https://doi.org/10.4081/gh.2022.1070
Post-pandemic COVID-19 estimated and forecasted hotspots in the Association of Southeast Asian Nations (ASEAN) countries in connection to vaccination rate
Submitted: 9 January 2022
Accepted: 8 March 2022
Published: 22 March 2022
Accepted: 8 March 2022
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
After a two-year pandemic, coronavirus disease 2019 (COVID-19) is still a serious public health problem and economic stability worldwide, particularly in the Association of Southeast Asian Nations (ASEAN) countries. The objective of this study was to identify the wave periods, provide an accurate space-time forecast of COVID-19 disease and its relationship to vaccination rates. We combined a hierarchical Bayesian pure spatiotemporal model and locally weighted scatterplot smoothing techniques to identify the wave periods and to provide weekly COVID-19 forecasts for the period 15 December 2021 to 5 January 2022 and to identify the relationship between the COVID-19 risk and the vaccination rate. We discovered that each ASIAN country had a unique COVID-19 time wave and duration. Additionally, we discovered that the number of COVID-19 cases was quite low and that no weekly hotspots were identified during the study period. The vaccination rate showed a nonlinear relationship with the COVID-19 risk, with a different temporal pattern for each ASEAN country. We reached the conclusion that vaccination, in comparison to other interventions, has a large influence over a longer time span.
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How to Cite
Jaya, I. G. N. M., Andriyana, Y., & Tantular, B. (2022). Post-pandemic COVID-19 estimated and forecasted hotspots in the Association of Southeast Asian Nations (ASEAN) countries in connection to vaccination rate. Geospatial Health, 17(s1). https://doi.org/10.4081/gh.2022.1070
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