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.
|Number of pages
|Stochastic Environmental Research and Risk Assessment
|Published - Apr 2023
Bibliographical noteFunding Information:
The authors acknowledge the support of Professor Francisco J. Rodríguez-Cortés for his valuable help in obtaining the agreements for using the COVID-19 dataset and the Municipal Public Health Secretary of Cali, Colombia, for providing it.
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
- 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