Spatio-temporal modeling of COVID-19 prevalence and mortality using artificial neural network algorithms

Spatial and Spatio-temporal Epidemiology - Tập 40 - Trang 100471 - 2022
Nima Kianfar1, Mohammad Saadi Mesgari1, Abolfazl Mollalo2, Mehrdad Kaveh1
1Faculty of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
2Department of Public Health and Prevention Science, School of Health Sciences, Baldwin Wallace University, Berea, OH 44017, USA

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World Health Organization (WHO), Archived: WHO Timeline- COVID-19. 2020ab; Available from: www.who.int/news/item/27-04-2020-who-timeline-covid-19.

World Health Organization (WHO), WHO Coronavirus (COVID-19) Dashboard. 2021b; Available from: https://covid19.who.int/.

Wang, 2020, A novel coronavirus outbreak of global health concern, Lancet, 395, 470, 10.1016/S0140-6736(20)30185-9

Mansour, 2021, Sociodemographic determinants of COVID-19 incidence rates in Oman: geospatial modelling using multiscale geographically weighted regression (MGWR), Sustain. Cities Soc., 65, 10.1016/j.scs.2020.102627

Mollalo, 2020, Artificial neural network modeling of novel coronavirus (COVID-19) incidence rates across the continental United States, Int. J. Environ. Res. Public Health, 17, 4204, 10.3390/ijerph17124204

Mollalo, 2019, A GIS-based artificial neural network model for spatial distribution of tuberculosis across the continental United States, Int. J. Environ. Res. Public Health, 16, 157, 10.3390/ijerph16010157

Ripley, 2007

Duh, 1998, Epidemiologic interpretation of artificial neural networks, Am. J. Epidemiol., 147, 1112, 10.1093/oxfordjournals.aje.a009409

Olden, 2002, Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks, Ecol. Modell., 154, 135, 10.1016/S0304-3800(02)00064-9

Olden, 2004, An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data, Ecol. Modell., 178, 389, 10.1016/j.ecolmodel.2004.03.013

Ibrahim, 2013, A comparison of methods for assessing the relative importance of input variables in artificial neural networks, J. Appl. Sci. Res., 9, 5692

Özesmi, 1999, An artificial neural network approach to spatial habitat modelling with interspecific interaction, Ecol. Modell., 116, 15, 10.1016/S0304-3800(98)00149-5

Ferretti, 2016, Trends in sensitivity analysis practice in the last decade, Sci. Total Environ., 568, 666, 10.1016/j.scitotenv.2016.02.133

Wei, 2015, Variable importance analysis: a comprehensive review, Reliab. Eng. Syst. Saf., 142, 399, 10.1016/j.ress.2015.05.018

Dfuf, 2020, Variable importance analysis in imbalanced datasets: A new approach, IEEE Access, 8, 127404, 10.1109/ACCESS.2020.3008416

Casiraghi, 2020, Explainable machine learning for early assessment of COVID-19 risk prediction in emergency departments, IEEE Access, 8, 196299, 10.1109/ACCESS.2020.3034032

Pasha, 2021, An analysis to identify the important variables for the spread of COVID-19 using numerical techniques and data science, Case Stud. Chem. Environ. Eng., 3, 10.1016/j.cscee.2020.100067

Shaffiee Haghshenas, 2020, Prioritizing and analyzing the role of climate and urban parameters in the confirmed cases of COVID-19 based on artificial intelligence applications, Int. J. Environ. Res. Public Health, 17, 3730, 10.3390/ijerph17103730

Mollalo, 2015, Geographic information system-based analysis of the spatial and spatio-temporal distribution of zoonotic cutaneous leishmaniasis in Golestan Province, north-east of Iran, Zoonoses Public Health, 62, 18, 10.1111/zph.12109

Mollalo, 2018, Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis in Golestan province, Iran, Acta Trop., 188, 187, 10.1016/j.actatropica.2018.09.004

Bashir, 2020, Correlation between climate indicators and COVID-19 pandemic in New York, USA, Sci. Total Environ., 728, 10.1016/j.scitotenv.2020.138835

Zhang, 2020, Spatial disparities in coronavirus incidence and mortality in the United States: an ecological analysis as of May 2020, J. Rural Health, 36, 433, 10.1111/jrh.12476

Jia, 2020, Population flow drives spatio-temporal distribution of COVID-19 in China, Nature, 582, 389, 10.1038/s41586-020-2284-y

Ahmadi, 2020, Investigation of effective climatology parameters on COVID-19 outbreak in Iran, Sci. Total Environ., 729, 10.1016/j.scitotenv.2020.138705

Ramírez, 2020, COVID-19 emergence and social and health determinants in Colorado: a rapid spatial analysis, Int. J. Environ. Res. Public Health, 17, 3856, 10.3390/ijerph17113856

Moonsammy, 2021, COVID-19 modelling in the Caribbean: spatial and statistical assessments, Spatial Spatio Temp. Epidemiol., 37

Ambade, 2021, COVID-19 lockdowns reduce the Black carbon and polycyclic aromatic hydrocarbons of the Asian atmosphere: source apportionment and health hazard evaluation, Environ. Develop. Sustain., 1

Gautam, 2020, The influence of COVID-19 on air quality in India: a boon or inutile, Bull. Environ. Contam. Toxicol., 104, 724, 10.1007/s00128-020-02877-y

Gautam, 2020, COVID-19: air pollution remains low as people stay at home, Air Quality Atmos. Health, 13, 853, 10.1007/s11869-020-00842-6

Wang, 2020, Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak, Resour. Conserv. Recycl., 158, 10.1016/j.resconrec.2020.104814

World Bank, World Bank Open Data 2021. Available from: https://data.worldbank.org/. Accessed February 1, 2021.

Helliwell, 2018

Pew Research Center. (2014) Washington, D.C.Religious diversity index scores by country4 April. Available from: https://www.pewforum.org/2014/04/04/religious-diversity-index-scores-by-country/. Accessed March 21, 2021.

Shrestha, 2020, Detecting multicollinearity in regression analysis, Am. J. Appl. Math. Stat., 8, 39, 10.12691/ajams-8-2-1

Civco, 1993, Artificial neural networks for land-cover classification and mapping, Int. J. Geogr. Inform. Sci., 7, 173, 10.1080/02693799308901949

Kang, H.-Y., R. Rule, and P. Noble, Artificial neural network modeling of phytoplankton blooms and its application to sampling sites within the same estuary.2011.

Yonaba, 2010, Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting, J. Hydrol. Eng., 15, 275, 10.1061/(ASCE)HE.1943-5584.0000188

Ojha, 2017, Metaheuristic design of feedforward neural networks: a review of two decades of research, Eng. Appl. Artif. Intell., 60, 97, 10.1016/j.engappai.2017.01.013

Kayri, 2016, Predictive abilities of Bayesian regularization and levenberg–marquardt algorithms in artificial neural networks: a comparative empirical study on social data, Math. Comput. Appl., 21, 20

Eğrioğlu, 2008, A new model selection strategy in artificial neural networks, Appl. Math. Comput., 195, 591

Li, 2019, Cluster-based bagging of constrained mixed-effects models for high spatiotemporal resolution nitrogen oxides prediction over large regions, Environ. Int., 128, 310, 10.1016/j.envint.2019.04.057

Wechsler, 2006, Quantifying DEM uncertainty and its effect on topographic parameters, Photogramm. Eng. Remote Sens., 72, 1081, 10.14358/PERS.72.9.1081

Garson, 1991, Interpreting Neural Network Connection Weights, AI Expert, 6, 47

Sung, 1998, Ranking importance of input parameters of neural networks, Expert Syst. Appl., 15, 405, 10.1016/S0957-4174(98)00041-4

Gevrey, 2003, Review and comparison of methods to study the contribution of variables in artificial neural network models, Ecol. Modell., 160, 249, 10.1016/S0304-3800(02)00257-0

do Nascimento, 2019, Sensitivity analysis of chaos in a nonlinear pendulum through artificial neural networks, Math. Eng. Sci. Aerospace (MESA), 10

Welford, 1962, Note on a method for calculating corrected sums of squares and products, Technometrics, 4, 419, 10.1080/00401706.1962.10490022

Augusta, 2019, Deep learning for supervised classification of spatial epidemics, Spat. Spatio Temp. Epidemiol., 29, 187, 10.1016/j.sste.2018.08.002

Meliker, 2011, Spatio-temporal epidemiology: Principles and opportunities, Spat. Spatio Temp. Epidemiol., 2, 1, 10.1016/j.sste.2010.10.001

Shrestha, 2020, Spatial epidemiology: an empirical framework for syndemics research, Soc. Sci. Med.

Ghayvat, 2021, Sustain. CitiesSoc., 69

Jin, 1995, The impact of unemployment on health: a review of the evidence, CMAJ, 153, 529

Malik, 2020, Determinants of COVID-19 vaccine acceptance in the US, EClinicalMedicine, 26, 10.1016/j.eclinm.2020.100495

Mollalo, 2021, Spatial Modeling of COVID-19 vaccine hesitancy in the United States, Int. J. Environ. Res. Public Health, 18, 9488, 10.3390/ijerph18189488

Sigler, 2021, The socio-spatial determinants of COVID-19 diffusion: the impact of globalisation, settlement characteristics and population, Glob. Health, 17, 1, 10.1186/s12992-021-00707-2

Sirkeci, 2020, Coronavirus and migration: analysis of human mobility and the spread of Covid-19, Migr. Lett., 17, 379, 10.33182/ml.v17i2.935

Coşkun, 2021, The spread of COVID-19 virus through population density and wind in Turkey cities, Sci. Total Environ., 751, 10.1016/j.scitotenv.2020.141663

Rocklöv, 2020, High population densities catalyse the spread of COVID-19, J. Travel Med., 27, 10.1093/jtm/taaa038

Bhadra, 2021, Impact of population density on Covid-19 infected and mortality rate in India, Model. Earth Syst. Environ., 7, 623, 10.1007/s40808-020-00984-7

Zheng, 2020, Spatial transmission of COVID-19 via public and private transportation in China, Travel Med. Infect. Dis., 34, 10.1016/j.tmaid.2020.101626

Cordes, 2020, Spatial analysis of COVID-19 clusters and contextual factors in New York City, Spat. Spat.Tempor. Epidemiol., 34

Critchley, 2018, Glycemic control and risk of infections among people with type 1 or type 2 diabetes in a large primary care cohort study, Diabetes Care., 41, 2127, 10.2337/dc18-0287

Muniyappa, 2020, COVID-19 pandemic, coronaviruses, and diabetes mellitus, Am. J. Physiol.Endocrinol. Metab., 318, E736, 10.1152/ajpendo.00124.2020

Gazzaz, 2021, Diabetes and COVID-19, Open Life Sci., 16, 297, 10.1515/biol-2021-0034

Kumar, 2020, Is diabetes mellitus associated with mortality and severity of COVID-19? A meta-analysis, Diabetes Metab. Syndr. Clin. Res. Rev., 14, 535, 10.1016/j.dsx.2020.04.044

Huang, 2020, Diabetes mellitus is associated with increased mortality and severity of disease in COVID-19 pneumonia–a systematic review, meta-analysis, and meta-regression, Diabetes Metab. Syndr. Clin. Res. Rev., 14, 395, 10.1016/j.dsx.2020.04.018

Guo, 2020, Diabetes is a risk factor for the progression and prognosis of COVID-19, Diabetes Metab. Res. Rev., 36, e3319, 10.1002/dmrr.3319

Giovanni, Greenbelt. NASA/GSFC, MD,USA, NASA goddard earth sciences data and information services center (GES DISC) (2021). Available from: https://giovanni.gsfc.nasa.gov/. Accessed March 1, 2021.