Mathematical modeling of COVID-19 fatality trends: Death kinetics law versus infection-to-death delay rule

The COVID-19 pandemic has world-widely motivated numerous attempts to properly adjust classical epidemiological models, namely those of the SEIR-type, to the spreading characteristics of the novel Corona virus. In this context, the fundamental structure of the differential equations making up the SEIR models has remained largely unaltered—presuming that COVID-19 may be just “another epidemic”. We here take an alternative approach, by investigating the relevance of one key ingredient of the SEIR models, namely the death kinetics law. The latter is compared to an alternative approach, which we call infection-todeath delay rule. For that purpose, we check how well these two mathematical formulations are able to represent the publicly available country-specific data on recorded fatalities, across a selection of 57 different nations. Thereby, we consider that the model-governing parameters—namely, the death transmission coefficient for the death kinetics model, as well as the apparent fatality-to-case fraction and the characteristic fatal illness period for the infection-to-death delay rule—are time-invariant. For 55 out of the 57 countries, the infection-to-death delay rule turns out to represent the actual situation significantly more precisely than the classical death kinetics rule. We regard this as an important step towards making SEIR-approaches more fit for the COVID-19 spreading prediction challenge.

• Tipo de recurso: Article of journal
• Fecha de publicación: 2020
• Institución: Universidad de Bogotá Jorge Tadeo Lozano
• Repositorio: Expeditio: repositorio UTadeo
• Idioma: eng
• OAI Identifier: oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/12032
• Acceso en línea: https://doi.org/10.1016/j.chaos.2020.109891; http://hdl.handle.net/20.500.12010/12032
• Palabra clave: Population kinetics; Optimization; Pandemic; Prediction; Corona; SARS-CoV-2; Síndrome respiratorio agudo grave; COVID-19; SARS-CoV-2; Coronavirus
• License: Acceso restringido