21% [10‑30%]
***
25 4,897 391
RCT mortality8% [-21‑30%]11 3,482 283
RCT hospitalizationRCT hosp.14% [-13‑35%]10 1,205 205
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval. * p<0.05  ** p<0.01  **** p<0.0001.
Early treatment Late treatment
All studies23% [-13‑48%]19% [7‑28%]
**
After exclusions23% [-13‑48%]21% [7‑32%]
**
Peer-reviewed studiesPeer-reviewed24% [-13‑49%]21% [8‑31%]
**
Randomized Controlled TrialsRCTs18% [-22‑45%]24% [7‑38%]
**
Mortality44% [-33‑76%]13% [-4‑27%]
VentilationVent.-2% [-60‑35%]0% [-69‑41%]
ICU admissionICU-381% [-4086‑45%]-26% [-55‑-3%]
*
HospitalizationHosp.-4% [-137‑54%]-8% [-34‑13%]
Recovery10% [-16‑31%]16% [2‑28%]
*
Viral10% [-9‑26%]40% [23‑54%]
****
RCT mortality67% [-712‑99%]7% [-22‑29%]
RCT hospitalizationRCT hosp.-55% [-177‑14%]25% [9‑39%]
**
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Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for ventilation.
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Figure 6. Random effects meta-analysis for ICU admission.
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Figure 7. Random effects meta-analysis for hospitalization.
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Figure 8. Random effects meta-analysis for progression.
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Figure 9. Random effects meta-analysis for recovery.
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Figure 10. Random effects meta-analysis for viral clearance.
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Figure 11. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] 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. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 12 shows a comparison of results for RCTs and non-RCT studies. Figure 13, 14, and 15 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.
Bias in clinical research may be defined as something that tends to make conclusions differ systematically from the truth. RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases [Jadad], and analysis of double-blind RCTs has identified extreme levels of bias [Gøtzsche]. 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, 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.
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 51 treatments we have analyzed, 64% 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).
RCTs have a bias against finding an effect for interventions that are widely available — patients that believe they need the intervention are more likely to decline participation and take the intervention. RCTs for favipiravir are more likely to enroll low-risk participants that do not need treatment to recover, making the results less applicable to clinical practice. This bias is likely to be greater for widely known treatments, and may be greater when the risk of a serious outcome is overstated. This bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
Evidence shows that non-RCT trials can also provide reliable results. [Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows 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 Internet survey bias could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see [Deaton, Nichol].
Currently, 37 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of the 37 treatments with statistically significant efficacy/harm, 23 have been confirmed in RCTs, with a mean delay of 4.4 months. For the 14 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 10 are all consistent with the overall results (benefit or harm), with 8 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and aspirin.
We need to evaluate each trial on its own merits. 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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Figure 12. Results for RCTs and non-RCT studies.
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Figure 13. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 14. Random effects meta-analysis for RCT mortality results.
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Figure 15. Random effects meta-analysis for RCT hospitalization results.
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 may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.
The studies excluded are as below. Figure 16 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Abdulrahman], very late stage, ICU patients.
[Acar Sevinc], very late stage, ICU patients.
[Al Mutair], very late stage, ICU patients.
[Arfijanto], unadjusted results with no group details.
[Assiri], unadjusted results with no group details; very late stage, ICU patients.
[Babayigit], substantial unadjusted confounding by indication possible.
[Cilli], unadjusted results with no group details.
[Damayanti], minimal details provided.
[Khamis], study compares against another treatment showing significant efficacy.
[Kurniyanto], unadjusted results with no group details.
[Tawfik], unadjusted results with minimal group details.
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Figure 16. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Heterogeneity in COVID-19 studies arises from many factors including:
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 hours [McLean, Treanor]. Baloxavir studies for influenza also show that treatment delay is critical — [Ikematsu] report an 86% reduction in cases for post-exposure prophylaxis, [Hayden] 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] report only 2.5 hours improvement for inpatient treatment.
Table 3. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post exposure prophylaxis86% fewer cases [Ikematsu]
<24 hours-33 hours symptoms [Hayden]
24-48 hours-13 hours symptoms [Hayden]
Inpatients-2.5 hours to improvement [Kumar]
Figure 17 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 51 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 17. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 51 treatments.
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 (as in [López-Medina]).
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics [Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. [Williams] analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. [Xu] analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 18. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
Currently, 37 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 94% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.1 months. When restricting to RCTs only, 55% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 2.9 months.
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Figure 18. 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.
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. This may have a greater effect than pooling different outcomes such as mortality and hospitalization. For example a treatment may have 50% efficacy for mortality but only 40% for hospitalization when used within 48 hours. However efficacy could be 0% when used late.
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.
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 results [Boulware, Meeus, Meneguesso].
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.
Figure 19 shows a scatter plot of results for prospective and retrospective studies. 38% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 56% of prospective studies, consistent with a bias toward publishing negative results. The median effect size for retrospective studies is 38% improvement, compared to 10% for prospective studies, suggesting a potential bias towards publishing results showing higher efficacy.
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Figure 19. Prospective vs. retrospective studies. The diamonds show the results of random effects meta-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. Figure 20 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 < 0.05 [Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley]. 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.
Figure 20. Example funnel plot analysis for simulated perfect trials.
Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Favipiravir for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 favipiravir 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 favipiravir trials represent the optimal conditions for efficacy.
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 by 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 affiliated with special interests 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 alone [Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu]. 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, vaccine, 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.
11 of the 61 studies compare against other treatments, which may reduce the effect seen. 4 of 61 studies combine treatments. The results of favipiravir alone may differ. 3 of 30 RCTs use combined treatment. Other meta analyses for favipiravir can be found in [Hung, Lan], showing significant improvements for one or more of hospital discharge, improvement, and viral clearance.
Favipiravir is an effective treatment for COVID-19. Statistically significant improvements are seen for recovery and viral clearance. 29 studies from 29 independent teams in 16 different countries show statistically significant improvements in isolation (14 for the most serious outcome). Meta analysis using the most serious outcome reported shows 20% [10‑30%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies.
Studies to date do not show a significant benefit for mortality. Potential risks of the mechanism of action include the creation of dangerous variants, and mutagenicity, carcinogenicity, teratogenicity, and embryotoxicity [Hadj Hassine, Waters, Zhirnov].
0 0.5 1 1.5 2+ Mortality 3% Improvement Relative Risk c19early.org/a Abdulrahman et al. Favipiravir for COVID-19 ICU Is very late treatment with favipiravir beneficial for COVID-19? Retrospective 1,699 patients in Saudi Arabia (Jun - Aug 2020) No significant difference in mortality Abdulrahman et al., J. Ayub Medical College Abbo.., doi:10.55519/JAMC-03-10305 Favors favipiravir Favors control
[Abdulrahman] Retrospective 1,699 ICU patients in Saudi Arabia, 193 treated with favipiravir, showing no significant difference in mortality.
0 0.5 1 1.5 2+ Mortality 16% Improvement Relative Risk Ventilation 10% c19early.org/a Acar Sevinc et al. NCT04645433 Favipiravir ICU Is very late treatment with favipiravir beneficial for COVID-19? Retrospective 100 patients in Turkey (March - May 2020) Study compares with lopinavir/ritonavir, results vs. placebo may differ Lower mortality with favipiravir (not stat. sig., p=0.38) Acar Sevinc, S., SiSli Etfal Hastanesi Tip Bulte.., doi:10.14744/SEMB.2021.35902 Favors favipiravir Favors lopinavir/ri..
[Acar Sevinc] Retrospective 100 ICU patients in Turkey, showing improved survival with favipiravir vs. lopinavir/ritonavir.
0 0.5 1 1.5 2+ Improvement -40% Improvement Relative Risk Improvement (b) -36% Improvement (c) -64% c19early.org/a Adhikari et al. Favipiravir for COVID-19 RCT EARLY Is early treatment with favipiravir beneficial for COVID-19? RCT 70 patients in Nepal Worse improvement with favipiravir (not stat. sig., p=0.57) Adhikari et al., Int. J. Infectious Diseases, doi:10.1016/j.ijid.2021.12.109 Favors favipiravir Favors control
[Adhikari] Preliminary report for an RCT in Nepal with 38 favipiravir patients and 32 control patients, showing no significant differences. There were no serious side effects.
0 0.5 1 1.5 2+ Mortality 26% Improvement Relative Risk c19early.org/a Aghajani et al. Favipiravir for COVID-19 LATE Is late treatment with favipiravir beneficial for COVID-19? Retrospective 991 patients in Iran Lower mortality with favipiravir (not stat. sig., p=0.28) Aghajani et al., J. Medical Virology, doi:10.1002/jmv.27053 Favors favipiravir Favors control
[Aghajani] Retrospective 991 hospitalized patients in Iran focusing on aspirin use but also showing results for HCQ, remdesivir, and favipiravir.
0 0.5 1 1.5 2+ Mortality 7% Improvement Relative Risk ARDS -9% ICU time -34% Hospitalization time -37% c19early.org/a Al Mutair et al. Favipiravir for COVID-19 ICU Is very late treatment with favipiravir beneficial for COVID-19? Retrospective 538 patients in Saudi Arabia (April 2020 - March 2021) Study compares with various, results vs. placebo may differ Longer ICU admission (p=0.001) and hospitalization (p=0.001) Al Mutair et al., J. Infection and Public Health, doi:10.1016/j.jiph.2022.01.013 Favors favipiravir Favors various
[Al Mutair] Retrospective 269 favipiravir ICU patients in Saudi Arabia and 269 matched controls receiving different treatments, showing no significant difference.
0 0.5 1 1.5 2+ Mortality -263% Improvement Relative Risk Oxygen therapy 41% Hospitalization time -40% c19early.org/a Al-Muhsen et al. Favipiravir for COVID-19 LATE Is late treatment with favipiravir beneficial for COVID-19? Prospective study of 598 patients in Saudi Arabia (Jun 2020 - Jan 2021) Higher mortality (p=0.04) and lower need for oxygen therapy (p<0.0001) Al-Muhsen et al., Frontiers in Medicine, doi:10.3389/fmed.2022.826247 Favors favipiravir Favors control
[Al-Muhsen] Prospective observational study of 598 hospitalized patients in Saudi Arabia, showing higher risk of mortality and longer hospitalization time with favipiravir.
0 0.5 1 1.5 2+ Mortality -56% Improvement Relative Risk Ventilation 90% Adjusted discharge ratio 49% c19early.org/a Alamer et al. Favipiravir for COVID-19 LATE TREATMENT Is late treatment with favipiravir beneficial for COVID-19? Retrospective 456 patients in Saudi Arabia Lower ventilation (p<0.0001) and higher discharge (p<0.0001) Alamer et al., Current Medical Research and Opin.., doi:10.1080/03007995.2021.1920900 Favors favipiravir Favors control
[Alamer] Retrospective 234 favipiravir and 223 control patients in Saudi Arabia, showing shorter time to discharge and lower progression to ventilation, but no significant difference in mortality.
0 0.5 1 1.5 2+ Mortality 33% Improvement Relative Risk Clinical improvement -2% primary Days to clinical improvem.. -6% Viral clearance 44% c19early.org/a Alattar et al. Favipiravir for COVID-19 EARLY Is early treatment with favipiravir beneficial for COVID-19? PSM retrospective 774 patients in Qatar (May - July 2020) Improved viral clearance with favipiravir (p=0.0000014) Alattar et al., J. Infection and Public Health, doi:10.1016/j.jiph.2022.08.011 Favors favipiravir Favors control
[Alattar] PSM retrospective with 1,493 patients, showing significantly improved viral clearance with favipiravir. There were no significant differences in clinical improvement or mortality. Mortality was lower (2.1% vs 3.1%), without statistical significance with the small number of events.
0 0.5 1 1.5 2+ Mortality -42% Improvement Relative Risk Mortality, day 28 -149% Mortality, day 14 62% ICU admission -90% Recovery time -11% c19early.org/a Almoosa et al. Favipiravir for COVID-19 LATE Is late treatment with favipiravir beneficial for COVID-19? Retrospective 226 patients in Saudi Arabia Higher ICU admission with favipiravir (p=0.02) Almoosa et al., J. Infection and Public Health, doi:10.1016/j.jiph.2021.08.022 Favors favipiravir Favors control
[Almoosa] Retrospective 226 COVID-19 pneumonia patients, 110 treated with favipiravir, showing higher mortality (p=0.1) and ICU admission (p=0.02) with treatment in multivariate analysis.
0 0.5 1 1.5 2+ Mortality 80% Improvement Relative Risk Hospitalization time -75% Time to discharge -40% c19early.org/a Alosaimi et al. Favipiravir for COVID-19 LATE Is late treatment with favipiravir beneficial for COVID-19? PSM retrospective 74 patients in Saudi Arabia (Apr 2020 - Mar 2021) Study compares with HCQ, results vs. placebo may differ Lower mortality with favipiravir (not stat. sig., p=0.49) Alosaimi et al., Pharmaceuticals, doi:10.3390/ph15121456 Favors favipiravir Favors HCQ
[Alosaimi] Retrospective 200 hospitalized COVID-19 patients in Saudi Arabia, showing no significant difference in outcomes between HCQ and favipiravir.
0 0.5 1 1.5 2+ Mortality 57% Improvement Relative Risk c19early.org/a Alotaibi et al. Favipiravir for COVID-19 LATE Is late treatment with favipiravir beneficial for COVID-19? Retrospective 437 patients in Saudi Arabia Study compares with HCQ, results vs. placebo may differ Lower mortality with favipiravir (not stat. sig., p=0.051) Alotaibi et al., Int. J. General Medicine, 2021:14 Favors favipiravir Favors HCQ
[Alotaibi] Retrospective hospitalized patients in Saudi Arabia, showing lower mortality with favipiravir compared to HCQ, not quite reaching statistical significance. Authors do not indicate the factors behind which therapy was chosen. May be subject to significant confounding by indication and confounding by time.
0 0.5 1 1.5 2+ Mortality -196% Improvement Relative Risk ICU admission 76% Recovery -42% Viral clearance 43% c19early.org/a AlQahtani et al. NCT04387760 Favipiravir RCT LATE Is late treatment with favipiravir beneficial for COVID-19? RCT 106 patients in Bahrain (August 2020 - March 2021) Lower ICU admission (p=0.2) and worse recovery (p=0.51), not stat. sig. AlQahtani et al., Scientific Reports, doi:10.1038/s41598-022-08794-w Favors favipiravir Favors control
[AlQahtani] RCT with 54 favipiravir, 51 HCQ, and 52 SOC hospitalized patients in Bahrain, showing no significant differences. Viral clearance improved with both treatments, but did not reach statistical significance with the small sample size.
0 0.5 1 1.5 2+ Mortality -14% Improvement Relative Risk Progression -2% ICU time -19% Hospitalization time -29% c19early.org/a Alshamrani et al. Favipiravir for COVID-19 LATE Is late treatment with favipiravir beneficial for COVID-19? PSM retrospective 2,539 patients in Saudi Arabia (Mar 2020 - Jan 2021) Longer ICU admission (p=0.005) and hospitalization (p=0.001) Alshamrani et al., Saudi Pharmaceutical J., doi:10.1016/j.jsps.2023.02.004 Favors favipiravir Favors control
[Alshamrani] PSM retrospective 29 hospitals in Saudi Arabia, showing higher mortality with favipiravir treatment, without statistical significance.
0 0.5 1 1.5 2+ Delayed viral clearance 51% Improvement Relative Risk c19early.org/a Arfijanto et al. Favipiravir for COVID-19 LATE Is late treatment with favipiravir beneficial for COVID-19? Retrospective 162 patients in Indonesia (June - December 2021) Improved viral clearance with favipiravir (p=0.02) Arfijanto et al., Pathophysiology, doi:10.3390/pathophysiology30020016 Favors favipiravir Favors control
[Arfijanto] Retrospective 162 hospitalized COVID-19 patients in Indonesia, showing lower incidence of delayed viral clearance with favipiravir treatment in unadjusted results.
0 0.5 1 1.5 2+ Mortality -79% Improvement Relative Risk c19early.org/a Assiri et al. Favipiravir for COVID-19 ICU PATIENTS Is very late treatment with favipiravir beneficial for COVID-19? Retrospective 118 patients in Saudi Arabia Higher mortality with favipiravir (not stat. sig., p=0.5) Assiri et al., J. Infection and Public Health, doi:10.1016/j.jiph.2021.08.030 Favors favipiravir Favors control
[Assiri] Retrospective 118 ICU patients in Saudi Arabia showing no significant differences in unadjusted results with zinc, vitamin D, and favipiravir treatment.
0 0.5 1 1.5 2+ Mortality 23% Improvement Relative Risk Progression 60% Time to viral- 9% primary Time to viral- (b) 9% primary c19early.org/a Atipornwanich et al. NCT04303299 FIGHT-COVID-19 Favipiravir RCT LATE Is late treatment with favipiravir+combined treatments beneficial for COVID-19? RCT 200 patients in Thailand Trial compares with oseltamivir, results vs. placebo may differ Lower progression with favipiravir+combined treatments (p=0.0085) Atipornwanich et al., SSRN Electronic J., doi:10.2139/ssrn.3936499 Favors favipiravir Favors oseltamivir
[Atipornwanich] RCT 200 moderate/severe patients in Thailand, showing significantly lower progression with favipiravir vs. oseltamivir. NCT04303299.
0 0.5 1 1.5 2+ Ventilation -184% Improvement Relative Risk ICU admission -181% Hospitalization time -100% c19early.org/a Babayigit et al. Favipiravir for COVID-19 LATE Is late treatment with favipiravir beneficial for COVID-19? Retrospective 1,302 patients in Turkey (March - July 2020) Higher ventilation (p=0.011) and ICU admission (p=0.001) Babayigit et al., Frontiers in Medicine, doi:10.3389/fmed.2022.894126 Favors favipiravir Favors control
[Babayigit] Retrospective 1,472 hospitalized patients in Turkey, showing a higher ICU admission and ventilation with favipiravir. Results may be subject to confounding by indication.
0 0.5 1 1.5 2+ Mortality 68% Improvement Relative Risk c19early.org/a Behboodikhah et al. Favipiravir for COVID-19 LATE Is late treatment with favipiravir beneficial for COVID-19? Retrospective 2,174 patients in Iran Lower mortality with favipiravir (not stat. sig., p=0.2) Behboodikhah et al., Iranian J. Science and Tech.., doi:10.1007/s40995-022-01351-0 Favors favipiravir Favors control
[Behboodikhah] Retrospective 2,174 hospitalized patients showing significantly shorter length of stay with favipiravir treatment.
0 0.5 1 1.5 2+ ICU admission -619% Improvement Relative Risk Hospitalization -219% Time to clinical improve.. -12% Time to viral clearance -15% primary c19early.org/a Bosaeed et al. NCT04464408 Favipiravir RCT EARLY TREATMENT Is early treatment with favipiravir beneficial for COVID-19? Double-blind RCT 231 patients in Saudi Arabia (Jul 2020 - Aug 2021) Higher ICU admission (p=0.11) and hospitalization (p=0.16), not stat. sig. Bosaeed et al., Clinical Microbiology and Infect.., doi:10.1016/j.cmi.2021.12.026 Favors favipiravir Favors control
[Bosaeed] RCT with 112 favipiravir and 119 control patients showing no significant differences in outcomes. Viral clearance and clinical recovery for patients treated within 48 hours was better than those treated later. NCT04464408.
0 0.5 1 1.5 2+ Progression -227% Improvement Relative Risk c19early.org/a Bruminhent et al. Favipiravir for COVID-19 EARLY Is early treatment with favipiravir beneficial for COVID-19? Retrospective study in Thailand (April - May 2021) Higher progression with favipiravir (p=0.005) Bruminhent et al., Tropical Medicine and Infecti.., doi:10.3390/tropicalmed7090238 Favors favipiravir Favors control
[Bruminhent] Retrospective 514 patients in Thailand, showing higher risk of progression with favipiravir treatment.
0 0.5 1 1.5 2+ Improvement in CT 69% Improvement Relative Risk Viral clearance 71% c19early.org/a Cai et al. Favipiravir for COVID-19 LATE TREATMENT Is late treatment with favipiravir beneficial for COVID-19? Retrospective 80 patients in China Lower pneumonia (p=0.042) and improved viral clearance (p=0.025) Cai et al., Engineering, doi:10.1016/j.eng.2020.03.007 Favors favipiravir Favors control
[Cai] Comparison of 35 FPV patients and 35 LPV/RTV patients, showing significant improvements in chest CT and faster viral clearance with FPV.
0 0.5 1 1.5 2+ Progression -13% Improvement Relative Risk