A dengue fever predicting model based on Baidu search index data and climate data in South China
Autoři:
Dan Liu aff001; Songjing Guo aff002; Mingjun Zou aff001; Cong Chen aff001; Fei Deng aff003; Zhong Xie aff002; Sheng Hu aff002; Liang Wu aff002
Působiště autorů:
School of Medicine, Wuhan University of Science and Technology, Wuhan, China
aff001; School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
aff002; State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, China
aff003; National Engineering Research Center for GIS, Wuhan, China
aff004
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0226841
Souhrn
With the acceleration of global urbanization and climate change, dengue fever is spreading worldwide. Different levels of dengue fever have also occurred in China, especially in southern China, causing enormous economic losses. Unfortunately, there is no effective treatment for dengue, and the most popular dengue vaccine does not exhibit good curative effects. Therefore, we developed a Generalized Additive Mixed Model (GAMM) that gathered climate factors (mean temperature, relative humidity and precipitation) and Baidu search data during 2011–2015 in Guangzhou city to improve the accuracy of dengue fever prediction. Firstly, the time series dengue fever data were decomposed into seasonal, trend and remainder components by the seasonal-trend decomposition procedure based on loess (STL). Secondly, the time lag of variables was determined in cross-correlation analysis and the order of autocorrelation was estimated using autocorrelation (ACF) and partial autocorrelation functions (PACF). Finally, the GAMM was built and evaluated by comparing it with Generalized Additive Mode (GAM). Experimental results indicated that the GAMM (R2: 0.95 and RMSE: 34.1) has a superior prediction capability than GAM (R2: 0.86 and RMSE: 121.9). The study could help the government agencies and hospitals respond early to dengue fever outbreak.
Klíčová slova:
Public and occupational health – Internet – China – Meteorology – Dengue fever – Humidity – Mosquitoes
Zdroje
1. Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and burden of dengue. Nature. 2013;496:504. doi: 10.1038/nature12060 23563266
2. Tatem AJ, Hay SI, Rogers DJ. Global traffic and disease vector dispersal. Proceedings of the National Academy of Sciences of the United States of America. 2006;103(16):6242–7. doi: 10.1073/pnas.0508391103 16606847
3. Brady OJ, Gething PW, Bhatt S, Messina JP, Brownstein JS, Hoen AG, et al. Refining the global spatial limits of dengue virus transmission by evidence-based consensus. PLoS neglected tropical diseases. 2012;6(8):e1760. doi: 10.1371/journal.pntd.0001760 22880140.
4. Diseases PFIT, Organization WH. Dengue: guidelines for diagnosis, treatment, prevention and control. Geneva World Health Organization. 2009;6(12):990.
5. Foster JE, Bennett SN, Vaughan H, Vorndam V, Mcmillan WO, Carrington CV. Molecular evolution and phylogeny of dengue type 4 virus in the Caribbean. Virology. 2003;306(1):126–34. doi: 10.1016/s0042-6822(02)00033-8 12620805
6. Lanciotti RS, Lewis JG, Gubler DJ, Trent DW. Molecular evolution and epidemiology of dengue-3 viruses. J Gen Virol. 1994;75(Pt 1):65–75. doi: 10.1099/0022-1317-75-1-65 8113741.
7. Lai S, Huang Z, Zhou H, Anders KL, Perkins TA, Yin W, et al. The changing epidemiology of dengue in China, 1990–2014: a descriptive analysis of 25 years of nationwide surveillance data. BMC Medicine. 2015;13(1):100. doi: 10.1186/s12916-015-0336-1 25925417
8. Simmons CP, Farrar JJ, Vv N, Wills B. Dengue. New England Journal of Medicine. 2012;366(15):399–401.
9. Guo P, Liu T, Zhang Q, Wang L, Xiao J, Zhang Q, et al. Developing a dengue forecast model using machine learning: A case study in China. PLoS neglected tropical diseases. 2017;11(10):e0005973. doi: 10.1371/journal.pntd.0005973 29036169.
10. Wu JY, Lun ZR, James AA, Chen XG. Dengue Fever in Mainland China. American Journal of Tropical Medicine & Hygiene. 2010;83(3):664.
11. Lo CL, Yip SP, Leung PH. Seroprevalence of dengue in the general population of Hong Kong. Tropical Medicine & International Health. 2013;18(9):1097–102.
12. Capeding MR, Tran NH, Hadinegoro SRS, Ismail HIHJM, Chotpitayasunondh T, Chua MN, et al. Clinical efficacy and safety of a novel tetravalent dengue vaccine in healthy children in Asia: a phase 3, randomised, observer-masked, placebo-controlled trial. The Lancet. 2014;384(9951):1358–65. doi: 10.1016/s0140-6736(14)61060-6
13. Achee NL, Gould F, Perkins TA, Reiner RC Jr., Morrison AC, Ritchie SA, et al. A critical assessment of vector control for dengue prevention. PLoS Negl Trop Dis. 2015;9(5):e0003655. doi: 10.1371/journal.pntd.0003655 25951103.
14. Halstead SB. Dengue vaccine development: a 75% solution? The Lancet. 2012;380(9853):1535–6. https://doi.org/10.1016/S0140-6736(12)61510-4
15. Villar L, Dayan GH, Arredondo-García JL, Rivera DM, Cunha R, Deseda C, et al. Efficacy of a tetravalent dengue vaccine in children in Latin America. N Engl J Med. 2015;372(2):113–23. doi: 10.1056/NEJMoa1411037 25365753.
16. Ooi EE. The re-emergence of dengue in China. BMC Med. 2015;13:99. doi: 10.1186/s12916-015-0345-0 25925732.
17. Sang S, Yin W, Bi P, Zhang H, Wang C, Liu X, et al. Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability. PloS one. 2014;9(7):e102755. doi: 10.1371/journal.pone.0102755 25019967.
18. Banu S, Hu W, Guo Y, Hurst C, Tong S. Projecting the impact of climate change on dengue transmission in Dhaka, Bangladesh. Environment International. 2014;63(3):137–42.
19. Acharya BK, Cao C, Xu M, Khanal L, Naeem S, Pandit S. Present and Future of Dengue Fever in Nepal: Mapping Climatic Suitability by Ecological Niche Model. International Journal of Environmental Research & Public Health. 2018;15(2):187.
20. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Present and Future of Dengue Fever in Nepal: Mapping Climatic Suitability by Ecological Niche Model. Nature. 2008;457:1012.
21. Althouse BM, Ng YY, Cummings DA. Prediction of dengue incidence using search query surveillance. Plos Neglected Tropical Diseases. 2011;5(8):e1258. doi: 10.1371/journal.pntd.0001258 21829744
22. Chan EH, Sahai V, Conrad C, Brownstein JS. Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance. PLoS neglected tropical diseases. 2011;5(5):e1206. doi: 10.1371/journal.pntd.0001206 21647308.
23. Huang J, Hui Z, Jie Z, editors. Detecting Flu Transmission by Social Sensor in China. IEEE International Conference on Green Computing & Communications & IEEE Internet of Things & IEEE Cyber; 2013.
24. Kang M, Zhong H, He J, Rutherford S, Yang F. Using Google Trends for influenza surveillance in South China. PLoS One. 2013;8(1):e55205. doi: 10.1371/journal.pone.0055205 23372837.
25. Qingyu Y, Nsoesie EO, Benfu L, Geng P, Rumi C, Brownstein JS. Monitoring influenza epidemics in china with search query from baidu. PLoS One. 2013;8(5):e64323. doi: 10.1371/journal.pone.0064323 23750192
26. McIver DJ, Brownstein JS. Wikipedia usage estimates prevalence of influenza-like illness in the United States in near real-time. PLoS Comput Biol [Internet]. 2014/4//; 10(4):[e1003581 p.]. doi: 10.1371/journal.pcbi.1003581 24743682
27. Yang W, Li Z, Lan Y, Wang J, Ma J, Jin L, et al. A nationwide web-based automated system for outbreak early detection and rapid response in China. Western Pacific Surveillance & Response Journal Wpsar. 2011;2(1):10.
28. Halide H, Ridd P. A predictive model for Dengue Hemorrhagic Fever epidemics. Int J Environ Health Res. 2008;18(4):253–65. doi: 10.1080/09603120801966043 18668414.
29. Sang S, Yin W, Bi P, Zhang H, Wang C, Liu X, et al. Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability. PLoS One [Internet]. 2014; 9(7):[e102755 p.]. doi: 10.1371/journal.pone.0102755 25019967
30. Li Z, Liu T, Zhu G, Lin H, Zhang Y, He J, et al. Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China. Plos Negl Trop Dis. 2017;11(3):e0005354. doi: 10.1371/journal.pntd.0005354 28263988
31. Ho CC, Ting CY. Time Series Analysis and Forecasting of Dengue Using Open Data. 2015.
32. Shen JC, Luo L, Li LI, Jing QL, Chun Quan OU, Yang ZC, et al. The Impacts of Mosquito Density and Meteorological Factors on Dengue Fever Epidemics in Guangzhou, China, 2006–2014: a Time-series Analysis. Biomedical & Environmental Sciences. 2015;28(5):321–9.
33. Siregar FA, Makmur T, Saprin S. Forecasting dengue hemorrhagic fever cases using ARIMA model: a case study in Asahan district. IOP Conference Series: Materials Science and Engineering. 2018;300:012032. doi: 10.1088/1757-899x/300/1/012032
34. Louis VR, Phalkey R, Horstick O, Ratanawong P, Wilder-Smith A, Tozan Y, et al. Modeling tools for dengue risk mapping—a systematic review. International Journal of Health Geographics. 2014;13(1):50.
35. Bouzid M, Colón-González FJ, Lung T, Lake IR, Hunter PR. Climate change and the emergence of vector-borne diseases in Europe: case study of dengue fever. BMC Public Health. 2014;14(1):781. doi: 10.1186/1471-2458-14-781 25149418
36. Xu L, Stige LC, Chan K-S, Zhou J, Yang J, Sang S, et al. Climate variation drives dengue dynamics. Proceedings of the National Academy of Sciences. 2017;114(1):113–8. doi: 10.1073/pnas.1618558114 27940911
37. Sang S, Gu S, Bi P, Yang W, Yang Z, Xu L, et al. Predicting unprecedented dengue outbreak using imported cases and climatic factors in Guangzhou, 2014. PLoS neglected tropical diseases. 2015;9(5):e0003808. doi: 10.1371/journal.pntd.0003808 26020627
38. Shen JC, Luo L, Li L, Jing QL, Ou CQ, Yang ZC, et al. The Impacts of Mosquito Density and Meteorological Factors on Dengue Fever Epidemics in Guangzhou, China, 2006–2014: a Time-series Analysis. Biomedical and Environmental Sciences. 2015;28(5):321–9. https://doi.org/10.3967/bes2015.046 26055559
39. Sun J, Lin J, Yan J, Fan W, Lu L, Lv H, et al. Dengue virus serotype 3 subtype III, Zhejiang Province, China. Emerg Infect Dis. 2011;17(2):321–3. doi: 10.3201/eid1702.100396 21291623.
40. Yuan Q, O Nsoesie E, Lv B, Peng G, Chunara R, Brownstein J. Detecting Flu Transmission by Social Sensor in China2013. e64323 p.
41. Gu Y, Chen F, Liu T, Lv X, Shao Z, Lin H, et al. Early detection of an epidemic erythromelalgia outbreak using Baidu search data. Sci Rep [Internet]. 2015; 5:[12649 p.]. doi: 10.1038/srep12649 26218589
42. Bao JX, Lv BF, Geng P, Na L, editors. Gonorrhea incidence forecasting research based on Baidu search data. International Conference on Management Science & Engineering; 2013.
43. Ying L, Lv B, Geng P, Yuan Q, editors. A preprocessing method of internet search data for prediction improvement: Application to Chinese stock market. Data Mining & Intelligent Knowledge Management Workshop; 2012.
44. Hulth A, Rydevik G, Linde A. Web queries as a source for syndromic surveillance. PLoS One. 2009;4(2):e4378. doi: 10.1371/journal.pone.0004378 19197389.
45. Yang L, Qin G, Zhao N, Wang C, Song G. Using a generalized additive model with autoregressive terms to study the effects of daily temperature on mortality. Bmc Medical Research Methodology. 2012;12(1):165. doi: 10.1186/1471-2288-12-165 23110601
46. Hoffmann AA, Montgomery BL, Popovici J, Iturbe-Ormaetxe I, Johnson PH, Muzzi F, et al. Successful establishment of Wolbachia in Aedes populations to suppress dengue transmission. Nature [Internet]. 2011/8//; 476(7361):[454–7 pp.]. doi: 10.1038/nature10356 21866160
47. Walker T, Johnson PH, Moreira LA, Iturbe-Ormaetxe I, Frentiu FD, McMeniman CJ, et al. The wMel Wolbachia strain blocks dengue and invades caged Aedes aegypti populations. Nature [Internet]. 2011/8//; 476(7361):[450–3 pp.]. doi: 10.1038/nature10355 21866159
48. Harris AF, Nimmo D, McKemey AR, Kelly N, Scaife S, Donnelly CA, et al. Field performance of engineered male mosquitoes. Nat Biotechnol [Internet]. 2011/11//; 29(11):[1034–7 pp.]. doi: 10.1038/nbt.2019 22037376
49. Acharya BK, Cao C, Xu M, Chen W, Pandit S. Spatiotemporal Distribution and Geospatial Diffusion Patterns of 2013 Dengue Outbreak in Jhapa District, Nepal. Asia-Pacific journal of public health. 2018;30(4):1010539518769809.
50. Focks, Dana A, Alexander, Neal, Villegas, Elci. Multicountry study of Aedes aegypti pupal productivity survey methodology: findings and recommendations. 2006.
51. Burattini MN, Chen M, Chow A, Coutinho FAB, Goh KT, Lopez LF, et al. Modelling the control strategies against dengue in Singapore. Epidemiol Infect. 2008;136(3):309–19. doi: 10.1017/S0950268807008667 17540051.
52. Corwin AL, Larasati RP, Bangs MJ, Wuryadi S, Arjoso S, Sukri N, et al. Epidemic dengue transmission in southern Sumatra, Indonesia. Trans R Soc Trop Med Hyg. 2001;95(3):257–65. doi: 10.1016/s0035-9203(01)90229-9 11490992.
53. Arcari P, Tapper N, Pfueller S. Regional variability in relationships between climate and dengue/DHF in Indonesia. Singapore Journal of Tropical Geography. 2010;28(3):251–72.
54. Bangs MJ, Larasati RP, Corwin AL, Suharyono W. Climatic factors associated with epidemic dengue in Palembang, Indonesia: implications of short-term meteorological events on virus transmission. Southeast Asian J Trop Med Public Health. 2006;37(6):1103–16. 17333762
55. Barrera R, Delgado N, Jiménez M, Villalobos I, Romero I. Estratificación de una ciudad hiperendémica en dengue hemorrágico. Revista Panamericana De Salud Pública. 2000;8(4):225–33.
56. Chadee DD, Shivnauth B, Rawlins SC, Chen AA. Climate, mosquito indices and the epidemiology of dengue fever in Trinidad (2002–2004). Ann Trop Med Parasitol. 2007;101(1):69–77. doi: 10.1179/136485907X157059 17244411.
57. Yang HM, Macoris MLG, Galvani KC, Andrighetti MTM, Wanderley DMV. Assessing the effects of temperature on the population of Aedes aegypti, the vector of dengue. Epidemiol Infect. 2009;137(8):1188–202. doi: 10.1017/S0950268809002040 19192322.
58. Mariangela B, Giuliano G, Xioaguang C, James AA. The invasive mosquito species Aedes albopictus: current knowledge and future perspectives. Trends in Parasitology. 2013;29(9):460–8. doi: 10.1016/j.pt.2013.07.003 23916878
59. Su T, Mulla MS. Effects of temperature on development, mortality, mating and blood feeding behavior of Culiseta incidens (Diptera: Culicidae). Journal of Vector Ecology. 2001;26(1):83–92. 11469189
60. Githeko AK, Lindsay SW, Confalonieri UE, Patz JA. Climate change and vector-borne diseases: a regional analysis. Bull World Health Organ. 2000;78(9):1136–47. 11019462
61. Sang S, Gu S, Bi P, Yang W, Yang Z, Xu L, et al. Predicting unprecedented dengue outbreak using imported cases and climatic factors in Guangzhou, 2014. PLoS neglected tropical diseases [Internet]. 2015/5//; 9(5):[e0003808 p.]. doi: 10.1371/journal.pntd.0003808 26020627
62. Chen S-C, Liao C-M, Chio C-P, Chou H-H, You S-H, Cheng Y-H. Lagged temperature effect with mosquito transmission potential explains dengue variability in southern Taiwan: insights from a statistical analysis. Sci Total Environ. 2010;408(19):4069–75. doi: 10.1016/j.scitotenv.2010.05.021 20542536.
63. 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 [Internet]. 2014; 14:[167 p.]. doi: 10.1186/1471-2334-14-167 24669859
64. Dugas AF, Jalalpour M, Gel Y, Levin S, Torcaso F, Igusa T, et al. Influenza forecasting with Google Flu Trends. PLoS One. 2013;5(1):e56176.
65. Milinovich GJ, Williams GM, Clements AC, Hu W. Internet-based surveillance systems for monitoring emerging infectious diseases. Lancet Infectious Diseases. 2014;14(2):160–8. doi: 10.1016/S1473-3099(13)70244-5 24290841
Článok vyšiel v časopise
PLOS One
2019 Číslo 12
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Nejasný stín na plicích – kazuistika
- Masturbační chování žen v ČR − dotazníková studie
- Těžké menstruační krvácení může značit poruchu krevní srážlivosti. Jaký management vyšetření a léčby je v takovém případě vhodný?
- Fixní kombinace paracetamol/kodein nabízí synergické analgetické účinky
Najčítanejšie 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