Table (3):
K-means Cluster results on COVID-19 data set.

The objective of the experiment is to analyze the effect of temperature on the covid-19 cases including suspected, confirmed, and death in the popular cities of China (as per WHO report). The analysis is made on Temperature Þ {death, con f irmed, suspected}. In each case the null values are ignored from Table 3. Based on Equation 1, we have created three sub Tables 4 to 7 and they are graphically represented in Figure 2 to 4, where X-axis represents the cluster results of temperature with death, confirmed, and suspected and Y-axis represents the different regions.

Table{ 4, 5, 7} Ì Table 3      …… (1)

From the k-means clustering technique 34 clusters are found as shown in Table 3. Three possible patterns are revealed, as discussed in subsequent subsections.

Table (4):
K-means Cluster results on COVID-19 for death Vs Temperature.

Trend 1: Effect of temperature on Death cases
From the analysis of clustering results shown in Table 4, we see the trends that Hubei has the highest number of death cases i.e., 33.14%, in Figure 2, we can see a clear jump in Hubei. After that, three regions, Hainan, Henan, and Inner Mongolia have the same death rate i.e., 0.14% at different temperatures. Out of four regions, three have death cases when the recorded temperature ≥ 14◦C. Hainan has the highest jump in temperature, but the death rate is same as with Henan and Inner Mongolia. Interestingly, Inner Mongolia has a death case at less temperature 4.85◦C compare to others.

Trend 2: Effect of temperature on Confirmed cases
Filtered cluster results manipulated in Table 5, same trend has been detected as we have seen in Table 4 that cluster#30, Hubei has the highest number of cases (confirmed) i.e., around 189.85, compare to other regions. Some regions such as Hong Kong SAR, Taipei, Beijing and Zhejiang attains value ≥ 1. While other regions such as Liaoning, Shangai, Shandong, and Sichuan have less confirmed cases. If we interrelate the cluster results with temperature value as shown in Figure 3, the trend seems there is no as such direct relationship between the temperature and confirmed Covid-19 cases. Some exception cases such as when the cluster value for Hubei temperature reaches to ≥ 14◦C then the number confirmed cases escalates higher and reaches up to 189.95. However, Table 5 shows the maximum temperature is 21.71◦C for region Guangdong but have confirmed cases are around 0.42. It is also noted from Table 5 that Guangdong is the highly populous region, which means spread the COVID-19 pandemic is controlled here by certain precautionary measures.

Table (5):
K-means Cluster results on Confirmed COVID-19 cases Vs Temperature.

Table (6):
Evidence from Primary Data Set for Hainan Region claims Suspected Covid-19 cases to Death cases without getting Confirmed.

Trend 3: Effect of temperature on Suspected cases
The clustered suspected cases are presented in Table 7, for region Hubei the common trend has been seen i.e., compare to other cities.  As depicted in Figure 4, this city has the highest number suspected cases. Another trend is that Hubei, afflicted at the same temperature of 14◦C, which was clearly seen in the trends of death and confirmed cases (refer to Table 4 and 5). Another trend has been explored that region Hainan is common with the clusters of death as shown in Table 4 with the temperature of 26.71◦C. The similarity in these trends is that these two cities/regions are affected at the same temperature. Here, the analytical results, stress us to argument that the suspected cases of Hainan city comes directly into the death cases, without going to confirmed phase as evidence provided in Table 6.

Table (7):
K-means Cluster results on Suspected COVID-19 cases Vs Temperature.