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Nigella Sativa for COVID-19: real-time meta analysis of 9 studies
Covid Analysis, August 10, 2022, DRAFT
https://c19ns.com/meta.html
0 0.5 1 1.5+ All studies 61% 9 2,161 Improvement, Studies, Patients Relative Risk Mortality 73% 4 1,192 ICU admission 61% 1 381 Hospitalization 53% 4 672 Recovery 68% 3 751 Cases 62% 2 481 Viral clearance 71% 2 250 RCTs 66% 7 1,532 RCT mortality 73% 4 1,192 Peer-reviewed 61% 8 1,848 Prophylaxis 51% 3 734 Early 83% 4 967 Late 39% 2 460 Nigella Sativa for COVID-19 c19ns.com Aug 2022 Favorsnigella sativa Favorscontrol after exclusions
Statistically significant improvements are seen for mortality, hospitalization, recovery, cases, and viral clearance. 6 studies from 5 independent teams in 4 different countries show statistically significant improvements in isolation (3 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 61% [54‑68%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Early treatment is more effective than late treatment.
0 0.5 1 1.5+ All studies 61% 9 2,161 Improvement, Studies, Patients Relative Risk Mortality 73% 4 1,192 ICU admission 61% 1 381 Hospitalization 53% 4 672 Recovery 68% 3 751 Cases 62% 2 481 Viral clearance 71% 2 250 RCTs 66% 7 1,532 RCT mortality 73% 4 1,192 Peer-reviewed 61% 8 1,848 Prophylaxis 51% 3 734 Early 83% 4 967 Late 39% 2 460 Nigella Sativa for COVID-19 c19ns.com Aug 2022 Favorsnigella sativa Favorscontrol after exclusions
While many treatments have some level of efficacy, they do not replace vaccines and other measures to avoid infection. Only 22% of nigella sativa studies show zero events in the treatment arm. Multiple treatments are typically used in combination, and other treatments may be more effective.
No treatment, vaccine, or intervention is 100% available and effective for all variants. All practical, effective, and safe means should be used. Denying the efficacy of treatments increases mortality, morbidity, collateral damage, and endemic risk.
All data to reproduce this paper and sources are in the appendix.
Highlights
Studies to date suggest that Nigella Sativa reduces risk for COVID-19 with very high confidence for recovery, cases, and in pooled analysis, and high confidence for mortality, hospitalization, and viral clearance.
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 43 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ashraf (RCT) 82% 0.18 [0.04-0.80] death 2/157 11/156 CT​1 Improvement, RR [CI] Treatment Control Al-Haidari (RCT) 96% 0.04 [0.00-0.70] death 0/160 14/259 Koshak (RCT) 75% 0.25 [0.03-2.22] hosp. 1/91 4/92 Bencheqr.. (DB RCT) 69% 0.31 [0.01-7.19] hosp. 0/29 1/23 Tau​2 = 0.00, I​2 = 0.0%, p = 0.0011 Early treatment 83% 0.17 [0.06-0.49] 3/437 30/530 83% improvement Karimi (RCT) 51% 0.49 [0.09-2.66] death 2/192 4/189 CT​1 Improvement, RR [CI] Treatment Control Setayesh (RCT) -8% 1.08 [0.07-16.7] death 1/38 1/41 CT​1 Tau​2 = 0.00, I​2 = 0.0%, p = 0.51 Late treatment 39% 0.61 [0.15-2.57] 3/230 5/230 39% improvement Al-Haidari 62% 0.38 [0.31-0.46] symp. case 68/188 180/188 Improvement, RR [CI] Treatment Control Shehab 0% 1.00 [0.36-2.74] severe case 4/39 22/214 Chandra (RCT) 49% 0.51 [0.16-1.59] cases 4/52 8/53 CT​1 Tau​2 = 0.13, I​2 = 45.0%, p = 0.013 Prophylaxis 51% 0.49 [0.28-0.86] 76/279 210/455 51% improvement All studies 61% 0.39 [0.32-0.46] 82/946 245/1,215 61% improvement 9 nigella sativa COVID-19 studies c19ns.com Aug 2022 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors nigella sativa Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ashraf (RCT) 82% death CT​1 Improvement Relative Risk [CI] Al-Haidari (RCT) 96% death Koshak (RCT) 75% hospitalization Bencheqr.. (DB RCT) 69% hospitalization Tau​2 = 0.00, I​2 = 0.0%, p = 0.0011 Early treatment 83% 83% improvement Karimi (RCT) 51% death CT​1 Setayesh (RCT) -8% death CT​1 Tau​2 = 0.00, I​2 = 0.0%, p = 0.51 Late treatment 39% 39% improvement Al-Haidari 62% symp. case Shehab 0% severe case Chandra (RCT) 49% case CT​1 Tau​2 = 0.13, I​2 = 45.0%, p = 0.013 Prophylaxis 51% 51% improvement All studies 61% 61% improvement 9 nigella sativa COVID-19 studies c19ns.com Aug 2022 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors nigella sativa Favors control
Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the distribution of effects reported in studies. C. History of all reported effects (chronological within treatment stages).
Introduction
We analyze all significant studies concerning the use of nigella sativa 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, for studies within each treatment stage, for individual outcomes, for peer-reviewed studies, for 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.
Preclinical and Phase I Research
9 In Silico studies support the efficacy of nigella sativa [Banerjee, Bouchentouf, Duru, Hardianto, Khan, Maiti, Mir, Rizvi, Sherwani].
2 In Vitro studies support the efficacy of nigella sativa [Esharkawy, Sherwani].
[Thomas] present a phase I clinical study investigating a novel formulation of nigella sativa that may be more effective for COVID-19.
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
Figure 3 shows a visual overview of the results, with details in Table 1 and Table 2. Figure 4, 5, 6, 7, 8, 9, 10, and 11 show forest plots for a random effects meta-analysis of all studies with pooled effects, mortality results, ICU admission, hospitalization, recovery, cases, viral clearance, and peer reviewed studies.
0 0.5 1 1.5+ ALL STUDIES MORTALITY ICU ADMISSION HOSPITALIZATION RECOVERY CASES VIRAL CLEARANCE RANDOMIZED CONTROLLED TRIALS RCT MORTALITY PEER-REVIEWED After Exclusions ALL STUDIES All Prophylaxis Early Late Nigella Sativa for COVID-19 C19NS.COM AUG 2022
Figure 3. Overview of results.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Early treatment 4 4 100% 83% improvement
RR 0.17 [0.06‑0.49]
p = 0.0011
Late treatment 1 2 50.0% 39% improvement
RR 0.61 [0.15‑2.57]
p = 0.51
Prophylaxis 3 3 100% 51% improvement
RR 0.49 [0.28‑0.86]
p = 0.013
All studies 8 9 88.9% 61% improvement
RR 0.39 [0.32‑0.46]
p < 0.0001
Table 1. Results by treatment stage.
Studies Early treatment Late treatment Prophylaxis PatientsAuthors
All studies 983% [51‑94%]39% [-157‑85%]51% [14‑72%] 2,161 152
With exclusions 883% [51‑94%]39% [-157‑85%]62% [54‑68%] 1,908 145
Peer-reviewed 884% [29‑97%]39% [-157‑85%]51% [14‑72%] 1,848 104
Randomized Controlled TrialsRCTs 783% [51‑94%]39% [-157‑85%]49% [-59‑84%] 1,532 142
Table 2. Results by treatment stage for all studies and with different exclusions.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ashraf (RCT) 82% 0.18 [0.04-0.80] death 2/157 11/156 CT​1 Improvement, RR [CI] Treatment Control Al-Haidari (RCT) 96% 0.04 [0.00-0.70] death 0/160 14/259 Koshak (RCT) 75% 0.25 [0.03-2.22] hosp. 1/91 4/92 Bencheqr.. (DB RCT) 69% 0.31 [0.01-7.19] hosp. 0/29 1/23 Tau​2 = 0.00, I​2 = 0.0%, p = 0.0011 Early treatment 83% 0.17 [0.06-0.49] 3/437 30/530 83% improvement Karimi (RCT) 51% 0.49 [0.09-2.66] death 2/192 4/189 CT​1 Improvement, RR [CI] Treatment Control Setayesh (RCT) -8% 1.08 [0.07-16.7] death 1/38 1/41 CT​1 Tau​2 = 0.00, I​2 = 0.0%, p = 0.51 Late treatment 39% 0.61 [0.15-2.57] 3/230 5/230 39% improvement Al-Haidari 62% 0.38 [0.31-0.46] symp. case 68/188 180/188 Improvement, RR [CI] Treatment Control Shehab 0% 1.00 [0.36-2.74] severe case 4/39 22/214 Chandra (RCT) 49% 0.51 [0.16-1.59] cases 4/52 8/53 CT​1 Tau​2 = 0.13, I​2 = 45.0%, p = 0.013 Prophylaxis 51% 0.49 [0.28-0.86] 76/279 210/455 51% improvement All studies 61% 0.39 [0.32-0.46] 82/946 245/1,215 61% improvement 9 nigella sativa COVID-19 studies c19ns.com Aug 2022 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors nigella sativa Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ashraf (RCT) 82% death CT​1 Improvement Relative Risk [CI] Al-Haidari (RCT) 96% death Koshak (RCT) 75% hospitalization Bencheqr.. (DB RCT) 69% hospitalization Tau​2 = 0.00, I​2 = 0.0%, p = 0.0011 Early treatment 83% 83% improvement Karimi (RCT) 51% death CT​1 Setayesh (RCT) -8% death CT​1 Tau​2 = 0.00, I​2 = 0.0%, p = 0.51 Late treatment 39% 39% improvement Al-Haidari 62% symp. case Shehab 0% severe case Chandra (RCT) 49% case CT​1 Tau​2 = 0.13, I​2 = 45.0%, p = 0.013 Prophylaxis 51% 51% improvement All studies 61% 61% improvement 9 nigella sativa COVID-19 studies c19ns.com Aug 2022 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors nigella sativa Favors control
Figure 4. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ashraf (RCT) 82% 0.18 [0.04-0.80] 2/157 11/156 CT​1 Improvement, RR [CI] Treatment Control Al-Haidari (RCT) 96% 0.04 [0.00-0.70] 0/160 14/259 Tau​2 = 0.00, I​2 = 0.0%, p = 0.0026 Early treatment 87% 0.13 [0.04-0.49] 2/317 25/415 87% improvement Karimi (RCT) 51% 0.49 [0.09-2.66] 2/192 4/189 CT​1 Improvement, RR [CI] Treatment Control Setayesh (RCT) -8% 1.08 [0.07-16.7] 1/38 1/41 CT​1 Tau​2 = 0.00, I​2 = 0.0%, p = 0.51 Late treatment 39% 0.61 [0.15-2.57] 3/230 5/230 39% improvement All studies 73% 0.27 [0.09-0.77] 5/547 30/645 73% improvement 4 nigella sativa COVID-19 mortality results c19ns.com Aug 2022 Tau​2 = 0.15, I​2 = 12.3%, p = 0.014 1 CT: study uses combined treatment Favors nigella sativa Favors control
Figure 5. Random effects meta-analysis for mortality results.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Karimi (RCT) 61% 0.39 [0.08-2.00] 2/192 5/189 CT​1 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.26 Late treatment 61% 0.39 [0.08-2.00] 2/192 5/189 61% improvement All studies 61% 0.39 [0.08-2.00] 2/192 5/189 61% improvement 1 nigella sativa COVID-19 ICU result c19ns.com Aug 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.26 1 CT: study uses combined treatment Favors nigella sativa Favors control
Figure 6. Random effects meta-analysis for ICU admission.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Koshak (RCT) 75% 0.25 [0.03-2.22] hosp. 1/91 4/92 Improvement, RR [CI] Treatment Control Bencheqr.. (DB RCT) 69% 0.31 [0.01-7.19] hosp. 0/29 1/23 Tau​2 = 0.00, I​2 = 0.0%, p = 0.15 Early treatment 73% 0.27 [0.04-1.61] 1/120 5/115 73% improvement Karimi (RCT) 70% 0.30 [0.15-0.61] hosp. 184 (n) 174 (n) CT​1 Improvement, RR [CI] Treatment Control Setayesh (RCT) 29% 0.71 [0.63-0.81] hosp. time 38 (n) 41 (n) CT​1 Tau​2 = 0.31, I​2 = 81.7%, p = 0.1 Late treatment 50% 0.50 [0.22-1.15] 0/222 0/215 50% improvement All studies 53% 0.47 [0.24-0.92] 1/342 5/330 53% improvement 4 nigella sativa COVID-19 hospitalization results c19ns.com Aug 2022 Tau​2 = 0.21, I​2 = 54.1%, p = 0.027 1 CT: study uses combined treatment Favors nigella sativa Favors control
Figure 7. Random effects meta-analysis for hospitalization.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ashraf (RCT) 84% 0.16 [0.11-0.24] no recov. 107 (n) 103 (n) CT​1 Improvement, RR [CI] Treatment Control Koshak (RCT) 43% 0.57 [0.42-0.78] no recov. 34/91 60/92 Tau​2 = 0.76, I​2 = 96.2%, p = 0.059 Early treatment 69% 0.31 [0.09-1.05] 34/198 60/195 69% improvement Karimi (RCT) 67% 0.33 [0.17-0.65] no recov. 184 (n) 174 (n) CT​1 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.0013 Late treatment 67% 0.33 [0.17-0.65] 0/184 0/174 67% improvement All studies 68% 0.32 [0.13-0.76] 34/382 60/369 68% improvement 3 nigella sativa COVID-19 recovery results c19ns.com Aug 2022 Tau​2 = 0.54, I​2 = 92.4%, p = 0.0096 1 CT: study uses combined treatment Favors nigella sativa Favors control
Figure 8. Random effects meta-analysis for recovery.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Al-Haidari 62% 0.38 [0.31-0.46] symp. case 68/188 180/188 Improvement, RR [CI] Treatment Control Chandra (RCT) 49% 0.51 [0.16-1.59] cases 4/52 8/53 CT​1 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Prophylaxis 62% 0.38 [0.32-0.46] 72/240 188/241 62% improvement All studies 62% 0.38 [0.32-0.46] 72/240 188/241 62% improvement 2 nigella sativa COVID-19 case results c19ns.com Aug 2022 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 1 CT: study uses combined treatment Favors nigella sativa Favors control
Figure 9. Random effects meta-analysis for cases.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ashraf (RCT) 82% 0.18 [0.12-0.27] viral+ 107 (n) 103 (n) CT​1 Improvement, RR [CI] Treatment Control Bencheqr.. (DB RCT) 43% 0.57 [0.22-1.43] viral+ 5/21 8/19 Tau​2 = 0.52, I​2 = 79.7%, p = 0.03 Early treatment 71% 0.29 [0.10-0.89] 5/128 8/122 71% improvement All studies 71% 0.29 [0.10-0.89] 5/128 8/122 71% improvement 2 nigella sativa COVID-19 viral clearance results c19ns.com Aug 2022 Tau​2 = 0.52, I​2 = 79.7%, p = 0.03 1 CT: study uses combined treatment Favors nigella sativa Favors control
Figure 10. Random effects meta-analysis for viral clearance.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Al-Haidari (RCT) 96% 0.04 [0.00-0.70] death 0/160 14/259 Improvement, RR [CI] Treatment Control Koshak (RCT) 75% 0.25 [0.03-2.22] hosp. 1/91 4/92 Bencheqr.. (DB RCT) 69% 0.31 [0.01-7.19] hosp. 0/29 1/23 Tau​2 = 0.00, I​2 = 0.0%, p = 0.016 Early treatment 84% 0.16 [0.03-0.71] 1/280 19/374 84% improvement Karimi (RCT) 51% 0.49 [0.09-2.66] death 2/192 4/189 CT​1 Improvement, RR [CI] Treatment Control Setayesh (RCT) -8% 1.08 [0.07-16.7] death 1/38 1/41 CT​1 Tau​2 = 0.00, I​2 = 0.0%, p = 0.51 Late treatment 39% 0.61 [0.15-2.57] 3/230 5/230 39% improvement Al-Haidari 62% 0.38 [0.31-0.46] symp. case 68/188 180/188 Improvement, RR [CI] Treatment Control Shehab 0% 1.00 [0.36-2.74] severe case 4/39 22/214 Chandra (RCT) 49% 0.51 [0.16-1.59] cases 4/52 8/53 CT​1 Tau​2 = 0.13, I​2 = 45.0%, p = 0.013 Prophylaxis 51% 0.49 [0.28-0.86] 76/279 210/455 51% improvement All studies 61% 0.39 [0.33-0.47] 80/789 234/1,059 61% improvement 8 nigella sativa COVID-19 peer reviewed trials c19ns.com Aug 2022 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors nigella sativa Favors control
Figure 11. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] analyze 356 COVID-19 trials, finding no significant evidence that peer-reviewed studies are more trustworthy. They also show extremely slow review times during a 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.
Exclusions
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which 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 12 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Shehab], unadjusted results with no group details.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ashraf (RCT) 82% 0.18 [0.04-0.80] death 2/157 11/156 CT​1 Improvement, RR [CI] Treatment Control Al-Haidari (RCT) 96% 0.04 [0.00-0.70] death 0/160 14/259 Koshak (RCT) 75% 0.25 [0.03-2.22] hosp. 1/91 4/92 Bencheqr.. (DB RCT) 69% 0.31 [0.01-7.19] hosp. 0/29 1/23 Tau​2 = 0.00, I​2 = 0.0%, p = 0.0011 Early treatment 83% 0.17 [0.06-0.49] 3/437 30/530 83% improvement Karimi (RCT) 51% 0.49 [0.09-2.66] death 2/192 4/189 CT​1 Improvement, RR [CI] Treatment Control Setayesh (RCT) -8% 1.08 [0.07-16.7] death 1/38 1/41 CT​1 Tau​2 = 0.00, I​2 = 0.0%, p = 0.51 Late treatment 39% 0.61 [0.15-2.57] 3/230 5/230 39% improvement Al-Haidari 62% 0.38 [0.31-0.46] symp. case 68/188 180/188 Improvement, RR [CI] Treatment Control Chandra (RCT) 49% 0.51 [0.16-1.59] cases 4/52 8/53 CT​1 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Prophylaxis 62% 0.38 [0.32-0.46] 72/240 188/241 62% improvement All studies 63% 0.37 [0.31-0.45] 78/907 223/1,001 63% improvement 8 nigella sativa COVID-19 studies after exclusions c19ns.com Aug 2022 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors nigella sativa Favors control
Figure 12. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Randomized Controlled Trials (RCTs)
Figure 13 shows the distribution of results for Randomized Controlled Trials and other studies, and a chronological history of results. The median effect size for RCTs is 69% improvement, compared to 31% for other studies. Figure 14 and 15 show forest plots for a random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. Table 3 summarizes the results.
Figure 13. The distribution of results for Randomized Controlled Trials and other studies, and a chronological history of results.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ashraf (RCT) 82% 0.18 [0.04-0.80] death 2/157 11/156 CT​1 Improvement, RR [CI] Treatment Control Al-Haidari (RCT) 96% 0.04 [0.00-0.70] death 0/160 14/259 Koshak (RCT) 75% 0.25 [0.03-2.22] hosp. 1/91 4/92 Bencheqr.. (DB RCT) 69% 0.31 [0.01-7.19] hosp. 0/29 1/23 Tau​2 = 0.00, I​2 = 0.0%, p = 0.0011 Early treatment 83% 0.17 [0.06-0.49] 3/437 30/530 83% improvement Karimi (RCT) 51% 0.49 [0.09-2.66] death 2/192 4/189 CT​1 Improvement, RR [CI] Treatment Control Setayesh (RCT) -8% 1.08 [0.07-16.7] death 1/38 1/41 CT​1 Tau​2 = 0.00, I​2 = 0.0%, p = 0.51 Late treatment 39% 0.61 [0.15-2.57] 3/230 5/230 39% improvement Chandra (RCT) 49% 0.51 [0.16-1.59] cases 4/52 8/53 CT​1 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.25 Prophylaxis 49% 0.51 [0.16-1.59] 4/52 8/53 49% improvement All studies 66% 0.34 [0.17-0.67] 10/719 43/813 66% improvement 7 nigella sativa COVID-19 Randomized Controlled Trials c19ns.com Aug 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.0018 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors nigella sativa Favors control
Figure 14. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ashraf (RCT) 82% 0.18 [0.04-0.80] 2/157 11/156 CT​1 Improvement, RR [CI] Treatment Control Al-Haidari (RCT) 96% 0.04 [0.00-0.70] 0/160 14/259 Tau​2 = 0.00, I​2 = 0.0%, p = 0.0026 Early treatment 87% 0.13 [0.04-0.49] 2/317 25/415 87% improvement Karimi (RCT) 51% 0.49 [0.09-2.66] 2/192 4/189 CT​1 Improvement, RR [CI] Treatment Control Setayesh (RCT) -8% 1.08 [0.07-16.7] 1/38 1/41 CT​1 Tau​2 = 0.00, I​2 = 0.0%, p = 0.51 Late treatment 39% 0.61 [0.15-2.57] 3/230 5/230 39% improvement All studies 73% 0.27 [0.09-0.77] 5/547 30/645 73% improvement 4 nigella sativa COVID-19 RCT mortality results c19ns.com Aug 2022 Tau​2 = 0.15, I​2 = 12.3%, p = 0.014 1 CT: study uses combined treatment Favors nigella sativa Favors control
Figure 15. Random effects meta-analysis for RCT mortality results.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Randomized Controlled Trials 6 7 85.7% 66% improvement
RR 0.34 [0.17‑0.67]
p = 0.0018
RCT mortality results 3 4 75.0% 73% improvement
RR 0.27 [0.09‑0.77]
p = 0.014
Table 3. Randomized Controlled Trial results.
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 hours [McLean, Treanor]. Figure 16 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 43 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 16. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 43 treatments. Early treatment is critical.
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 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.
Variants.
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].
Regimen.
Effectiveness may depend strongly on the dosage and treatment regimen.
Other treatments.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
Medication quality.
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.
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 the treatment may be very effective when used earlier.
In general, by combining heterogeneous studies, as all meta analyses do, we run the risk of obscuring an effect by including studies where the treatment is less effective, not effective, or harmful.
When including studies where a treatment is less effective we expect the estimated effect size to be lower than that for the optimal case. We do not a priori expect that pooling all studies will create a positive result for an effective treatment. 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 pooled results for all studies, we also present individual outcome and treatment time analyses, which are more relevant 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 results [Boulware, Meeus, Meneguesso]. For nigella sativa, 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.
0% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 75% of prospective studies, consistent with a bias toward publishing negative results. The median effect size for retrospective studies is 0% improvement, compared to 66% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy. Figure 17 shows a scatter plot of results for prospective and retrospective studies.
Figure 17. Prospective vs. retrospective studies.
Funnel plot analysis.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 18 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 18. Example funnel plot analysis for simulated perfect trials.
Conflicts of interest.
Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Nigella Sativa for COVID-19 lacks this because it is an inexpensive and widely available supplement. In contrast, most COVID-19 nigella sativa 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 nigella sativa trials represent the optimal conditions for efficacy.
Early/late vs. mild/moderate/severe.
Some analyses classify treatment based on early/late administration (as we do here), while others distinguish between mild/moderate/severe cases. We note that 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.
Notes.
4 of 9 studies combine treatments. The results of nigella sativa alone may differ. 4 of 7 RCTs use combined treatment.
Conclusion
Studies to date suggest that nigella sativa is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, hospitalization, recovery, cases, and viral clearance. 6 studies from 5 independent teams in 4 different countries show statistically significant improvements in isolation (3 for the most serious outcome). Meta analysis using the most serious outcome reported shows 61% [54‑68%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Early treatment is more effective than late treatment.
Study Notes
0 0.5 1 1.5 2+ Symptomatic case 62% Improvement Relative Risk c19ns.com Al-Haidari et al. Nigella Sativa for COVID-19 Prophylaxis Favors nigella sativa Favors control
[Al-Haidari (B)] Prophylaxis study with 376 mostly high-risk patients, 188 treated with nigella sativa, showing significantly lower cases with treatment. Black seeds 40mg/kg orally once daily.
0 0.5 1 1.5 2+ Mortality 96% Improvement Relative Risk Severe case 93% c19ns.com Al-Haidari et al. Nigella Sativa for COVID-19 RCT EARLY Favors nigella sativa Favors control
[Al-Haidari] Open-label RCT with 419 patients in Iraq, 160 treated with Nigella Sativa, showing lower mortality and severe cases with treatment. Black seeds 40mg/kg orally once daily for 14 days.
0 0.5 1 1.5 2+ Mortality 82% Improvement Relative Risk Mortality (b) 67% Mortality (c) 79% Recovery 84% Recovery (b) 75% Viral clearance 82% Viral clearance (b) 77% c19ns.com Ashraf et al. NCT04347382 Nigella Sativa RCT EARLY Favors nigella sativa Favors control
[Ashraf] RCT with 157 patients treated with honey and nigella sativa, and 156 control patients, showing significantly faster recovery and viral clearance.

Honey (1gm/kg/day) plus encapsulated Nigella sativa seeds (80mg/kg/day) orally in 2-3 divided doses daily for up to 13 days.
0 0.5 1 1.5 2+ Hospitalization 69% Improvement Relative Risk Time to sustained clinical.. 9% Time to sustained clini.. (b) 35% Viral clearance 43% c19ns.com Bencheqroun et al. Nigella Sativa for COVID-19 RCT EARLY Favors nigella sativa Favors control
[Bencheqroun] 52 patient RCT in the USA with nigella sativa component thymoquinone, showing improved recovery with treatment. There was a significantly faster decline in the total symptom burden, and a significant increase in CD8+ and helper CD4+ central memory T lymphocytes. The treatment group contained 5 more vaccinated patients and 7 more overweight patients. Authors also present in vitro results showing an inhibitory effect with five SARS-CoV-2 variants including omicron.
0 0.5 1 1.5 2+ Case 49% Improvement Relative Risk Case (b) 87% Case (c) 74% c19ns.com Chandra et al. CTRI/2020/08/027222 Nigella Sativa RCT Prophylaxis Favors nigella sativa Favors control
[Chandra] RCT 251 high-risk individuals in India, mostly with direct contact with COVID-19 positive patients, testing polyherbal formulations Infuza, which includes nigella sativa, and Kulzam. Both formulations showed lower risk, without statisical significance, while the best results were from the combination of both.