https://doi.org/10.4081/gh.2021.1012
Association between climate variables and dengue incidence in Nakhon Si Thammarat Province, Thailand
Submitted: 2 May 2021
Accepted: 11 September 2021
Published: 28 October 2021
Accepted: 11 September 2021
Abstract Views: 3198
PDF: 1316
SUPPLEMENTARY: 115
HTML: 555
SUPPLEMENTARY: 115
HTML: 555
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.
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.
Authors
The tropical climate of Thailand encourages very high mosquito densities in certain areas and is ideal for dengue transmission, especially in the southern region where the province Nakhon Si Thammarat is located. It has the longest dengue fever transmission duration that is affected by some important climate predictors, such as rainfall, number of rainy days, temperature and humidity. We aimed to explore the relationship between weather variables and dengue and to analyse transmission hotspots and coldspots at the district-level. Poisson probability distribution of the generalized linear model (GLM) was used to examine the association between the monthly weather variable data and the reported number of dengue cases from January 2002 to December 2018 and geographic information system (GIS) for dengue hotspot analysis. Results showed a significant association between the environmental variables and dengue incidence when comparing the seasons. Temperature, sea-level pressure and wind speed had the highest coefficients, i.e. β=0.17, β= -0.12 and β= -0.11 (P<0.001), respectively. The risk of dengue incidence occurring during the rainy season was almost twice as high as that during monsoon. Statistically significant spatial clusters of dengue cases were observed all through the province in different years. Nabon was identified as a hotspot, while Pak Phanang was a coldspot for dengue fever incidence, explained by the fact that the former is a rubber-plantation hub, while the agricultural plains of the latter lend themselves to the practice of pisciculture combined with rice farming. This information is imminently important for planning apt sustainable control measures for dengue epidemics.
Andraud M, Hens N, Beutels P, 2013. A simple periodic-forced model for dengue fitted to incidence data in Singapore. Math Biosci. 244:22-8.
DOI: https://doi.org/10.1016/j.mbs.2013.04.001
Benedum CM, Seidahmed OM, Eltahir EA, Markuzon N, 2018. Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore. PLoS Negl Trop Dis 12:e0006935.
DOI: https://doi.org/10.1371/journal.pntd.0006935
Braks M, Van Ginkel R, Wint W, Sedda L, Sprong H, 2014. Climate change and public health policy: translating the science. Int J Environ Res Public Health 11:13-29.
DOI: https://doi.org/10.3390/ijerph110100013
Brownstein JS, Rosen H, Purdy D, Miller JR, Merlino M, Mostashari F, Fish D, 2002. Spatial analysis of West Nile virus: rapid risk assessment of an introduced vector-borne zoonosis. Vector Borne Zoonotic Dis 2:157-164.
DOI: https://doi.org/10.1089/15303660260613729
Bureau of Epidemiology, 2019. BOE National Disease Surveillance website. Bureau of Epidemiology (BOE), Ministry of Public Health (MOPH), Thailand. Available from: http://www.boe.moph.go.th/boedb/surdata/disease.php?ds=66 Accessed: 12 September 2019.
Christophers S, 1960. Aëdes aegyptì (L.) the yellow fever mosquito; its life history, bionomics and structure. Cambridge University Press, New York, 1960. xii+ 739 pp. Illus.
Chumpu R, Khamsemanan N, Nattee C, 2019. The association between dengue incidences and provincial-level weather variables in Thailand from 2001 to 2014. PLos One 14:e0226945.
DOI: https://doi.org/10.1371/journal.pone.0226945
CityPopulation. 2019. Thailand: major cities, towns & communes. Available from: http://citypopulation.de/Thailand-Cities.html Accessed: 7 October 2019.
Clark DV, Mammen Jr MP, Nisalak A, Puthimethee V, Endy TP, 2005. Economic impact of dengue fever/dengue hemorrhagic fever in Thailand at the family and population levels. Am J Trop Med Hyg 72:786-91.
DOI: https://doi.org/10.4269/ajtmh.2005.72.786
Cuong HQ, Hien NT, Duong TN, Phong TV, Cam NN, Farrar J, Nam VS, Thai KT, Horby P, 2011. Quantifying the emergence of dengue in Hanoi, Vietnam: 1998-2009. PLoS Negl Trop Dis 5:e1322.
DOI: https://doi.org/10.1371/journal.pntd.0001322
Department of Disease Control, 2001. Case definition for surveillance. Factsheet (in Thai). Available from: http://203.157.15.4/surdata Accessed: 17 September 2019.
Department Provincial Administration, 2019. Official statistics registration systems Thailand: Registration statistics system. Department Provincial Administration. Available from: http://stat.bora.dopa.go.th/stat/statnew/statTDD/views/showDistrictData.php?rcode=80&statType=1&year=61 Accessed: 02 August 2019.
Depradine C, Lovell E, 2004. Climatological variables and the incidence of Dengue fever in Barbados. Int J Environ Health Res 14:429-41.
DOI: https://doi.org/10.1080/09603120400012868
Edwards H, 1982. Ion concentration and activity in the haemolymph of Aedes aegypti larvae. J Exp Biol 101:143-51.
DOI: https://doi.org/10.1242/jeb.101.1.143
Ehelepola N, Ariyaratne K, Buddhadasa W, Ratnayake S, Wickramasinghe M, 2015. A study of the correlation between dengue and weather in Kandy City, Sri Lanka (2003-2012) and lessons learned. Infect Dis Poverty 4:42.
DOI: https://doi.org/10.1186/s40249-015-0075-8
ESRI, 2021. How Hot Spot Analysis (Getis-Ord Gi*) works. ESRI. Available from: https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/h-how-hot-spot-analysis-getis-ord-gi-spatial-stati.htm Accessed: 2 January 2021.
Getis A, Ord JK. 2010. The analysis of spatial association by use of distance statistics. In: Perspectives on spatial data analysis. Springer, Berlin, Germany.
DOI: https://doi.org/10.1111/j.1538-4632.1992.tb00261.x
Githeko AK, Lindsay SW, Confalonieri UE, Patz JA, 2000. Climate change and vector-borne diseases: a regional analysis. Bull WHO 78:1136-47.
Gubler DJ, 2002. Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. Trends Microbiol 10:100-3.
DOI: https://doi.org/10.1016/S0966-842X(01)02288-0
Gubler DJ, Reiter P, Ebi KL, Yap W, Nasci R, Patz JA, 2001. Climate variability and change in the United States: potential impacts on vector-and rodent-borne diseases. Environ Health Perspect 109:223-33.
DOI: https://doi.org/10.1289/ehp.109-1240669
Guha-Sapir D, Schimmer B, 2005. Dengue fever: new paradigms for a changing epidemiology. Emerg Themes Epidemiol 2:1.
DOI: https://doi.org/10.1186/1742-7622-2-1
Ho CC, Ting C-Y, Raja DB, 2018. Using public open data to predict dengue epidemic: assessment of weather variability, population density, and land use as predictor variables for dengue outbreak prediction using support vector machine. Indian J Sci. Technol 11:1-8.
DOI: https://doi.org/10.17485/ijst/2018/v11i4/115405
Jeefoo P, Tripathi NK, Souris M, 2011. Spatio-temporal diffusion pattern and hotspot detection of dengue in Chachoengsao province, Thailand. Int J Environ Res Public Health 8:51-74.
DOI: https://doi.org/10.3390/ijerph8010051
Jetten TH, Focks DA, 1997. Potential changes in the distribution of dengue transmission under climate warming. Am J Trop Med Hyg 57:285-97.
DOI: https://doi.org/10.4269/ajtmh.1997.57.285
Lawless JF, 1987. Negative binomial and mixed Poisson regression. Canad J Stat 209-25.
DOI: https://doi.org/10.2307/3314912
Meteorological Department of Thailand, 2015. The climate of Thailand. Meteorological Department of Thailand. Available from: https://www.tmd.go.th/en/archive/thailand_climate.pdf Accessed: 20 October 2019.
Ministry of Public Health, 2019. Annual epidemiological surveillance report. War Veterans Organization, Bangkok (1996-2005) (in Thai). Department of Disease Control, Ministry of Public Health, Bangkok.
Mondini A, Chiaravalloti-Neto F, 2008. Spatial correlation of incidence of dengue with socioeconomic, demographic and environmental variables in a Brazilian city. Sci Total Environ 393:241-8.
DOI: https://doi.org/10.1016/j.scitotenv.2008.01.010
Ongart C, Pojaman S, Orathai S, Sirisamphan B, 2017. Applied epidemiology for prevention and control of D.H.F. Ministry of Public Health, National Library of Thailand Cataloging in Publication Data.
Ord JK, Getis A, 1995. Local spatial autocorrelation statistics: distributional issues and an application. Geogr Anal 27:286-306.
DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00912.x
Osei FB, Duker AA, 2008. Spatial and demographic patterns of cholera in Ashanti region-Ghana. Int J Health Geogr 7:44.
DOI: https://doi.org/10.1186/1476-072X-7-44
Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R, Church JA, Clarke L, Dahe Q, Dasgupta P, 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change / R. Pachauri and L. Meyer (editors) , Geneva, Switzerland, IPCC, 151 p., ISBN:978-92-9169-143-2 . Available from 10013/epic.45156.d001
Paily K, Chandhiran K, Vanamail P, Kumar NP, Jambulingam P, 2013. Efficacy of a mermithid nematode Romanomermis iyengari (Welch)(Nematoda: Mermithidae) in controlling tree hole-breeding mosquito Aedes albopictus (Skuse)(Diptera: Culicidae) in a rubber plantation area of Kerala, India. Parasitol Res 112:1299-304.
DOI: https://doi.org/10.1007/s00436-012-3265-3
Petric D, 1989. Seasonal and daily mosquito (Diptera, Culicidae) activity in Vojvodina. University of Novi Sad, Faculty of Agriculture, Novi Sad, Serbia.
Provincial Office, 2021. Geography of Nakhonsi Thammarat Province. Ministry of Labour, Nakhonsi Thammarat. Available from: https://nakhonsithammarat.mol.go.th/en/overall/geography Accessed: 05 February 2021.
Ramasamy R, Surendran SN, 2011. Possible impact of rising sea levels on vector-borne infectious diseases. BMC Infect Dis 11:1-6.
DOI: https://doi.org/10.1186/1471-2334-11-18
Reid C, 2000. Implications of climate change on malaria in Karnataka, India. Brown University, Providence, RI, USA.
Rotela C, Fouque F, Lamfri M, Sabatier P, Introini V, Zaidenberg M, Scavuzzo C, 2007. Space-time analysis of the dengue spreading dynamics in the 2004 Tartagal outbreak, Northern Argentina. Acta Trop 103:1-13.
DOI: https://doi.org/10.1016/j.actatropica.2007.05.003
Sánchez-MartÃn J-M, Rengifo-Gallego J-I, Blas-Morato R, 2019. Hot spot analysis versus cluster and outlier analysis: An enquiry into the grouping of rural accommodation in Extremadura (Spain). ISPRS Int J Geoinf 8:176.
DOI: https://doi.org/10.3390/ijgi8040176
Schmidt W-P, Suzuki M, Thiem VD, White RG, Tsuzuki A, Yoshida L-M, Yanai H, Haque U, Anh DD, Ariyoshi K, 2011. Population density, water supply, and the risk of dengue fever in Vietnam: cohort study and spatial analysis. PLoS Med 8:e1001082.
DOI: https://doi.org/10.1371/journal.pmed.1001082
Shepard DS, Undurraga EA, Halasa YA, 2013. Economic and disease burden of dengue in Southeast Asia. PLoS Negl Trop Dis 7:e2055.
DOI: https://doi.org/10.1371/journal.pntd.0002055
Si Y, Debba P, Skidmore A, Toxopeus A, Li L, 2008. Spatial and temporal patterns of global H5N1 outbreaks International Society for Photogrammetry and Remote Sensing. Available from:??
Stewart-Ibarra AM, Lowe R, 2013. Climate and non-climate drivers of dengue epidemics in southern coastal Ecuador. Am J Trop Med Hyg 88:971-81.
DOI: https://doi.org/10.4269/ajtmh.12-0478
Sulekan A, Suhaila J, Wahid NAA, 2021. Assessing the effect of climate factors on dengue incidence via a generalized linear model. Open J Appl Sci 10:549.
DOI: https://doi.org/10.4236/ojapps.2021.104039
Tangena J-AA, Thammavong P, Wilson AL, Brey PT, Lindsay SW, 2016. Risk and control of mosquito-borne diseases in Southeast Asian rubber plantations. Trends Parasitol 32:402-15.
DOI: https://doi.org/10.1016/j.pt.2016.01.009
Thailand Health Ministry, 2001. The outlook for dengue fever in 2001 was higher than that of 2000. Communicable Disease Control Department. Ministry of Public Health, Thailand. RYT9. Available from: https://www.ryt9.com/s/prg/259907 Accessed: 26 April 2021.
Thailand Interior Ministry, 2016. Department of Lands. Lands Department. Available from: https://www.dol.go.th/Pages/internet.aspx Accessed: 8 January 2021.
Thaivbd, 2019. Thailand vectorborne diseases. Thaivbd Thailand, Ministry of Public Health, MOPH. Available from: http://www.thaivbd.org/n/
Thammapalo S, Chongsuwiwatwong V, Geater A, Lim A, Choomalee K, 2005. Socio-demographic and environmental factors associated with Aedes breeding places in Phuket, Thailand. Southeast Asian J Trop Med Public Health 36:426-33.
Thammapalo S, Wonghiranrat W, Moonmek S, Sriplong W, 2011. Biting time of Aedes albopictus in the rubber plantation and the orchard, the southern-most of Thailand. J Vector Borne Dis 6:2.
Tian H, Huang S, Zhou S, Bi P, Yang Z, Li X, Chen L, Cazelles B, Yang J, Luo L, 2016. Surface water areas significantly impacted 2014 dengue outbreaks in Guangzhou, China. Environ Res 150:299-305.
DOI: https://doi.org/10.1016/j.envres.2016.05.039
Tian H, Zhou S, Dong L, Van Boeckel TP, Cui Y, Newman SH, Takekawa JY, Prosser DJ, Xiao X, Wu Y, 2015. Avian influenza H5N1 viral and bird migration networks in Asia. Proc Natl Acad Sci 112:172-7.
DOI: https://doi.org/10.1073/pnas.1405216112
Tipayamongkholgul M, Fang C-T, Klinchan S, Liu C-M, King C-C, 2009. Effects of the El Niño-Southern Oscillation on dengue epidemics in Thailand, 1996-2005. BMC Public Health 9:1-15.
DOI: https://doi.org/10.1186/1471-2458-9-422
Tran A, l'Ambert G, Lacour G, Benoît R, Demarchi M, Cros M, Cailly P, Aubry-Kientz M, Balenghien T, Ezanno P, 2013. A rainfall-and temperature-driven abundance model for Aedes albopictus populations. Int J Environ Res Public Health 10:1698-719.
DOI: https://doi.org/10.3390/ijerph10051698
Tsai P-J, Lin M-L, Chu C-M, Perng C-H, 2009. Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006. BMC Public Health 9:1-13.
DOI: https://doi.org/10.1186/1471-2458-9-464
Victor T, Chandrasekaran B, Reuben R, 1994. Composite fish culture for mosquito control in rice fields in southern India. Southeast Asian J Trop Med Public Healthn 25:522-7.
WHO, 1997. Dengue haemorrhagic fever: diagnosis, treatment, prevention and control. World Health Organization, Geneva, Switzerland.
WHO, 2005. Using climate to predict infectious disease epidemics. World Health Organization, Geneva, Switzerland.
WHO, 2011. Comprehensive guideline for prevention and control of dengue and dengue haemorrhagic fever. World Health Organization, Geneva, Switzerland.
WHO, 2012. Global strategy for dengue prevention and control 2012-2020. World Health Organization, Geneva, Switzerland.
WHO, 2014. Dengue and severe dengue. World Health Organization. Regional Office for the Eastern Mediterranean, Geneva, Switzerland.
Wongkoon S, Jaroensutasinee M, Jaroensutasinee K, 2016a. Spatio-temporal climate-based model of dengue infection in Southern, Thailand. Trop Biomed 33:55-70.
Wongkoon S, Jaroensutasinee M, Jaroensutasinee K, 2016b. Spatio-temporal climate-based model of dengue infection in Southern, Thailand. TropBiomed 33:55-70.
Wu N, Liao G, Li D, Luo Y, Zhong G, 1991. The advantages of mosquito biocontrol by stocking edible fish in rice paddies. Southeast Asian J Trop Med Public Health 22:436-42.
Wu P-C, Lay J-G, Guo H-R, Lin C-Y, Lung S-C, Su H-J, 2009. Higher temperature and urbanization affect the spatial patterns of dengue fever transmission in subtropical Taiwan. Sci Total Environ 407:2224-33.
DOI: https://doi.org/10.1016/j.scitotenv.2008.11.034
Wu X, Lu Y, Zhou S, Chen L, Xu B, 2016. Impact of climate change on human infectious diseases: Empirical evidence and human adaptation. Environ Int 86:14-23.
DOI: https://doi.org/10.1016/j.envint.2015.09.007
Xiao J, Liu T, Lin H, Zhu G, Zeng W, Li X, Zhang B, Song T, Deng A, Zhang M, 2018. Weather variables and the El Nino Southern Oscillation may drive the epidemics of dengue in Guangdong Province, China. Sci Total Environ 624:926-34.
DOI: https://doi.org/10.1016/j.scitotenv.2017.12.200
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: https://doi.org/10.1016/j.scitotenv.2018.11.395
Yeshiwondim AK, Gopal S, Hailemariam AT, Dengela DO, Patel HP, 2009. Spatial analysis of malaria incidence at the village level in areas with unstable transmission in Ethiopia. Int J Health Geogr 8:1-11.
DOI: https://doi.org/10.1186/1476-072X-8-5
How to Cite
Ibrahim Abdulsalam, F. ., Yimthiang, S. ., La-Up, A. ., Ditthakit , P., Cheewinsiriwat, P., & Jawjit, W. (2021). Association between climate variables and dengue incidence in Nakhon Si Thammarat Province, Thailand. Geospatial Health, 16(2). https://doi.org/10.4081/gh.2021.1012
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Muhammad Aidil Roslan, Romano Ngui, Muhammad Fathi Marzuki, Indra Vythilingam, Aziz Shafie, Sabri Musa, Wan Yusoff Wan Sulaiman, Spatial dispersal of Aedes albopictus mosquitoes captured by the modified sticky ovitrap in Selangor, Malaysia , Geospatial Health: Vol. 17 No. 1 (2022)
You may also start an advanced similarity search for this article.
