Publication: Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction
Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction
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Bracher, J., & Held, L. (2022). Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction. International Journal of Forecasting, 38(3), 1221–1233. https://doi.org/10.1016/j.ijforecast.2020.07.002
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Multivariate count time series models are an important tool for analyzing and predicting the spread of infectious disease. We consider the endemic-epidemic framework, a class of autoregressive models for infectious disease surveillance counts, and replace the default autoregression on counts from the previous time period with more flexible weighting schemes inspired by discrete-time serial interval distributions. We employ three different parametric formulations, each with an additional unknown weighting parameter estimated via a prof
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- Bracher, Johannes
- Held, Leonhard
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Bracher, J., & Held, L. (2022). Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction. International Journal of Forecasting, 38(3), 1221–1233. https://doi.org/10.1016/j.ijforecast.2020.07.002
