Susceptibility and Sustainability of India against CoVid19: a multivariate approach

Soumi Ray; Mitu Roy

To keep the result as unbiased as possible, we have collected data of multiple cities and countries all over the world having different geographical locations and climatic conditions. We have conducted analysis of several relevant factors to look into the situation from all possible corners. Possible dependency of the number of total infection and death has been examined against the environmental conditions taking different weather parameters as independent variables. For this purpose, data of 43 cities all over the world has been considered. These cities are significantly affected by SARS-CoV 2. We have collected the CoVid19 related data from WHO site and other data from particular websites for each individual type of parameter to minimize the biases. The duration considered to check dependency of weather parameters is from 1st to 27 th March, as because by 1 st of March, a large number of countries got significantly infected. The days have been limited to 27 th , as by 3 rd week of March majority of the countries applied social distancing and isolation. Data beyond that time may have significant impact of isolation. Isolation includes noise in the measure of infection and death, as it puts restriction in virus transmission due to lower availability of hosts. The weather information of all those locations have been collected from a single website [15] to keep it uniform even if the information includes any noise or bias. We also have taken a measure of the population to compare the infection rate. Human to human infection depends on the population for transmission. Other than these affecting parameters, another checking has been done on the impact due to the lockdown. How the duration of lockdown has been affecting the number of new infection, have also been examined to understand its importance. We have considered life expectancy also to inspect its impact on the number of infected cases and deaths. The life expectancy includes the impact of different parameters like average living standard, socioeconomic situation, health service qualities, natural calamities etc. A comparison with life expectancy refers to be have a relation with all those hidden parameters though the insights of each are not accessible. The data is collected from United Nations Development Program reports [16] . As proposed in a paper [17] , vaccination may have great impact on death. In This article, the data of Bacillus Calmette-Guérin (BCG) vaccination has been compared with present death rate of different countries. This observation has a significant impact in our final outcome. This data is collected from WHO and review articles [18] [19] [20] . The additional factors included in this study are the impact of lung cancer (LC), chronic obstructive pulmonary disease (COPD) and Lower respiratory infect (LRI). These diseases have shown an impact on death rate in many countries which are badly affected by coronavirus. The required data are retried from online resources [21] .