Spatio-temporal modeling of infectious diseases by integrating compartment and point process models

André Victor Ribeiro Amaral*, Jonatan A. González, Paula Moraga

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Infectious disease modeling plays an important role in understanding disease spreading dynamics and can be used for prevention and control. The well-known SIR (Susceptible, Infected, and Recovered) compartment model and spatial and spatio-temporal statistical models are common choices for studying problems of this kind. This paper proposes a spatio-temporal modeling framework to characterize infectious disease dynamics by integrating the SIR compartment and log-Gaussian Cox process (LGCP) models. The method’s performance is assessed via simulation using a combination of real and synthetic data for a region in São Paulo, Brazil. We also apply our modeling approach to analyze COVID-19 dynamics in Cali, Colombia. The results show that our modified LGCP model, which takes advantage of information obtained from the previous SIR modeling step, leads to a better forecasting performance than equivalent models that do not do that. Finally, the proposed method also allows the incorporation of age-stratified contact information, which provides valuable decision-making insights.

Original languageEnglish (US)
Pages (from-to)1519-1533
Number of pages15
JournalStochastic Environmental Research and Risk Assessment
Volume37
Issue number4
DOIs
StatePublished - Apr 2023

Keywords

  • Compartment SIR model
  • Infectious diseases
  • Log-Gaussian Cox process
  • Spatial point process
  • Spatio-temporal modeling

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Water Science and Technology
  • Safety, Risk, Reliability and Quality
  • General Environmental Science

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