# A Novel Non-Parametric Spatiotemporal Scan Statistic: An Application to Detect Disease Outbreaks
## 3 Experimental Results
### An Application to COVID-19 Data  
We secondly applied the proposed scan statistic to reported COVID-19 cases in China corresponding to the time period from January 2020 to May 2020. This dataset contains cases reported in 33 major spatial areas in China. The data were extracted through the data repository of the Center for Systems Science and Engineering at Johns Hopkins University.  
Our main objective in this context is to determine disease outbreak areas with a significantly higher number of reported cases using the improved Spatiotemporal Mann-Whitney Scan Statistic. According to the results, 30 out of 33 locations are included in the most significant cluster. In order to further confirm these results, we came up with the following spatiotemporal visualizations.  
The chart in Figure 1 was obtained excluding the Hubei region since it was an outlier with a significantly higher number of cases over the period. The regions which are not included in the highly likely cluster are boxed in Figure 1. According to the visualization, these regions have little or no fluctuation over the considered period compared to the other regions. Furthermore, no significant peaks of cases can be seen in those regions over the five months.  
Table 1 shows the spatial averages of each region in ascending order. Accordingly, three of the areas which are not included in the cluster have the lowest spatial averages.  
\begin{table}
\begin{tabular}{c c} \hline
**Region** & **Average Number of Cases** \\ \hline
**Tibet** & **0.007633588** \\ \hline
**Qinghai** & **0.13740458** \\ \hline Macau & 0.34351145 \\ \hline Ningxia & 0.572519084 \\ \hline
**Xinjiang** & **0.580152672** \\ \hline Gansu & 1.061068702 \\ \hline Guizhou & 1.13740458 \\ \hline Liaoning & 1.13740458 \\ \hline Jilin & 1.183206107 \\ \hline Hainan & 1.290076336 \\ \hline Yunnan & 1.41221374 \\ \hline Tianjin & 1.465648855 \\ \hline Shanxi & 1.511450382 \\ \hline Inner Mongolia & 1.79389313 \\ \hline Guangxi & 1.938931298 \\ \hline Shaanxi & 2.351145038 \\ \hline Hebei & 2.503816794 \\ \hline \end{tabular}
\end{table}
Table 1: Spatial averages of COVID-19 cases.  
Figure 1: Spatiotemporal plot of COVID-19 cases in China.  
Moreover, according to Figure 2, the three areas which are not included in the cluster are located away (towards left) from the rest of the locations. Even though the regions Macau and Ningxia have relatively low spatial averages, they are located closer to the larger outbreak areas and hence they could have been included in the most significant cluster.  
\begin{table}
\begin{tabular}{c c} \hline
**Region** & **Average Number of Cases** \\ \hline Fujian & 2.732824427 \\ \hline Sichuan & 4.389312977 \\ \hline Chongqing & 4.419847328 \\ \hline Beijing & 4.526717557 \\ \hline Jiangsu & 4.984732824 \\ \hline Shanghai & 5.129770992 \\ \hline Shandong & 6.045801527 \\ \hline Jiangqi & 7.152671756 \\ \hline Heilongjiang & 7.213740458 \\ \hline Anhui & 7.564885496 \\ \hline Hunan & 7.778625954 \\ \hline Hong Kong & 8.27480916 \\ \hline Zhejiang & 9.679389313 \\ \hline Henan & 9.740458015 \\ \hline Guangdong & 12.17557252 \\ \hline Hubei & 520.1145038 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: Spatial plot of average number of cases.