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The Impact of Population, Contact, and Spatial Heterogeneity on Epidemic Model Predictions

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  • Francisco J. Zagmutt
  • Mark A. Schoenbaum
  • Ashley E. Hill

Abstract

Our objective was to evaluate the effect that complexity in the form of different levels of spatial, population, and contact heterogeneity has in the predictions of a mechanistic epidemic model. A model that simulates the spatiotemporal spread of infectious diseases between animal populations was developed. Sixteen scenarios of foot‐and‐mouth disease infection in cattle were analyzed, involving combinations of the following factors: multiple production‐types (PT) with heterogeneous contact and population structure versus single PT, random versus actual spatial distribution of population units, high versus low infectivity, and no vaccination versus preemptive vaccination. The epidemic size and duration was larger for scenarios with multiple PT versus single PT. Ignoring the actual unit locations did not affect the epidemic size in scenarios with multiple PT/high infectivity, but resulted in smaller epidemic sizes in scenarios using multiple PT/low infectivity. In conclusion, when modeling fast‐spreading epidemics, knowing the actual locations of population units may not be as relevant as collecting information on population and contact heterogeneity. In contrast, both population and spatial heterogeneity might be important to model slower spreading epidemic diseases. Our findings can be used to inform data collection and modeling efforts to inform health policy and planning.

Suggested Citation

  • Francisco J. Zagmutt & Mark A. Schoenbaum & Ashley E. Hill, 2016. "The Impact of Population, Contact, and Spatial Heterogeneity on Epidemic Model Predictions," Risk Analysis, John Wiley & Sons, vol. 36(5), pages 939-953, May.
    • Handle: RePEc:wly:riskan:v:36:y:2016:i:5:p:939-953
    DOI: 10.1111/risa.12482
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    1. Alison P. Galvani & Robert M. May, 2005. "Dimensions of superspreading," Nature, Nature, vol. 438(7066), pages 293-295, November.
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    Cited by:

    1. Liang, Zhenglin & Jiang, Chen & Sun, Muxia & Xue, Zongqi & Li, Yan-Fu, 2023. "Resilience analysis for confronting the spreading risk of contagious diseases," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    2. Jidong Wu & Ying Li & Ning Li & Peijun Shi, 2018. "Development of an Asset Value Map for Disaster Risk Assessment in China by Spatial Disaggregation Using Ancillary Remote Sensing Data," Risk Analysis, John Wiley & Sons, vol. 38(1), pages 17-30, January.
    3. L. Robin Keller & Jay Simon, 2019. "Preference Functions for Spatial Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 244-256, January.
    4. Nikolaos Argyris & Valentina Ferretti & Simon French & Seth Guikema & Gilberto Montibeller, 2019. "Advances in Spatial Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 1-8, January.
    5. Duncan A. Robertson, 2019. "Spatial Transmission Models: A Taxonomy and Framework," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 225-243, January.

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