Prediction model for dengue fever based on interactive effects between multiple meteorological factors in Guangdong, China (2008–2016)


Autoři: Binghua Zhu aff001;  Ligui Wang aff001;  Haiying Wang aff003;  Zhidong Cao aff004;  Lei Zha aff001;  Ze Li aff001;  Zhongyang Ye aff001;  Jinping Zhang aff002;  Hongbin Song aff001;  Yansong Sun aff005
Působiště autorů: Chinese PLA Center for Disease Control and Prevention, Beijing, China aff001;  305 Hospital of PLA, Beijing, China aff002;  Joint Service Institute, National Defense University of PLA, Beijing, China aff003;  The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China aff004;  College of Military Medicine, Academy of Military Sciences, Beijing, China aff005
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225811

Souhrn

Introduction

In order to improve the prediction accuracy of dengue fever incidence, we constructed a prediction model with interactive effects between meteorological factors, based on weekly dengue fever cases in Guangdong, China from 2008 to 2016.

Methods

Dengue fever data were derived from statistical data from the China National Notifiable Infectious Disease Reporting Information System. Daily meteorological data were obtained from the China Integrated Meteorological Information Sharing System. The minimum temperature for transmission was identified using data fitting and the Ross-Macdonald model. Correlations and interactive effects were examined using Spearman’s rank correlation and multivariate analysis of variance. A probit regression model to describe the incidence of dengue fever from 2008 to 2016 and forecast the 2017 incidence was constructed, based on key meteorological factors, interactive effects, mosquito-vector factors, and other important factors.

Results

We found the minimum temperature suitable for dengue transmission was ≥18°C, and as 97.91% of cases occurred when the minimum temperature was above 18 °C, the data were used for model training and construction. Epidemics of dengue are related to mean temperature, maximum/minimum and mean atmospheric pressure, and mean relative humidity. Moreover, interactions occur between mean temperature, minimum atmospheric pressure, and mean relative humidity. Our weekly probit regression prediction model is 0.72. Prediction of dengue cases for the first 41 weeks of 2017 exhibited goodness of fit of 0.60.

Conclusion

Our model was accurate and timely, with consideration of interactive effects between meteorological factors.

Klíčová slova:

Dengue fever – Humidity – China – Infectious diseases – Mosquitoes – Statistical data


Zdroje

1. World Health Organization.(2013)Dengue and severe dengue. Fact sheet N8117. http://www.who.int/mediacentre/factsheets/en/.

2. Sang S, Chen B, Wu H, Yang Z, Di B, Wang L, et al.(2015)Dengue is still an imported disease in china: a case study in guangzhou. Infection, Genetics and Evolution, 32:178–190. doi: 10.1016/j.meegid.2015.03.005 25772205

3. Sun J, Wu D, Zhou H, Zhang H, Guan D, He X,et al.(2016)The epidemiological characteristics and genetic diversity of dengue virus during the third largest historical outbreak of dengue in Guangdong, China, in 2014. Journal of Infection 72:80–90. doi: 10.1016/j.jinf.2015.10.007 26546854

4. Li MT, Sun GQ, Yakob L, Zhu HP, Jin Z, Zhang WY.(2016)The Driving Force for 2014 Dengue Outbreak in Guangdong, China. Plos One 11:11.

5. Li TG, Yang ZC, Luo L, DI B, Wang M.(2013)Dengue Fever Epidemiological Status and Relationship with Meteorological Variables in Guangzhou, Southern China, 2007–2012. Biomedical and Environmental Sciences 26:994–997. 24393510

6. Minh An DT, Rocklöv J.(2014)Epidemiology of dengue fever in Hanoi from 2002 to 2010 and its meteorological determinants. Global health action 7:23074–23074. doi: 10.3402/gha.v7.23074 25511882

7. Adde A, Roucou P, Mangeas M, Ardillon V, Desenclos JC, Rousset D, et al.(2016)Predicting Dengue Fever Outbreaks in French Guiana Using Climate Indicators. Plos Neglected Tropical Diseases 10:e0004681. doi: 10.1371/journal.pntd.0004681 27128312

8. Gharbi M, Quenel P, Gustave J, Cassadou S, La Ruche G, Girdary L, et al.(2011)Time series analysis of dengue incidence in Guadeloupe, French West Indies: Forecasting models using climate variables as predictors. Bmc Infectious Diseases 11:166. doi: 10.1186/1471-2334-11-166 21658238

9. Fan J, Lin H, Wang C, Bai L, Yang S, Chu C,et al. (2014)Identifying the high-risk areas and associated meteorological factors of dengue transmission in Guangdong Province, China from 2005 to 2011. Epidemiology and infection 142:634–643. doi: 10.1017/S0950268813001519 23823182

10. Tang B, Xiao Y, Tang S, Wu J.(2016)Modelling weekly vector control against Dengue in the Guangdong Province of China. Journal of theoretical biology 410:65–76. doi: 10.1016/j.jtbi.2016.09.012 27650706

11. Xiao J, Liu T, Lin H, Zhu G, Zeng W, Li X, et al.(2018)Weather variables and the El Nino Southern Oscillation may drive the epidemics of dengue in Guangdong Province, China. The Science of the total environment 624:926–934. doi: 10.1016/j.scitotenv.2017.12.200 29275255

12. Alkhaldy I.(2017)Modelling the association of dengue fever cases with temperature and relative humidity in Jeddah, Saudi Arabia A generalised linear model with break-point analysis. ActaTropica 168:9–15.

13. Watts DM, Burke DS, Harrison BA, Whitmire RE, Nisalak A.(1987)Effect of temperature on the vector efficiency of Aedesaegypti for dengue 2 virus. The American journal of tropical medicine and hygiene 36:143–152. doi: 10.4269/ajtmh.1987.36.143 3812879

14. Smith CE. (1970)Prospects for the control of infectious disease.Proceedings of the Royal Society of Medicine 63:1181–1190. 5530322

15. Watts DM, Burke DS, Harrison BA, Whitmire RE, Nisalak A.(1987)Effect of temperature on the vector efficiency of Aedesaegypti for dengue 2 virus. The American journal of tropical medicine and hygiene 36:143–152. doi: 10.4269/ajtmh.1987.36.143 3812879

16. Newton EA, Reiter P.(1992)A model of the transmission of dengue fever with an evaluation of the impact of ultra-low volume (ULV) insecticide applications on dengue epidemics. The American journal of tropical medicine and hygiene 47:709–720. doi: 10.4269/ajtmh.1992.47.709 1361721

17. Focks DA, Daniels E, Haile DG, Keesling JE. (1992)A simulation-model of the epidemiology of urban dengue fever—literature analysis, model development, preliminary validation, and samples of simulation results. American Journal of Tropical Medicine and Hygiene 53:489–506.

18. Fouque F, Carinci R, Gaborit P, Issaly J, Bicout DJ, Sabatier P.(2006)Aedesaegypti survival and dengue transmission patterns in French Guiana. Journal of Vector Ecology 31: 390–399. doi: 10.3376/1081-1710(2006)31[390:aasadt]2.0.co;2 17249358

19. Almeida AP, Baptista SS, Sousa CA, Novo MT, Ramos HC, Panella NA, et al.(2005)Bioecology and vectorial capacity of Aedesalbopictus (Diptera: Culicidae) in Macao, China, in relation to dengue virus transmission. J Med Entomol 42:419–4. doi: 10.1093/jmedent/42.3.419 15962796

20. Brady OJ, Johansson MA, Guerra CA, et al. Modelling adult Aedes aegypti and Aedes albopictus survival at different temperatures in laboratory and field settings[J]. Parasites & Vectors, 2013, 6(1):351–363.

21. Delatte H, Gimonneau G, Triboire A, et al. Influence of Temperature on Immature Development, Survival, Longevity, Fecundity, and Gonotrophic Cycles of Aedes albopictus, Vector of Chikungunya and Dengue in the Indian Ocean[J]. Journal of Medical Entomology, 2009, 46(1):33–41. doi: 10.1603/033.046.0105 19198515

22. Murdock CC, Paaijmans KP, Bell AS, et al. Complex effects of temperature on mosquito immune function[J]. Proceedings: Biological Sciences, 2012, 279(1741):3357–3366.

23. Colón-González FJ, Lake IR, Bentham G.(2011)Climate Variability and Dengue Fever in Warm and Humid Mexico. American Journal of Tropical Medicine and Hygiene 84:757–763. doi: 10.4269/ajtmh.2011.10-0609 21540386

24. Gubler DJ, Reiter P, Ebi KL, Yap W, Nasci R, Patz JA. et al.(2001)Climate variability and change in the United States: Potential impacts on vector- and rodent-borne diseases. Environmental Health Perspectives 109:223–233. doi: 10.1289/ehp.109-1240669 11359689

25. Pinto E, Coelho M, Oliver L, Massad E. (2011)The influence of climate variables on dengue in Singapore. International journal of environmental health research 21:415–426. doi: 10.1080/09603123.2011.572279 21557124

26. Chowell G, Cazelles B, Broutin H, Munayco CV.(2011)The influence of geographic and climate factors on the timing of dengue epidemics in Peru, 1994–2008. Bmc Infectious Diseases 11: 164. doi: 10.1186/1471-2334-11-164 21651779

27. Bambrick HJ, Woodruff RE, Hanigan IC.(2009)Climate change could threaten blood supply by altering the distribution of vector-borne disease: an Australian case-study. Global health action 2: 2059.

28. Pinto E, Coelho M, Oliver L, Massad E.(2011)The influence of climate variables on dengue in Singapore. International journal of environmental health research 21:415–426. doi: 10.1080/09603123.2011.572279 21557124


Článek vyšel v časopise

PLOS One


2019 Číslo 12