A Monte Carlo approach to model COVID-19 deaths and infections using Gompertz functions
Tulio Rodrigues, Otaviano Helene

TL;DR
This paper presents a Monte Carlo method using Gompertz functions to model COVID-19 deaths and infections across six countries, providing forecasts and analyzing the effects of interventions with quantified uncertainties.
Contribution
It introduces a novel Monte Carlo framework with Gompertz functions for modeling COVID-19 dynamics, including uncertainty propagation and multi-peak analysis.
Findings
Peak death rates vary across countries with different trends.
Model predicts total deaths with specific confidence intervals.
Infection prevalence estimates align with serological studies.
Abstract
This study describes the dynamics of COVID-19 deaths and infections via a Monte Carlo approach. The analyses include death's data from USA, Brazil, Mexico, UK, India and Russia, which comprise the four countries with the highest number of deaths/confirmed cases, as of Aug 07, 2020, according to the WHO. The Gompertz functions were fitted to the data of weekly averaged confirmed deaths per day by mapping the values. The uncertainties, variances and covariances of the model parameters were calculated by propagation. The fitted functions for the average deaths per day for USA and India have an upward trend, with the former having a higher growth rate and quite huge uncertainties. For Mexico, UK and Russia, the fits are consistent with a slope down pattern. For Brazil we found a subtle trend down, but with significant uncertainties. The USA, UK and India data shown a first peak…
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