The association between dengue incidences and provincial-level weather variables in Thailand from 2001 to 2014
Autoři:
Romrawin Chumpu aff001; Nirattaya Khamsemanan aff001; Cholwich Nattee aff001
Působiště autorů:
Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
aff001
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0226945
Souhrn
Dengue and dengue hemorrhagic pose significant burdens in many tropical countries. Dengue incidences have perpetually increased, leading to an annual (uncertain) peak. Dengue cases cause an enormous public health problem in Thailand because there is no anti-viral drug against the dengue virus. Searching for means to reduce the dengue incidences is a challenging and appropriate strategy for primary prevention in a dengue outbreak. This study constructs the best predictive model from past statistical dengue incidences at the provincial level and studies the relationships among dengue incidences and weather variables. We conducted experiments for 65 provinces (out of 77 provinces) in Thailand since there is no dengue information for the remaining provinces. Predictive models were constructed using weekly data during 2001-2014. The training set are data during 2001-2013, and the test set is the data from 2014. Collected data were separated into two parts: current dengue cases as the dependent variable, and weather variables and previous dengue cases as the independent variables. Eight weather variables are used in our models: average pressure, maximum temperature, minimum temperature, average humidity, precipitation, vaporization, wind direction, wind power. Each weather variable includes the current week and one to three weeks of lag time. A total of 32 independent weather variables are used for each province. The previous one to three weeks of dengue cases are also used as independent variables. There is a total of 35 independent variables. Predictive models were constructed using five methods: Poisson regression, negative binomial regression, quasi-likelihood regression, ARIMA(3,1,4) and SARIMA(2,0,1)(0,2,0). The best model is determined by combinations of 1–12 variables, which are 232,989,800 models for each province. We construct a total of 15,144,337,000 models. The best model is selected by the average from high to low of the coefficient of determination (R2) and the lowest root mean square error (RMSE). From our results, the one-week lag previous case variable is the most frequent in 55 provinces out of a total of 65 provinces (coefficient of determinations with a minimum of 0.257 and a maximum of 0.954, average of 0.6383, 95% CI: 0.57313 to 0.70355). The most influential weather variable is precipitation, which is used in most of the provinces, followed by wind direction, wind power, and barometric pressure. The results confirm the common knowledge that dengue incidences occur most often during the rainy season. It also shows that wind direction, wind power, and barometric pressure also have influences on the number of dengue cases. These three weather variables may help adult mosquitos to survive longer and spread dengue. In conclusion, The most influential factor for further cases is the number of dengue cases. However, weather variables are also needed to obtain better results. Predictions of the number of dengue cases should be done locally, not at the national level. The best models of different provinces use different sets of weather variables. Our model has an accuracy that is sufficient for the real prediction of future dengue incidences, to prepare for and protect against severe dengue outbreaks.
Klíčová slova:
Dengue fever – Humidity – Meteorology – Thailand – Time series analysis – Vaporization – Wind power
Zdroje
1. Gubler DJ. Dengue and dengue hemorrhagic fever. Clin Microbiol Rev. 1998;11(3):480–496. doi: 10.1128/CMR.11.3.480 9665979
2. Gubler DJ. Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. Trends Microbiol. 2002;10(2):100–103. doi: 10.1016/s0966-842x(01)02288-0 11827812
3. Rigau-Pérez JG, Clark GG, Gubler DJ, Reiter P, Sanders EJ, Vorndam AV. Dengue and dengue haemorrhagic fever. Lancet. 1998;352(9132):971–977. doi: 10.1016/s0140-6736(97)12483-7 9752834
4. Singh M, Chakraborty A, Kumar S, Kumar A. The epidemiology of dengue viral infection in developing countries: A systematic review. J Health Res Rev. 2017;4(3):104–107. doi: 10.4103/jhrr.jhrr_24_17
5. World Health Organization. Dengue and severe dengue; 13 September 2018 [cited 1 February 2019]. Available from: https://www.who.int/en/news-room/fact-sheets/detail/dengue-and-severe-dengue.
6. Halstead SB. Mosquito-borne haemorrhagic fevers of South and South-East Asia. B World Health Organ. 1966;35(1):3–15.
7. Hammon WM, Rundnick A, Sather GE. Viruses Associated with Epidemic Hemorrhagic Fevers of the Philippines and Thailand. Science. 1960;131(3407):1102–1103. doi: 10.1126/science.131.3407.1102 14399343
8. Chareonsook O, Foy HM, Teeraratkul A, Silarug N. Changing epidemiology of dengue hemorrhagic fever in Thailand. Epidemiol Infect. 1999;122(1):161–166. doi: 10.1017/s0950268898001617 10098800
9. Nimmannitya S, Halstead SB, Cohen SN, Margiotta MR. Dengue and Chikungunya Virus Infection in Man in Thailand, 1962–1964. Am J Trop Med Hyg. 1969;18(6):954–971.
10. Stanaway JD, Shepard DS, Undurraga EA, Halasa YA, Coffeng LE, Brady OJ, et al. The global burden of dengue: an analysis from the Global Burden of Disease Study 2013. Lancet Infect Dis. 2016;16(6):712–723. doi: 10.1016/S1473-3099(16)00026-8 26874619
11. Barbazan P, Yoksan S, Gonzalez JP. Dengue hemorrhagic fever epidemiology in Thailand: description and forecasting of epidemics. Microb Infect. 2002;4(7):699–705. doi: 10.1016/S1286-4579(02)01589-7
12. Clark DV, Mammen MP, Nisalak A, Puthimethee V, Endy TP. Economic impact of dengue fever/dengue hemorrhagic fever in Thailand at the family and population levels. Am J Trop Med Hyg. 2005;72(6):786–791. doi: 10.4269/ajtmh.2005.72.786 15964964
13. Khetarpal N, Khanna I. Dengue fever: causes, complications, and vaccine strategies. J Immunol Res. 2016; doi: 10.1155/2016/6803098 27525287
14. Naish S, Dale P, Mackenzie JS, McBride J, Mengersen K, Tong S. Climate change and dengue: a critical and systematic review of quantitative modelling approaches. BMC Infect Dis. 2014;14(1):167. doi: 10.1186/1471-2334-14-167 24669859
15. Hales S, de Wet N, Maindonald J, Woodward A. Potential effect of population and climate changes on global distribution of dengue fever: an empirical model. Lancet. 2002;360(9336):830–834. doi: 10.1016/S0140-6736(02)09964-6 12243917
16. Chen SC, Liao CM, Chio CP, Chou HH, You SH, Cheng YH. Lagged temperature effect with mosquito transmission potential explains dengue variability in southern Taiwan: Insights from a statistical analysis. Sci Total Environ. 2010;408(19):4069–4075. doi: 10.1016/j.scitotenv.2010.05.021 20542536
17. Wang Z, Chan HM, Hibberd ML, Lee GKK. Delayed Effects of Climate Variables on Incidence of Dengue in Singapore during 2000-2010. APCBEE Proc. 2012;1:22–26. doi: 10.1016/j.apcbee.2012.03.005
18. Choi Y, Tang CS, McIver L, Hashizume M, Chan V, Abeyasinghe RR, et al. Effects of weather factors on dengue fever incidence and implications for interventions in Cambodia. BMC Public Health. 2016;16(1):241. doi: 10.1186/s12889-016-2923-2 26955944
19. Huang X, Williams G, Clements ACA, Hu W. Imported Dengue Cases, Weather Variation and Autochthonous Dengue Incidence in Cairns, Australia. PLoS One. 2013;8(12):1–7.
20. Wu X, Lang L, Ma W, Song T, Kang M, He J, et al. Non-linear effects of mean temperature and relative humidity on dengue incidence in Guangzhou, China. Sci Total Environ. 2018;628-629:766–771. https://doi.org/10.1016/j.scitotenv.2018.02.136
21. Cheong YL, Burkart K, Leitão PJ, Lakes T. Assessing Weather Effects on Dengue Disease in Malaysia. Int J Environ Res Public Health. 2013;10(12):6319–6334. doi: 10.3390/ijerph10126319 24287855
22. Fairos W, Azaki W, Alias L, Yap B. Modelling dengue fever (DF) and dengue haemorrhagic fever (DHF) outbreak using Poisson and Negative Binomial model. World Acad Sci Eng Technol. 2010;62:903–908.
23. Hii YL, Zhu H, Ng N, Ng LC, Rocklöv J. Forecast of Dengue Incidence Using Temperature and Rainfall. PLoS Negl Trop Dis. 2012;6(11):1–9. doi: 10.1371/journal.pntd.0001908
24. Hii YL. Climate and dengue fever: early warning based on temperature and rainfall [Doctoral Thesis]. Department of Public Health and Clinical Medicine, Faculty of Medicine, Umeå University; 2013.
25. Hii YL, Rocklöv J, Ng N, Tang CS, Pang FY, Sauerborn R. Climate variability and increase in intensity and magnitude of dengue incidence in Singapore. Glob Health Action. 2009;2(1):2036. doi: 10.3402/gha.v2i0.2036
26. Lee HS, Nguyen-Viet H, Nam VS, Lee M, Won S, Duc PP, et al. Seasonal patterns of dengue fever and associated climate factors in 4 provinces in Vietnam from 1994 to 2013. BMC Infect Dis. 2017;17(1):218. doi: 10.1186/s12879-017-2326-8 28320341
27. Li C, Wang X, Wu X, Liu J, Ji D, Du J. Modeling and projection of dengue fever cases in Guangzhou based on variation of weather factors. Sci Total Environ. 2017;605-606:867–873. doi: 10.1016/j.scitotenv.2017.06.181 28683431
28. Lu L, Lin H, Tian L, Yang W, Sun J, Liu Q. Time series analysis of dengue fever and weather in Guangzhou, China. BMC Public Health. 2009;9(1):395. doi: 10.1186/1471-2458-9-395 19860867
29. Wongkoon S, Jaroensutasinee M, Jaroensutasinee K. Climatic variability and dengue virus transmission in Chiang Rai, Thailand. Biomedica. 2011;27(19):5–13.
30. Wu PC, Guo HR, Lung SC, Lin CY, Su HJ. Weather as an effective predictor for occurrence of dengue fever in Taiwan. Acta Trop. 2007;103(1):50–57. https://doi.org/10.1016/j.actatropica.2007.05.014.
31. Beck HE, Zimmermann NE, McVicar TR, Vergopolan N, Berg A, Wood EF. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci Data. 2018;5.
32. Arcari P, Tapper N, Pfueller S. Regional variability in relationships between climate and dengue/DHF in Indonesia. Singap J Trop Geogr. 2007;28(3):251–272. doi: 10.1111/j.1467-9493.2007.00300.x
33. Beretta E, Hara T, Ma W, Takeuchi Y. Global asymptotic stability of an SIR epidemic model with distributed time delay. Nonlinear analysis, theory, methods & applications. 2001;47(6):4107–4115. doi: 10.1016/S0362-546X(01)00528-4
34. Sharmin S, Glass K, Viennet E, Harley D. Interaction of Mean Temperature and Daily Fluctuation Influences Dengue Incidence in Dhaka, Bangladesh. PLOS Negl Trop Dis. 2015;9(7):1–13. doi: 10.1371/journal.pntd.0003901
35. Ehelepola NDB, Ariyaratne K. The correlation between dengue incidence and diurnal ranges of temperature of Colombo district, Sri Lanka 2005-2014. Glob Health Action. 2016;9(32267).
36. Chu PC, Qi Y, Chen Y, Shi P, Mao Q. South China Sea Wind-Wave Characteristics. Part I: Validation of Wavewatch-III Using TOPEX/Poseidon Data. Journal of Atmospheric and Oceanic Technology. 2004;21(11):1718–1733. doi: 10.1175/JTECH1661.1
37. Meteorological Department of Thailand. The climate of Thailand; 2015 [cited 1 February 2019]. Available from: https://www.tmd.go.th/en/archive/thailand_climate.pdf.
Článek vyšel v časopise
PLOS One
2019 Číslo 12
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Je libo čepici místo mozkového implantátu?
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- AI může chirurgům poskytnout cenná data i zpětnou vazbu v reálném čase
- Nová metoda odlišení nádorové tkáně může zpřesnit resekci glioblastomů
Nejčtenější v tomto čísle
- Methylsulfonylmethane increases osteogenesis and regulates the mineralization of the matrix by transglutaminase 2 in SHED cells
- Oregano powder reduces Streptococcus and increases SCFA concentration in a mixed bacterial culture assay
- The characteristic of patulous eustachian tube patients diagnosed by the JOS diagnostic criteria
- Parametric CAD modeling for open source scientific hardware: Comparing OpenSCAD and FreeCAD Python scripts
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy