Statistical Study of Relationship and Comparison of Average Monthly Temperature and nCoV-SARS-2: A Case Study of India

In this study we employed statistical methods to relate and make comparison between monthly average temperature and number of confirmed cases for COVID-19 disease. R-studio were used to achieve the results. We extracted our data for the analysis from the Ministry of Health and Family Welfare Government of India for the cumulative confirmed cases of nCoV-SARS-2, the average monthly temperature from 1 March to 31 May 2020 were also extracted via internet from Current Results weather and science facts for Indian Weather. The descriptive analysis of the generated data was presented graphically. Our findings show that, the average temperature so far has no effect on the number of nCoV-SARS-2 in all the four regions of India, in fact number of cases are still in the rise with daily increase.


INTRODUCTION.
As a result of high rate of spread of COVID-19, serious attention has been given to study on rate of the pandemic, transmission ways, methods of prevention and how the virus will survive when exposed to certain amount of temperature. The activities of the virus in association with environmental features is critical. (1) The CoV-SARS-2 infection 2019 or COVID-19 pandemic has turn out to be a very serious health issue of concern by government and general public. The newness of the syndrome encourages an investigation for thoughtful of how natural dynamics influence the spread and existence of the disease. Many researchers have tried vigorously in finding a connection between temperature and COVID-19 number of confirmed cases. But, there is no exact study for the four regions of India. Cascella et al. (2020) performed a study on the novel nCoV-SARS-2 structure. Their examination revealed that the disease fits to the family of single-stranded RNA viruses (+ ssRNA), which has a length of about 30 kb and an envelope with spear structures and they verified the sensitivity of the virus to UV light and temperature. (2) Pirouz et al. (2020) studied the relationship between environmental features and the number of confirmed cases of Coronavirus using the artificial intelligence techniques. The findings of this investigation placed indication on the role of weather conditions on the pandemic rate. (3) Chen et al. (2020) established a time dependent mathematical model for the estimation of the total number of confirmed cases. (4) Examination of the prior studies shows that a further investigation about the consequences of ecological factors on the nCoV-SARS-2 is required. Since some of the earlier studies have indicate the influence of weather situations, particularly temperature, on COVID-19. The Coronavirus Disease 2019 (COVID-19) is steady in faeces at room temperature for a minimum of one to two (1-2) days and it can be steady also in an infected person for up to four days. Thermal heat at 56 0 C destroys the COVID-19 at about ten thousand (10000) units per 0.25hrs. Thus, temperature is a vital feature in existence of COVID-19 disease . (5) The objective of this study is to determine if there exist a significant relationship between temperature and COVID-19 confirmed cases from March to May 2020 as well as their comparison for the 4 regions of India. In this study we employed statistical methods to relate and make comparison between temperature and number of confirmed case for COVID-19 disease. T-test was used for comparison and correlation analysis have been used to find if significant relationship exists.

STUDY SCOPE
The scope of this study covers only the four region of India, Western, Northern, Southern and Eastern regions. Below are the maps of India showing the location of each region. (6) We consider their average monthly temperature and the cumulative confirmed cases of nCoV-SARS-2 from the early month of March to the end of May, 2020. The t-test statistic is among the type of inferential statistical techniques used in determining if significant differences exist among the means of two sets of variables, which may be associated in certain structures. A t-test is applied as a hypothesis testing instrument, which permits analysis of a guess suitable to a population. A t-test (t-t) gazes at the test statistic, the t-distribution outcomes, and the degrees of freedom (DF) to ascertain the statistical significance adequacy. (7) The statistical procedure for testing hypothesis on two differences means ( 12  − ) for normality of two distributions such that variance one ( 2 1  ) and variance two ( 2 2  ) are not known. A t-test statistics is appropriate to test our claim and assumption. We follow the following steps with an assumption that the two variances of the given dual normal distribution are equal and unknown.

Decision and conclusion
The final steps is to take decision based upon the step 3 & 4 and draw a statistical conclusion for the users and policy maker.

Techniques of Correlation Analysis
A Correlation is a statistical term refers to statistical tools that aimed in helping researcher to measure and analysed extend of or degree of association between two variables. Usually, correlation investigation deals with the relationship between two or more variables. The mathematical and statistical formula for measuring such relationship and its significance is as follows: It is usually useful to test significant of the true correlation value through the application and formulating statistical hypothesis. The used hypothesis for the significant of the correlation estimate is: In this study, we applied R-Studio to estimate the relationship and its significance for the four regions of India. We extracted our data for the analysis from the Ministry of Health and Family Welfare Government of India for the cumulative confirmed cases of nCoV-SARS-2,the average monthly temperature from 1 st March to 31 st May 2020 were also extracted via internet from Current Results weather and science facts for Indian Weather. (9)(10)(11) The descriptive analysis of the generated data were presented graphically. Comparable to standard values of correlation coefficients varies in between 11 r −   inclusive. Our p-values shows a non-significant correlation between the study variables. We further employ the two independent sample t-test which gives the comparison of means of our study variables. Figure 1a is a multiple bar chart of Eastern regional state of India. This chart clearly depicts the average temperature and cumulative cases of nCoV-SARS-2 in hundreds of the region. The chart practically shows higher temperature amount in the month of May while in April the temperature value is moderate but in March there is low temperature in all the state of this region. West-Bengel record higher cases of nCoV-SARS-2 but its temperature value remained almost similar to all other states. States like Meghalaya, Nagaland, Tripura, Assam, and Odisha have recorded lowest number of confirmed cases. In summary, higher, moderate and low average temperature value have no correlation with an increase or decrease of nCoV-SARS-2 cases. Even though relationship may seem to exist in one way or the other, but it might likely be insignificant and inversely correlated. The line graph in figure 1b shows also same result as depicted by figure 1a.  The average temperature for April and May is almost same and is higher than that of March for all the states. It was observed that the highest average temperature in the region is 30 0 C from March to May. The lowest temperature was found to be in Jammu and Kashmir followed by Himachal Pradesh, though Himachal Pradesh have less number of confirmed cases as Jammu and Kashmir do have. Our findings shows that, the highest number of cases was not recorded in the state with highest average temperature value. The lowest number of confirmed cases was not recorded in the state with lowest average temperature in the Northern region. So as the region concern, temperature has no contribution in the spread of the virus.  May a strong relationship was indicated. Same results was r ecorded as the other three regions when t-test was applied f or the test of comparison.

RESULTS AND DISCUSSION
The strong relationship in Eastern India were attributed to h igh cumulative number of COVID-19 confirmed cases and t he average temperature. But still the strong relationship has no effect to buttress the saying that COVID-19 cases decrea se with increasing temperature. The negative relationship w itness in Western region is due to the low cumulative numb er of COVID-19. Though significant relationship may exist when the magnitude of the temperature reaches 60 o C which may likely not be viable in India, because the highest avera ge value of the temperature is usually recorded in the month of May.

CONCLUSION
Our findings show that, the average temperature so far has n o effect on the number of nCoV-SARS-2 in all the four regi ons of India, in fact number of cases are still in the rise with daily increase. The highest number of cases was not recorde d in the state with highest average temperature value. The st ate with lowest average temperature also has not reported th e least number of confirmed cases. This has also confirmed our study hypothesis