tetano
Editor, Senior Moderator
Healthcare (Basel)
. 2020 Nov 2;8(4):E453.
doi: 10.3390/healthcare8040453.
Prediction and Potential Spatially Explicit Spread of COVID-19 in Mexico's Megacity North Periphery
Maria de la Luz Hern?ndez-Flores[SUP] 1 [/SUP], Jair Escobar-S?nchez[SUP] 1 [/SUP], Jes?s Eduardo Paredes-Zarco[SUP] 2 [/SUP], Giorgio Alberto Franyuti Kelly[SUP] 3 [/SUP], Lam?n Carranza-Ram?rez[SUP] 1 [/SUP]
Affiliations
Abstract
The novel COVID-19, detected in Wuhan, China, has reached almost every city across the globe, and researchers from many countries have used several epidemiologic models to describe the epidemic trends. In this context, it is also important to know the geographic extent of the infected population. Following this approach, a Gumpertz model was adapted with official data from the state of Hidalgo, Mexico, in order to estimate the people infected during this COVID-19 pandemic. We found, based on the adjusted data, the highest value in infected people according to official and theoretical data. Furthermore, using a geographical analysis based on geostatistical measures related to density of demographic and economic data, traffic level and geolocation, raster files were generated to estimate probability of coronavirus cases occurrence using the areas where the contagion may occur. We also distributed the maximum contagion obtained by the epidemic model, using these raster files, and a regression model to weight factors according their importance. Based on this estimated distribution, we found that most of the infected people were located in the southern border, a trend related to the economic strip in the southern part of Hidalgo State, associated with its vicinity to the Megacity of Mexico.
Keywords: COVID-19; density metrics; epidemic; modeling; spatial analysis.
. 2020 Nov 2;8(4):E453.
doi: 10.3390/healthcare8040453.
Prediction and Potential Spatially Explicit Spread of COVID-19 in Mexico's Megacity North Periphery
Maria de la Luz Hern?ndez-Flores[SUP] 1 [/SUP], Jair Escobar-S?nchez[SUP] 1 [/SUP], Jes?s Eduardo Paredes-Zarco[SUP] 2 [/SUP], Giorgio Alberto Franyuti Kelly[SUP] 3 [/SUP], Lam?n Carranza-Ram?rez[SUP] 1 [/SUP]
Affiliations
- PMID: 33147698
- DOI: 10.3390/healthcare8040453
Abstract
The novel COVID-19, detected in Wuhan, China, has reached almost every city across the globe, and researchers from many countries have used several epidemiologic models to describe the epidemic trends. In this context, it is also important to know the geographic extent of the infected population. Following this approach, a Gumpertz model was adapted with official data from the state of Hidalgo, Mexico, in order to estimate the people infected during this COVID-19 pandemic. We found, based on the adjusted data, the highest value in infected people according to official and theoretical data. Furthermore, using a geographical analysis based on geostatistical measures related to density of demographic and economic data, traffic level and geolocation, raster files were generated to estimate probability of coronavirus cases occurrence using the areas where the contagion may occur. We also distributed the maximum contagion obtained by the epidemic model, using these raster files, and a regression model to weight factors according their importance. Based on this estimated distribution, we found that most of the infected people were located in the southern border, a trend related to the economic strip in the southern part of Hidalgo State, associated with its vicinity to the Megacity of Mexico.
Keywords: COVID-19; density metrics; epidemic; modeling; spatial analysis.