Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics.

Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics.

Publication date: Oct 09, 2024

Epidemiological parameters such as the reproduction number, latent period, and infectious period provide crucial information about the spread of infectious diseases and directly inform intervention strategies. These parameters have generally been estimated by mathematical models that involve an unrealistic assumption of history-independent dynamics for simplicity. This assumes that the chance of becoming infectious during the latent period or recovering during the infectious period remains constant, whereas in reality, these chances vary over time. Here, we find that conventional approaches with this assumption cause serious bias in epidemiological parameter estimation. To address this bias, we developed a Bayesian inference method by adopting more realistic history-dependent disease dynamics. Our method more accurately and precisely estimates the reproduction number than the conventional approaches solely from confirmed cases data, which are easy to obtain through testing. It also revealed how the infectious period distribution changed throughout the COVID-19 pandemic during 2020 in South Korea. We also provide a user-friendly package, IONISE, that automates this method.

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Concepts Keywords
Epidemiology Basic Reproduction Number
Infectious Bayes Theorem
Korea Bias
Mathematical COVID-19
Models Epidemiological Models
Humans
Pandemics
Republic of Korea
SARS-CoV-2

Semantics

Type Source Name
disease IDO history
pathway REACTOME Reproduction
disease MESH infectious diseases
disease IDO intervention
disease MESH COVID-19 pandemic
disease IDO host
disease MESH secondary infections
disease MESH influenza
disease IDO contact tracing
drug DRUGBANK Coenzyme M
disease MESH infection
drug DRUGBANK N-Cyclohexyltaurine
pathway REACTOME Infectious disease
disease IDO infectious disease
disease MESH uncertainty
disease IDO country
disease MESH aids
drug DRUGBANK Etoperidone
disease MESH emergency

Original Article

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