Analysis of spatial point patterns: An application to Covid-19 in Peru
DOI:
https://doi.org/10.36881/yachay.v10i1.268Keywords:
spatial statistics, spatial patterns, spatial association, Covid-19Abstract
In December 2019, Covid 19 disease emerged from the Wuhan province, and then spread throughout the world, Peru has not been the exception. The main objective of this work is to estimate the spatial patterns of Covid 19 in Peru, in addition to establishing clusters at the regional level. Spatial association was found using spatial statistical techniques using the global I Moran of 0.056 (p <0.05). On the other hand, four clusters were established: first, regions in the north of the country where there are more cases, the south where there are fewer numbers and clusters made up of outliers such as Lima and Callao.
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References
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Anselin, L., & Forax, R. (1995). New Directions in Spatial Econometrics. Verlag: Springer.
Arashi, M., Bekker, A., Salehi, M., Millard, S., Erasmus, B., Cronje, T., & Golpaygani, M. (2020). Spatial analysis and prediction of COVID-19 spread in South Africa after lockdown. https://arxiv.org/abs/2005.09596.
Bayes, C., Sal y Rosas, V., & Valdivieso, L. (2020). Modelling death rates due to COVID-19: A Bayesian approach. https://arxiv.org/abs/2004.02386.
Bivand, R. (1992). SYSTAT-compatible software for modeling spatial dependence among observations. Comput Geosci, 951–963.
Bivand, R., Pebesma, E., & Gómez-Rubio, V. (2013). Applied Spatial Data Analysis with R. New York: Springer.
Chen, Y. (2013). New Approaches for Calculating Moran’s Index of Spatial Autocorrelation. PLoS ONE 8(7): e68336. https://doi.org/10.1371/journal.pone.0068336.
Cliff, A., & Ord, K. (1981). Spatial Processes: Models & Applications. London: Pion.
Cressie, N. (1992). Statistics for spatial data. New York: Wiley interscience publication.
Ebdon, D. (1977). Statistics in geography: A practical approach. Oxford: Wiley-Blackwell.
Fanelli, D., & Piazza, F. (2020). Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons and Fractals, 1-5.
Geary, R. (1954). he contiguity ratio and statistical mapping,. The Incorporated Statistician, 115–145.
Getis, A., & Ord, J. (1996). Local spatial statistics: An overview. En P. Longley, & M. Batty, Spatial Analysis: Modeling in A GIS Environment (págs. 261-277). New York: John Wiley & Sons.
Giuliani, D., Dickson, M. M., Espa, G., & Santi, F. (2020). COVID-19, Italy, epidemiology, diffusion model, spatio-temporal diffusion models. Disponible en: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3559569.
Gozzer, E., Canchihuamán, F., & Espinoza, R. (2020). COVID-19 y la necesidad de actuar para mejorar las capacidades del Perú frente a las pandemias. Revista Peruana de Medicina Experimental y Salud Pública.
Herrera, M., Paz, J., & Cid, J. (2012). Introducción a la Econometría Espacial. Una Aplicación al Estudio de la Fecundidad en la Argentina usando R. MPRA, 1-30.
Huang, R., Liu, M., & Ding, Y. (2020). Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis. The Journal of infection in Developing Countries.
Kim, J.-H., Choi, J., Kang, D., & Choi, H. (2020). Spatial epidemic dynamics of the COVID-19 outbreak in China. International Journal of Infectious Diseases.
Matel, N. (1967). he Detection of Disease Clustering and a Generalized Regression Approach. Cancer Research,, 209-220.
MINSA. (2020). Situación actual "Covid-19" al 18 de Abril 2020. Lima: Ministerio de Salud.
Moran, P. (1950). Notes on Continuous Stochastic Phenomena. Biometrika, 17-23.
Murat Yüceşahin, M., & Sirkeci, I. (2020). Coronavirus and Migration: Analysis of Human Mobility and the Spread of COVID-19. Migration Letters, 379-398.
Topler, W. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region in the Detroit Region. Economic Geography, 234-240.
Zhang, X., Rao, H.-X., Wu, Y., Huang, Y., & Dai, H. (2020). Comparison of the spatiotemporal characteristics of the COVID-19 and SARS outbreaks in mainland China. medRxiv.
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Published
2021-12-12
How to Cite
Calderón Canto, L. E. (2021). Analysis of spatial point patterns: An application to Covid-19 in Peru. Yachay, 10(1), 596–599. https://doi.org/10.36881/yachay.v10i1.268
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