TY - GEN
T1 - Impact of Community Mobility and Weather Variability on COVID-19 Case in the Provinces of Java Island
AU - Muttaqien, Furqon H.
AU - Naufal, Faishal Z.
AU - Arymurthy, Aniati Murni
AU - Syafarina, Inna
AU - Latifah, Arnida L.
N1 - Funding Information:
This study is supported by Degree By Research Program, BRIN. We thank to Google Mobility Report, Meteorological, Climatological, and Geophysical Agency, and Indonesian COVID-19 Task Force for providing the data. We also thank to Taufiq Wirahman from Scientific Computing Research Group in National Research and Innovation Agency for collecting the COVID-19 data.
Publisher Copyright:
© 2021 ACM.
PY - 2021/10/5
Y1 - 2021/10/5
N2 - COVID-19 is easy to transmit from one infected person to a susceptible person through droplets. Human mobility and weather variable become the factors affecting COVID-19. However, the most influence variable needs to be investigated to effectively control COVID-19 spread. This paper studied the correlation between COVID-19, community mobility and weather variability in Java Island. We used the confirmed cases of COVID-19, community mobility data and weather data from the beginning of March 2020 until the end of February 2021 in each province of Java Island. Two decision tree-based models (Random Forest and XGBoost) in four experimental setups were implemented in this paper. We found that there is similarity trend between Random Forest and XGBoost method in prediction results. The performance of both has also no significant difference. The Capital City of Jakarta, Banten and the Special Region of Yogyakarta shows the best prediction result in the third experiment which used the community mobility variable as features. While, West Java shows the best result with a combination of all weather variables and mobility, Central Java and East Java with the combination of temperature and mobility. This shows that the community mobility gives an impact on COVID-19 cases in all provinces. The correlation analysis found that the community mobility percentage change in transit stations has a significant role in predicting COVID-19 cases. Based on the model performance, the prediction of COVID-19 cases in the Capital City of Jakarta has the best result. While the Special Region of Yogyakarta has the highest error.
AB - COVID-19 is easy to transmit from one infected person to a susceptible person through droplets. Human mobility and weather variable become the factors affecting COVID-19. However, the most influence variable needs to be investigated to effectively control COVID-19 spread. This paper studied the correlation between COVID-19, community mobility and weather variability in Java Island. We used the confirmed cases of COVID-19, community mobility data and weather data from the beginning of March 2020 until the end of February 2021 in each province of Java Island. Two decision tree-based models (Random Forest and XGBoost) in four experimental setups were implemented in this paper. We found that there is similarity trend between Random Forest and XGBoost method in prediction results. The performance of both has also no significant difference. The Capital City of Jakarta, Banten and the Special Region of Yogyakarta shows the best prediction result in the third experiment which used the community mobility variable as features. While, West Java shows the best result with a combination of all weather variables and mobility, Central Java and East Java with the combination of temperature and mobility. This shows that the community mobility gives an impact on COVID-19 cases in all provinces. The correlation analysis found that the community mobility percentage change in transit stations has a significant role in predicting COVID-19 cases. Based on the model performance, the prediction of COVID-19 cases in the Capital City of Jakarta has the best result. While the Special Region of Yogyakarta has the highest error.
KW - community mobility
KW - COVID-19
KW - Random Forest
KW - weather variability
KW - XGBoost
UR - http://www.scopus.com/inward/record.url?scp=85125371526&partnerID=8YFLogxK
U2 - 10.1145/3489088.3489122
DO - 10.1145/3489088.3489122
M3 - Conference contribution
AN - SCOPUS:85125371526
T3 - ACM International Conference Proceeding Series
SP - 141
EP - 145
BT - Proceedings of the 2021 International Conference on Computer, Control, Informatics and Its Applications - Learning Experience
PB - Association for Computing Machinery
T2 - 2021 International Conference on Computer, Control, Informatics and Its Applications - Learning Experience: Raising and Leveraging the Digital Technologies During the COVID-19 Pandemic, IC3INA
Y2 - 5 October 2021 through 7 October 2021
ER -