# Country-Based COVID-19 DNA Sequence Classification in Relation with International Travel Policy
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Country Name**} & \multicolumn{6}{c}{**MLP Model**} & \multicolumn{6}{c}{**CNN Model**} & \multicolumn{6}{c}{**BiLSTM Model**} \\ \cline{2-13}  & **Acc.** & **Prec.** & **Recall** & **F1-Score** & **Time** & **Acc.** & **Prec.** & **Recall** & **F1-Score** & **Time** & **Acc.** & **Prec.** & **Recall** & **F1-Score** & **Time** \\ \hline Brunei & 0.98 & 0.98 & 0.98 & 0.98 & 00.00:58 & 0.98 & 0.98 & 0.98 & 0.98 & 0.98 & 00.02:09 & 0.98 & 0.98 & 0.98 & 0.98 & 0.12:17 \\ Cambodia & 0.85 & 0.87 & 0.85 & 0.85 & 00.01:18 & 0.85 & 0.87 & 0.85 & 0.85 & 00.00:16 & 0.84 & 0.86 & 0.84 & 0.83 & 0.13:29 \\ Indonesia & 0.69 & 0.71 & 0.69 & 0.69 & 00.01:35 & 0.71 & 0.72 & 0.71 & 0.70 & 00.08:54 & 0.69 & 0.7 & 0.69 & 0.69 & 0.23:30 \\ Laos & 0.84 & 0.86 & 0.84 & 0.83 & 00.01:22 & 0.86 & 0.88 & 0.86 & 0.86 & 0.00:14 & 0.85 & 0.88 & 0.85 & 0.10:50 \\ Malaysia & 0.66 & 0.68 & 0.66 & 0.65 & 00.01:26 & 0.70 & 0.72 & 0.7 & 0.69 & 00.11:15 & 0.66 & 0.67 & 0.66 & 0.65 & 0.18:12 \\ Philippines & 0.81 & 0.82 & 0.81 & 0.80 & 00.01:47 & 0.83 & 0.84 & 0.83 & 0.82 & 00.07:58 & 0.81 & 0.83 & 0.81 & 0.81 & 0.20:47 \\ Singapore & 0.79 & 0.79 & 0.79 & 0.79 & 00.01:34 & 0.82 & 0.82 & 0.82 & 0.82 & 00.11:22 & 0.78 & 0.8 & 0.78 & 0.77 & 0.19:29 \\ Thailand & 0.79 & 0.79 & 0.79 & 0.79 & 00.01:40 & 0.80 & 0.80 & 0.80 & 0.80 & 00.00:27 & 0.80 & 0.80 & 0.80 & 0.14:34 \\ Vietnam & 0.70 & 0.71 & 0.70 & 0.70 & 00.01:38 & 0.73 & 0.74 & 0.73 & 0.72 & 00.10:02 & 0.70 & 0.71 & 0.70 & 0.69 & 0.13:30 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Execution time and performance metrics for classification evaluation for each model, involving Delta variant sequences. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Country Name**} & **Average of Accuracy for All Folds** & **Standard Deviation** & **Execution Time** \\ \hline Brunei & 97.97 & 0.05 & 00:16:06 \\ Cambodia & 85.22 & 0.23 & 00:14:07 \\ Indonesia & 66.72 & 0.52 & 00:16:08 \\ Laos & 83.44 & 0.27 & 00:18:33 \\ Malaysia & 65.07 & 0.44 & 00:16:46 \\ Philippines & 78.37 & 0.58 & 00:20:27 \\ Singapore & 78.64 & 0.22 & 00:22:03 \\ Thailand & 78.38 & 0.30 & 00:18:00 \\ Vietnam & 67.01 & 0.31 & 00:21:02 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Execution time and average of the classification accuracy with 10-fold cross-validation for MLP model, involving Delta variant sequences. COVID-19 condition with fewer accumulated cases and deaths, we consider this as trigger information to be explored separately in the future. For additional context, without considering the DNA sequence, a previous study examining pandemic trajectories at the beginning of the outbreak in four ASEAN countries revealed that Malaysia and Vietnam started with strong early performances, while the crisis began severely in the Philippines and Singapore [28]. However, challenges persisted in Malaysia and the Philippines, with relative stability in Vietnam and Singapore. The study emphasized that significant political uncertainty persisted, exemplified by the fragile political situation in Malaysia in 2021. Given that the effectiveness of planning and executing a comprehensive multisectoral response is heavily dependent on national leadership, initial resources obviously play a crucial role, but policy actions also carry significant weight amid the fight against the pandemic. Moving to the main purpose of the study, we further explored the potential correlation between classification accuracy and policy implementation in each country. We utilized the international travel policy index resulting from the processing of the third dataset, accessed from The Oxford COVID-19 Government Response Tracker (OxCGRT) project [27]. The strict implementation of international travel policy, compared with other government policies, might correlate with the evolution of the virus within a country.