Poisson spatial autoregressive (SAR) for estimating factors influencing covid-19
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Published:
Sep 30, 2023
Keywords:
Covid-19, Poisson Distribution, Spatial Autoregressive
Section
Computer Science
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Abstract
People was afraid to engage in activities outside the home because of Covid-19, which has affected several countries throughout the world, but these activities must be carried out so that people's needs can still be met. The community must abide by established health protocol standards, such as wearing masks, washing hands, and keeping a safe distance, in order to carry out this activity. Interactions between communities that invected by Covid-19 to other communities can transmit the disease. This interactions not only happen betwen sub division and sub region but also happen in larger area for example between provinces. Thus make the Covid-19 spread rapidly and easily. The existence of a relationship between location areas that affect the response variable can be referred to as Spatial Autoregressive Model (SAR). Based on the model, the dominant factors that causing the significant case of Covid-19 infected patients on the island of Sumatra are: income, local temperature and compliance of keeping save distance. The coefficient ? of 0.28309, interprets that the number of Covid-19 patient is affected by the province that surrounds it by 0.28309 times.
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How to Cite
Kholijah, G., Rarasati, N., & Sormin, C. (2023). Poisson spatial autoregressive (SAR) for estimating factors influencing covid-19 . Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA), 4(1), 57–64. https://doi.org/10.54076/jumpa.v3i2.287
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Anselin, L. (1988b). Spatial Econometrics: Methods and Models (Vol. 4). Springer Netherlands. https://doi.org/10.1007/978-94-015-7799-1
A’yunin, Q., & Sutijo, B. (2011). Pemodelan Gizi Buruk pada Balita di Kota Surabaya dengan Spatial Autoregressive Model (SAR). ITS.
Bayode, T., Popoola, A., Akogun, O., Siegmund, A., Magidimisha-Chipungu, H., & Ipingbemi, O. (2022). Spatial variability of COVID-19 and its risk factors in Nigeria: A spatial regression method. Applied Geography, 138. https://doi.org/10.1016/j.apgeog.2021.102621
Bermudi, P. M. M., Lorenz, C., Aguiar, B. S. de, Failla, M. A., Barrozo, L. V., & Chiaravalloti-Neto, F. (2021). Spatiotemporal ecological study of COVID-19 mortality in the city of São Paulo, Brazil: Shifting of the high mortality risk from areas with the best to those with the worst socio-economic conditions. Travel Medicine and Infectious Disease, 39. https://doi.org/10.1016/j.tmaid.2020.101945
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Calabrese, R. (2023). Contagion effects of UK small business failures: A spatial hierarchical autoregressive model for binary data. European Journal of Operational Research, 305(2), 989–997. https://doi.org/10.1016/j.ejor.2022.06.027
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Draper, N. R., & Smtih, H. (1998). Applied Regression Analysis, Third Edition (Third Edition). John Wiley & Sons.
D’Urso, P., Mucciardi, M., Otranto, E., & Vitale, V. (2022). Community mobility in the European regions during COVID-19 pandemic: A partitioning around medoids with noise cluster based on space–time autoregressive models. Spatial Statistics, 49. https://doi.org/10.1016/j.spasta.2021.100531
Franch-Pardo, I., Napoletano, B. M., Rosete-Verges, F., & Billa, L. (2020). Spatial analysis and GIS in the study of COVID-19. A review. Science of the Total Environment, 739. https://doi.org/10.1016/j.scitotenv.2020.140033
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Neyens, T., Faes, C., Vranckx, M., Pepermans, K., Hens, N., Van Damme, P., Molenberghs, G., Aerts, J., & Beutels, P. (2020). Can COVID-19 symptoms as reported in a large-scale online survey be used to optimise spatial predictions of COVID-19 incidence risk in Belgium? Spatial and Spatio-Temporal Epidemiology, 35. https://doi.org/10.1016/j.sste.2020.100379
Permai, S. D., Jauri, R., & Chowanda, A. (2019). Spatial autoregressive (SAR) model for average expenditure of Papua Province. Procedia Computer Science, 157, 537–542. https://doi.org/10.1016/j.procs.2019.09.011
Sakamaki, T., Morita, A., Touyama, S., Wanatabe, Y., Suzuki, S., & Kawai, T. (2022). Effects of watershed land use on coastal marine environments: A multiscale exploratory analysis with multiple biogeochemical indicators in fringing coral reefs of Okinawa Island. Marine Pollution Bulletin, 183. https://doi.org/10.1016/j.marpolbul.2022.114054
Sannigrahi, S., Pilla, F., Basu, B., Basu, A. S., & Molter, A. (2020). Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach. Sustainable Cities and Society, 62. https://doi.org/10.1016/j.scs.2020.102418
Sir’, O., Arsine, A., Syam’, I., Despitasari3 ’bagian, M., Prodi, E., Masyarakat, K., Fakultas, E., Teknologi, P., Kesehatan, I., & Litbangkes, B. (2014). Faktor-Faktor yang Berhubungan dengan Kejadian Malaria di Kecamatan Kabola, Kabupaten Alor, Provinsi Nusa Tenggara Timur (NTT) Tahun 2014. Jurnal Ekologi Kesehatan, 14(04), 334–341.
Skevas, I. (2020). Inference in the spatial autoregressive efficiency model with an application to Dutch dairy farms. European Journal of Operational Research, 283(1), 356–364. https://doi.org/10.1016/j.ejor.2019.10.033
Susilo, A., Martin Rumende, C., Pitoyo, C. W., Djoko Santoso, W., Yulianti, M., Sinto, R., Singh, G., Nainggolan, L., Nelwan, E. J., Khie Chen, L., Widhani, A., Wijaya, E., Wicaksana, B., Maksum, M., Annisa, F., Jasirwan, C. O., & Yunihastuti, E. (2020). TINJAUAN PUSTAKA. In Jurnal Penyakit Dalam Indonesia | (Vol. 7, Issue 1). https://www.ncbi.nlm.nih.gov/nuccore/
Walpole, R. E., Myers, R. H., Myers, S. L., & Ye, K. (2011). Probability & Statistics for Engineers & Scientists ninth edition (ninth edition). Pearson Education, Inc.
WHO. (2021). emergencies/diseases/novel-coronavirus-2019. Https://Www.Who.Int/Emergencies/Diseases/Novel-Coronavirus-2019.
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