A Mathematical Model for COVID-19 with Variable Transmissibility and Hospitalizations: A Case Study in Paraguay

Abstract

Forecasting the dynamics of the number of cases with coronavirus disease 2019 (COVID-19) in a given population is a challenging task due to behavioural changes which occur over short periods. Planning of hospital resources and containment measures in the near term require a scenario analysis and the use of predictive models to gain insight into possible outcomes for each scenario. In this paper, we present the SEIR-H epidemiological model for the spread dynamics in a given population and the impact of COVID-19 in the local health system. It was developed as an extension of the classic SEIR model to account for required hospital resources and behavioural changes of the population in response to containment measures. Time-varying parameters such as transmissibility are estimated using Bayesian methods, based on the database of reported cases with a moving time-window strategy. The assessment of the model offers reasonable results with estimated parameters and simulations, reflecting the observed dynamics in Paraguay. The proposed model can be used to simulate future scenarios and possible effects of containment strategies, to guide the public institution response based on the available resources in the local health system.

Transportation and Reserve of Emergency Medical Supplies during Public Health Events

“…First, Stăncioi, C.-M., and other scholars have developed a mathematical model for the COVID-19 pandemic dynamics to predict the trends; the outputs of the model are the number of new cases, the number of new deaths, the total number of cases, or the total number of deaths [7]. Shin, H.H., and others constructed a SEIR-H epidemiological model for the spread dynamics in a given population [8]. This model extends the SEIR model, while researchers have carried out parameter simulation and estimation work, using Paraguay as a case study.…”