ABSTRACT: 

Background: The coronavirus disease 2019 (COVID-19) is an emerging and rapidly evolving profound pandemic, which causes severe acute respiratory syndrome and results in significant case fatality around the world. We conducted this study to identify locally transmitted COVID-19 spatial clusters and hotspots in this phrase of the fourth wave in Vietnam.
Data used and Methods: A total of 9,192 locally transmitted cases confirmed in this phrase in the fourth wave were used in study. Global and local Moran’s I and Getis-Ord’s G_i^* statistics were employed to identify spatial autocorrelation and hotspots of COVID-19 cases.
Results: It was found that global Moran’s I statistic indicates a robust spatial autocorrelation of COVID-19 cases. Local Moran’s I statistic successfully identified three high-high spatial clusters of COVID-19 cases in Bac Giang (5,083 cases), Bac Ninh (1,407 cases), and Hanoi (464 cases). In addition, hotspots of COVID-19 cases were mainly detected in Bac Giang (5,083 cases), Bac Ninh (1,470 cases), Hanoi (464 cases), Hai Duong (51 cases), and Thai Nguyen (7 cases).
Conclusion: The results of this work offer new perspectives on the geostatistical analysis of COVID-19 clusters and hotspots, which could help policy planners anticipate the dynamics of spatiotemporal transmission and develop critical control measures for SARS-CoV-2 in Vietnam. Future pandemics and epidemics can be avoided and controlled with the help of geospatial analysis techniques.

KEYWORDS:

Identification, Spatial Clusters, Hotspots, Locally transmitted COVID-19, Local Moran’s I statistic, Local Getis Ord statistic, Vietnam.

REFERENCES :

1) Paules CI, Marston HD, Fauci AS. Coronavirus infections—more than just the common cold. Jama. 2020;323(8):707–8.
2) Guo Y-R, Cao Q-D, Hong Z-S, Tan Y-Y, Chen S-D, Jin H-J, et al. The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak–an update on the status. Mil Med Res. 2020;7:1–10.
3) Chan JF-W, Yuan S, Kok K-H, To KK-W, Chu H, Yang J, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet. 2020;395(10223):514–23.
4) Coronavirus E. 13,968 Cases and 223 Deaths: https://www. worldometers. info/coronavirus/country/ethiopia. Accessed on. 2020;27.
5) WHO. WHO Coronavirus (COVID-19) Dashboard [Internet]. 2023. Available from: https://covid19.who.int/
6) Islam A, Sayeed MA, Rahman MK, Ferdous J, Islam S, Hassan MM. Geospatial dynamics of COVID‐19 clusters and hotspots in Bangladesh. Transbound Emerg Dis. 2021;68(6):3643–57.
7) Kirby RS, Delmelle E, Eberth JM. Advances in spatial epidemiology and geographic information systems. Ann Epidemiol. 2017;27(1):1–9.
8) Tami A, Grillet ME, Grobusch MP. Applying geographical information systems (GIS) to arboviral disease surveillance and control: a powerful tool. Vol. 14, Travel medicine and infectious disease. 2016. p. 9–10.
9) 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.
10) Rosenkrantz L, Schuurman N, Bell N, Amram O. The need for GIScience in mapping COVID-19. Health Place. 2021;67:102389.
11) 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.
12) 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.
13) Huang R, Liu M, Ding Y. Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis. J Infect Dev Ctries. 2020;14(03):246–53.
14) Pourghasemi HR, Pouyan S, Heidari B, Farajzadeh Z, Shamsi SRF, Babaei S, et al. Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020). Int J Infect Dis. 2020;98:90–108.
15) Wang F-S, Zhang C. What to do next to control the 2019-nCoV epidemic? Lancet. 2020;395(10222):391–3.
16) 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.
17) 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.
18) 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.
19) Ghosh P, Cartone A. A Spatio‐temporal analysis of COVID‐19 outbreak in Italy. Reg Sci Policy Pract. 2020;12(6):1047–62.
20) 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.
21) Tough R. Ho Chi Minh City during the fourth wave of COVID‐19 in Vietnam. City Soc. 2021;33(3).
22) MHV. COVD-19 Information Website of Ministry of Health, Vietnam [Internet]. vaMinistry of Health of Vietnam. 2023 [cited 2007 Mar 20]. Available from: https://ncov.moh.gov.vn/
23) Cliff AD, Ord JK. Spatial processes: models & applications. (No Title). 1981;
24) Getis A, Ord JK. Local spatial statistics: An overview. Spatial analysis: Modeling in a GIS environment. Longley, P., and M. Batty. Wiley, New York; 1996.
25) 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).
26) Nguyen TT, Vu TD. Identification of multivariate geochemical anomalies using spatial autocorrelation analysis and robust statistics. Ore Geol Rev. 2019;111.
27) Anselin L. Local indicators of spatial association—LISA. Geogr Anal. 1995;27(2):93–115.
28) Nguyen TT, Vu TD. Use of hot spot analysis to detect underground coal fires from landsat-8 TIRS data: A case study in the Khanh Hoa coal field, North-East of Vietnam. Environ Nat Resour J. 2019;17(3).
29) 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.
30) Phan LT, Nguyen T V, Luong QC, Nguyen T V, Nguyen HT, Le HQ, et al. Importation and human-to-human transmission of a novel coronavirus in Vietnam. N Engl J Med. 2020;382(9):872–4.