Environmental and social analysis as risk factors for the spread of the novel coronavirus (SARS-CoV-2) using remote sensing, GIS and analytical hierarchy process (AHP): Case of Peru

The novel coronavirus disease (COVID-19) generated by the SARS-CoV-2 virus was originated in the city of Wuhan (China) in December 2019, the virus began to spread in other regions of China until it spread to the rest of the world. In this research, an analysis was made of environmental factors (tropospheric column of NO₂, vertical airflow, percentage of solid waste disposed of in open dumps and percentage of the population without any mechanism of faeces disposal) and social factors (levels of monetary poverty, percentage of the number of hospitals per population and vulnerable population) that could directly and indirectly affect the spread of SARS-CoV-2 virus in the regions of Peru. Remote sensing techniques, geographic information systems and an analysis under the multi-parametric statistical approach proposed by Saaty were used to determine which regions present greater susceptibility, vulnerability and risk of spreading the SARS-CoV-2 virus. The results show that the prevalence of high values of tropospheric NO₂and values close to 0 Pa/s of the vertical airflow were directly related to the number of positive cases by COVID-19. In addition, it was found that 68% of the regions of Peru are at a “high” and “very high” risk of spreading SARS-CoV-2 virus, and most of them are in northern and central Peru (Callao, Tumbes, Piura, Loreto, Lambayeque, Huancavelica, Amazonas, Cajamarca, Ucayali, Huanuco and among others), therefore, special care should be taken with the measures adopted after social isolation in order to avoid the resurgence and collapse of the of health systems. It concludes that public policies on air quality management, integrated solid waste management and sanitation services should be improved in order to reduce the risk of spreading the SARS-CoV-2 virus. This research can be replicated on a longer scale, including more variables.