Projecting the criticality of COVID-19 transmission in India using GIS and machine learning methods

There is a new public health catastrophe forbidding the world. With the advent and spread of 2019 novel coronavirus (2019-nCoV). Learning from the experiences of various countries and the World Health Organization (WHO) guidelines, social distancing, use of sanitizers, thermal screening, quarantining, and provision of lock down in the cities being the effective measure that can contain the spread of the pandemic. Though complete lockdown helps in containing the spread, it generates complexity by breaking the economic activity chain. Besides, laborers, farmers, and workers may lose their daily earnings. Owing to these detrimental effects, the government has to open the lockdown strategically. Prediction of the COVID-19 spread and analyzing when the cases would stop increasing helps in developing a strategy. An attempt is made in this paper to predict the time after which the number of new cases stops rising, considering the strong implementation of lockdown conditions using three different techniques such as Decision Tree, Support Vector Machine, and Gaussian Process Regression algorithm are used to project the number of cases. Thus, the projections are used in identifying inflection points, which would help in planning the easing of lockdown in a few of the areas strategically. The criticality in a region is evaluated using the criticality index (CI), which is proposed by authors in one of the past of research works. This research work is made available in a dashboard to enable the decision-makers to combat the pandemic.