ArticleScientific reports2022
Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Detecting changepoints in dynamical systems: Modeling time-varying transmission of seasonal influenza.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Article
- The disutility of compartmental model forecasts during the COVID-19 pandemic.Frontiers in epidemiology · 2024Article
- The impact of multiple non-pharmaceutical interventions for China-bound travel on domestic COVID-19 outbreaks.Frontiers in public health · 2023Article
- Monitoring real-time transmission heterogeneity from incidence data.PLoS computational biology · 2022Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of mitigation strategies and do not account for under-reporting of active cases, thus introducing bias in the estimation of model parameters. To infer more accurate parameter estimates and to reduce the uncertainty of these estimates, we extend the SIR and SEIR epidemiological models with two time-varying parameters that capture the transmission rate and the rate at which active cases are reported to health officials. Using two real data sets of COVID-19 cases, we perform Bayesian inference via our SIR and SEIR models with time-varying transmission and reporting rates and via their standard counterparts with constant rates; our approach provides parameter estimates with more realistic interpretation, and 1-week ahead predictions with reduced uncertainty. Furthermore, we find consistent under-reporting in the number of active cases in the data that we consider, suggesting that the initial phase of the pandemic was more widespread than previously reported.
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