tetano
Editor, Senior Moderator
One Health
. 2021 Jun;12:100235.
doi: 10.1016/j.onehlt.2021.100235. Epub 2021 Mar 10.
How the global health security index and environment factor influence the spread of COVID-19: A country level analysis
Bruno Grespan Leichtweis[SUP] 1 [/SUP], Let?cia de Faria Silva[SUP] 1 [/SUP], Felipe Lopes da Silva[SUP] 2 [/SUP], Luiz Alexandre Peternelli[SUP] 3 [/SUP]
Affiliations
Abstract
The progress of viral diseases such as the new coronavirus (COVID-19) can be influenced not only by social isolation policies, but also by climatic factors. Understanding how these factors affect the progress of the pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be essential to know the risks each country is facing because of the disease. In this study, we verified the existence of a relationship between the basic reproduction number (R0) of SARS-CoV-2 with different climate variables, while also considering the Global Health Security Index (GHS). We collected data from confirmed cases of COVID-19 along with their respective GHS notes and climate data, from December 31, 2019 to April 13, 2020, for 52 countries. The generalized additive model (GAM) was applied to explore the effect of temperature, relative humidity, solar radiation index, and GHS score on the spread rate of COVID-19. The countries that showed similarity to each other were grouped into clusters using the Kohonen self-organizing map methodology to investigate the importance of each variable in the dissemination of the disease. The temperature variable presented a linear relationship (p < 0.001) with the R0, with an explained variation of 36.2%, while the relative humidity variable did not present a significant relationship with the R0. The response curve of the solar radiation variable presented a significant nonlinear relationship (p < 0.001) with an explained variation of 32.3%. The GHS index variable, with a significant nonlinear relationship (p < 0.001), presented the largest explanatory response in the control of COVID-19, with an explained variation of 38.4%; further, it was observed that the countries with the largest GHS index scores were less influenced by climate variables.
Keywords: Basic reproduction number (R0); Coronavirus disease; Generalized additive model (GAM); Solar radiation index; Temperature.
. 2021 Jun;12:100235.
doi: 10.1016/j.onehlt.2021.100235. Epub 2021 Mar 10.
How the global health security index and environment factor influence the spread of COVID-19: A country level analysis
Bruno Grespan Leichtweis[SUP] 1 [/SUP], Let?cia de Faria Silva[SUP] 1 [/SUP], Felipe Lopes da Silva[SUP] 2 [/SUP], Luiz Alexandre Peternelli[SUP] 3 [/SUP]
Affiliations
- PMID: 33723518
- PMCID: PMC7943391
- DOI: 10.1016/j.onehlt.2021.100235
Abstract
The progress of viral diseases such as the new coronavirus (COVID-19) can be influenced not only by social isolation policies, but also by climatic factors. Understanding how these factors affect the progress of the pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be essential to know the risks each country is facing because of the disease. In this study, we verified the existence of a relationship between the basic reproduction number (R0) of SARS-CoV-2 with different climate variables, while also considering the Global Health Security Index (GHS). We collected data from confirmed cases of COVID-19 along with their respective GHS notes and climate data, from December 31, 2019 to April 13, 2020, for 52 countries. The generalized additive model (GAM) was applied to explore the effect of temperature, relative humidity, solar radiation index, and GHS score on the spread rate of COVID-19. The countries that showed similarity to each other were grouped into clusters using the Kohonen self-organizing map methodology to investigate the importance of each variable in the dissemination of the disease. The temperature variable presented a linear relationship (p < 0.001) with the R0, with an explained variation of 36.2%, while the relative humidity variable did not present a significant relationship with the R0. The response curve of the solar radiation variable presented a significant nonlinear relationship (p < 0.001) with an explained variation of 32.3%. The GHS index variable, with a significant nonlinear relationship (p < 0.001), presented the largest explanatory response in the control of COVID-19, with an explained variation of 38.4%; further, it was observed that the countries with the largest GHS index scores were less influenced by climate variables.
Keywords: Basic reproduction number (R0); Coronavirus disease; Generalized additive model (GAM); Solar radiation index; Temperature.