Predictive Analysis of COVID-19 Epidemic in Thailand: Evaluating Control Lockdown Measures using LSTM Networks

This study addresses the critical objective of evaluating the effectiveness of non-pharmaceutical lockdown measures implemented during COVID-19 outbreaks in Thailand. Assessing the outcome of these measures provides valuable insight that can inform and guide response to future outbreaks. Utilizing a closed-loop forecasting model built on Long Short-Term Memory (LSTM) networks, the research focuses on achieving precise daily forecasts of COVID-19 cases. The methodology involves optimizing hyperparameters through grid-search and incorporating training data from other countries that implemented similar measures. The LSTM, configured with an optimal number of hidden processing units, utilizes past lagged data of daily infected cases as predictors to generate multi-step-ahead predicted values, which are subsequently used as predictors in a recursive approach. As a result, the predicted cases closely align with measured data, facilitating the estimation of the effective reproduction number (Reff) to assess the performance of lockdown measures. The effectiveness of the lockdown measures is quantified at different time intervals: 51%, 41%, and 23% one day after implementation, increasing to 84%, 98%, and 34% after one week, and reaching 96%, 99%, and 73% at the endpoint of the first, second, and fourth waves of infection, respectively. Throughout these waves, the final Reff remains below 1, indicating ongoing but controllable COVID-19, demonstrating the efficacy of the implemented lockdown measures. It is noted that these results are based on specific LSTM model, as the effectiveness of lockdown measures may vary with alternative modeling approaches. Therefore, the findings should be interpreted in the context of this LSTM-framework.