TY - JOUR
T1 - Spatio-temporal modeling of infectious diseases by integrating compartment and point process models
AU - Amaral, André Victor Ribeiro
AU - González, Jonatan A.
AU - Moraga, Paula
N1 - Funding 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.
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2023/4
Y1 - 2023/4
N2 - 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.
AB - 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.
KW - Compartment SIR model
KW - Infectious diseases
KW - Log-Gaussian Cox process
KW - Spatial point process
KW - Spatio-temporal modeling
UR - http://www.scopus.com/inward/record.url?scp=85143912775&partnerID=8YFLogxK
U2 - 10.1007/s00477-022-02354-4
DO - 10.1007/s00477-022-02354-4
M3 - Article
C2 - 36530377
AN - SCOPUS:85143912775
SN - 1436-3240
VL - 37
SP - 1519
EP - 1533
JO - Stochastic Environmental Research and Risk Assessment
JF - Stochastic Environmental Research and Risk Assessment
IS - 4
ER -