The Processing Spatial Data for Statistical Modeling and Visualization Case study: INLA model for COVID-19 in Alabama, USA

Getachew Engidaw; György Terdik

doi:10.14513/actatechjaur.00746

Authors

Getachew Engidaw Doctoral School of Informatics, University of Debrecen, 4028, Debrecen, Hungary
György Terdik Department of Information Technology, Faculty of Informatics, University of Debrecen, 4028, Debrecen, Hungary https://orcid.org/0000-0002-9663-6892

DOI:

https://doi.org/10.14513/actatechjaur.00746

Keywords:

COVID-19, Spatial Data, Disease mapping, Bayesian analysis, hot spot

Abstract

This research emphasizes the visualization of spatial data for statistical modelling and analysis of the relative risk associated with the COVID-19 pandemic in Alabama, USA. We used Bayesian analysis and the Integrated Nested Laplace Approximation (INLA) approach on data ranging from March 11, 2020, to December 31, 2022, which included observed COVID-19 cases, the population for each of the Alabama counties, and a Geographical map of the state. The geographical distribution of COVID-19’s relative risk was determined using various spatial statistical techniques, indicating high-risk locations. The study used Besag-York-Mollié (BYM) models to assess the posterior relative risk of COVID-19, and it found a statistically significant average decrease in COVID-19 case rates across the 67 counties evaluated. These findings have practical implications for evidence-based policymaking in pandemic prevention, mitigation, and preparation.

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Author Biography

György Terdik, Department of Information Technology, Faculty of Informatics, University of Debrecen, 4028, Debrecen, Hungary

University of Debrecen. Faculty of Informatics. Department of Information Technology

References

T. Alamo D. G. Reina, P. Millán. Data-driven methods to monitor, model, forecast and control COVID-19 pandemic: Leveraging data science, epidemiology and control theory. arXiv preprint arXiv: 2006.01731 (2020). https://doi.org/10.1016/j.coi.2020.09.011

M. A. S. Alhdiri N. A. Samat, Z. Mohamed. Disease mapping for stomach cancer in libya based on besag–york–mollié (bym) model. Asian Pacific Journal of Cancer Prevention: APJCP 18 (6) (2017) 1479. https://doi.org/10.1007/s11356-022-23319-6

M. P. Armstrong G. Rushton, D. L. Zim- merman. Geographically masking health data to preserve confidentiality. Statistics in medicine 18 (5) (1999) pp. 497–525. https://doi.org/10.1002/(SICI)1097- 0258(19990315)18:5

J. Besag J. York, A. Mollié. Bayesian image restoration, with two applications in spatial statis- tics. Annals of the institute of statistical mathematics 43 (1991) pp. 1–20. https://doi.org/10.1007/BF00058655

M. Blangiardo, M. Cameletti. Spatial and spatio-temporal Bayesian models with R-INLA (2015). John Wiley & Sons.

M. Blangiardo M. Cameletti G. Baio, H. Rue. Spatial and spatio-temporal models with r-inla. Spatial and spatio-temporal epidemiology 4 (2013) pp. 33–49. https://doi.org/10.1016/j.sste.2012.12.001

P. J. Brantingham. Crime diversity. Crimi- nology 54 (4) (2016) pp. 553–586. https://doi.org/https://doi.org/10.1111/doi:1745- 9125.12116.

M. J. Breslow, O. Badawi. Severity scoring in the critically iii: Part 2: Maximizing value from outcome prediction scoring systems. Chest 141 (2) (2012) pp. 518–527. https://doi.org/10.1378/chest.11-0331

A. C. Cameron, P. K. Trivedi. Regression analysis of count data (2013). Number 53. Cam- bridge university press.

J. Chen J. J. Song, J. D. Stamey. A Bayesian hierarchical spatial model to correct for misreporting in count data: application to state-level COVID-19 data in the United States. International Journal of Environmental Research and Public Health 19 (6) (2022) 3327. https://doi.org/10.3390/ijerph19063327

J. T. Chen. 11 multilevel and hierarchical models for disease mapping. Geographic health data: Fundamental techniques for analysis (2013), pages183. https://doi.org/10.1079/9781780640891.0183

E. Clement. Small area estimation with appli- cation to disease mapping. International Journal of Probability and Statistics 3 (1) (2014) pp. 15–22. https://doi.org/10.5923/j.ijps.20140301.03

A. Comunian R. Gaburro, M. Giudici. Inver- sion of a sir-based model: A critical analysis about the application to covid-19 epidemic. Physica D: Nonlinear Phenomena 413 (2020) 132674. https://doi.org/10.1016/j.cossms.2021.100411

E. Cuevas. An agent-based model to evaluate the COVID-19 transmission risks in facilities. Computers in Biology and Medicine 121 (2020) 103827. https://doi.org/10.1016/j.compbiomed.2020.103827

P. Elliott, D. Wartenberg. Spatial epidemiology: current approaches and future challenges. Environmental health perspectives 112 (9) (2004) pp. 998–1006. https://doi.org/10.1093/ije/dyz047

I. Franch-Pardo B. M. Napoletano F. Rosete- Verges, L. Billa. Spatial analysis and gis in the study of COVID-19. a review. Science of the total environment 739 (2020) 140033. https://doi.org/10.1016/j.scitotenv.2020.140033

V. Gómez-Rubio, F. Palm Perales. Multi- variate posterior inference for spatial models with the integrated nested Laplace approximation. Journal of the Royal Statistical Society Series C: Ap- plied Statistics 68 (1) (2019) pp. 199–215. https://doi.org/10.1111/rssc.12292

G. Grekousis Z. Feng I. Marakakis Y. Lu, R. Wang. Ranking the importance of demographic, socioeconomic, and underlying health factors on us COVID-19 deaths: A geographical random forest approach. Health & Place 74 (2022) 102744. https://doi.org/10.1016/j.healthplace.2022.102744

R. P. Haining. Spatial data analysis: theory and practice (2003). Cambridge university press.

A. Jalilian, J. Mateu. A hierarchical spatio temporal model to analyse relative risk variations of COVID-19: a focus on Spain, Italy and Germany. Stochastic Environmental Research and Risk Assessment 35 (2021) pp. 797–812.

H. Joe, R. Zhu. Generalized poison distribution: the property of mixture of poison and comparison with negative binomial distribution. Biometrical Journal: Journal of Mathematical Methods in Biosciences 47 (2) (2005) pp. 219–229. https://doi.org/10.1002/bimj.200410102Citations: 140

M. R. Karim. Bayesian hierarchical spatial modeling of COVID-19 cases in bangladesh. An- nals of Data Science (2023) pp. 1–27. https://doi.org/10.1007/s40745-022-00381-1

M. U. Kraemer S. I. Hay D. M. Pigott D. L. Smith G. W. Wint, N. Golding. Progress and chal- lenges in infectious disease cartography. Trends in parasitology 32 (1) (2016) pp. 19–29. https://doi.org/10.1016/j.pt.2015.09.006

E. Krainski V. Gómez-Rubio H. Bakka A. Lenzi D. Castro-Camilo D. Simpson F. Lind- gren, H. Rue. Advanced spatial modeling with stochastic partial differential equations using R and INLA (2018). Chapman and Hall/CRC.

A. Lal J. Marshall J. Benschop A. Brock S. Hales M. G. Baker, N. P. French. A Bayesian spatio-temporal framework to identify outbreaks and examine environmental and social risk fac- tors for infectious diseases monitored by routine surveillance. Spatial and spatio-temporal epidemi- ology 25 (2018) pp. 39–48.

K. Lancaster T. Rhodes, M. Rosengarten. Making evidence and policy in public health emergencies: lessons from COVID-19 for adap- tive evidence-making and intervention. Evidence & policy 16 (3) (2020) pp. 477–490. https://doi.org/10.1332/174426420X15913559981103

A. B. Lawson. Bayesian disease mapping: hierarchical modeling in spatial epidemiology (2018). Chapman and Hall/CRC.

P. Legendre. Spatial autocorrelation: trouble or new paradigm? Ecology 74 (6) (1993) pp. 1659– 1673. https://doi.org/10.2307/1939924

J. W. Lichstein T. R. Simons S. A. Shriner, K. E. Franzreb. Spatial autocorrelation and autoregressive models in ecology. Ecological monographs 72 (3) (2002) pp. 445–463. https://doi.org/10.1890/0012- 9615(2002)072[0445:SAAAMI]2.0.CO;2

J. Ma H. Zhu P. Li C. Liu F. Li Z. LuoM. Zhang, L. Li. Spatial patterns of the spread of COVID-19 in singapore and the influenc- ing factors. ISPRS International Journal of Geo- Information 11 (3) (2022) 152. https://doi.org/10.3390/ijgi11030152

Maiti Q. Zhang S. Sannigrahi S. Pra- manik S. Chakraborti A. Cerda, F. Pilla. Ex- ploring spatiotemporal effects of the driving fac- tors on COVID-19 incidences in the contigu- ous united states. Sustainable cities and society 68 (2021) 102784. https://doi.org/10.1016/j.scs.2021.102784

T. J. Mason. Atlas of Cancer Mortality for US counties, 1950-1969 (1975). US Department of Health, Education, and Welfare, Public Health Service.

J. C. Miller, E. M. Volz. Incorporating disease and population structure into models of sir disease in contact networks. PLOS ONE 8 (8) (2013) e69162. https://doi.org/10.1371/journal.pone.0069162

P. Moraga. Geospatial health data: Modeling and visualization with R-INLA and shiny (2019). Chapman and Hall/CRC.

K. Muoka O. O. Ngesa, A. G. Waititu. Statistical models for count data. Science Jour- nal of Applied Mathematics and Statistics 4 (6) (2016) pp. 256–262. https://doi.org/10.11648/j.sjams.20160406.12

N. Nazia Z. A. Butt M. L. Bedard W.-C. Tang H. Sehar, J. Law. Methods used in the spatial and spatiotemporal analysis of COVID-19 epidemi- ology: a systematic review. International Jour- nal of Environmental Research and Public Health 19 (14) (2022) 8267. https://doi.org/10.3390/ijerph19148267

K. W. Pettis T. A. Bailey A. K. Jain, R. C. Dubes. An intrinsic dimensionality estimator from near-neighbor information. IEEE Transactions on pattern analysis and machine intelligence 1 (1979) pp. 25–37.

P. Puvanachandra C. Hoe T. Özkan, T. La- junen. Burden of road traffic injuries in turkey. Traffic injury prevention 13 (1) (2012) pp. 64–75. https://doi.org/15389588.2011.633135

A. Riebler S. H. Sørbye D. Simpson, H. Rue. An intuitive bayesian spatial model for disease mapping that accounts for scaling. Statistical methods in medical research 25 (4) (2016) pp. 1145–1165. https://doi.org/10.1016/j.sste.2016.10.014

D. Roux C. I. Kiefe D. R. Jacobs Jr M. Haan S. A. Jackson F. J. Nieto C. C. Paton,R. Schulz.Area characteristics and individual-level socioeconomic position indicators in three population-based epidemiologic studies. Annals of epidemiology 11 (6) (2001) pp. 395–405. https://doi.org/10.1016/S1047-2797(01)00221-6

H. Rue S. Martino, N. Chopin. Approximate Bayesian inference for latent gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society Series B: Statistical Methodology 71 (2) (2009) pp. 319–392. https://doi.org/10.1111/j.1467- 9868.2008.00700.x

P. Saavedra A. Santana L. Bello J.-M. Pacheco, E. Sanjuán. A bayesian spatio-temporal analysis of mortality rates in spain: application to the covid-19 2020 outbreak. Population Health Metrics 19 (1) (2021) 27. https://doi.org/10.1186/s12963-021-00259-y

A. K. Sahu V. T. Amritha Nand R. Mathew P. Aggarwal J. Nayer, S. Bhoi. Covid-19 in health care workers–a systematic review and meta- analysis. The American Journal of Emergency Medicine 38 (9) (2020) pp. 1727–1731. https://doi.org/10.1016/j.ajem.2020.05.113

S. K. Sahu, D. Böhning. Bayesian spatio- temporal joint disease mapping of COVID-19 cases and deaths in local authorities of england. Spatial Statistics 49 (2022) 100519. https://doi.org/10.1007/s11356-021-12925-2

P. Satorra, C. Tebé. Bayesian spatio-temporal analysis of the covid-19 pandemic in Catalonia Scientific Reports 14 (1) (2024) 4220. https://doi.org/10.1007/s11749-021-00769-4

O. Schabenberger, C. A. Gotway. Statistical methods for spatial data analysis (2017). Chapman and Hall/CRC.

K. F. Sellers S. Borle, G. Shmueli. The com- poison model for count data: a survey of methods and applications. Applied Stochastic Models in Business and Industry 28 (2) (2012) pp. 104–116.

S. Sisman, A. C. Aydinoglu. A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in istanbul. Land use policy 119 (2022) 106183. https://doi.org/10.1016/j.landusepol.2022.106183

R. J. Thomas. Female consumption and evaluation of traditionally male orientated products: a self-monitoring perspective (2010). University of South Wales (United Kingdom).

P. H. Verburg K. Kok R. G. Pontius Jr, A. Veldkamp. Modelling land-use and land-cover change. In: Land-use and land-cover change: local processes and global impacts, pp. 117–135 Springer (2006).

L. A. Waller L. Zhu C. A. Gotway D. M. Gor- man, P. J. Gruenewald. Quantifying geographic variations in associations between alcohol distribution and violence: a comparison of geographically weighted regression and spatially varying coefficient models. Stochastic Environmental Re- search and Risk Assessment 21 (2007) pp. 573–588. https://doi.org/10.1007/s00477-007-0139-9

S. Wang X. Yang L. Li P. Nadler R. Arcucci Y. Huang Z. Teng, Y. Guo. A Bayesian updating scheme for pandemics: estimating the infection dynamics of COVID-19. IEEE Computational Intelligence Magazine 15 (4) (2020) pp. 23–33. https://doi.org/10.1016/j.jbi.2020.103347

Y. Wang X. Chen, F. Xue. A review of Bayesian spatiotemporal models in spatial epidemiology. ISPRS International Journal of Geo-Information 13 (3) (2024) 97. https://doi.org/10.3390/ijgi13030097

A. Woo ditch N. J. Johnson R. Solymosi J. Medina Ariza, S. Langton. Getting to know your data. In: A Beginner’s Guide to Statistics for Criminology and Criminal Justice Using R, pp. 21–38. Springer (2021).