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.
Abente LG, Aragonés N, GarcÃa-Pérez J, Fernández NP, 2018. Disease mapping and spatio-temporal analysis: importance of expected-case computation criteria. Geospat Health 9:27-33.
DOI: https://doi.org/10.4081/gh.2014.3
Bakka H, Rue H, Fuglstad GA, Riebler A, Bolin D, Illian J, Krainski E, Simpson D, Lindgren F, 2018. Spatial modeling with R-INLA: A review. Wires Comput Stat 10:e1443.
DOI: https://doi.org/10.1002/wics.1443
Carroll R, Prentice C, 2021. Using spatial and temporal modeling to visualize the effects of U.S. state issued stay at home orders on COVID 19. Sci Rep 11:1-7.
DOI: https://doi.org/10.1038/s41598-021-93433-z
Cleveland W, Loader C, 1996. Smoothing by local regression: principles and methods. In W. HardIe, and M. Schimek (Eds.), Statistical theory and computational aspects of smoothing. Physica-Verlag Heidelberg, Berlin, Germany, pp. 10-49.
DOI: https://doi.org/10.1007/978-3-642-48425-4_2
Cucinotta D, Vanelli M, 2020. WHO declares COVID-19 a pandemic. Acta Biomed 911:157-60.
D’Angelo N, Abbruzzo A, Adelfio G, 2021. Spatio-temporal spread pattern of COVID-19 in Italy. Mathematics 92454:1-14.
DOI: https://doi.org/10.3390/math9192454
Dyer O, 2021. Covid-19: Indonesia becomes Asia’s new pandemic epicentre as Delta variant spreads. BMJ 374:n1815.
DOI: https://doi.org/10.1136/bmj.n1815
Gamio L, Symonds A, 2021. Global virus cases reach new peak, driven by India and South America. Available from: https://nyti.ms/3xYVO94 Accessed: 31 December 2021.
Gelman A, 2006. Prior distribution for variance parameters in hierarchical models. Bayesian Anal 1:515-33.
DOI: https://doi.org/10.1214/06-BA117A
Giuliani D, Dickson M, Espa G, Santi F, 2020. Modelling and predicting the spatio-temporal spread of COVID-19 in Italy. Infect Dis 20700:1-10.
DOI: https://doi.org/10.1186/s12879-020-05415-7
Haseltine W, 2021. A proposal for long-term COVID-19 Control: Universal Vaccination, Prophylactic Drugs, Rigorous Mitigation, and International Cooperation. Global Economy and Development program: Brookings. Available from: https://www.brookings.edu/wp-content/uploads/2021/08/Proposal-for-long-term-COVID-19-control_v1.0.pdf
Ioannidis J, Cripps S, Tanner M, 2020. Forecasting for COVID-19 has failed. Int J Forecast 2-17.
Jaya IGNM, Folmer H, 2020. Bayesian spatiotemporal mapping of relative dengue disease risk in Bandung, Indonesia. J Geogr Syst 221:105-42.
DOI: https://doi.org/10.1007/s10109-019-00311-4
Jaya IGNM, Folmer H, 2021. Bayesian spatiotemporal forecasting and mapping of COVIDâ€19 Risk with application to West Java Province, Indonesia. J Reg Sci 61:1-45.
DOI: https://doi.org/10.1111/jors.12533
Jaya IGNM, Folmer H, 2021.Spatiotemporal high-resolution prediction and mapping: methodology and application to Dengue disease. J Geogr Syst [Epub ahead of print].
DOI: https://doi.org/10.1007/s10109-021-00368-0
Jaya IGNM, Folmer H, Ruchjana BN, Kristiani F, Andriyana Y, 2017. Modeling of infectious diseases: a core research topic for the next hundred years. In: R. Jackson and P. Schaeffer (Eds.), Regional research frontiers-Vol. 2, Methodological advances, regional systems modeling and open sciences. Springer, West Virginia, USA, pp 239-255.
DOI: https://doi.org/10.1007/978-3-319-50590-9_15
Knorr-Held L, 2000. Bayesian modeling of inseparable space-time variation in disease risk. Stat Med 19:2555-67.
DOI: https://doi.org/10.1002/1097-0258(20000915/30)19:17/18<2555::AID-SIM587>3.0.CO;2-#
Lawson A, 2010. Hotspot detection and clustering: ways and means. Environ Ecol Stat 171: 231-45.
DOI: https://doi.org/10.1007/s10651-010-0142-z
Lawson A, Rotejanaprasert C, 2014. Childhood brain cancer in Florida: A Bayesian clustering approach. Statist Public Policy 11:99-107.
DOI: https://doi.org/10.1080/2330443X.2014.970247
Mahmud KH, Hafsa B, Ahmed R, 2021. Roel of transport network accessibility in the spread of COVID-19 a case study in Savar Upzila, Bangladesh. Geospat Health 16:1-11.
DOI: https://doi.org/10.4081/gh.2021.954
Massad E, Burattini M, Lopez L, Coutinho F, 2005. Forecasting versus projection models in epidemiology: The case of the SARS epidemics. Med Hypotheses 65:17-22.
DOI: https://doi.org/10.1016/j.mehy.2004.09.029
Mlcochova P, Kemp S, Dhar M, Papa G, Meng B, Ferreira I, 2021. SARS-CoV-2 B.1.617.2 Delta variant replication and immune evasion. Nature 599:114-9.
Mohammad Ebrahimi S, Mohammadi S, Bergquist S, Dolatkhah S, Olia M, Tavakolian A, Pishgar E, Kiani B, 2021. Epidemiological characteristics and initial spatiotemporal visualisation of COVID-19 in a major city in the Middle East. BMC Public Health 21:1-18.
DOI: https://doi.org/10.1186/s12889-021-11326-2
Nicola M, Alsafi Z, Sohrabi C, Kerwan A, Al-Jabir A, Losifidis C, 2000. The socio-economic implications of the coronavirus pandemic (COVID-19): a review. Int J Surg 78:185-93.
DOI: https://doi.org/10.1016/j.ijsu.2020.04.018
Nikparvar B, Rahman M, Hatami F, Thill JC, 2021. Spatio temporal prediction of the COVID 19 pandemic in US counties: modeling with a deep LSTM neural network. Sci Rep 1121715:1-12.
DOI: https://doi.org/10.1038/s41598-021-01119-3
OECD 2020. COVID-19 crisis response in ASEAN Member States. OECD, Paris, France.
PTI, 2021. Vaccination is the only long-term solution to COVID-19 crisis in India, says Fauci. Available from: https://www.thehindu.com/news/national/vaccination-is-the-only-long-term-solution-to-covid-19-crisis-in-india-says-fauci/article34522378.ece Accessed: December 20, 2021 [In Hindu].
Rue H, Martino S, Chopin N, 2009. Approximate Bayesian inference for latent gaussian models by using integrated nested Laplace approximations. J R Stat Soc 72:319-92.
DOI: https://doi.org/10.1111/j.1467-9868.2008.00700.x
Sahu S, Böhning D, 2021. Bayesian spatio-temporal joint disease mapping of Covid-19 cases and deaths in local authorities of England. Spat Stat 100519. [Epub ahead of print].
DOI: https://doi.org/10.1016/j.spasta.2021.100519
Sasongko T, 2021. COVID-19 in Southeast Asia: all eyes on Indonesia. The Conversation. Available from: https://theconversation.com/covid-19-in-southeast-asia-all-eyes-on-indonesia-164244 Accessed: March 2, 2022.
Shinde G, Kalamkar A, Dey N, Chaki J, Hassanien A, 2020. Forecasting models for coronavirus disease COVID 19: a survey of the state of the art. SN Comput Sci 1197:1-15.
DOI: https://doi.org/10.1007/s42979-020-00209-9
Valente F, Laurini M, 2021. Estimating spatiotemporal patterns of deaths by COVID-19 outbreak on a global scale. BMJ Open 11:e047002.
DOI: https://doi.org/10.1136/bmjopen-2020-047002
Wagner C, Saad-Roy C, Grenfell B, 2022. Modelling vaccination strategies for COVID-19. Nat Rev Immunol 22:139-41.
DOI: https://doi.org/10.1038/s41577-022-00687-3
Watson S, Diggle P, Chipeta M, Lilford R, 2021. Spatiotemporal analysis of the first wave of COVID-19 hospitalisations in Birmingham, UK. BMJ Open 1110:e050574.
DOI: https://doi.org/10.1136/bmjopen-2021-050574
Webb G, 2021. A COVID-19 epidemic model predicting the effectiveness of vaccination in the US. Infect Dis Rep 13:654-67.
DOI: https://doi.org/10.3390/idr13030062
WHO, 2021a. WHO coronavirus COVID-19 dashboard. WHO. Available from: https://covid19.who.int/ Accessed: December 21, 2021.
WHO, 2021b. Vaccine efficacy, effectiveness and protection. WHO. Available from: https://www.who.int/news-room/feature-stories/detail/vaccine-efficacy-effectiveness-and-protection Accessed: March 4, 2022.
Widadio N, 2021. Indonesia new coronavirus epicenter as Delta variant spreads Country 'has become the epicenter at least in Asia,’ says epidemiologist. AA. Available from: https://www.aa.com.tr/en/asia-pacific/indonesia-new-coronavirus-epicenter-as-delta-variant-spreads/2305735 Accessed: March 4, 2022.
Yang W, Shaman J, 2021. COVID-19 pandemic dynamics in India, the SARS-CoV-2 Delta variant, and implications for vaccination. medRxiv [Preprint]:1-2. doi:10.1101/2021.06.21.21259268.
DOI: https://doi.org/10.1101/2021.06.21.21259268
Zambrano A, Calderón X, Jaramillo S, Zambrano O, Esteve M, Palau C, 2017. Community early warning systems. In: D. Câmara & N. Nikaein (Eds.), Wireless public safety networks - 3. Elsevier, UK, pp. 39-66.
DOI: https://doi.org/10.1016/B978-1-78548-053-9.50003-2
Zhao Zy, Niu Y, Luo L, Hu Qq, Yang Tl, Chu Mj, 2021. The optimal vaccination strategy to control COVID-19: a modeling study in Wuhan City, China. Infect Dis Poverty 10:1-26.
DOI: https://doi.org/10.1186/s40249-021-00922-4
Zipfel C, Bansal S, 2020. Assessing the interactions between COVID-19 and influenza in the United States. medRxiv. Preprint, 1-13.
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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