COVID-19 transmission risk in Surabaya and Sidoarjo: an inhomogeneous marked Poisson point process approach

Springer Science and Business Media LLC - Tập 37 - Trang 2271-2282 - 2023
Achmad Choiruddin1, Firdaus Fabrice Hannanu2, Jorge Mateu3, Vanda Fitriyanah1
1Department of Statistics, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia
2AGEIS EA 7404, Faculty of Medicine, Université Grenoble Alpes, Grenoble, France
3Department of Mathematics, Universitat Jaume I, Castellon, Spain

Tóm tắt

Understanding the spatio-temporal dynamics of COVID-19 transmission is necessary to plan better strategies for controlling the spread of the disease. However, only a few studies explore the COVID-19 transmission risk over a fine spatial resolution while considering relevant spatial and temporal factors. To this aim, we consider an inhomogeneous marked Poisson point process model to assess COVID-19 transmission risk using data of home addresses of confirmed cases, in relation to locations of sources of crowd (enterprise, market, and place of worship) and population density in Surabaya and Sidoarjo, Indonesia. Our marked model is able to analyze how the spatial covariates are varying with time, helping authorities to evaluate the information of covariates depending on the period in which restrictions are taking place. Our results show that enterprise, place of worship, and population densities have significant impact to the transmission risk in Surabaya and Sidoarjo. We finally provide predicted risk maps which provide additional information based on the demographic-based risk analysis to help conduct more efficient testing, tracing, and vaccination programs.

Tài liệu tham khảo

Baddeley A, Rubak E, Turner R (2015) Spatial point patterns: methodology and applications with R. CRC Press Briz-Redón A, Iftimi A, Mateu J, Romero-García C (2022) A mechanistic spatio-temporal modeling of Covid-19 data. Biom J, pp 1–18 Carozzi F (2020) Urban density and Covid-19. IZA paper, 13440 Chen Z, Dassios A, Kuan V, Lim JW, Qu Y, Surya B, Zhao H (2021) A two-phase dynamic contagion model for Covid-19. Result Phys 26:104264 Choiruddin A, Aisah Trisnisa F, Iriawan N (2021) Quantifying the effect of geological factors on distribution of earthquake occurrences by inhomogeneous Cox processes. Pure Appl Geophys 178(5):1579–1592 Choiruddin A, Coeurjolly J-F, Letué F (2018) Convex and non-convex regularization methods for spatial point processes intensity estimation. Electron J Stat 12(1):1210–1255 Choiruddin A, Coeurjolly J-F, Waagepetersen R (2021) Information criteria for inhomogeneous spatial point processes. Aust New Zealand J Stat 63(1):119–143 Choiruddin A, Susanto TY, Metrikasari R (2021) Two-step estimation for modeling the earthquake occurrences in sumatra by Neyman-Scott Cox point processes. In: Mohamed A, Yap BW, Zain JM, Berry MW (eds) Soft computing in data science, pp 146–159. Springer. Singapore Cordes J, Castro MC (2020) Spatial analysis of Covid-19 clusters and contextual factors in New York city. Spatial Spatio-tempor Epidemiol 34:100355 Covid 19, STP (2022) Data sebaran. Retrieved from https://Covid19.go.id Covid 19 Jatim S (2021) Berita Covid-19. Retrieved from http://infoCovid19.jatimprov.go.id/ Cronie O, Van Lieshout MNM (2018) A non-model-based approach to bandwidth selection for kernel estimators of spatial intensity functions. Biometrika 105(2):455–462 Franch-Pardo I, Napoletano B.M, Rosete-Verges F, Billa L (2020) Spatial analysis and GIS in the study of Covid-19. A review. Sci Total Environ 739:140033 Hamidi S, Sabouri S, Ewing R (2020) Does density aggravate the Covid19 pandemic? early findings and lessons for planners. J Am Plann Assoc 86(4):495–509 Husain A, Choiruddin A (2021) Poisson and logistic regressions for inhomogeneous multivariate point processes: a case study in the Barro Colorado Island plot. In: Mohamed A, Yap BW, Zain JM, Berry MW (eds) Soft computing in data science, pp 301–311. Springer, Singapore Illian J, Penttinen A, Stoyan H, Stoyan D (2008) Statistical analysis and modelling of spatial point patterns. Wiley Jalilian A, Mateu J (2021) A hierarchical spatio-temporal model to analyze relative risk variations of Covid-19: a focus on Spain, Italy and Germany. Stoch Env Res Risk Assess 35(4):797–812 Kadi N, Khelfaoui M (2020) Population density, a factor in the spread of Covid-19 in Algeria: statistic study. Bull Natl Res Centre 44(1):1–7 Kang D, Choi H, Kim J-H, Choi J (2020) Spatial epidemic dynamics of the Covid-19 outbreak in China. Int J Infect Dis 94:96–102 Kwok CYT, Wong MS, Chan KL, Kwan M-P, Nichol JE, Liu CH, Kan Z (2021) Spatial analysis of the impact of urban geometry and socio-demographic characteristics on Covid-19, a study in Hongkong. Sci Total Environ 764:144455 Niraula P, Mateu J, Chaudhuri S (2022) A Bayesian machine learning approach for spatio-temporal prediction of Covid-19 cases. Stoch Environ Res Risk Assessm, pp 1–19 Park J, Chang W, Choi B (2022) An interaction Neyman–Scott point process model for coronavirus disease-19. Spat Stat 47:100561 Rocklöv J, Sjödin H (2020) High population densities catalyse the spread of Covid-19. J Travel Med 27(3):1–2 Scarpone C, Brinkmann ST, Große T, Sonnenwald D, Fuchs M, Walker BB (2020) A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: a cross-sectional case study of Covid-19 incidence in germany. Int J Health Geogr 19(1):1–17 Waagepetersen R (2007) An estimating function approach to inference for inhomogeneous Neyman–Scott processes. Biometrics 63(1):252–258 WHO (2022) Knuth: computers and typesetting. Retrieved from https://www.who.int/ Wong DW, Li Y (2020) Spreading of Covid-19: density matters. PLoS ONE 15(12):1–16