The Spatial Epidemiology models on Covid-19 cases in India Mathematical models and data used in Spatial Epidemiology Spatial Epidemiology
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Abstract
Geographical epidemiology has been description of geographical patterns of mortality rates as part of descriptive epidemiological investigations, with the goal of developing theories regarding disease causation. Disease mapping, disease clustering, and ecological analysis are the predominant methods of geographical epidemiology, having close relationships between them. For describing the transmission of an illness within a geographically dispersed population, many models incorporating frameworks based on individuals, networks, stochastic processes, as well as partial derivative equations have been made. However, these models need a large amount of information and even a large amount of computational performance. Keeping this in mind, we have tried to create deterministic models formulated as partial differential equations to model spatial epidemics in spatial domains. This has been by assuming two types of population, the susceptible population, and the infective population, considering the functions of space and time. COVID-19 is a global tragedy, with India likely to be among the most hit. The fluctuation in the dispersion of COVID-19-related well-being results is most likely connected with numerous basic factors, like segment, financial, or natural poisons related factors.
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References
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Auler, A. C., Cássaro, F. A. M., Da Silva, V. O., and Pires, L. F. (2020). Evidence that high temperatures and intermediate relative humidity might favor the spread of COVID-19 in tropical climate: A case study for the most affected Brazilian cities. Science of the Total Environment, 729, 139090, DOI: 10.1016/j.scitotenv.2020.139090
Taghizadeh-Hesary, F., and Akbari, H. (2020). The powerful immune system against powerful COVID-19: A hypothesis. Medical hypotheses, 140, 109762. doi: 10.1016/j.mehy.2020.109762
Das, A., Ghosh, S., Das, K., Dutta, I., Basu, T., and Das, M. (2020). Re:(In) visible impact of inadequate WaSH Provision on COVID-19 incidences can be not be ignored in large and megacities of India. Public Health, 185, 34. doi: 10.1016/j.puhe.2020.05.035
Kang, J. Y., Michels, A., Lyu, F., Wang, S., Agbodo, N., Freeman, V. L., and Wang, S. (2020). Rapidly measuring spatial accessibility of COVID-19 healthcare resources: a case study of Illinois, USA. International journal of health geographics, 19(1), 1-17. doi: 10.1186/s12942-020-00229-x
Khalatbari-Soltani, S., Cumming, R. C., Delpierre, C., and Kelly-Irving, M. (2020). Importance of collecting data on socioeconomic determinants from the early stage of the COVID-19 outbreak onwards. J Epidemiol Community Health, 74(8), 620-623.DOI: 10.1136/jech-2020-214297
Tewara, M. A., Mbah-Fongkimeh, P. N., Dayimu, A., Kang, F., and Xue, F. (2018). Small-area spatial statistical analysis of malaria clusters and hotspots in Cameroon; 2000–2015. BMC infectious diseases, 18(1), 1-15. doi: 10.1186/s12879-018-3534-6.
Goovaerts, P. (2005). Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging. International Journal of Health Geographics, 4(1), 1-33. doi: 10.1186/1476-072X-4-31
Zarikas, V., Poulopoulos, S. G., Gareiou, Z., and Zervas, E. (2020). Clustering analysis of countries using the COVID-19 cases dataset. Data in brief, 31, 105787. doi: 10.1016/j.dib.2020.105787
Azevedo, L., Pereira, M. J., Ribeiro, M. C., and Soares, A. (2020). Geostatistical COVID-19 infection risk maps for Portugal. International Journal of Health Geographics, 19(1), 1-8. DOI:10.1186/s12942-020-00221-5
Gangwar, H. S., and Ray, P. C. (2021). Geographic information system-based analysis of COVID-19 cases in India during pre-lockdown, lockdown, and unlock phases. International Journal of Infectious Diseases, 105, 424-435. DOI: 10.1016/j.ijid.2021.02.070
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Franch-Pardo, I., Napoletano, B. M., Rosete-Verges, F., and Billa, L. (2020). Spatial analysis and GIS in the study of COVID-19. A review. Science of the total environment, 739, 140033. https://doi.org/10.1016/j.scitotenv.2020.140033
Gupta, R., Ghosh, A., Singh, A. K., and Misra, A. (2020). Clinical considerations for patients with diabetes in times of COVID-19 epidemic. Diabetes and metabolic syndrome, 14(3), 211.DOI: 10.1016/j.dsx.2020.03.002
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Kumar, A. (2020). Modeling geographical spread of COVID-19 in India using network-based approach. Medrxiv. doi: https://doi.org/10.1101/2020.04.23.20076489
Ma, Y., Zhao, Y., Liu, J., He, X., Wang, B., Fu, S., Jun Y., Jingping N., Ji Zhou , and Luo, B. (2020). Effects of temperature variation and humidity on the mortality of COVID-19 in Wuhan. medRxiv. DOI: 10.1016/j.scitotenv.2020.138226
Magal, P., Webb, G. F., and Wu, Y. (2020). Spatial spread of epidemic diseases in geographical settings: Seasonal influenza epidemics in Puerto Rico. arXiv preprint arXiv:1801.01856. Doi: 10.3934/dcdsb.2019223
Middya, A. I., and Roy, S. (2021). Geographically varying relationships of COVID-19 mortality with different factors in India. Scientific reports, 11(1), 1-12. DOI: 10.1038/s41598-021-86987-5
Murray, J. D. (1993). Geographic spread and control of epidemics. In Mathematical Biology (pp. 661-721). Springer, New York, NY. https://doi.org/10.1007/0-387-22438-6_13
Riley, S., Eames, K., Isham, V., Mollison, D., and Trapman, P. (2015). Five challenges for spatial epidemic models. Epidemics, 10, 68-71. DOI: 10.1016/j.epidem.2014.07.001
Subramanian, S. V., Karlsson, O., Zhang, W., and Kim, R. (2020). Geo-mapping of COVID-19 risk correlates across districts and parliamentary constituencies in India. DOI:10.1162/99608f92.68bb12e4
Talisuna A., Ali E. A., Yahaya A., Stephen M., Bonkoungou B., Musa E. O., Minkoulou E. M., Okeibunor J., Impouma B., Djingarey H.M., Yao N.K.M., Oka S., Yoti Z., and Fall I.S. (2020). Spatial and temporal distribution of infectious disease epidemics, disasters and other potential public health emergencies in the World Health Organisation Africa region, 2016–2018. Globalization and health, 16(1), 1-12. doi: 10.1186/s12992-019-0540-4.
Wang, L., Li, J., Guo, S., Xie, N., Yao, L., Cao, Y., Day S.D., Howard S.C., Graff J.C., Tianshu G., Jiafu Ji. Weikuan Gu. and Sun, D. (2020). Real-time estimation and prediction of mortality caused by COVID-19 with patient information-based algorithm. Science of the total environment, 727, 138394. doi: 10.1016/j.scitotenv.2020.138394.
Xie, J., and Zhu, Y. (2020). Association between ambient temperature and COVID-19 infection in 122 cities from China. Science of the Total Environment, 724, 138201. DOI: 10.1016/j.scitotenv.2020.138201.
Xiong, Y., Wang, Y., Chen, F., and Zhu, M. (2020). Spatial statistics and influencing factors of the novel coronavirus pneumonia 2019 epidemic in Hubei Province, China. doi: 10.3390/ijerph17113903
Xu, F., McCluskey, C. C., and Cressman, R. (2013). Spatial spread of an epidemic through public transportation systems with a hub. Mathematical biosciences, 246(1), 164-175. doi: 10.1016/j.mbs.2013.08.014
Zhu, Y., Xie, J., Huang, F., and Cao, L. (2020). Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China. Science of the total environment, 727, 138704. doi: 10.1016/j.scitotenv.2020.138704
Lau, H., Khosrawipour, V., Kocbach, P., Mikolajczyk, A., Ichii, H., Zacharski, M., Bania, J. and Khosrawipour, T. (2020). The association between international and domestic air traffic and the coronavirus (COVID-19) outbreak. Journal of Microbiology, Immunology and Infection, 53(3), 467-472. https://doi.org/10.1016/j.jmii.2020.03.026
Capitalblog, T. (2020). What are the Steps Taken by the Government to Fight Coronavirus in India. https://www.tatacapital.com/blog/trends/what-are-the-steps-taken-by-the-government-to-fight-coronavirus-in-india/
Liu, J., Zhou, J., Yao, J., Zhang, X., Li, L., Xu, X., Xiaotao H., Bo W., Shihua F., Tingting N., Jun Y., Yanjun S., Xiaowei R., Jingping N., Weihao Z. Sheng Li, Bin, L. and Zhang, K. (2020). Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China. Science of the total environment, 726, 138513. https://doi.org/10.1016/j.scitotenv.2020.138513
Wu, X., Nethery, R. C., Sabath, M. B., Braun, D., and Dominici, F. (2020). Exposure to air pollution and COVID-19 mortality in the United States: A nationwide cross-sectional study. MedRxiv. doi: https://doi.org/10.1101/2020.04.05.20054502
Auler, A. C., Cássaro, F. A. M., Da Silva, V. O., and Pires, L. F. (2020). Evidence that high temperatures and intermediate relative humidity might favor the spread of COVID-19 in tropical climate: A case study for the most affected Brazilian cities. Science of the Total Environment, 729, 139090, DOI: 10.1016/j.scitotenv.2020.139090
Taghizadeh-Hesary, F., and Akbari, H. (2020). The powerful immune system against powerful COVID-19: A hypothesis. Medical hypotheses, 140, 109762. doi: 10.1016/j.mehy.2020.109762
Das, A., Ghosh, S., Das, K., Dutta, I., Basu, T., and Das, M. (2020). Re:(In) visible impact of inadequate WaSH Provision on COVID-19 incidences can be not be ignored in large and megacities of India. Public Health, 185, 34. doi: 10.1016/j.puhe.2020.05.035
Kang, J. Y., Michels, A., Lyu, F., Wang, S., Agbodo, N., Freeman, V. L., and Wang, S. (2020). Rapidly measuring spatial accessibility of COVID-19 healthcare resources: a case study of Illinois, USA. International journal of health geographics, 19(1), 1-17. doi: 10.1186/s12942-020-00229-x
Khalatbari-Soltani, S., Cumming, R. C., Delpierre, C., and Kelly-Irving, M. (2020). Importance of collecting data on socioeconomic determinants from the early stage of the COVID-19 outbreak onwards. J Epidemiol Community Health, 74(8), 620-623.DOI: 10.1136/jech-2020-214297
Tewara, M. A., Mbah-Fongkimeh, P. N., Dayimu, A., Kang, F., and Xue, F. (2018). Small-area spatial statistical analysis of malaria clusters and hotspots in Cameroon; 2000–2015. BMC infectious diseases, 18(1), 1-15. doi: 10.1186/s12879-018-3534-6.
Goovaerts, P. (2005). Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging. International Journal of Health Geographics, 4(1), 1-33. doi: 10.1186/1476-072X-4-31
Zarikas, V., Poulopoulos, S. G., Gareiou, Z., and Zervas, E. (2020). Clustering analysis of countries using the COVID-19 cases dataset. Data in brief, 31, 105787. doi: 10.1016/j.dib.2020.105787
Azevedo, L., Pereira, M. J., Ribeiro, M. C., and Soares, A. (2020). Geostatistical COVID-19 infection risk maps for Portugal. International Journal of Health Geographics, 19(1), 1-8. DOI:10.1186/s12942-020-00221-5
Gangwar, H. S., and Ray, P. C. (2021). Geographic information system-based analysis of COVID-19 cases in India during pre-lockdown, lockdown, and unlock phases. International Journal of Infectious Diseases, 105, 424-435. DOI: 10.1016/j.ijid.2021.02.070
Bluhm, R., and Pinkovskiy, M. (2021). The spread of COVID-19 and the BCG vaccine: A natural experiment in reunified Germany. The Econometrics Journal, 24(3), 353-376. https://dx.doi.org/10.2139/ssrn.3604314
Franch-Pardo, I., Napoletano, B. M., Rosete-Verges, F., and Billa, L. (2020). Spatial analysis and GIS in the study of COVID-19. A review. Science of the total environment, 739, 140033. https://doi.org/10.1016/j.scitotenv.2020.140033
Gupta, R., Ghosh, A., Singh, A. K., and Misra, A. (2020). Clinical considerations for patients with diabetes in times of COVID-19 epidemic. Diabetes and metabolic syndrome, 14(3), 211.DOI: 10.1016/j.dsx.2020.03.002
Kramer, A. M., Pulliam, J. T., Alexander, L. W., Park, A. W., Rohani, P., and Drake, J. M. (2016). Spatial spread of the West Africa Ebola epidemic. Royal Society open science, 3(8), 160294. https://doi.org/10.1098/rsos.160294
Kumar, A. (2020). Modeling geographical spread of COVID-19 in India using network-based approach. Medrxiv. doi: https://doi.org/10.1101/2020.04.23.20076489
Ma, Y., Zhao, Y., Liu, J., He, X., Wang, B., Fu, S., Jun Y., Jingping N., Ji Zhou , and Luo, B. (2020). Effects of temperature variation and humidity on the mortality of COVID-19 in Wuhan. medRxiv. DOI: 10.1016/j.scitotenv.2020.138226
Magal, P., Webb, G. F., and Wu, Y. (2020). Spatial spread of epidemic diseases in geographical settings: Seasonal influenza epidemics in Puerto Rico. arXiv preprint arXiv:1801.01856. Doi: 10.3934/dcdsb.2019223
Middya, A. I., and Roy, S. (2021). Geographically varying relationships of COVID-19 mortality with different factors in India. Scientific reports, 11(1), 1-12. DOI: 10.1038/s41598-021-86987-5
Murray, J. D. (1993). Geographic spread and control of epidemics. In Mathematical Biology (pp. 661-721). Springer, New York, NY. https://doi.org/10.1007/0-387-22438-6_13
Riley, S., Eames, K., Isham, V., Mollison, D., and Trapman, P. (2015). Five challenges for spatial epidemic models. Epidemics, 10, 68-71. DOI: 10.1016/j.epidem.2014.07.001
Subramanian, S. V., Karlsson, O., Zhang, W., and Kim, R. (2020). Geo-mapping of COVID-19 risk correlates across districts and parliamentary constituencies in India. DOI:10.1162/99608f92.68bb12e4
Talisuna A., Ali E. A., Yahaya A., Stephen M., Bonkoungou B., Musa E. O., Minkoulou E. M., Okeibunor J., Impouma B., Djingarey H.M., Yao N.K.M., Oka S., Yoti Z., and Fall I.S. (2020). Spatial and temporal distribution of infectious disease epidemics, disasters and other potential public health emergencies in the World Health Organisation Africa region, 2016–2018. Globalization and health, 16(1), 1-12. doi: 10.1186/s12992-019-0540-4.
Wang, L., Li, J., Guo, S., Xie, N., Yao, L., Cao, Y., Day S.D., Howard S.C., Graff J.C., Tianshu G., Jiafu Ji. Weikuan Gu. and Sun, D. (2020). Real-time estimation and prediction of mortality caused by COVID-19 with patient information-based algorithm. Science of the total environment, 727, 138394. doi: 10.1016/j.scitotenv.2020.138394.
Xie, J., and Zhu, Y. (2020). Association between ambient temperature and COVID-19 infection in 122 cities from China. Science of the Total Environment, 724, 138201. DOI: 10.1016/j.scitotenv.2020.138201.
Xiong, Y., Wang, Y., Chen, F., and Zhu, M. (2020). Spatial statistics and influencing factors of the novel coronavirus pneumonia 2019 epidemic in Hubei Province, China. doi: 10.3390/ijerph17113903
Xu, F., McCluskey, C. C., and Cressman, R. (2013). Spatial spread of an epidemic through public transportation systems with a hub. Mathematical biosciences, 246(1), 164-175. doi: 10.1016/j.mbs.2013.08.014
Zhu, Y., Xie, J., Huang, F., and Cao, L. (2020). Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China. Science of the total environment, 727, 138704. doi: 10.1016/j.scitotenv.2020.138704