Studies on Ivermectin show positive results as Covid-19 treatment

The use of Ivermectin for the prevention and treatment of Covid-19 has been the subject of much debate. The World Health Organisation‘s recommendation against Ivermectin as an alternative treatment for Covid-19 is shrouded in suspicion as the WHO’s second biggest donor is the Bill and Melinda Gates Foundation (BMGF). Bill Gates also founded and funds The Vaccine Alliance (GAVI). The connection and clear conflict of interest is thus astounding. This 3,000 word synopsis, done by Rubin van Niekerk, is on Bryant’s peer reviewed meta analysis published in the American Journal of Therapeutics about 60 studies on the treatment impact of Ivermectin on Covid-19. Van Niekerk notes that ‘Ivermectin studies vary widely, which makes the consistently positive results even more remarkable. It is both insightful and important. – Nadya Swart

Ivermectin Meta Analysis Synopsis

By Rubin van Niekerk*

Meta analysis of 60 studies on Ivermectin and Covid 19 by Bryant, published in the American Journal of Therapeutics. (Version 93 Updated 21/6/21)

This is a brief 3000-word synopsis of the analysis of all significant studies concerning the use of ivermectin 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. Random effects of meta-analysis results for all studies, for studies within each treatment stage, for mortality results, for COVID-19 case results, for viral clearance results, for peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after exclusions are presented.

Please read the original 18 000-word comprehensive research analysis should you need more detail and insight into the methodology on

Studies Prophylaxis Early treatment Late treatment Patients Authors
All studies 60 85% [75‑91%] 76% [59‑86%] 46% [29‑59%] 18,931 549
With exclusions 51 87% [75‑93%] 78% [69‑84%] 54% [33‑68%] 14,554 495
Peer-reviewed 34 88% [70‑95%] 77% [62‑86%] 38% [15‑55%] 7,431 343
Randomized Controlled Trials 31 83% [39‑95%] 69% [57‑77%] 40% [11‑60%] 5,316 340
Mortality results 22 96% [42‑100%] 81% [46‑93%] 61% [38‑76%] 7,690 205
Percentage improvement with ivermectin treatment

•The probability that an ineffective treatment generated results as positive as the 60 studies to date is estimated to be 1 in 2 trillion (p = 0.00000000000045).

Heterogeneity arises from many factors including treatment delay, population, effect measured, variants, and regimens. The consistency of positive results is remarkable. Heterogeneity is low in specific cases, for example early treatment mortality.

•While many treatments have some level of efficacy, they do not replace vaccines and other measures to avoid infection. Only 27% of ivermectin studies show zero events in the treatment arm.

•Elimination of COVID-19 is a race against viral evolution. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. All practical, effective, and safe means should be used. Not doing so increases the risk of COVID-19 becoming endemic; and increases mortality, morbidity, and collateral damage.

•Administration with food, often not specified, may significantly increase plasma and tissue concentration.

•The evidence base is much larger and has much lower conflict of interest than typically used to approve drugs.

•All data to reproduce this paper and sources are in the appendix. See [Bryant, Hariyanto, Hill, Kory, Lawrie, Nardelli] for other meta analyses, all with similar results confirming effectiveness.



Treatment time Number of studies reporting positive effects Total number of studies Percentage of studies reporting positive effects Probability of an equal or greater percentage of positive results from an ineffective treatment Random effects meta-analysis results
Early treatment 23 25 92.0% 0.0000097
1 in 103 thousand
76% improvement
RR 0.24 [0.14‑0.41] p < 0.0001
Late treatment 19 21 90.5% 0.00011
1 in 9 thousand
46% improvement
RR 0.54 [0.41‑0.71] p < 0.0001
Prophylaxis 14 14 100% 0.000061
1 in 16 thousand
85% improvement
RR 0.15 [0.09‑0.25] p < 0.0001
All studies 56 60 93.3% 0.00000000000045
1 in 2 trillion
71% improvement
RR 0.29 [0.23‑0.38] p < 0.0001

Prophylaxis refers to regularly taking medication before becoming sick, to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.

Randomized Controlled Trials (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].

Random effects meta-analysis for Randomized Controlled Trial mortality results only.

Treatment time Number of studies reporting positive effects Total number of studies Percentage of studies reporting positive effects Probability of an equal or greater percentage of positive results from an ineffective treatment Random effects meta-analysis results
Randomized Controlled Trials 28 31 90.3% 0.0000023
1 in 430 thousand
64% improvement
RR 0.36 [0.26‑0.51] p < 0.0001
Randomized Controlled Trials (excluding late treatment) 18 19 94.7% 0.000038
1 in 26 thousand
75% improvement
RR 0.25 [0.17‑0.38] p < 0.0001

Table 2. Summary of RCT results.


All studies are included in the main analysis to avoid bias in the selection of studies. This bias evaluation is based on full analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study.


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. An antiviral may be very effective when used early but may not be effective in late stage disease and may even be harmful. Other medications might be beneficial for late-stage complications, while early use may not be effective or may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours [McLeanTreanor].

Effectiveness may depend critically on treatment delay.

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 (as in [López-Medina]).

Effect measured.

Efficacy may differ significantly depending on the effect measured, for example a treatment may be highly 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 thousands of different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study.


Effectiveness may depend strongly on the dosage and treatment regimen. Higher dosages have been found to be more successful for ivermectin [Hill]. Method of administration may also be critical. [Guzzo] show that the plasma concentration of ivermectin is much higher when administered with food (Figure 20: geometric mean AUC 2.6 times higher). Many ivermectin studies specify fasting, or they do not specify administration. Fasting administration is expected to reduce effectiveness for COVID-19 due to lower plasma and tissue concentrations. Note that this is different to anthelmintic use in the gastrointestinal tract where fasting is recommended.


The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.

Figure 20. Mean plasma concentration (ng/ml) profiles of ivermectin following single oral doses of 30mg (fed and fasted administration), from [Guzzo].
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 extremely late treatment, the outcome may be negative, even though the treatment may be highly effective when used earlier.

Looking at all studies is valuable for providing an overview of all research, and important to avoid cherry-picking, but the resulting estimate does not apply to specific cases such as early treatment in high-risk populations.

Ivermectin studies vary widely in all the factors above, which makes the consistently positive results even more remarkable.

The probability that an ineffective treatment generated results as positive as the 60 studies to date is estimated to be 1 in 2 trillion (p = 0.00000000000045).


Publishing is often biased towards positive results, which we would need to adjust for when analysing the percentage of positive results. For ivermectin, 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 likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. News coverage of ivermectin studies is extremely biased. Only one study to date has received significant press coverage in western media [López-Medina], which is neither the largest or the least biased study, and is one of the two studies with the most critical issues as discussed earlier.

4 of the 60 studies compare against other treatments rather than placebo. Currently ivermectin shows better results than these other treatments, however ivermectin may show greater improvement when compared to placebo. 13 of 60 studies combine treatments, for example ivermectin + doxycycline. The results of ivermectin alone may differ. 4 of 31 RCTs use combined treatment, three with doxycycline, and one with iota-carrageenan. 1 of 60 studies currently have minimal published details available.

Typical meta-analyses involve subjective selection criteria, effect extraction rules, and study bias evaluation, which can be used to bias results towards a specific outcome. To avoid bias we include all studies and use a pre-specified method to extract results from all studies (we also present results after exclusions). The results to date are overwhelmingly positive, very consistent, and very insensitive to potential selection criteria, effect extraction rules, and/or bias evaluation.

Additional meta analyses confirming the effectiveness of ivermectin can be found in [Bryant, Hill, Kory, Lawrie]. Figure 22 shows a comparison of mortality results across meta-analyses. [Kory] also review epidemiological data and provide suggested treatment regimens.

The evidence supporting ivermectin for COVID-19 far exceeds the typical amount of evidence used for the approval of treatments. [Lee] shows that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTsTable 3 and Table 4 compare the amount of evidence for ivermectin compared to that used for other COVID-19 approvals, and that used by WHO for the approval of ivermectin for scabies and strongyloidiasis. Table 5 compares US CDC recommendations for ibuprofen and ivermectin.

Indication Studies Patients Status
Strongyloidiasis [Kory (B)] 5 591 Approved
Scabies [Kory (B)] 10 852 Approved
COVID‑19 60 18,931 Pending
COVID‑19 RCTs 31 5,316

Table 3. WHO ivermectin approval status.

Medication Studies Patients Improvement Status
Budesonide (UK) 1 1,779 17% Approved
Remdesivir (USA) 1 1,063 31% Approved
Casiri/imdevimab (USA) 1 799 66% Approved
Ivermectin evidence 60 18,931 71% [62‑77%] Pending

Table 4. Evidence base used for other COVID-19 approvals compared with the ivermectin evidence base.

Ibuprofen Ivermectin
(for scabies)
(for COVID-19)
Lives saved 0 0 >500,000
Deaths per year ~450 <1 <1
CDC recommended Yes Yes No
Based on 0 RCTs 10 RCTs
852 patients
31 RCTs
5,316 patients

Table 5. Comparison of CDC recommendations [Kory (B)].

WHO Analysis

WHO updated their treatment recommendations on 3/30/2021 [WHO]. For ivermectin they reported a mortality odds ratio of 0.19 [0.09-0.36] based on 7 studies with 1,419 patients. They do not specify which trials they included. The report is inconsistent, with a forest plot that only shows 4 studies with mortality results.

Despite this extremely positive result, they recommended only using ivermectin in clinical trials. The analysis contains many flaws [Kory (C)]:

  • Of the 60 studies (31 RCTs), they only included 16.
  • They excluded all 14 prophylaxis studies (4 RCTs).
  • There was no protocol for data exclusion.
  • Trials included in the original UNITAID search protocol [Hill]were excluded.
  • They excluded all epidemiological evidence, although WHO has considered such evidence in the past.
  • They combine early treatment and late treatment studies and do not provide heterogeneity information. As above, early treatment is more successful, so pooling late treatment studies will obscure the effectiveness of early treatment. They chose not to do subgroup analysis by disease severity across trials, although treatment delay is clearly a critical factor in COVID-19 treatment, the analysis is easily done (as above), and it is well known that the studies for ivermectin and many other treatments clearly show greater effectiveness for early treatment.
  • WHO downgraded the quality of trials compared to the UNITAID systematic review team [Hill]and a separate international expert guideline group that has long worked with the WHO [Bryant].
  • They disregarded their own guidelines that stipulate quality assessments should be upgraded when there is evidence of a large magnitude effect (which there is), and when there is evidence of a dose-response relationship (which there is). They claim there is no dose-response relationship, while the UNITAID systematic review team found a clear relationship [Hill].
  • Their risk of bias assessments does not match the actual risk of bias in studies. For example they classify [López-Medina]as low risk of bias, however this study has many issues making the results unreliable [Covid Analysis], even prompting an open letter from over 170 physicians concluding that the study is fatally flawed [Open Letter][Gonzalez]is also classified as low risk of bias but is a study with very late-stage severe condition high-comorbidity patients. There is a clear treatment delay-response relationship and very late-stage treatment is not expected to be as effective as early treatment. Conversely, much higher quality studies were classified as high risk of bias.
  • Although WHO’s analysis is called a “living guideline”, it is rarely updated and very outdated.
  • A single person served as Methods Chair, member of the Guidance Support Collaboration Committee, and member of the Living Systematic Review/NMA team.
  • Public statements from people involved in the analysis suggest substantial bias. For example, a co-chair reportedly said that “the data available was sparse and likely based on chance” [Reuters]. As above, the data is comprehensive, and we estimate the probability that an ineffective treatment generated results as positive as observed to be 1 in 2 trillion (p= 0.00000000000045). The clinical team lead refers to their analysis of ivermectin as “fighting this overuse of unproven therapies … without evidence of efficacy” [Reuters], despite the extensive evidence of efficacy from the 60 studies by 549 scientists with 18,931 patients. People involved may be more favourable to late stage treatment of COVID-19, for example the co-chair recommended treating severe COVID-19 with remdesivir [Rochwerg].

In summary, although WHO’s analysis predicts that over 2 million fewer people would be dead if ivermectin was used from early in the pandemic, they recommend against use outside trials.

Use early in the pandemic was proposed by Kitasato University including the co-discoverer of ivermectin, Dr. Satoshi Ōmura. They requested Merck conduct clinical trials of ivermectin for COVID-19 in Japan, because Merck has priority to submit an application for an expansion of ivermectinʼs indications. Merck declined [Yagisawa].

Merck Analysis

Merck has recommended against ivermectin [Merck]. They stated that there is “no scientific basis for a potential therapeutic effect against COVID-19 from pre-clinical studies”. This is contradicted by many papers and studies, including [Arévalo, Bello, Choudhury, de Melo, DiNicolantonio, DiNicolantonio (B), Errecalde, Eweas, Francés-Monerris, Heidary, Jans, Jeffreys, Kalfas, Kory, Lehrer, Li, Mody, Mountain Valley MD, Qureshi, Saha, Surnar, Udofia, Wehbe, Yesilbag, Zaidi, Zatloukal].

They state that there is “no meaningful evidence for clinical activity or clinical efficacy in patients with COVID-19 disease”. This is contradicted by numerous studies including [Afsar, Alam, Aref, Babalola, Behera, Behera (B), Bernigaud, Budhiraja, Bukhari, Cadegiani, Carvallo (B), Carvallo (C), Chaccour, Chahla, Chahla (B), Chowdhury, Elalfy, Elgazzar, Elgazzar (B), Espitia-Hernandez, Faisal, Hashim, Huvemek, Khan, Kirti, Lima-Morales, Loue, Mahmud, Merino, Mohan, Morgenstern, Mourya, Niaee, Okumuş, Samaha, Seet].

They also claim that there is “a concerning lack of safety data in the majority of studies”. Safety analysis is found in [Descotes, Errecalde, Guzzo, Kory, Madrid], and safety data can be found in most studies, including [Abd-Elsalam, Afsar, Ahmed, Aref, Babalola, Behera (B), Bhattacharya, Biber, Bukhari, Camprubí, Carvallo, Chaccour, Chahla (B), Chowdhury, Elalfy, Elgazzar, Espitia-Hernandez, Gorial, Huvemek, Khan, Kishoria, Krolewiecki, Lima-Morales, Loue, López-Medina, Mahmud, Mohan, Morgenstern, Mourya, Niaee, Okumuş, Pott-Junior, Seet, Shahbaznejad, Shouman, Spoorthi, Szente Fonseca].

Merck has several conflicts of interest:

  • Merck has committed to give ivermectin away for free “as much as needed, for as long as needed” in the Mectizan® Donation Program [Merck (B)], to help eliminate river blindness.
  • Merck has their own new COVID-19 treatments MK-7110 (formerly CD24Fc) [Adams] and Molnupiravir (MK-4482) [Wikipedia]. Merck has a ~$1.2B agreement to supply molnupiravir to the US government, if it receives EUA or approval [Khan (B)].
  • Ivermectin is off patent, there are many manufacturers, and Merck is unlikely to be able to compete with low cost manufacturers.
  • Promoting the use of low cost off-patent medications compared to new products may be undesirable to some shareholders.
  • Japan requested Merck conduct clinical trials early in the pandemic and they declined. Merck may be reluctant to admit this mistake [Yagisawa].


Ivermectin is an effective treatment for COVID-19. The probability that an ineffective treatment generated results as positive as the 60 studies to date is estimated to be 1 in 2 trillion (p = 0.00000000000045). As expected for an effective treatment, early treatment is more successful, with an estimated reduction of 76% in the effect measured using random effects meta-analysis (RR 0.24 [0.14-0.41]). 81% and 96% lower mortality is observed for early treatment and prophylaxis (RR 0.19 [0.07-0.54] and 0.04 [0.00-0.58]). Statistically significant improvements are seen for mortality, ventilation, hospitalization, cases, and viral clearance. The consistency of positive results across a wide variety of heterogeneous studies is remarkable, with 93% of the 60 studies reporting positive effects (28 statistically significant in isolation).

Appendix 1. Methods and Study Results

We performed ongoing searches of PubMed, medRxiv,, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site, which regularly receives submissions of studies upon publication. Search terms were ivermectin and COVID-19 or SARS-CoV-2, or simply ivermectin. Automated searches are performed every hour with notifications of new matches. The broad search terms result in a large volume of new studies daily which are reviewed for inclusion. All studies regarding the use of ivermectin for COVID-19 that report a comparison with a control group are included in the main analysis. Sensitivity analysis is performed, excluding studies with critical issues, epidemiological studies, and studies with minimal available information. This is a living analysis and is updated regularly.

Clinical outcome is considered more important than PCR testing status. 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 no room for an effective treatment to do better). When results provide an odds ratio, we computed the relative risk when possible, or converted to a relative risk according to [Zhang]. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported including propensity score matching (PSM), the PSM results are used. 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 1 [Sweeting]. Results are all expressed with RR < 1.0 suggesting effectiveness. Most results are the relative risk of something negative. If studies report relative times, results are expressed as the ratio of the time for the ivermectin group versus the time for the control group. Calculations are done in Python (3.9.1) with scipy (1.6.3), pythonmeta (1.23), numpy (1.20.3), statsmodels (0.12.2), and plotly (4.14.3). The forest plots are computed using PythonMeta [Deng] with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case). The forest plots show simplified dosages for comparison, these are the total dose in the first four days for treatment, and the monthly dose for prophylaxis, for a 70kg person.

We received no funding, and this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.


We will update the paper as new studies are released or with any corrections. Please submit updates and corrections at the bottom of this page. Please send us corrections, updates, or comments. Vaccines and treatments are both extremely valuable and complementary. All practical, effective, and safe means should be used. Elimination of COVID-19 is a race against viral evolution. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Denying the efficacy of any method increases the risk of COVID-19 becoming endemic and increases mortality, morbidity, and collateral damage. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. Treatment protocols for physicians are available from the FLCCC.

  • Rubin van Niekerk. Editor of @gaypagessa Magazine. SAGMJ President. Co-organiser of South African Car of the Year.

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