tetano
Editor, Senior Moderator
Front Public Health
. 2024 Jun 18:12:1298177.
doi: 10.3389/fpubh.2024.1298177. eCollection 2024. Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis
David De Ridder[SUP] 1 2 3 4 [/SUP], Anaïs Ladoy[SUP] 1 2 [/SUP], Yangji Choi[SUP] 5 [/SUP], Damien Jacot[SUP] 5 [/SUP], Séverine Vuilleumier[SUP] 6 [/SUP], Idris Guessous[SUP] #[/SUP][SUP] 1 3 4 [/SUP], Stéphane Joost[SUP] #[/SUP][SUP] 1 2 3 6 [/SUP], Gilbert Greub[SUP] #[/SUP][SUP] 5 7 [/SUP]
Affiliations
Introduction: Since its emergence in late 2019, the SARS-CoV-2 virus has led to a global health crisis, affecting millions and reshaping societies and economies worldwide. Investigating the determinants of SARS-CoV-2 diffusion and their spatiotemporal dynamics at high spatial resolution is critical for public health and policymaking.
Methods: This study analyses 194,682 georeferenced SARS-CoV-2 RT-PCR tests from March 2020 and April 2022 in the canton of Vaud, Switzerland. We characterized five distinct pandemic periods using metrics of spatial and temporal clustering like inverse Shannon entropy, the Hoover index, Lloyd's index of mean crowding, and the modified space-time DBSCAN algorithm. We assessed the demographic, socioeconomic, and environmental factors contributing to cluster persistence during each period using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), to consider non-linear and spatial effects.
Results: Our findings reveal important variations in the spatial and temporal clustering of cases. Notably, areas with flatter epidemics had higher total attack rate. Air pollution emerged as a factor showing a consistent positive association with higher cluster persistence, substantiated by both immission models and, to a lesser extent, tropospheric NO[SUB]2[/SUB] estimations. Factors including population density, testing rates, and geographical coordinates, also showed important positive associations with higher cluster persistence. The socioeconomic index showed no significant contribution to cluster persistence, suggesting its limited role in the observed dynamics, which warrants further research.
Discussion: Overall, the determinants of cluster persistence remained across the study periods. These findings highlight the need for effective air quality management strategies to mitigate air pollution's adverse impacts on public health, particularly in the context of respiratory viral diseases like COVID-19.
Keywords: SARS-CoV-2; air pollution; geoAI; machine learning; remote sensing; sociodemographic and environmental determinants; spatial epidemiology; spatial modeling.
. 2024 Jun 18:12:1298177.
doi: 10.3389/fpubh.2024.1298177. eCollection 2024. Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis
David De Ridder[SUP] 1 2 3 4 [/SUP], Anaïs Ladoy[SUP] 1 2 [/SUP], Yangji Choi[SUP] 5 [/SUP], Damien Jacot[SUP] 5 [/SUP], Séverine Vuilleumier[SUP] 6 [/SUP], Idris Guessous[SUP] #[/SUP][SUP] 1 3 4 [/SUP], Stéphane Joost[SUP] #[/SUP][SUP] 1 2 3 6 [/SUP], Gilbert Greub[SUP] #[/SUP][SUP] 5 7 [/SUP]
Affiliations
- PMID: 38957202
- PMCID: PMC11217542
- DOI: 10.3389/fpubh.2024.1298177
Introduction: Since its emergence in late 2019, the SARS-CoV-2 virus has led to a global health crisis, affecting millions and reshaping societies and economies worldwide. Investigating the determinants of SARS-CoV-2 diffusion and their spatiotemporal dynamics at high spatial resolution is critical for public health and policymaking.
Methods: This study analyses 194,682 georeferenced SARS-CoV-2 RT-PCR tests from March 2020 and April 2022 in the canton of Vaud, Switzerland. We characterized five distinct pandemic periods using metrics of spatial and temporal clustering like inverse Shannon entropy, the Hoover index, Lloyd's index of mean crowding, and the modified space-time DBSCAN algorithm. We assessed the demographic, socioeconomic, and environmental factors contributing to cluster persistence during each period using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), to consider non-linear and spatial effects.
Results: Our findings reveal important variations in the spatial and temporal clustering of cases. Notably, areas with flatter epidemics had higher total attack rate. Air pollution emerged as a factor showing a consistent positive association with higher cluster persistence, substantiated by both immission models and, to a lesser extent, tropospheric NO[SUB]2[/SUB] estimations. Factors including population density, testing rates, and geographical coordinates, also showed important positive associations with higher cluster persistence. The socioeconomic index showed no significant contribution to cluster persistence, suggesting its limited role in the observed dynamics, which warrants further research.
Discussion: Overall, the determinants of cluster persistence remained across the study periods. These findings highlight the need for effective air quality management strategies to mitigate air pollution's adverse impacts on public health, particularly in the context of respiratory viral diseases like COVID-19.
Keywords: SARS-CoV-2; air pollution; geoAI; machine learning; remote sensing; sociodemographic and environmental determinants; spatial epidemiology; spatial modeling.