Sustainable ambient environment to prevent future outbreaks: How ambient environment relates to covid-19 local transmission in Lima, Peru

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), universally recognized as COVID-19, is currently is a global issue. Our study uses multivariate regression for determining the relationship between the ambient environment and COVID-19 cases in Lima. We also forecast the pattern trajectory of COVID-19 cases with variables using an Auto-Regressive Integrated Moving Average Model (ARIMA). There is a significant association between ambient temperature and PM10 and COVID-19 cases, while no significant correlation has been seen for PM2.5. All variables in the multivariate regression model have R2 = 0.788, which describes a significant exposure to COVID-19 cases in Lima. ARIMA (1,1,1), during observation time of PM2.5, PM10, and average temperature, is found to be suitable for forecasting COVID-19 cases in Lima. This result indicates that the expected high particle concentration and low ambient temperature in the coming season will further facilitate the transmission of the coronavirus if there is no other policy intervention. A suggested sustainable policy related to ambient environment and the lessons learned from different countries to prevent future outbreaks are also discussed in this study.