COVID-19 Pandemic vis-à-vis Zamboanga Peninsula Aviation: A Predictive Analysis

The use of time-series analysis to examine aviation data trends through time comes crucial in planning its future. The prophet is an additive model that fits non-linear patterns. It functions best with historical data from various seasons and time series with significant seasonal impacts. This research looked closely into the aviation data in Zamboanga Peninsula, Jolo, and Tawi-Tawi to give a clearer picture of its impact on the sector and forecast passenger and aircraft movement in the coming months to see whether the impact of the opening in the aviation industry can be sustained. The final data comprise 51 data points for flight arrivals and departures and 51 data points for passenger arrivals and departures. Data show the decline in passengers and aircrafts arriving and departing in major airports in Zamboanga Peninsula, Jolo, and Tawi-Tawi during the pandemic. However, an increasing trend was observed years after the pandemic hit the region. Findings during the training and testing phase revealed that different models attained varied results;however, there are models which attained a higher degree of accuracy as depicted in the RMSE and R2. This indicates that predicting passenger and aircraft movement using models with higher accuracy is similar to real data thus, it is viable in predicting future values. Forecasting results further show a gradually increasing trend of aircraft and passenger arrivals in the major airports in Zamboanga Peninsula, Jolo, and Tawi-Tawi despite some observed smaller forecasted values. © 2022 IEEE.

Time Series Analysis of Infected COVID-19 Cases in the Zamboanga Peninsula, Philippines using Long Short-Term Memory Neural Networks

Infectious disease outbreaks, such as COVID-19 pandemics, exhibit patterns that can be described by the dynamics of a mathematical model This study seeks to explore the use of LSTM in order to develop models that will capture the non-linear dynamic changes of COVID-19 cases in Zamboanga Peninsula. The study uses 436 data points where the latest timestamp for the dataset is on May 29, 2021 and the oldest is on March 20, 2020. These data are taken from the DOH repositories and revalidated using the data from the DOH Regional Office. The training and testing phase results show that among the different LSTM variants, convLSTM trained using Adam and RMSProp attained the smallest RMSE result of 42.34 and 43.67 and a correlation coefficient of 0.94 0.93, respectively. ConvLSTM, when trained with Adam and RMSProp, produces the best results, as evidenced by the shortest RMSE and highest correlation coefficient. Results revealed that convLSTM appears to be a viable choice for modeling the time series of the COVID 19 infected cases in Zamboanga Peninsula Region in compared with the different variants of LSTM. © 2021 IEEE.