The COVID-19 pandemic highlighted health systems vulnerabilities, as well as thoughtlessness by governments and society. Due to the nature of this contingency, the use of geographic information systems (GIS) is essential to understand the SARS-CoV-2 distribution dynamics within a defined geographic area. This work was performed in Tepic, a medium-sized city in Mexico. The residence of 834 COVID-19 infected individuals was georeferenced and categorized by viral load (Ct). The analysis took place during the maximum contagion of the first four waves of COVID-19 in Mexico, analyzing 158, 254, 143, and 279 cases in each wave respectively. Then heatmaps were built and categorized into five areas ranging from very low to very high risk of contagion, finding that the second wave exhibited a greater number of cases with a high viral load. Additionally, a spatial analysis was performed to measure urban areas with a higher risk of contagion, during this wave this area had 19,203.08 km2 (36.11% of the city). Therefore, a kernel density spatial model integrated by meaningful variables such as the number of infected subjects, viral load, and place of residence in cities, to establish geographic zones with different degrees of infection risk, could be useful for decision-making in future epidemic events.
Keywords: COVID-19; GIS; Mexico; SARS-CoV-2; heatmap; kernel density; qRT-PCR; risk; vaccination; viral load.