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Letter to the Editor Re:(In) visible impact of inadequate WaSH Provision on COVID-19 incidences can be not be ignored in large and megacities of India The number of coronavirus cases in India has crossed the 82,000-mark on May 15, 2020. 1 As on May 15, 2020, there are 51,401 active COVID-19 cases in the country, 27,919 patients have been cured or discharged, whereas 2649 people have died from this deadly disease. The geographic distribution of COVID-19epositive cases is not uniform in India and 10 big cities account for more than 52% of the total reported cases with Mumbai, Delhi, Ahmadabad, Chennai, and Pune are most affected 2 indicates a spatial clustering of the disease. Recent studies have shown multiple environmental factors such as temperature 3,4,5,6,7 , humidity 8,9,10 , air pollution 11 and smoking 12 determine the severity and rate of spread of COVID-19. Poor availability and accessibility of water, sanitation, and hygiene (WaSH) may increase the risk of COVID-19 spread by affecting the survival and transmission of the virus in a variety of ways. Higher incidences of certain infectious diseases, such as diarrhoea, cholera, typhoid and hepatitis 13 reported in poor WaSH conditions. Poor status of WaSH is also associated with stunted growth, impaired cognitive and mental development, especially among children. 14 Surprisingly, no study has made yet that aimed to provide scientific evidence about the provision of WaSH services and incidence of COVID-19. WaSH are essential components of human health. 15 Hence, universal, sustainable, and equitable provisions of WaSH services by 2030 stand for the UN Sustainable Development Goal 6. In the Indian context, despite several policies and programme interventions

The first incident of COVID-19 case in India was recorded on 30th January, 2020 which turns to 100,000 marks on May 19th and by June 3rd it was over 200,000 active cases and 5,800 deaths. Geographic Information System (GIS) based spatial models can be helpful for better understanding of different factors that have triggered COVID-19 spread at district level in India. In the present study, 19 variables were considered that can explain the variability of the disease. Different spatial statistical techniques were used to describe the spatial distribution of COVID-19 and identify significant clusters. Spatial lag and error models (SLM and SEM) were employed to examine spatial dependency, geographical weighted regression (GWR) and multi-scale GWR (MGWR) were employed to examine at local level. The results show that the global models perform poorly in explaining the factors for COVID-19 incidences. MGWR shows the best-fit-model to explain the variables affecting COVID-19 (R2= 0.75) with lowest AICc value. Population density, urbanization and bank facility were found to be most susceptible for COVID-19 cases. These indicate the necessity of effective policies related to social distancing, low mobility. Mapping of different significant variables using MGWR can provide significant insights for policy makers for taking necessary actions.

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