https://doi.org/10.4081/gh.2023.1161
Does mobility restriction significantly control infectious disease transmission? Accounting for non-stationarity in the impact of COVID-19 based on Bayesian spatially varying coefficient models
Submitted: 6 October 2022
Accepted: 29 November 2022
Published: 25 May 2023
Accepted: 29 November 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
COVID-19 is the most severe health crisis of the 21st century. COVID-19 presents a threat to almost all countries worldwide. The restriction of human mobility is one of the strategies used to control the transmission of COVID-19. However, it has yet to be determined how effective this restriction is in controlling the rise in COVID-19 cases, particularly in small areas. Using Facebook's mobility data, our study explores the impact of restricting human mobility on COVID-19 cases in several small districts in Jakarta, Indonesia. Our main contribution is showing how the restriction of human mobility data can give important information about how COVID-19 spreads in different small areas. We proposed modifying a global regression model into a local regression model by accounting for the spatial and temporal interdependence of COVID-19 transmission across space and time. We applied Bayesian hierarchical Poisson spatiotemporal models with spatially varying regression coefficients to account for non-stationarity in human mobility. We estimated the regression parameters using an Integrated Nested Laplace Approximation. We found that the local regression model with spatially varying regression coefficients outperforms the global regression model based on DIC, WAIC, MPL, and R2 criteria for model selection. In Jakarta's 44 districts, the impact of human mobility varies significantly. The impacts of human mobility on the log relative risk of COVID-19 range from –4.445 to 2.353. The prevention strategy involving the restriction of human mobility may be beneficial in some districts but ineffective in others. Therefore, a cost-effective strategy had to be adopted.
Adin A, Goicoa T, Hodges JS, Schnell PM, Ugarte MD, 2022. Alleviating confounding in spatio-temporal areal models with an application on crimes against women in India. Stat Modelling: 1-22.
DOI: https://doi.org/10.1177/1471082X211015452
Bauer C, Wakefield J, Rue H, Self S, Feng Z, Wang Y, 2016. Bayesian penalized spline models for the analysis of spatiotemporal count data. Stat Med 35:1848–65.
DOI: https://doi.org/10.1002/sim.6785
Congdon P, 2018. Spatial heterogeneity in Bayesian disease mapping. Geo Journal 1:1-14.
Facebook Data for Good, 2022. Disease Prevention Maps. Accessed 21 August 2022. Available from: https://dataforgoodfacebookcom/
Fiebig DG, Bartels R, Aigner D J, 1991. A random coefficient approach to the estimation of residential end-use load profiles. J Econom 50:297-327.
DOI: https://doi.org/10.1016/0304-4076(91)90023-7
Finley AO, 2011 Comparing spatially-varying coefficients models for analysis of ecological data with non-stationary and anisotropic residual dependence. Methods Ecol Evol 2:143–54.
DOI: https://doi.org/10.1111/j.2041-210X.2010.00060.x
Firza N, Monaco A, 2022. forecasting model based on lifestyle risk and health factors to predict COVID-19 severity. Int J Environ Res Public Health 19:12538.
DOI: https://doi.org/10.3390/ijerph191912538
Hou X, Gao S, Li Q, Kang Y, Chen N, Chen K, Rao J, Ellenberg JS, Patz JA, 2021. Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic age and race. PNAS 118:e2020524118.
DOI: https://doi.org/10.1073/pnas.2020524118
Jaya IGNM, Folmer H, 2021a. Bayesian spatiotemporal forecasting and mapping of COVID-19 risk with application to West Java Province Indonesia. J Reg Sci 61:849-81.
DOI: https://doi.org/10.1111/jors.12533
Jaya IGNM, Folmer H, 2021b. Identifying spatiotemporal clusters by means of agglomerative hierarchical clustering and Bayesian regression analysis with spatiotemporally varying coefficients: methodology and application to dengue disease in Bandung Indonesia. Geogr Anal 53:1-57.
DOI: https://doi.org/10.1111/gean.12264
Jaya IGNM, Folmer H, 2020. Bayesian spatiotemporal mapping of relative dengue disease risk in Bandung Indonesia. J Geogr Syst 22:105-142.
DOI: https://doi.org/10.1007/s10109-019-00311-4
Jaya IGNM, Folmer H, Ruchjana BN, Kristiani F, Yudhie, 2017. A Modeling of infectious diseases: A core research topic for the next hundred years. In Regional Research Frontiers: Methodological Advances Regional Systems Modeling and Open Sciences; Jackson R Schaeffer P Eds, Springer International Publishing: USA 2 pp 239-254.
DOI: https://doi.org/10.1007/978-3-319-50590-9_15
Karcıoğlu O, Yüksel A, Baha A, Er BA, Esendağlı D, Gülhan P Y, Karaoğlanoğlu S, Erçelik M, Şerifoğlu İ, 2020. Covid-19: The biggest threat of the 21st century: In respectful memory of the warriors all over the world. Turk Thorax J 21:409-18.
DOI: https://doi.org/10.5152/TurkThoracJ.2020.20069
Knorr-Held L, 2000. Bayesian modelling of inseparable space‐time variation in disease risk Stat Med 19: 15-30.
DOI: https://doi.org/10.1002/1097-0258(20000915/30)19:17/18<2555::AID-SIM587>3.0.CO;2-#
Kraemer MUG, Yang CH, Gutierrez B, Wu CH, Klein B, Pigott DM Open COVID-19 Data Working Group; Plessi LD The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 368:493-497.
DOI: https://doi.org/10.1126/science.abb4218
Kucharski AJ, Russell TW, Diamo C, Liu Y, Edmunds J, Funk S, Eggo RM, 2020. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infect Dis 20:553–558.
DOI: https://doi.org/10.1016/S1473-3099(20)30144-4
Lome-Hurtado A, Lartigue-Mendoza J, Trujillo JC, 2021. Modelling local patterns of child mortality risk: a Bayesian Spatio-temporal analysis. BMC Public Health 21:1-12.
DOI: https://doi.org/10.1186/s12889-020-10016-9
Osei F, Stein A, 2017. Diarrhea morbidities in small areas: Accounting for non-stationarity in sociodemographic impacts using Bayesian spatially varying coefficient modelling Sci Rep 7:1-15.
DOI: https://doi.org/10.1038/s41598-017-10017-6
Rue H, Martino S, Chopin N, 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J R Stat Soc 7:319–392.
DOI: https://doi.org/10.1111/j.1467-9868.2008.00700.x
Rue H, Riebler A, Sørbye SH, Illian JB, Simpson DP, Lindgren FK, 2017. Bayesian computing with INLA: A review. Annu Rev Stat Appl 4:395–421.
DOI: https://doi.org/10.1146/annurev-statistics-060116-054045
Shepherd HE, Atherden FS, Chan HMT, Loveridge A, Tatem AJ, 2021. Domestic and international mobility trends in the United Kingdom during the COVID-19 pandemic: an analysis of facebook data. Int J Health Geogr 20:1-13.
DOI: https://doi.org/10.1186/s12942-021-00299-5
Sparks C, 2015. An examination of disparities in cancer incidence in Texas using Bayesian random coefficient models. Peer J 3:e1283.
DOI: https://doi.org/10.7717/peerj.1283
Sperrin M, McMillan B, 2020. Prediction models for covid-19 outcomes. BMJ 371:m3777.
DOI: https://doi.org/10.1136/bmj.m3777
Vicente G, Goicoa T, Ugarte M, 2020. Bayesian inference in multivariate spatio-temporal areal models using INLA: analysis of gender-based violence in small areas. Stoch Environ Res Risk Assess 34:1421–40.
DOI: https://doi.org/10.1007/s00477-020-01808-x
Wakefield J, 2007. Disease mapping and spatial regression with count data. Biostatistics 8:158-83.
DOI: https://doi.org/10.1093/biostatistics/kxl008
Wang S, Liu Y, Hu T, 2020. Examining the change of human mobility adherent to social restriction policies and its effect on COVID-19 cases in Australia. Int J Environ Res Public Health 17:7930.
DOI: https://doi.org/10.3390/ijerph17217930
WHO, 2020. Briefing 11 March Accessed on 18 Novemeber, 2022. Available from: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020
WHO, 2022. Coronavirus (COVID-19) dashboard. Accessed 25 August 2022. Available from: https://covid19whoint/
Yabe T, Tsubouchi K, Fujiwara N, Wada T, Sekimoto Y, Ukkusur SV, 2020. Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic. Sci Rep 10:18053.
DOI: https://doi.org/10.1038/s41598-020-75033-5
Yuan Z, Xiao Y, Dai Z, Huang J, Zhang Z, Chen Y, 2020. Modelling the effects of Wuhan’s lockdown during COVID-19 China. Bull World Health Organ 98:84–494.
DOI: https://doi.org/10.2471/BLT.20.254045
Zhang M, Wang S, Hu T, Fu X, Wang X, Halloran B, Li Z, Cui Y, Liu H, Liu Z, Bao S, 2022. Human mobility and COVID-19 transmission: a systematic review and future directions. Ann GIS 1-14.
DOI: https://doi.org/10.1101/2021.02.02.21250889
How to Cite
Jaya, I. G. N. M., Chadidjah, A., Kristiani, F., Darmawan, G., & Christine Princidy, J. (2023). Does mobility restriction significantly control infectious disease transmission? Accounting for non-stationarity in the impact of COVID-19 based on Bayesian spatially varying coefficient models. Geospatial Health, 18(1). https://doi.org/10.4081/gh.2023.1161
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