Estimating the number of deaths due to COVID-19 in Lima and Peru during March and April 2020 using ARIMA time series and modeling

Abstract Background: The SARS-CoV-2 virus which causes the COVID-19 disease is a large family of viruses that cause respiratory diseases and complications in human beings. Nowadays, the pandemic is studied by the rate of infection and mortality in different countries. Since there are discrepancies regarding the number of deaths due to the pandemic, our main goal is to estimate the number of deaths in Lima and Peru due to COVID-19. Methods: By using inference modeling techniques and statistical analysis in time series together with ARIMA-type predictions, it is possible to estimate the number of deaths caused by COVID-19. Our study took place in the city of Lima and Peru and our detailed analysis was carried out during March and April of 2020. Results: By comparing the death toll provided by the Ministry of Health (MINSA), we have obtained approximately a difference of 325.9% regarding the number of deaths due to COVID-19 in the city of Lima and a difference of 185.9% regarding the number of deaths in Peru. Conclusions: ARIMA time series modeling are a powerful statistical tool that predict and forecast data that are widely used in various fields of science, health, and economics. In this study, we have shown the discrepancy between the data reported by MINSA and the projection obtained in our ARIMA time series modeling. Therefore, we have a confidence level of 95% about the study that was carried out in March and April of 2020.