# A Novel Non-Parametric Spatiotemporal Scan Statistic: An Application to Detect Disease Outbreaks
## 3 Experimental Results  
### A Simulation Study  
The proposed scan statistic is first applied in the year 2004 to the simulated datasets used in [14], which have been specifically designed for evaluating and comparing the statistical power of spatiotemporal disease outbreak detection methods. These simulated datasets can be found in [[http://www.satscan.org/datasets](http://www.satscan.org/datasets)]([http://www.satscan.org/datasets](http://www.satscan.org/datasets)) (accessed on 25 December 2021). These datasets were simulated by [PERSON] based on the geography and population of New York City, including the effects of disease outbreaks of a hypothetical disease of varying size and location. Accordingly, geographic coordinates (representing the approximate center of each zip code) and population numbers for 176 New York City zip codes were used for these datasets. Assuming outbreak occurred in seventeen different locationsin New York City, with a high or moderate risk, 34 datafiles were created considering a period of 31 days, with a random number of cases of the hypothetical disease. Each file has 1000 simulated datasets. For each dataset, the total number of randomly allocated cases is 100 multiplied by the number of days (i.e., 31 \(\times\) 100 = 3100 cases). The number 100 was chosen to reflect the occurrence rate of certain syndromes common to the syndromic surveillance system of New York City emergency department visits. In the null model scenario of this context, each person living in New York City is equally likely to contact the disease, and hence each case is assigned to a particular zip code on any given day with a probability proportional to the population of that zip code. When generating data for geographically localized outbreaks, an increased risk was assigned to the zip codes in which the outbreaks are assumed to have occurred. Consequently, for each such zip code and day combination, the corresponding population was multiplied by an assigned relative risk. This study attempts to detect the areas in which a disease outbreak has occurred out of a larger geographic region using the improved Spatiotemporal Mann-Whitney Scan Statistic. The wider geographical region includes areas belonging to the four main boroughs of New York City: Brooklyn (A), Manhattan (B), Staten Island (C) and the Bronx (E). The simulated datasets, which were created assuming that outbreak occurred only in Williamsburg, Brooklyn (A), were selected to apply the novel spatiotemporal MW scan statistic. The chosen datasets are simulated with a **high risk of outbreak** for a period of 31 days. Of the datasets simulated, assuming outbreak occurred in Williamsburg, Brooklyn, 10 datasets were chosen randomly for this study. The main objective in this scenario is to determine whether the exact outbreak zip code, 11211, of area A is identified by this scan statistic. If this zip code is not included in the most likely cluster, secondary clusters are found using the method introduced in [1] until this area is detected. According to the results (see Appendix A), all the zip codes identified in the most likely cluster of each sample belong to area A. In other words, the scan statistic has detected A as the outbreak area in all cases, out of the four boroughs considered. Hence, the **Spatiotemporal MW Scan Statistic performs well in detecting the area of disease outbreak** in a larger geographical region. Furthermore, it detects four surrounding areas of the exact outbreak zip code on average, in the most likely cluster. Moreover, 80% of the time, the Spatiotemporal MW Scan Statistic identifies the exact outbreak zip code in the most likely cluster. Therefore, it is reasonable to suggest that the **Spatiotemporal MW Scan Statistic can effectively detect the exact disease outbreak zip code in the majority of cases.