Spatiotemporal early warning system for COVID-19 pandemic

I.G.N.M. Jaya, Y. Andriyana, B. Tantular, F. Krisiani


Wuhan, China reported the outbreak of COVID-19 in December 2019. The disease has aggressively spread around the world, including Indonesia. The emergence of COVID-19 has serious implications for public health and socio-economic development worldwide. No country is prepared to face COVID-19. Because of the rapid transmission of COVID-19, the early warning systems (EWS) in each country are not prepared to deal with it. Controlling and preventing COVID-19 transmission in an effective and efficient manner is critical not only for public health, but also for economic sustainability and long-term viability. Consequently, an efficient and effective EWS for COVID-19 is required. The EWS for COVID-19 must be capable of monitoring and forecasting the spatiotemporal transmission of COVID-19. This study demonstrates how an EWS could be a proactive system that would be able to predict the spatiotemporal distribution of COVID-19 and detect its sudden increase in small areas such as cities. Early COVID-19 data in Bandung, Indonesia from 17 March 2020 to 22 June 2020 was used to demonstrate the construction of an effective and efficient EWS using the spatiotemporal model. We observed that the relative risk of COVID-19 fluctuates geographically and temporally, gradually increasing throughout the estimate phase (17 March 2020-22 June 2020) and increasing slightly during the prediction period (23 June–06 July 2020). We discovered that human mobility is a major aspect that must be addressed in order to minimize COVID-19 transmission during the early pandemic phase.

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Published: 2022-01-10

How to Cite this Article:

I.G.N.M. Jaya, Y. Andriyana, B. Tantular, F. Krisiani, Spatiotemporal early warning system for COVID-19 pandemic, Commun. Math. Biol. Neurosci., 2022 (2022), Article ID 4

Copyright © 2022 I.G.N.M. Jaya, Y. Andriyana, B. Tantular, F. Krisiani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Commun. Math. Biol. Neurosci.

ISSN 2052-2541

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