Physical-mathematical Modeling for Decision-Making against COVID-19 in Cuba

Abstract

Objective: To apply physical-mathematical modeling to the dynamics of COVID19 to make decisions associated with mitigation and eradication of the pandemics in Cuba.

Methods: Modeling was applied in order to characterize the forecast peak timing, and the reproductive performance of the pandemic, through MATLAB tools and functions. Peak timing was determined with the application of the SIR model, after adjustments. It was fit using the GlobalSearch optimization strategy. Function ode23tb was used in the solution, including a Runge-Kutta algorithm combined with another trapezoidal rule algorithm. To determine reproductive performance, an exponential model was fit using Curve Fitting.

Main results: The parameters of the SIR model were identified, and the peak forecast was completed timely and accurately two weeks in advance. The susceptible, accumulated infected and recovered patients were predicted. The calculated basic reproduction number (R₀) helped conclude that to eradicate the pandemic by vaccination, the immunized population should be over 72 %. The effective reproduction number (R_ef) helped evaluate the efficacy of mitigation measures. Remarks are made concerning the proper conduct to follow for eradication.

Conclusions: The SIR model proved its capacity to predict the peak timing of the pandemic. The R₀ of SARS-CoV-2 corroborated the high transmissibility of the virus. The mitigation measures have been effective, and should be kept until the pandemic is eradicated, even with R_ef < 1, when 72 % of the population is immunized.