ABSTRACT
Resumen Si bien se han realizado múltiples intentos de modelar matemáticamente la pande-mia de la enfermedad por coronavirus 2019 (COVID-19), causada por SARS-CoV-2, pocos modeloshan sido pensados como herramientas interactivas accesibles para usuarios de distintos ámbitos.El objetivo de este trabajo fue desarrollar un modelo que tuviera en cuenta la heterogeneidadde las tasas de contacto de la población e implementarlo en una aplicación accesible, que per-mitiera estimar el impacto de posibles intervenciones a partir de información disponible. Sedesarrolló una versión ampliada del modelo susceptible-expuesto-infectado-resistente (SEIR),denominada SEIR-HL, que asume una población dividida en dos subpoblaciones, con tasas decontacto diferentes. Asimismo, se desarrolló una fórmula para calcular el número básico dereproducción (R0) para una población dividida en n subpoblaciones, discriminando las tasas decontacto de cada subpoblación según el tipo o contexto de contacto. Se compararon las pre-dicciones del SEIR-HL con las del SEIR y se demostró que la heterogeneidad en las tasas decontacto puede afectar drásticamente la dinámica de las simulaciones, aun partiendo de lasmismas condiciones iniciales y los mismos parámetros. Se empleó el SEIR-HL para mostrar elefecto sobre la evolución de la pandemia del desplazamiento de individuos desde posiciones dealto contacto hacia posiciones de bajo contacto. Finalmente, a modo de ejemplo, se aplicó elSEIR-HL al análisis de la pandemia de COVID-19 en Argentina; también se desarrolló un ejemplode uso de la fórmula del R0. Tanto el SEIR-HL como una calculadora del R0fueron implementadosinformáticamente y puestos a disposición de la comunidad.
Abstract Although multiple attempts have been made to mathematically model the currentepidemic of SARS-CoV-2, the causative agent of coronavirus disease 2019 (COVID-19), fewmodels have been conceived as accessible interactive tools for users from various backgrounds.The goal of this study was to develop a model that took into account the heterogeneity incontact rates within the population and to implement it in an accessible application allowingto estimate the impact of possible interventions based on available information. An extendedversion of the Susceptible-Exposed-Infected-Resistant (SEIR) model, named SEIR-HL, was deve-loped, assuming a population divided into two subpopulations, with different contact rates.Additionally, a formula for the calculation of the basic reproduction number (R0) for a popula-tion divided into n subpopulations was proposed, where the contact rates for each subpopulationcan be distinguished according to contact type or context. The predictions made by SEIR-HLwere compared to those of SEIR, showing that the heterogeneity in contact rates can drama-tically affect the dynamics of simulations, even when run from the same initial conditions andwith the same parameters. SEIR-HL was used to predict the effect on the epidemic evolution ofthe displacement of individuals from high-contact positions to low-contact positions. Lastly, byway of example, SEIR-HL was applied to the analysis of the SARS-CoV-2 epidemic in Argentinaand an example of the application of the R0formula was also developed. Both the SEIR-HLmodel and an R0calculator were computerized and made available to the community.
ABSTRACT
Although multiple attempts have been made to mathematically model the current epidemic of SARS-CoV-2, the causative agent of coronavirus disease 2019 (COVID-19), few models have been conceived as accessible interactive tools for users from various backgrounds. The goal of this study was to develop a model that took into account the heterogeneity in contact rates within the population and to implement it in an accessible application allowing to estimate the impact of possible interventions based on available information. An extended version of the Susceptible-Exposed-Infected-Resistant (SEIR) model, named SEIR-HL, was developed, assuming a population divided into two subpopulations, with different contact rates. Additionally, a formula for the calculation of the basic reproduction number (R0) for a population divided into n subpopulations was proposed, where the contact rates for each subpopulation can be distinguished according to contact type or context. The predictions made by SEIR-HL were compared to those of SEIR, showing that the heterogeneity in contact rates can dramatically affect the dynamics of simulations, even when run from the same initial conditions and with the same parameters. SEIR-HL was used to predict the effect on the epidemic evolution of the displacement of individuals from high-contact positions to low-contact positions. Lastly, by way of example, SEIR-HL was applied to the analysis of the SARS-CoV-2 epidemic in Argentina and an example of the application of the R0 formula was also developed. Both the SEIR-HL model and an R0 calculator were computerized and made available to the community.
