# EpiInfer: A Non-Markovian Method and System to Forecast Infection Rates in Epidemics
## 9 Multiple Locales
### Multilocale Experiment with EpiPolicy Data
Table 4 shows that both EpiInfer and EpiInfer-multi improve over ARIMA for forecasts more than 7 days in the future. EpiInfer-multi already has a statistically significantly lower Root Mean Squared Error (RMSE) for forecasts of four days in the future or more. EpiInfer on multiple locales having similar meeting distributions is more accurate than EpiInfer on a single locale no matter what the forecasting horizon is. Figure 12 shows a visual comparison for EpiPolicy data predictions from 1-20 days into the future. Figure 13 shows predictions from the two best methods ARIMA and EpiInfer-multi using locales having similar meeting distributions. Figure 14 shows a comparison of ARIMA and EpiInfer-multi that uses data from similar locales. There is a notable difference compared to Figure 3. EpiInfer with similar locales has a lower mean relative RMSE than ARIMA for 5-, 10-, and 20-day  
==WARNING: Truncated because of repetitions==  
+++ \begin{table}
\begin{tabular}{c c c c c} \hline \hline \# & **Inc Offset** & \(p_{1}\) **Multiplier** & \(p_{2}\) **Multiplier** & **RMSE** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 8: EpiPolicy data 1-day prediction root mean squared error (RMSE) values for combinations of inc offset, \(p_{1}\) multiplier, and \(p_{2}\) multiplier. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \# & **Inc Offset** & \(p_{1}\) **Multiplier** & \(p_{2}\) **Multiplier** & **RMSE** \\ \hline
1 & \(-3\) & 0.9 & 0.9 & 0.3562 \\
2 & \(-3\) & 0.9 & 1.0 & 0.3428 \\
3 & \(-3\) & 0.9 & 1.1 & 0.3316 \\
4 & \(-3\) & 1.0 & 0.9 & 0.2915 \\
5 & \(-3\) & 1.0 & 1.0 & 0.2886 \\
6 & \(-3\) & 1.0 & 1.1 & 0.2860 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: EpiPolicy data 10-day prediction root mean squared error (RMSE) values for combinations of inc offset, \(p_{1}\) multiplier, and \(p_{2}\) multiplier. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \# & **Inc Offset** & \(p_{1}\) **Multiplier** & \(p_{2}\) **Multiplier** & **RMSE** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._ \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Setting** \\ \hline
14 (_inc_, 1.0\(\cdot\)_p_\({}_{1}\), 1.0\(\cdot\)_p_\({}_{2}\)) \\
13 (_inc_, 1.0\(\cdot\)_p_\({}_{1}\), 0.9\(\cdot\)_p_\({}_{2}\)) \\
10 (_inc_, 0.9\(\cdot\)_p_\({}_{1}\), 0.9\(\cdot\)_p_\({}_{2}\)) \\
6 (_inc_-3, 1.0\(\cdot\)_p_\({}_{1}\), 1.1\(\cdot\)_p_\({}_{2}\)) \\
19 (_inc_+3, 0.9\(\cdot\)_p_\({}_{1}\), 0.9\(\cdot\)_p_\({}_{2}\)) \\
5 (_inc_-3, 1.0\(\cdot\)_p_\({}_{1}\), 1.0\(\cdot\)_p_\({}_{2}\)) \\
23 (_inc_+3, 1.0\(\cdot\)_p_\({}_{1}\), 1.0\(\cdot\)_p_\({}_{2}\)) \\
11 (_inc_, 0.9\(\cdot\)_p_\({}_{1}\), 1.0\(\cdot\)_p_\({}_{2}\)) \\
4 (_inc_-3, 1.0\(\cdot\)_p_\({}_{1}\), 0.9\(\cdot\)_p_\({}_{2}\)) \\
22 (_inc_+3, 1.0\(\cdot\)_p_\({}_{1}\), 0.9\(\cdot\)_p_\({}_{2}\)) \\
15 (_inc_, 1.0\(\cdot\)_p_\({}_{1}\), 1.1\(\cdot\)_p_\({}_{2}\)) \\
12 (_inc_, 0.9\(\cdot\)_p_\({}_{1}\), 1.1\(\cdot\)_p_\({}_{2}\)) \\
20 (_inc_+3, 0.9\(\cdot\)_p_\({}_{1}\), 1.0\(\cdot\)_p_\({}_{2}\)) \\
3 (_inc_-3, 0.9\(\cdot\)_p_\({}_{1}\), 1.1\(\cdot\)_p_\({}_{2}\)) \\
16 (_inc_, 1.1\(\cdot\)_p_\({}_{1}\), 0.9\(\cdot\)_p_\({}_{2}\)) \\
17 (_inc_, 1.1\(\cdot\)_p_\({}_{1}\), 1.0\(\cdot\)_p_\({}_{2}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Parameter settings ordered by best average rank determined by smallest RMSE on both real and simulated data. The default setting (14) has the best overall ranking, even though, for many forecast days, it is not the best. No other setting is consistently better.