Factors Affecting the Covid-19 Risk in South Sulawesi Province, Indonesia: A Bayesian Spatial Model

Aswi Aswi, Sukarna Sukarna

Abstract


The transmission of Coronavirus diseases 2019 (Covid-19) grows continuously around the world. Although a number of researches of modelling Covid-19 cases have been conducted, there was limited research implementing the Bayesian Spatial Conditional Autoregressive (CAR) model. Factors affecting the Covid-19 risk especially population density and distance to the capital city have been studied, but the results are inconsistent and limited research has been done in Indonesia. This study aims to assess the most appropriate Bayesian spatial CAR Leroux models and examine factors that affect the risk of Covid-19 in South Sulawesi Province. Data on the number of Covid-19 cases (19 March 2020 - 31 January 2022), population density, and distance to the capital city were used for every 24 districts. Several criteria were used in choosing the most appropriate model. The results depict that Bayesian spatial CAR Leroux with hyperprior IG (1, 0.01) model with the inclusion of population density were preferred. It is concluded that a factor that significantly affects the number of Covid-19 cases is population density. There was a positive correlation between the population density and Covid-19 risk. Makassar city has the highest relative risk (RR) among other districts while Bone has the lowest RR of Covid-19.

Keywords


Poisson; Bayesian; Spatial; Conditional Autoregressive (CAR)

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References


WHO. (2020, 14 April ). Coronavirus Disease 2019 (COVID-19) Situation Report-85. . Available: https://www.who.int/docs/default-source/coronaviruse/ situation-reports/20200414-sitrep-85-covid-19.pdf?sfvrsn=7b8629bb_4,

Y. Zhu, J. Xie, F. Huang, and L. Cao, "The mediating effect of air quality on the association between human mobility and COVID-19 infection in China," Environmental research, vol. 189, p. 109911, 2020.

P. R. Martins-Filho, "Relationship between population density and COVID-19 incidence and mortality estimates: A county-level analysis," Journal of infection and public health, vol. 14, no. 8, pp. 1087-1088, 2021.

I. A. Moosa and I. N. Khatatbeh, "The density paradox: Are densely‐populated regions more vulnerable to Covid‐19?," The International journal of health planning and management, vol. 36, no. 5, pp. 1575-1588, 2021.

Y. Jo, A. Hong, and H. Sung, "Density or Connectivity: What Are the Main Causes of the Spatial Proliferation of COVID-19 in Korea?," International journal of environmental research and public health, vol. 18, no. 10, p. 5084, 2021.

K. T. L. Sy, L. F. White, and B. E. Nichols, "Population density and basic reproductive number of COVID-19 across United States counties," PloS one, vol. 16, no. 4, pp. e0249271-e0249271, 2021.

Y. Arbel, C. Fialkoff, A. Kerner, and M. Kerner, "Do population density, socio-economic ranking and Gini Index of cities influence infection rates from coronavirus? Israel as a case study," The Annals of regional science, vol. 68, no. 1, pp. 181-206, 2021.

M. Agnoletti, S. Manganelli, and F. Piras, "Covid-19 and rural landscape: The case of Italy," Landscape and urban planning, vol. 204, pp. 103955-103955, 2020.

M. Dadar, Y. Fakhri, G. Bjørklund, and Y. Shahali, "The association between the incidence of COVID-19 and the distance from the virus epicenter in Iran," Archives of virology, vol. 165, no. 11, p. 2555, 2020.

A. Ehlert, "The socio-economic determinants of COVID-19: A spatial analysis of German county level data," Socio-economic planning sciences, vol. 78, pp. 101083-101083, 2021.

D. W. S. Wong and Y. Li, "Spreading of COVID-19: Density matters," PloS one, vol. 15, no. 12, pp. e0242398-e0242398, 2020.

N. D. Goldstein, D. C. Wheeler, P. Gustafson, and I. Burstyn, "A Bayesian approach to improving spatial estimates of prevalence of COVID-19 after accounting for misclassification bias in surveillance data in Philadelphia, PA," Spatial and spatio-temporal epidemiology, vol. 36, pp. 100401-100401, 2021.

G. Konstantinoudis, T. Padellini, J. Bennett, B. Davies, M. Ezzati, and M. Blangiardo, "Long-term exposure to air-pollution and COVID-19 mortality in England: A hierarchical spatial analysis," Environment international, vol. 146, p. 106316, 2021.

G. Carella, J. Pérez Trufero, M. Álvarez, and J. Mateu, "A Bayesian Spatial Analysis of the Heterogeneity in Human Mobility Changes During the First Wave of the COVID-19 Epidemic in the United States," The American statistician, vol. 76, no. 1, pp. 64-72, 2022.

R. S. Whittle and A. Diaz-Artiles, "An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City," BMC medicine, vol. 18, no. 1, pp. 271-271, 2020.

A. Aswi, M. Andi, T. Muhammad Arif, and B. Muhammad Nadjib, "RELATIVE RISK OF CORONAVIRUS DISEASE (COVID-19) IN SOUTH SULAWESI PROVINCE, INDONESIA: BAYESIAN SPATIAL MODELING," Media Statistika, vol. 14, no. 2, pp. 158-169, 2022.

M. A. Tiro, A. Aswi, and Z. Rais, "Association of Population Density and Distance to the City with the Risks of COVID-19: A Bayesian Spatial Analysis," Journal of physics. Conference series, vol. 2123, no. 1, p. 12001, 2021.

Badan Pusat Statistik, "Sulawesi Selatan dalam Angka 2021," 2021.

Badan Pusat Statistik, "Sulawesi Selatan dalam Angka 2015," 2015.

P. A. P. Moran, "Notes on continuous stochastic phenomena," Biometrika, vol. 37, no. 1-2, p. 17, 1950.

T. B. Carrijo and A. R. Da Silva, "Modified Moran's I for Small Samples," Geographical Analysis, vol. 49, no. 4, pp. 451-467, 2017.

A. Aswi, S. Cramb, E. Duncan, and K. Mengersen, "Evaluating the impact of a small number of areas on spatial estimation," International journal of health geographics, vol. 19, no. 1, pp. 39-39, 2020.

A. Aswi, S. Cramb, E. Duncan, and K. Mengersen, "Detecting Spatial Autocorrelation for a Small Number of Areas: a practical example," Journal of physics. Conference series, vol. 1899, no. 1, p. 12098, 2021.

T. J. Oyana and F. Margai, Spatial analysis: statistics, visualization, and computational methods Boca Raton: CRC Press, 2015.

A. Getis and J. Aldstadt, "Constructing the Spatial Weights Matrix Using a Local Statistic," Geographical analysis, vol. 36, no. 2, pp. 90-104, 2004.

B. G. Leroux, X. Lei, and N. Breslow, "Estimation of Disease Rates in Small Areas: A new Mixed Model for Spatial Dependence," Statistical Models in Epidemiology, the Environment, and Clinical Trials, vol. 116, pp. 179-191, 2000.

A. Aswi, S. M. Cramb, P. Moraga, and K. Mengersen, "Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review," Epidemiology And Infection, vol. 147, 2019.

D. Lee, "CARBayes: an R package for Bayesian spatial modeling with conditional autoregressive priors," Journal of Statistical Software, vol. 55, no. 13, pp. 1-24, 2013.

R Core Team, "R: A language and environment for statistical computing," ed. Vienna, Austria: R Foundation for Statistical Computing, 2019.




DOI: http://dx.doi.org/10.12962/j27213862.v5i1.12527

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