## 3 Materials and Methods
### Implementing Discrete Event Simulation Models
#### 3.5.2 Experiment 2 (Hypothetical Case Scenario)  
Let us assume that there is an outbreak of the Zika Virus in the town where this urgent care clinic is located. Based on the information given about the clinic and the simulated model generated in the above section, the new models produced are used to answer _what if_ type questions regarding Zika. Scenario 1: Suppose the arrival rate of adult patients increases with the onset of Zika. What will be, on average, the wait time for the patient (child or adult) to see a nurse and/or physician without increasing the number of physicians? After adjusting the values of multiple replications for DES and defining along factors that enable greater control of multiple design points with this simulated experiment (Figure 6), the results of these experiments are noted in Table 2. Table 2 demonstrates the results of 3 additional experiments (rows 2, 3, 4) compared to a regular day at this clinic (row 1). More specifically, it illustrates the results of the average statistics for each response within each experimental condition. These conditions are related to the number of adult patients arriving per hour at this clinic (8, 10, 12, and 15). Comparing the first row (8 patients per hour) with the fourth row (15 patients per hour), the mean wait time to see a nurse increased from 4.594 min to 36.630 min. Even more dramatically, the average wait time for an adult patient to see a medical doctor increased from 11.417 min to 126.101 min. Scenario 2: Suppose the arrival rate of adult patients increases with the onset of Zika. What will be, on average, the wait time for the patient (child or adult) to see a nurse and/or physician when increasing the number of physicians by one? While changing the number of doctors and pediatricians has no effect on the amount of time a patient will spend waiting to see a nurse, adding one doctor should reduce the overall adult patient \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Avg\_Arrival** & **AvgWait\_Nurse** & **AvgWait\_Ped** & **AvgWait\_MD** & **Treated\_Child** & **Treated\_Adult** \\ \hline
**8/hour** & & & & & \\ (\(\mu\)=7.5) & 4.594 & 0.486 & **11.417** & 51.4 & 55.2 \\ \hline
**10/hour** & & & & & \\ (\(\mu\)=6) & 8.070 & 0.403 & 35.167 & 51.4 & 84.2 \\ \hline
**12/hour** & & & & & \\ (\(\mu\)=5) & 14.961 & 0.277 & 71.328 & 51.4 & 101 \\ \hline
**15/hour** & & & & & \\ (\(\mu\)=4) & 36.630 & 0.246 & **126.101** & 51.4 & 122.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average waiting time for an adult patient without any increase in staffing in minutes. Figure 6: Simulation model when 15 adult patients arrive per hour (no additional staffing). wait time. The following scenario (Figure 7) examines the situation where one additional _Medical Doctor_ is utilized. At _Point 2_ in Table 3, 5 replications of this model are generated. These models had the same uniform distribution that was utilized in our previous experiment, except the number of medical doctors (_NumMDs_) is now 3. Table 4 presents the histograms of wait times for patients during one day for each experiment. The effect of the increased number of adult patients over time is evident. Comparing Cell 1-3 and Cell 2-3, the wait times for an adult patient to see a doctor during the Zika Virus outbreak increased dramatically, with a maximum wait time of 32.5 min to more than 4 h. Also, as demonstrated by the scatter plots and a comparison of Cell 1-3 and Cell 2-3, the average wait time per adult patient increases quickly at the beginning of the day and climbs, exponentially, until the doors close. Recall that on a typical day this clinic opens at 8:00 am and closes at 5:00 p.m., which corresponds to time ranges from 0 to 540 min. This is no longer the case with this emergency because this clinic has to stay open to accommodate all patients. Cell 2-3 indicates that there are still patients in this clinic at 10:00 p.m., which corresponds to time ranges of 0 to 840 min.