ArticleThe Canadian journal of statistics = Revue canadienne de statistique2022
Extended Bayesian endemic-epidemic models to incorporate mobility data into COVID-19 forecasting.
Article in The Canadian journal of statistics = Revue canadienne de statistique, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Refining Estimation of the Instantaneous Reproduction Number During Early Pandemic Stages: Addressing Case-Reporting Variability and Serial Interval Uncertainty.American journal of epidemiology · 2024Article
- Predicting subnational incidence of COVID-19 cases and deaths in EU countries.BMC infectious diseases · 2024Article
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Authors and funding
4 authors.
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Abstract
Forecasting the number of daily COVID-19 cases is critical in the short-term planning of hospital and other public resources. One potentially important piece of information for forecasting COVID-19 cases is mobile device location data that measure the amount of time an individual spends at home. Endemic-epidemic (EE) time series models are recently proposed autoregressive models where the current mean case count is modelled as a weighted average of past case counts multiplied by an autoregressive rate, plus an endemic component. We extend EE models to include a distributed-lag model in order to investigate the association between mobility and the number of reported COVID-19 cases; we additionally include a weekly first-order random walk to capture additional temporal variation. Further, we introduce a shifted negative binomial weighting scheme for the past counts that is more flexible than previously proposed weighting schemes. We perform inference under a Bayesian framework to incorporate parameter uncertainty into model forecasts. We illustrate our methods using data from four US counties.
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