Epidemiological characteristics and determination of spatio-temporal clusters during the 2013 dengue outbreak in Chiang Mai, Thailand

Submitted: 10 January 2020
Accepted: 4 August 2020
Published: 29 December 2020
Abstract Views: 1218
PDF: 514
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.


Dengue is the worldwide most important mosquito-borne viral disease in humans. A large dengue outbreak occurred in Chiang Mai, Thailand in 2013. The aims of this study were to describe the epidemiology of this outbreak and determine the spatio-temporal pattern in the sub-district with the highest number of dengue cases. Data on patients, including date of illness, were obtained from the Chiang Mai Provincial Public Health Center and analyzed descriptively using R statistical software. The geographic location of patients' residences was determined from available geographical information databases supplemented with coordinated data collection in the field. A space-time permutation model from SaTScan„¢ was used to determine disease clusters corresponding to space and time. Results showed that Muang District, the centre of the province, had a higher number of cases than the other 25 districts. The Suthep subdistrict, part of Muang District, had most of the patients: 625 subjects distributed between 213 residences. The space-time analysis identified a primary cluster and 7 secondary clusters in different time periods. The primary cluster had 128 patients in a period of approximately 3 months. The number of patients in the secondary clusters ranged between 7 and 65. Most of the clusters occurred in densely populated areas during June and July (the rainy season). The finding from this study may support health agencies to plan surveillance campaigns for people at specified local areas with a high incidence of the disease.



PlumX Metrics


Download data is not yet available.


CMPPHO.Dengue fever report 2013. Chiang Mai Provincial Public Health Office (CMPPHO) (http://www.chiangmaihealth.go.th/cmpho_web/all_it.php). Accessed on 5 January 2016.
Barbazan, P., Yoksan, S., & Gonzalez, J.P. (2002). Dengue hemorrhagic fever epidemiology in Thailand: description and forecasting of epidemics. Microbes Infect 4(7), 699-705. DOI: https://doi.org/10.1016/S1286-4579(02)01589-7
BVBD.Dengue situation . Bureau of Vector Borne Diseases, Ministry of Public Health, Thailand (http://www.thaivbd.org/n/home). Accessed on 1 january 2017.
BVBD.Dengue situation . Bureau of Vector Borne Diseases, Ministry of Public Health, Thailand (http://www.thaivbd.org/n/home). Accessed on 1 october 2019.
Ferreira, GLC.2001.Global dengue epidemiology trends. Revista do Instituto de Medicina Tropical de São Paulo 54:5-6. DOI: https://doi.org/10.1590/S0036-46652012000700003
Gubler DJ. 2011. Dengue, Urbanization and Globalization: The Unholy Trinity of the 21(st) Century. Tropical Medicine and Health 39(4 Suppl):3-11.
Guzman MG, Kouri G. 2002.Dengue: an update. Lancet Infect Dis 2(1):33-42. DOI: https://doi.org/10.1016/S1473-3099(01)00171-2
Kulldorff M. 2017. SaTScan users guide for version 8.0. (http://www.satscan.org). Accessed on 5 December 2017
Kulldorff M. 2001. Prospective time periodic geographical disease surveillance using a scan statistic. Journal of the Royal Statistical Society: Series A (Statistics in Society) 164(1): 61-72. DOI: https://doi.org/10.1111/1467-985X.00186
Kulldorff M, Feuer EJ, Miller, BA, Freedman LS. 1997. Breast cancer clusters in the northeast United States: a geographic analysis. Am J Epidemiol 146(2):161-170. DOI: https://doi.org/10.1093/oxfordjournals.aje.a009247
Limkittikul K, Brett, J, L'Azou M. 2014. Epidemiological Trends of Dengue Disease in Thailand (2000–2011): A Systematic Literature Review. PLoS Neglected Tropical Diseases 8(11):e3241. DOI: https://doi.org/10.1371/journal.pntd.0003241
Loshini T, Asirvadam VS, Dass SC, Gill BS. 2015. Predicting localized dengue incidences using ensemble system identification, in: 2015 International Conference on Computer, Control, Informatics and its Applications (IC3INA)):6-11.
Mohd-Zaki, AH, Brett J, Ismail, E, L'Azou, M. 2014.Epidemiology of Dengue Disease in Malaysia (2000–2012): A Systematic Literature Review. PLoS Neglected Tropical Diseases 8(11):e3159. doi: 10.1371/journal.pntd.0003159. DOI: https://doi.org/10.1371/journal.pntd.0003159
QGIS Development Team. 2016.QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org.
R Core Team.2017. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.
Shepard DS., Undurraga EA, Halasa YA. Economic and Disease Burden of Dengue in Southeast Asia. PLoS Neglected Tropical Diseases 2013; 7(2), e2055. doi: 10.1371/journal.pntd.0002055. DOI: https://doi.org/10.1371/journal.pntd.0002055
Sudjaritruk PO. 2011. Clinical Characteristics and Outcomes of Dengue-infected Children Admitted to the Chiang Mai University Hospital During an Outbreak in 2008. Chiang Mai Medical Journal 50(4):95-104.
Phanitchat T, Zhao B, Haque U, Pientong C, Ekalaksanana T, Aromseree S, Thaewnongiew K, Fustec B, Bangs MJ, Alexander N, Overgaard HJ. 2019. Spatio and temporal patterns of dengue incidence in northeast Thailand 2006-2016. BMC Infectious Diseases 19:743 doi:10.1186/s12879-019-4379-3. DOI: https://doi.org/10.1186/s12879-019-4379-3
Wen TH, Lin NH, Lin CH, King CC, Su MD. 2006. Spatial mapping of temporal risk characteristics to improve environmental health risk identification: a case study of a dengue epidemic in Taiwan. Sci Total Environ 367(2-3): 631-640. doi: 10.1016/j.scitotenv.2006.02.009. DOI: https://doi.org/10.1016/j.scitotenv.2006.02.009
Wongkoon S, Jaroensutasinee M, Jaroensutasinee K. 2013.Distribution, seasonal variation & dengue transmission prediction in Sisaket, Thailand. Indian J Med Res 138(3):347-353.
WHO, 2012.World Health Organization.Global Strategy for dengue prevention and control, 2012–2020 (https://www.who.int/denguecontrol/9789241504034/en/). Accessed 1 March 2017.
WHO, 2017. Factsheet on dengue dated April 2027. Accessed 25 March 2020 and available at http://www9.who.int/mediacentre/factsheets/fs117/en/
Xu Z, Bambrick H, Yakob L, Devine G, Lu J. Frentiu FD, Yang W, Williams G, Hu W. 2019. Spatiotemporal patterns and climatic drivers of severe dengue in Thailand. Sci Total Environ 656(889-901). doi: 10.1016/j.scitotenv.2018.11.395. DOI: https://doi.org/10.1016/j.scitotenv.2018.11.395

How to Cite

Punyapornwithaya, V., Sansamur, C., & Charoenpanyanet, A. (2020). Epidemiological characteristics and determination of spatio-temporal clusters during the 2013 dengue outbreak in Chiang Mai, Thailand. Geospatial Health, 15(2). https://doi.org/10.4081/gh.2020.857

List of Cited By :

Crossref logo