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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 estimate the heterogeneity level of under-reporting across different places. The reproductive number derived within the current framework is little sensitive to both diagnosis and infection rates during the asymptomatic states. The methods described here can be extended to more general cases if data is available and adapted to other epidemiological approaches and surveillance data.

SUBMITTER: Costa GS

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

REPOSITORIES: biostudies-literature

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

Costa Guilherme S GS Cota Wesley W Ferreira Silvio C SC

Chaos, solitons, and fractals 20220817

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 ...[more]

PMID: 35996714

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Dataset Information

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

Publications

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

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