Sharp reductions in COVID-19 case fatalities and excess deaths in Peru in close time conjunction, state-by-state, with ivermectin treatments
Bibliographic record
Abstract
On May 8, 2020, Peru’s Ministry of Health approved ivermectin (IVM) for the treatment of COVID-19. A drug of Nobel Prize-honored distinction, IVM has been safely distributed in 3.7 billion doses worldwide since 1987. It has exhibited major, statistically significant reductions in case mortality and severity in 11 clinical trials for COVID-19, three with randomized controls. The indicated biological mechanism of IVM is the same as that of antiviral antibodies generated by vaccines—binding to SARS-CoV-2 viral spike protein, blocking viral attachment to host cells.Mass distributions of IVM for COVID-19 treatments, inpatient and outpatient, were conducted in different timeframes with local autonomy in the 25 states (departamentos) of Peru. These treatments were conducted early in the pandemic’s first wave in 24 states, in some cases beginning even a few weeks before the May 8 national authorization, but delayed four months in Lima. Analysis was performed using Peruvian public health data for all-cause deaths and for COVID-19 case fatalities, as independently tracked for ages 60 and above. These daily figures were retrieved and analyzed by state. Case incidence data were not analyzed due to variations in testing methods and other confounding factors. These clinical data associated with IVM treatments beginning in different time periods, April through August 2020, in each of 25 Peruvian states, spanning an area equivalent to that from Denmark to Italy and Greece in Europe or from north to south along the US, with a total population of 33 million, provided a rich source for analysis.For the 24 states with early IVM treatment (and Lima), excess deaths dropped 59% (25%) at +30 days and 75% (25%) at +45 days after day of peak deaths. Case fatalities likewise dropped sharply in all states but Lima, yet six indices of Google-tracked community mobility rose over the same period. For nine states having mass distributions of IVM in a short timeframe through a national program, Mega-Operación Tayta (MOT), excess deaths at +30 days dropped by a population-weighted mean of 74%, each drop beginning within 11 day after MOT start. Extraneous causes of mortality reductions were ruled out. These sharp major reductions in COVID-19 mortality following IVM treatment thus occurred in each of Peru’s states, with such especially sharp reductions in close time conjunction with IVM treatments in each of the nine states of operation MOT. Its safety well established even at high doses, IVM is a compelling option for immediate, large scale national deployments as an interim measure and complement to pandemic control through vaccinations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".