COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models

Since December 2019, many statistical spatial–temporal methods have been developed to track and predict the spread of the COVID-19 pandemic. In this paper, we analyzed the COVID-19 dataset which includes the number of biweekly infected cases registered in Ontario from March 2020 to the end of June 2021. We made use of Bayesian Spatial–temporal models and Area-to-point (ATP) and Area-to-area (ATA) Poisson Kriging models. With the Bayesian models, spatial–temporal effects and government intervention effects on infection risk are considered while the ATP Poisson Kriging models are used to display the spread of the pandemic over space.

[1]  H. Folmer,et al.  Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease , 2022, Journal of Geographical Systems.

[2]  H. Folmer,et al.  Bayesian spatiotemporal forecasting and mapping of COVID‐19 risk with application to West Java Province, Indonesia , 2021, Journal of regional science.

[3]  Álvaro Briz-Redón,et al.  The impact of modelling choices on modelling outcomes: a spatio-temporal study of the association between COVID-19 spread and environmental conditions in Catalonia (Spain) , 2020, Stochastic Environmental Research and Risk Assessment.

[4]  K. Salako,et al.  Predicting COVID-19 spread in the face of control measures in West Africa , 2020, Mathematical Biosciences.

[5]  Hojeong Park,et al.  A Study on Herd Immunity of COVID-19 in South Korea: Using a Stochastic Economic-Epidemiological Model , 2020, Environmental and Resource Economics.

[6]  J. Nieto,et al.  Modeling and forecasting the COVID-19 pandemic in India , 2020, Chaos, Solitons & Fractals.

[7]  Liangxu Wang,et al.  Prediction of the COVID-19 spread in African countries and implications for prevention and control: A case study in South Africa, Egypt, Algeria, Nigeria, Senegal and Kenya , 2020, Science of The Total Environment.

[8]  Antonio López-Quílez,et al.  Spatiotemporal modeling of relative risk of dengue disease in Colombia , 2018, Stochastic Environmental Research and Risk Assessment.

[9]  Antonio López-Quílez,et al.  Relative risk estimation of dengue disease at small spatial scale , 2017, International Journal of Health Geographics.

[10]  M. D. Ugarte,et al.  On fitting spatio-temporal disease mapping models using approximate Bayesian inference , 2014, Statistical methods in medical research.

[11]  Aki Vehtari,et al.  Understanding predictive information criteria for Bayesian models , 2013, Statistics and Computing.

[12]  Gianluca Baio,et al.  Spatial and spatio-temporal models with R-INLA. , 2013, Spatial and spatio-temporal epidemiology.

[13]  Finn Lindgren,et al.  Bayesian computing with INLA: New features , 2012, Comput. Stat. Data Anal..

[14]  Leonhard Held,et al.  Spatio‐temporal disease mapping using INLA , 2011 .

[15]  Duncan Lee,et al.  A comparison of conditional autoregressive models used in Bayesian disease mapping. , 2011, Spatial and spatio-temporal epidemiology.

[16]  Leonhard Held,et al.  A primer on disease mapping and ecological regression using $${\texttt{INLA}}$$ , 2011, Comput. Stat..

[17]  Radford M. Neal.  MCMC Using Hamiltonian Dynamics , 2011, 1206.1901.

[18]  Leonhard Held,et al.  Using integrated nested Laplace approximations for the evaluation of veterinary surveillance data from Switzerland: a case‐study , 2011 .

[19]  Ying C MacNab,et al.  On Gaussian Markov random fields and Bayesian disease mapping , 2011, Statistical methods in medical research.

[20]  H. Rue,et al.  Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations , 2009 .

[21]  P. Goovaerts,et al.  How does Poisson kriging compare to the popular BYM model for mapping disease risks? , 2008, International journal of health geographics.

[22]  Pierre Goovaerts,et al.  International Journal of Health Geographics Geostatistical Analysis of Disease Data: Estimation of Cancer Mortality Risk from Empirical Frequencies Using Poisson Kriging , 2022 .

[23]  Sylvia Richardson,et al.  A comparison of Bayesian spatial models for disease mapping , 2005, Statistical methods in medical research.

[24]  P. Kyriakidis.  A Geostatistical Framework for Area-to-Point Spatial Interpolation , 2004 .

[25]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[26]  Hoon Kim,et al.  Monte Carlo Statistical Methods , 2000, Technometrics.

[27]  L Knorr-Held,et al.  Bayesian modelling of inseparable space-time variation in disease risk. , 2000, Statistics in medicine.

[28]  J. Besag,et al.  Bayesian image restoration, with two applications in spatial statistics , 1991 .

[29]  Szymon Moliński.  Pyinterpolate: Spatial interpolation in Python for point measurements and aggregated datasets , 2022, J. Open Source Softw..

[30]  Pierre Goovaerts,et al.  Kriging and Semivariogram Deconvolution in the Presence of Irregular Geographical Units , 2008, Mathematical geology.

[31]  Peter Green,et al.  Markov chain Monte Carlo in Practice , 1996 .

[32]  Pierre Goovaerts,et al.  International Journal of Health Geographics Geostatistical Analysis of Disease Data: Visualization and Propagation of Spatial Uncertainty in Cancer Mortality Risk Using Poisson Kriging and P-field Simulation , 2022 .