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Covid-19 Data Analysis in Tarakan with Poisson Regression and Spatial Poisson Process
Abstract
COVID-19 entered Indonesia in March 2020 and included North Kalimantan Province, Tarakan. COVID-19 cases have outspread in Tarakan. The cause of the outspread and the patterns were not known yet. One relevant approach was to use Generalized Linear Models. The two methods are Poisson Regression and Stochastic with Spatial Poisson Process. The variables used were rainfall, population density, and temperature in each village in Tarakan. The Poisson Regression analysis founds that only one factor affected temperature. Then, the results were refined with the Spatial Poisson Process, where in addition to the influencing factors also, the distribution patterns are obtained. The analysis showed that the pattern of case distribution was included in the non-homogeneous Poisson process criteria. Then the model of the case density intensity was obtained using regression. From the model, it was known that the covariate variables significantly influence rainfall and temperature. Compared with general Poisson regression analysis, the results showed that only the average temperature variables had a significant effect. Thus, a better method was used, namely the Spatial Poisson Process. It was also shown by the two models' AIC values, where the AIC value of the Spatial Poisson Process model was smaller than the Poisson Regression.
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L. Kuhn, L. L. Davidson, and M. S. Durkin, “Use of Poisson regression and time series analysis for detecting changes over time in rates of child injury following a prevention program,” Am J Epidemiol, vol. 140, no. 10, pp. 943–955, 1994.
D. L. Preston, “Poisson regression in epidemiology,” Encyclopedia of biostatistics, vol. 6, 2005.
R. Bender, “Introduction to the use of regression models in epidemiology,” in Cancer Epidemiology, Springer, 2009, pp. 179–195.
E. Gabriel, “A. Baddeley, E. Rubak, R. Turner: Spatial Point Patterns: Methodology and Applications with R.” Springer, 2017.
Z. Sun, H. Zhang, Y. Yang, H. Wan, and Y. Wang, “Impacts of geographic factors and population density on the COVID-19 spreading under the lockdown policies of China,” Science of The Total Environment, vol. 746, p. 141347, 2020.
J. F. Lawless, “Regression methods for Poisson process data,” J Am Stat Assoc, vol. 82, no. 399, pp. 808–815, 1987.
A. N. Syaifulloh, N. Iriawan, and P. P. Oktaviana, “Analisis Pola Persebaran Stasiun Pengisian Bahan Bakar Umum (SPBU) Wilayah Surabaya Menggunakan Spatial Poisson Point Process,” Jurnal Sains dan Seni ITS, vol. 8, no. 2, pp. D57–D64, 2020.
C. Mufudza and H. Erol, “Poisson mixture regression models for heart disease prediction,” Comput Math Methods Med, vol. 2016, 2016.
X. Wang, A. Mueen, H. Ding, G. Trajcevski, P. Scheuermann, and E. Keogh, “Experimental comparison of representation methods and distance measures for time series data,” Data Min Knowl Discov, vol. 26, no. 2, pp. 275–309, 2013.
R. Ramírez-Aldana, J. C. Gomez-Verjan, and O. Y. Bello-Chavolla, “Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level,” PLoS Negl Trop Dis, vol. 14, no. 11, p. e0008875, 2020.
H. Guliyev, “Determining the spatial effects of COVID-19 using the spatial panel data model,” Spat Stat, vol. 38, p. 100443, 2020.
M. D. Shokouhi, F. Miralles-Wilhelm, M. D. A. Amoroso, and M. M. Sajadi, “Temperature, Humidity, and Latitude Analysis to Predict Potential Spread and Seasonality for COVID-19.”,” Working paper, 2020.
W. M. Meredith, “THE POISSON DISTRIBUTION AND POISSON PROCESS IN PSYCHOMETRIC THEORY 1,” ETS Research Bulletin Series, vol. 1968, no. 2, pp. i–81, 1968.
R. E. Walpole, R. H. Myers, S. L. Myers, and K. Ye, Probability and statistics for engineers and scientists, vol. 5. Macmillan New York, 1993.
J. K. Lindsey, “Applying Generalized Linear Models. Springer, New York.,” 1997.
S. Yang and G. Berdine, “Poisson regression,” The Southwest Respiratory and Critical Care Chronicles, vol. 3, no. 9, pp. 61–64, 2015.
J. A. Santos and M. M. Neves, “A local maximum likelihood estimator for Poisson regression,” Metrika, vol. 68, no. 3, pp. 257–270, 2008.
A. E. Gelfand, P. Diggle, P. Guttorp, and M. Fuentes, Handbook of spatial statistics. CRC press, 2010.
T. D. Johnson, “Introduction to spatial point processes,” Www-Ljk.Imag.Fr, 2008.
J. Møller and R. P. Waagepetersen, “Modern statistics for spatial point processes,” Scandinavian Journal of Statistics, vol. 34, no. 4, 2007, doi: 10.1111/j.1467-9469.2007.00569.x.
H. P. Keeler, “Notes on the Poisson point process,” Weierstrass Inst., Berlin, Germany, Tech. Rep, 2016.
A. Baddeley, “Analysing spatial point patterns in R,” in Workshop notes version, 2008, vol. 3.
M. Berman and T. R. Turner, “Approximating point process likelihoods with GLIM,” J R Stat Soc Ser C Appl Stat, vol. 41, no. 1, pp. 31–38, 1992.
L. C. Drazek, “Intensity estimation for Poisson processes,” The University of Leeds, School of Mathematics, 2013.
W. H. Finch and J. E. Bolin, Multilevel modeling using Mplus. Chapman and Hall/CRC, 2017.
W. Pan, “Akaike’s information criterion in generalized estimating equations,” Biometrics, vol. 57, no. 1, pp. 120–125, 2001.
J. E. Cavanaugh and A. A. Neath, “The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements,” Wiley Interdiscip Rev Comput Stat, vol. 11, no. 3, p. e1460, 2019.
DOI: https://doi.org/10.18860/ca.v7i4.19653
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