Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

Scientific Reports

Volumn 6, Issue , 2016, Pages

  Johansson, Michael A ^a,b   Reich, Nicholas G ^c   Hota, Aditi ^d   Brownstein, John S ^d,e   Santillana, Mauricio ^d,e,f  

^a CENTERS FOR DISEASE CONTROL AND PREVENTION   (Puerto Rico)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^c UNIVERSITY OF MASSACHUSETTS   (United States)

^d BOSTON CHILDREN S HOSPITAL   (United States)

^e HARVARD MEDICAL SCHOOL   (United States)

^f HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84988892913     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep33707     Document Type: Article

References (63)

1. Brady, O. J. et al. Refining the global spatial limits of dengue virus transmission by evidence-based consensus. PLoS Negl Trop Dis 6, e1760, doi: 10.1371/journal.pntd.0001760 (2012).
2. Mexico Secretariat of Health. Anuarios de Morbilidad, http://www.epidemiologia.salud.gob.mx/anuario/html/anuarios.html (accessed on May 1st, 2013).
3. Beatty, M. E. et al. Best practices in dengue surveillance: a report from the Asia-Pacific and Americas Dengue Prevention Boards. PLoS Negl Trop Dis 4, e890, doi: 10.1371/journal.pntd.0000890 (2010).
4. Racloz, V., Ramsey, R., Tong, S. & Hu, W. Surveillance of dengue fever virus: a review of epidemiological models and early warning systems. PLoS Negl Trop Dis 6, e1648, doi: 10.1371/journal.pntd.0001648 (2012).
5. Johansson, M. A., Hombach, J. & Cummings, D. A. Models of the impact of dengue vaccines: a review of current research and potential approaches. Vaccine 29, 5860-5868, doi: S0264-410X(11)00906-6 [pii]. 10.1016/j.vaccine.2011.06.042 (2011).
6. Reiner, R. C. et al. A systematic review of mathematical models of mosquito-borne pathogen transmission: 1970-2010. Journal of The Royal Society Interface 10, 20120921 (2013).
7. Christophers, S. R. Aedes aegypti (L.): The Yellow Fever Mosquito. (The University Press, 1960).
8. Focks, D. A., Haile, D. G., Daniels, E. & Mount, G. A. Dynamic life table model for Aedes aegypti (Diptera: Culicidae): analysis of the literature and model development. J. Med. Entomol. 30, 1003-1017 (1993).
9. Brady, O. J. et al. Modelling adult Aedes aegypti and Aedes albopictus survival at different temperatures in laboratory and field settings. Parasites & vectors 6, 351, doi: 10.1186/1756-3305-6-351 (2013).
10. Chan, M. & Johansson, M. A. The incubation periods of dengue viruses. PLoS One 7, e50972, doi: 10.1371/journal.pone.0050972 (2012).
11. Hu, W., Clements, A., Williams, G. & Tong, S. Dengue fever and El Nino/Southern Oscillation in Queensland, Australia: a time series predictive model. Occup. Environ. Med. 67, 307-311, doi: 10.1136/oem.2008.044966 (2010).
12. Karim, M. N., Munshi, S. U., Anwar, N. & Alam, M. S. Climatic factors influencing dengue cases in Dhaka city: a model for dengue prediction. Indian J. Med. Res. 136, 32-39 (2012).
13. Depradine, C. & Lovell, E. Climatological variables and the incidence of Dengue fever in Barbados. Int. J. Environ. Health Res. 14, 429-441 (2004).
14. Luz, P. M., Mendes, B. V., Codeco, C. T., Struchiner, C. J. & Galvani, A. P. Time series analysis of dengue incidence in Rio de Janeiro, Brazil. Am. J. Trop. Med. Hyg. 79, 933-939 (2008).
15. Martinez, E. Z., Silva, E. A. & Fabbro, A. L. A SARIMA forecasting model to predict the number of cases of dengue in Campinas, State of Sao Paulo, Brazil. Rev. Soc. Bras. Med. Trop. 44, 436-440 (2011).
16. Lowe, R. et al. The development of an early warning system for climate-sensitive disease risk with a focus on dengue epidemics in Southeast Brazil. Stat. Med. 32, 864-883, doi: 10.1002/sim.5549 (2013).
17. Gomes, A. F., Nobre, A. A. & Cruz, O. G. Temporal analysis of the relationship between dengue and meteorological variables in the city of Rio de Janeiro, Brazil, 2001-2009. Cad. Saude Publica 28, 2189-2197 (2012).
18. Rua-Uribe, G. L., Suarez-Acosta, C., Chauca, J., Ventosilla, P. & Almanza, R. Modelling the effect of local climatic variability on dengue transmission in Medellin (Colombia) by means of time series analysis. Biomedica: revista del Instituto Nacional de Salud 33 Suppl 1, 142-152 (2013).
19. Eastin, M. D., Delmelle, E., Casas, I., Wexler, J. & Self, C. Intra-and interseasonal autoregressive prediction of dengue outbreaks using local weather and regional climate for a tropical environment in Colombia. The American journal of tropical medicine and hygiene 91, 598-610 (2014).
20. Torres, C., Barguil, S., Melgarejo, M. & Olarte, A. Fuzzy model identification of dengue epidemic in Colombia based on multiresolution analysis. Artif. Intell. Med. 60, 41-51 (2014).
21. Fuller, D. O., Troyo, A. & Beier, J. C. El Nino Southern Oscillation and vegetation dynamics as predictors of dengue fever cases in Costa Rica. Environmental research letters: ERL [Web site] 4, 140111-140118, doi: 10.1088/1748-9326/4/1/014011 (2009).
22. Wang, C., Jiang, B., Fan, J., Wang, F. & Liu, Q. A Study of the Dengue Epidemic and Meteorological Factors in Guangzhou, China, by Using a Zero-Inflated Poisson Regression Model. Asia. Pac. J. Public Health, doi: 10.1177/1010539513490195 (2013).
23. Stewart-Ibarra, A. M. & Lowe, R. Climate and non-climate drivers of dengue epidemics in southern coastal Ecuador. The American journal of tropical medicine and hygiene 88, 971-981 (2013).
24. Gharbi, M. et al. Time series analysis of dengue incidence in Guadeloupe, French West Indies: forecasting models using climate variables as predictors. BMC Infect Dis 11, 166, doi: 10.1186/1471-2334-11-166 (2011).
25. Bhatnagar, S., Lal, V., Gupta, S. D. & Gupta, O. P. Forecasting incidence of dengue in Rajasthan, using time series analyses. Indian J. Public Health 56, 281-285, doi: 10.4103/0019-557X.106415 (2012).
26. Halide, H. & Ridd, P. A predictive model for Dengue Hemorrhagic Fever epidemics. Int. J. Environ. Health Res. 18, 253-265, doi: 10.1080/09603120801966043 (2008).
27. Sitepu, M. S. et al. Temporal Patterns and a Disease Forecasting Model of Dengue Hemorrhagic Fever in Jakarta Based on 10 Years of Surveillance Data. The Southeast Asian journal of tropical medicine and public health 44, 206-217 (2013).
28. Hurtado-Diáz, M., Riojas-Rodríguez, H., Rothenberg, S. J., Gomez-Dantés, H. & Cifuentes, E. Short communication: impact of climate variability on the incidence of dengue in Mexico. Trop. Med. Int. Health 12, 1327-1337 (2007).
29. Descloux, E. et al. Climate-based models for understanding and forecasting dengue epidemics. PLoS Negl Trop Dis 6, e1470, doi: 10.1371/journal.pntd.0001470 (2012).
30. Buczak, A. L., Koshute, P. T., Babin, S. M., Feighner, B. H. & Lewis, S. H. A data-driven epidemiological prediction method for dengue outbreaks using local and remote sensing data. BMC medical informatics and decision making 12, 124, doi: 10.1186/1472-6947-12-124 (2012).
31. Su, G. L. Correlation of climatic factors and dengue incidence in Metro Manila, Philippines. Ambio 37, 292-294 (2008).
32. Buczak, A. L. et al. Prediction of high incidence of dengue in the Philippines. PLoS Negl Trop Dis 8, e2771, doi: 10.1371/journal. pntd.0002771 (2014).
33. Schreiber, K. V. An investigation of relationships between climate and dengue using a water budgeting technique. Int. J. Biometeorol. 45, 81-89 (2001).
34. Wu, Y., Lee, G., Fu, X. & Hung, T. Detect climatic factors contributing to dengue outbreak based on wavelet, support vector machines and genetic algorithm. Institute of High Performance Computing. URL http://oar.a-star.edu.sg:80/jspui/handle/123456789/700 (2008).
35. Wilder-Smith, A., Earnest, A., Tan, S., Ooi, E. & Gubler, D. Lack of association of dengue activity with haze. Epidemiol. Infect. 138, 962-967 (2010).
36. Earnest, A., Tan, S. & Wilder-Smith, A. Meteorological factors and El Nino Southern Oscillation are independently associated with dengue infections. Epidemiol. Infect. 140, 1244-1251 (2012).
37. Earnest, A., Tan, S. B., Wilder-Smith, A. & Machin, D. Comparing statistical models to predict dengue fever notifications. Computational and mathematical methods in medicine 2012 (2012).
38. Hii, Y. L., Zhu, H., Ng, N., Ng, L. C. & Rocklöv, J. Forecast of dengue incidence using temperature and rainfall. PLoS Negl Trop Dis 6.11, e1908 (2012).
39. Goto, K. et al. Analysis of effects of meteorological factors on dengue incidence in Sri Lanka using time series data. PLoS One 8, e63717, doi: 10.1371/journal.pone.0063717 (2013).
40. Wu, P. C., Guo, H. R., Lung, S. C., Lin, C. Y. & Su, H. J. Weather as an effective predictor for occurrence of dengue fever in Taiwan. Acta Trop. 103, 50-57 (2007).
41. Yu, H.-L., Yang, S.-J., Yen, H.-J. & Christakos, G. A spatio-temporal climate-based model of early dengue fever warning in southern Taiwan. Stochastic Environmental Research and Risk Assessment 25, 485-494 (2011).
42. Barbazan, P., Yoksan, S. & Gonzalez, J. P. Dengue hemorrhagic fever epidemiology in Thailand: description and forecasting of epidemics. Microbes Infect 4, 699-705 (2002).
43. Silawan, T., Singhasivanon, P., Kaewkungwal, J., Nimmanitya, S. & Suwonkerd, W. Temporal patterns and forecast of dengue infection in Northeastern Thailand. Southeast Asian J. Trop. Med. Public Health 39, 90-98 (2008).
44. Tipayamongkholgul, M., Fang, C.-T., Klinchan, S., Liu, C.-M. & King, C.-C. Effects of the El Ninõ-Southern Oscillation on dengue epidemics in Thailand, 1996-2005. BMC public health 9, 422 (2009).
45. Wongkoon, S., Jaroensutasinee, M. & Jaroensutasinee, K. Development of temporal modeling for prediction of dengue infection in Northeastern Thailand. Asian Pacific journal of tropical medicine 5, 249-252 (2012).
46. Wongkoon, S., Jaroensutasinee, M. & Jaroensutasinee, K. Assessing the temporal modelling for prediction of dengue infection in northern and northeastern, Thailand. Tropical biomedicine 29, 339-348 (2012).
47. Phung, D. et al. Identification of the prediction model for dengue incidence in Can Tho city, a Mekong Delta area in Vietnam. Acta Trop. 141, 88-96, doi: 10.1016/j.actatropica.2014.10.005 (2015).
48. Brady, O. J., Smith, D. L., Scott, T. W. & Hay, S. I. Dengue disease outbreak definitions are implicitly variable. Epidemics 11, 92-102, doi: 10.1016/j.epidem.2015.03.002 (2015).
49. Box, G. E., Jenkins, G. M. & Reinsel, G. C. Time series analysis: forecasting and control. Vol. 734 (John Wiley & Sons, 2011).
50. Zeger, S. L. & Qaqish, B. Markov regression models for time series: a quasi-likelihood approach. Biometrics 44, 1019-1031 (1988).
51. Said, S. E. & Dickey, D. A. Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika 71, 599-607 (1984).
52. Nishiura, H., Chowell, G., Heesterbeek, H. & Wallinga, J. The ideal reporting interval for an epidemic to objectively interpret the epidemiological time course. Journal of The Royal Society Interface, rsif20090153 (2009).
53. R Core Team. R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org (2013).
54. Johansson, M. A., Dominici, F. & Glass, G. E. Local and Global Effects of Climate on Dengue Transmission in Puerto Rico. PLoS Negl Trop Dis 3, e382 (2009).
55. Reich, N. G. et al. Interactions between serotypes of dengue highlight epidemiological impact of cross-immunity. J R Soc Interface 10, 20130414, doi: 10.1098/rsif.2013.0414 (2013).
56. Johansson, M. A., Cummings, D. A. & Glass, G. E. Multiyear climate variability and dengue-El Nino southern oscillation, weather, and dengue incidence in Puerto Rico, Mexico, and Thailand: a longitudinal data analysis. PLoS Med. 6, e1000168, doi: 10.1371/journal.pmed.1000168 (2009).
57. Xu, H.-Y. et al. Statistical modeling reveals the effect of absolute humidity on dengue in Singapore. PLoS Negl Trop Dis 8, e2805 (2014).
58. Campbell, K. M., Lin, C., Iamsirithaworn, S. & Scott, T. W. The complex relationship between weather and dengue virus transmission in Thailand. The American journal of tropical medicine and hygiene 89, 1066-1080 (2013).
59. Santillana, M., Nguyen, A. T., Dredze, M., Paul, M. J. & Brownstein, J. S. Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance. arXiv preprint arXiv. 1508, 06941 (2015).
60. Murphy, K. P. Machine learning: a probabilistic perspective. (MIT press, 2012).
61. Ginsberg, J. et al. Detecting influenza epidemics using search engine query data. Nature 457, 1012-1014 (2009).
62. Lazer, D., Kennedy, R., King, G. & Vespignani, A. The parable of Google Flu: traps in big data analysis. Science 343 (2014).
63. Yang, S., Santillana, M. & Kou, S. Accurate estimation of influenza epidemics using Google search data via ARGO. Proceedings of the National Academy of Sciences 112, 14473-14478 (2015).