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Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction

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  • Bracher, Johannes
  • Held, Leonhard

Abstract

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 profile likelihood approach, and compare them to an unrestricted nonparametric approach. The new methods are illustrated in a univariate analysis of dengue fever incidence in San Juan, Puerto Rico, and a spatiotemporal study of viral gastroenteritis in the 12 districts of Berlin. We assess the predictive performance of the suggested models and several reference models at various forecast horizons. In both applications, the performance of the endemic-epidemic models is considerably improved by the proposed weighting schemes.

Suggested Citation

  • Bracher, Johannes & Held, Leonhard, 2022. "Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1221-1233.
    • Handle: RePEc:eee:intfor:v:38:y:2022:i:3:p:1221-1233
    DOI: 10.1016/j.ijforecast.2020.07.002
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    Cited by:

    1. Maria Bekker‐Nielsen Dunbar & Felix Hofmann & Leonhard Held, 2022. "Session 3 of the RSS Special Topic Meeting on Covid‐19 Transmission: Replies to the discussion," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 158-164, November.
    2. Maria Bekker‐Nielsen Dunbar & Felix Hofmann & Leonhard Held & the SUSPend modelling consortium, 2022. "Assessing the effect of school closures on the spread of COVID‐19 in Zurich," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 131-142, November.
    3. Chen, Cathy W.S. & Chen, Chun-Shu & Hsiung, Mo-Hua, 2023. "Bayesian modeling of spatial integer-valued time series," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).
    4. Mirko Armillotta & Konstantinos Fokianos, 2024. "Count network autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(4), pages 584-612, July.

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