Data-driven approach in a compartmental epidemic model to assess undocumented infections.

ABSTRACT: Nowcasting and forecasting of epidemic spreading rely on incidence series of reported cases to derive the fundamental epidemiological parameters for a given pathogen. Two relevant drawbacks for predictions are the unknown fractions of undocumented cases and levels of nonpharmacological interventions, which span highly heterogeneously across different places and times. We describe a simple data-driven approach using a compartmental model including asymptomatic and pre-symptomatic contagions that allows to estimate both the level of undocumented infections and the value of effective reproductive number Rt from time series of reported cases, deaths, and epidemiological parameters. The method was applied to epidemic series for COVID-19 across different municipalities in Brazil allowing to esti

SUBMITTER: Costa GS 

PROVIDER: S-EPMC9385215 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature