A multi-source global-local model for epidemic management.

José Ulises Márquez Urbina,D Iván Rodríguez González,L Leticia Ramírez Ramírez,Graciela González Farías,Alessandro Rizzo

doi:10.1371/journal.pone.0261650

José Ulises Márquez Urbina, D Iván Rodríguez González + Show 3 more

Open Access

https://doi.org/10.1371/journal.pone.0261650

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Abstract

The Effective Reproduction Number Rt provides essential information for the management of an epidemic/pandemic. Projecting Rt into the future could further assist in the management process. This article proposes a methodology based on exposure scenarios to perform such a procedure. The method utilizes a compartmental model and its adequate parametrization; a way to determine suitable parameters for this model in México’s case is detailed. In conjunction with the compartmental model, the projection of Rt permits estimating unobserved variables, such as the size of the asymptomatic population, and projecting into the future other relevant variables, like the active hospitalizations, using scenarios. The uses of the proposed methodologies are exemplified by analyzing the pandemic in a Mexican state; the main quantities derived from the compartmental model, such as the active and total cases, are included in the analysis. This article also presents a national summary based on the methodologies to illustrate how these procedures could be further exploited. The supporting information includes an application of the proposed methods to a metropolitan area to show that it also works well at other demographic disaggregation levels. The procedures developed in this article shed light on how to develop an effective surveillance system when information is incomplete and can be applied in cases other than México’s.

Highlights

Simple compartmentalized epidemiological models have proven their usefulness for different infectious agents
Even though several theoretical results can be derived, the parameter selection or statistical inference in these models has been done with numerically intensive methods like Markov Chain Monte Carlo (MCMC), Approximate Bayesian Computation (ABC), or Particle Filtering, that involve Monte Carlo simulations with
To show that the proposed epidemiological calculator (EC) works at other strata and demographic disaggregation levels, in the supplemental material, it is presented the case of the Metropolitan Area of Mexico City, known as Valle de Mexico (Valley of Mexico)

Summary

Introduction

Simple compartmentalized epidemiological models have proven their usefulness for different infectious agents. To model an infectious process at a regional or global level, it is important to consider the structure of subpopulations and their heterogeneous relations because, frequently, they are induced by the spatial structure and the associated mobility [1,2,3,4]. Following this approach, reaction-diffusion models on metapopulation networks have played an essential role in journals with a physics focus [5,6,7]. Even though several theoretical results can be derived, the parameter selection or statistical inference in these models has been done with numerically intensive methods like Markov Chain Monte Carlo (MCMC), Approximate Bayesian Computation (ABC), or Particle Filtering, that involve Monte Carlo simulations with

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