ABSTRACT: 

Background : The outbreak of the new coronavirus, also known as COVID-19, began in Wuhan, China, in January 2020, becoming a sudden public health crisis and a severe threat to lives in most parts of the world. This study aimed to use a Geographic Information System (GIS) to study the spatial distribution of COVID-19 incidence in Ho Chi Minh city.
Methods: the histogram was first used to study the distribution of the COVID-19 cases and COVID-19 incidence. A GIS was then employed to map the spatial distribution of the COVID-19 cases and COVID-19 incidence. Finally, the study results were discussed and concluded.
Results: a large proportion of COVID-19 infections mainly appeared in districts in the eastern region, then followed by the western districts of the city, while the average rate of infections was concentrated in districts near the city center. Low rates of COVID-19 infection were detected in the northern and southern districts of the city and some central districts of the city.
Conclusions: the study results indicated the effectiveness of a GIS for mapping COVID-19 incidence. Findings in this study provide an insight into the spatial distribution of infectious diseases and make great contributions to the control of the COVID-19 pandemic.

KEYWORDS:

Spatial distribution, Geographic Information System (GIS), COVID-19, Ho Chi Minh City, Vietnam

REFERENCES :

1) Nguyen T-Q, Ngo T-T, Than T-B. Spatial clustering analysis of COVID-19 using LISA: A case study of the 2022 winter Day in Hanoi, Vietnam. 2023;
2) Shi Z, Chen H, Fan K, Chen P. Some thoughts and strategies of planning for the impact of “COVID-19” epidemic in Yunnan plateau basin. In: E3S Web of Conferences. EDP Sciences; 2020. p. 3044.
3) Rothan HA, Byrareddy SN. The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J Autoimmun. 2020;109:102433.
4) Spagnuolo G, De Vito D, Rengo S, Tatullo M. COVID-19 outbreak: an overview on dentistry. Int J Environ Res Public Health. 2020;17(6):2094.
5) Kim JY, Song JY, Yoon YK, Choi S-H, Song YG, Kim S-R, et al. Middle East respiratory syndrome infection control and prevention guideline for healthcare facilities. Infect Chemother. 2015;47(4):278–302.
6) WHO. WHO Coronavirus (COVID-19) Dashboard [Internet]. 2023. Available from: https://covid19.who.int/
7) Hoang A, Nguyen T. Identifying Spatio-Temporal Clustering of the COVID-19 Patterns Using Spatial Statistics: Case Studies of Four Waves in Vietnam. Int J Appl Geospatial Res. 2022;13(1):1–15.
8) Vu D-T, Nguyen T-T, Hoang A-H. Spatial clustering analysis of the COVID-19 pandemic: A case study of the fourth wave in Vietnam. Geogr Environ Sustain. 2021;14(4).
9) Svendsen ER, Gonzales M, Mukerjee S, Smith L, Ross M, Walsh D, et al. GIS-modeled indicators of traffic-related air pollutants and adverse pulmonary health among children in El Paso, Texas. Am J Epidemiol. 2012;176(suppl_7):S131–41.
10) Shepherd M. Why Geography is a key-part of fighting the COVID-19 Coronavirus outbreak. Forbes. 2020;5:2020.
11) Razavi-Termeh SV, Sadeghi-Niaraki A, Choi S-M. Coronavirus disease vulnerability map using a geographic information system (GIS) from 16 April to 16 May 2020. Phys Chem Earth, Parts A/B/C. 2022;126:103043.
12) Rose-Redwood R, Kitchin R, Apostolopoulou E, Rickards L, Blackman T, Crampton J, et al. Geographies of the COVID-19 pandemic Dialogues in Human Geography 10 (2): 97–106. 2020.
13) Das A, Ghosh S, Das K, Basu T, Dutta I, Das M. Living environment matters: Unravelling the spatial clustering of COVID-19 hotspots in Kolkata megacity, India. Sustain Cities Soc. 2021;65:102577.
14) Xiong Y, Wang Y, Chen F, Zhu M. Spatial statistics and influencing factors of the COVID-19 epidemic at both prefecture and county levels in Hubei Province, China. Int J Environ Res Public Health. 2020;17(11):3903.
15) Alves JD, Abade AS, Peres WP, Borges JE, Santos SM, Scholze AR. Impact of COVID-19 on the indigenous population of Brazil: A geo-epidemiological study. Epidemiol Infect. 2021;149:e185.
16) Rosenkrantz L, Schuurman N, Bell N, Amram O. The need for GIScience in mapping COVID-19. Health Place. 2021;67:102389.
17) Han Y, Yang L, Jia K, Li J, Feng S, Chen W, et al. Spatial distribution characteristics of the COVID-19 pandemic in Beijing and its relationship with environmental factors. Sci Total Environ. 2021;761:144257.
18) Franch-Pardo I, Napoletano BM, Rosete-Verges F, Billa L. Spatial analysis and GIS in the study of COVID-19. A review. Sci Total Environ. 2020;739:140033.
19) Liu L, Hu T, Bao S, Wu H, Peng Z, Wang R. The spatiotemporal interaction effect of COVID-19 transmission in the United States. ISPRS Int J Geo-Information. 2021;10(6):387.
20) Huangfu P, Atkinson R. Long-term exposure to NO2 and O3 and all-cause and respiratory mortality: A systematic review and meta-analysis. Environ Int. 2020;144:105998.
21) Belvis F, Aleta A, Padilla-Pozo Á, Pericàs J-M, Fernández-Gracia J, Rodríguez JP, et al. Key epidemiological indicators and spatial autocorrelation patterns across five waves of COVID-19 in Catalonia. Sci Rep. 2023;13(1):1–11.
22) Ghosh P, Cartone A. A Spatio‐temporal analysis of COVID‐19 outbreak in Italy. Reg Sci Policy Pract. 2020;12(6):1047–62.
23) Arab-Mazar Z, Sah R, Rabaan AA, Dhama K, Rodriguez-Morales AJ. Mapping the incidence of the COVID-19 hotspot in Iran–Implications for Travellers. Travel Med Infect Dis. 2020;34:101630.
24) Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020;20(5):533–4.
25) Tough R. Ho Chi Minh City during the fourth wave of COVID‐19 in Vietnam. City Soc. 2021;33(3).
26) Pearson K. X. Contributions to the mathematical theory of evolution.—II. Skew variation in homogeneous material. Philos Trans R Soc London(A). 1895;(186):343–414.
27) Howitt D, Cramer D. Introduction to statistics in psychology. Pearson education; 2008.
28) Freedman DA. Statistical models and causal inference: a dialogue with the social sciences. Cambridge University Press; 2010.
29) Stangor C. Research methods for the behavior science. Cengage Learning; 2011.
30) Tague NR. Seven basic quality tools. Qual toolbox. 2004;15.
31) Magnello ME. Karl Pearson and the origins of modern statistics: An elastician becomes a statistician. New Zeal J Hist Philos Sci Technol. 2005;1.
32) Lambert JH. Notes and comments on the composition of terrestrial and celestial maps. In: Mathematical Geography in the Eighteenth Century: Euler, Lagrange and Lambert. Springer; 2022. p. 367–422.
33) Snyder JP, Voxland PM. An album of map projections. US Government Printing Office; 1989.
34) Ghaderpour E. Some equal-area, conformal and conventional map projections: A tutorial review. J Appl Geod. 2016;10(3):197–209.
35) Monmonier M. How to lie with maps. University of Chicago Press; 2018.
36) Snyder JP. Flattening the earth: two thousand years of map projections. University of Chicago Press; 1997.