ArticleNature communications2024
Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Impact of temporal patterns in working contacts on epidemic spread.Scientific reports · 2026Article
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- A multi-method study evaluating the inference of compartmental model parameters from a generative agent-based model.Infectious Disease Modelling · 2026Article
- Inferring structure and parameters of stochastic reaction networks with logistic regression.PloS one · 2026Article
- Multi-algorithm radiomics machine learning models integrating ultrasound imaging and inflammation-immune features for hepatic metastases identification.BMC medical imaging · 2025Article
- A history-dependent approach for accurate initial condition estimation in epidemic models.PLoS computational biology · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
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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