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• W4367295931 abstract "Mathematical modelling plays a key role in understanding and predicting the epidemiological dynamics of infectious diseases. We construct a flexible discrete-time model that incorporates multiple viral strains with different transmissibilities to estimate the changing patterns of human contact that generates new infections. Using a Bayesian approach, we fit the model to longitudinal data on hospitalisation with COVID-19 from the Republic of Ireland and Northern Ireland during the first year of the pandemic. We describe the estimated change in human contact in the context of government-mandated non-pharmaceutical interventions in the two jurisdictions on the island of Ireland. We take advantage of the fitted model to conduct counterfactual analyses exploring the impact of lockdown timing and introducing a novel, more transmissible variant. We found substantial differences in human contact between the two jurisdictions during periods of varied restriction easing and December holidays. Our counterfactual analyses reveal that implementing lockdowns earlier would have decreased subsequent hospitalisation substantially in most, but not all cases, and that an introduction of a more transmissible variant - without necessarily being more severe - can cause a large impact on the health care burden." @default.
• W4367295931 created "2023-04-29" @default.
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• W4367295931 date "2023-12-01" @default.
• W4367295931 modified "2023-09-30" @default.
• W4367295931 title "Estimating time-dependent contact: a multi-strain epidemiological model of SARS-CoV-2 on the island of Ireland" @default.
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• W4367295931 doi "https://doi.org/10.1016/j.gloepi.2023.100111" @default.
• W4367295931 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37162815" @default.
• W4367295931 hasPublicationYear "2023" @default.
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