COVID-19 dynamical evolution prediction in Mexico, decision making and social implementation: mid/low income countries study

Abstract

A normal distribution approach is implemented to predict the evolution of the COVID-19 epidemic. The fit to the COVID-19 daily cases in Mexico, in the rising stage of the epidemic, is a very good continuous approximation to the data with R2 = 0.976. The derivative of this function provides a measure of the increase/decrease or acceleration of new cases per day that are otherwise buried in the noise of the raw data. The predictions are depicted in a novel 3D way, so as to convey the evolution of the forecasts as data becomes available. The estimations are in accordance within standard deviation, with the logistic and Gompertz functions fitted to the corresponding epidemic models. This scheme can be used to model the epidemic and use it as an ancillary for decision making at a municipal or regional level. Simplicity with robust prediction is favoured, so that the model can be understood and implemented by local government advisors or personnel not familiar with specialized statistical methods.