The development of an early warning system for climate-sensitive disease risk with a focus on dengue epidemics in Southeast Brazil

Statistics in Medicine

Volumn 32, Issue 5, 2013, Pages 864-883

  Lowe, Rachel ^a   Bailey, Trevor C ^b   Stephenson, David B ^b   Jupp, Tim E ^b   Graham, Richard J ^c   Barcellos, Christovam ^d   Carvalho, Marilia Sá ^d  

^a Climate Dynamics and Impacts Unit Institut Català de Ciències del Clima IC3   (Spain)

^b UNIVERSITY OF EXETER   (United Kingdom)

^c MET OFFICE   (United Kingdom)

^d INSTITUTO OSWALDO CRUZ   (Brazil)

Author keywords

Climate; Dengue; Early warning systems; Random effects; Spatio temporal modelling

Indexed keywords

ARTICLE; BRAZIL; CLIMATE; CLINICAL DECISION MAKING; CONCEPTUAL FRAMEWORK; DENGUE; DISEASE SURVEILLANCE; EARLY WARNING SYSTEM; EPIDEMIC; FALSE NEGATIVE RESULT; HUMAN; INCIDENCE; INFECTION RISK; INFORMATION SYSTEM; PREDICTION; RISK ASSESSMENT; SEASONAL VARIATION; SENSITIVITY AND SPECIFICITY; VALIDATION PROCESS;

BAYES THEOREM; BIOSTATISTICS; BRAZIL; DENGUE; EPIDEMICS; HUMANS; LINEAR MODELS; MODELS, STATISTICAL; PUBLIC HEALTH; RISK FACTORS; SEASONS; TROPICAL CLIMATE;

EID: 84873447879     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.5549     Document Type: Article

References (69)

1. Lowe R, Bailey TC, Stephenson DB, Graham RJ, Coelho CAS, Carvalho MS, Barcellos C. Spatio-temporal modelling of climate-sensitive disease risk: towards an early warning system for dengue in Brazil. Computers & Geosciences 2011; 37:371-381.
2. Gage KL, Burkot TR, Eisen RJ, Hayes EB. Climate and vectorborne diseases. American Journal of Preventive Medicine 2008; 35(5):436-450.
3. Kovats RS, Bouma MJ, Hajat S, Worrall E, Haines A. El Niño and health. Lancet 2003; 362(9394):1481-1489.
4. Kelly-Hope L, Thomson MC. Climate and infectious diseases. Seasonal Forecasts, Climatic Change and Human Health 2008; 1:31-70.
5. Thomson MC, Doblas-Reyes FJ, Mason SJ, Hagedorn R, Connor SJ, Phindela T, Morse AP, Palmer TN. Malaria early warnings based on seasonal climate forecasts from multi-model ensembles. Nature 2006; 439(7076):576-579.
6. Cox J, Abeku TA. Early warning systems for malaria in Africa: from blueprint to practice. Trends in Parasitology 2007; 23(6):243-246.
7. Ebi KL. Malaria early warning systems. Biometeorology for Adaptation to Climate Variability and Change 2009; 1:49-74.
8. Githeko AK, Ndegwa W. Predicting malaria epidemics in the Kenyan highlands using climate data: a tool for decision makers. Global Change & Human Health 2001; 2(1):54-63.
9. Hoshen MB, Morse AP.A weather-driven model of malaria transmission. Malaria Journal 2004; 3(32):1-14.
10. Worrall E, Connor SJ, Thomson MC.A model to simulate the impact of timing, coverage and transmission intensity on the effectiveness of indoor residual spraying (IRS) for malaria control. Tropical Medicine & International Health 2007; 12(1):75-88.
11. Jones AE, Morse AP. Application and validation of a seasonal ensemble prediction system using a dynamic malaria model. Journal of Climate 2010; 23(15):4202-4215.
12. Lafferty KD. The ecology of climate change and infectious diseases. Ecology 2009; 90(4):888-900.
13. Teixeira MG, Costa MCN, Barreto F, Barreto ML. Dengue: twenty-five years since reemergence in Brazil. Cadernos de Saúde Pública 2009; 25:7-18.
14. Gubler DJ. How effectively is epidemiological surveillance used for dengue programme planning and epidemic response? Dengue Bulletin 2002; 26:96-106.
15. Gubler DJ. Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. Trends in Microbiology 2002; 10(2):100-103.
16. Guzman MG, Kouri G. Dengue and dengue hemorrhagic fever in the Americas: lessons and challenges. Journal of Clinical Virology 2003; 27(1):1-13.
17. Rigau-Pérez JG, Clark GG, Gubler DJ, Reiter P, Sanders EJ, VanceVorndam A. Dengue and dengue haemorrhagic fever. The Lancet 1998; 352(9132):971-977.
18. Reiter P. Climate change and mosquito-borne disease. Environmental Health Perspectives 2001; 109(Suppl 1):141-161.
19. Chambers TJ, Hahn CS, Galler R, Rice CM. Flavivirus genome organization, expression, and replication. Annual Reviews in Microbiology 1990; 44(1):649-688.
20. Halstead SB. The Alexander D. Langmuir Lecture. The pathogenesis of dengue. Molecular epidemiology in infectious disease. American Journal of Epidemiology 1981; 114(5):632-648.
21. McMichael AJ, Haines A, Slooff S R. Kovats. Climate change and human health: an assessment prepared by a Task Group on behalf of the World Health Organization, the World Meteorological Organization and the United Nations Environment Programme. World Health Organization, Geneva 1996; 1:1-297.
22. Gubler DJ. Dengue and dengue hemorrhagic fever. Clinical Microbiology Reviews 1998; 11(3):480-496.
23. Gubler DJ, Meltzer M. Impact of dengue/dengue hemorrhagic fever on the developing world. Advances in Virus Research 1999; 53:35-70.
24. Favier C, Degallier N, Dubois MA. Dengue epidemic modelling: stakes and pitfalls. Asia Pacific Biotech News 2005; 9(22):1191-1194.
25. Halstead SB, Deen J. The future of dengue vaccines. Lancet 2002; 360(9341):1243-1245.
26. Webster DP, Farrar J, Rowland-Jones S. Progress towards a dengue vaccine. The Lancet Infectious Diseases 2009; 9(11):678-687.
27. Siqueira JB, Martelli CMT, Coelho GE, Simplício ACR, Hatch DL. Dengue and dengue hemorrhagic fever, Brazil, 1981-2002. Emerging Infectious Diseases 2005; 11(1):48-53.
28. Nogueira RMR, daAraújo JMG, Schatzmayr HG. Aspects of dengue virus infections in Brazil 1986-2007. Virus Reviews and Research 2007; 12:1-17.
29. Adler RF, Susskind J, Huffman GJ, Bolvin D, Nelkin E, Chang A, Ferraro R, Gruber A, Xie PP, Janowiak J, Rudolf B, Schneider U, Curtis S, Arkin P. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). Journal of Hydrometeorology 2003; 4(6):1147-1167.
30. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D. The NCAR/NCEP 40-year reanalysis project. Bulletin of the American Meteorological Society 1996; 77(3):437-471.
31. Philander SG. El Niño, La Niña, and the Southern Oscillation. Academic Press: New York, 1990. 1-293.
32. R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria, 2009. http://www.R-project.org, ISBN 3-900051-07-0.
33. Hales S, deWet N, Maindonald J, Woodward A. Potential effect of population and climate changes on global distribution of dengue fever: an empirical model. The Lancet 2002; 360(9336):830-834.
34. Hales S, Weinstein P, Souares Y, Woodward A. El Niño and the dynamics of vectorborne disease transmission. Environmental Health Perspectives 1999; 107(2):99-102.
35. Gagnon AS, Bush ABG, Smoyer-Tomic KE. Dengue epidemics and the El Niño Southern Oscillation. Climate Research 2001; 19(1):35-43.
36. Chakravarti A, Kumaria R. Eco-epidemiological analysis of dengue infection during an outbreak of dengue fever, India. Virology Journal 2005; 2(1):32-38.
37. Câmara FP, Gomes AF, Santos GT, Câmara DC. Climate and dengue epidemics in State of Rio de Janeiro. Revista de Sociedade Brasileira de Medicina Tropical 2009; 42(2):137-140.
38. Nakhapakorn K, Tripathi NK. An information value based analysis of physical and climatic factors affecting dengue fever and dengue haemorrhagic fever incidence. International Journal of Health Geographics 2005; 4(13):1-13.
39. Promprou S, Jaroensutasinee M, Jaroensutasinee K. Climatic factors affecting dengue haemorrhagic fever incidence in Southern Thailand. Dengue Bulletin 2005; 29:41-48.
40. Tipayamongkholgul M, Fang CT, Klinchan S, Liu CM, King CC. Effects of the El Niño-Southern Oscillation on dengue epidemics in Thailand, 1996-2005. BMC Public Health 2009; 9(422):1-15.
41. Schreiber KV. An investigation of relationships between climate and dengue using a water budgeting technique. International Journal of Biometeorology 2001; 45:81-89.
42. Arcari P, Tapper N, Pfueller S. Regional variability in relationships between climate and dengue/DHF in Indonesia. Singapore Journal of Tropical Geography 2007; 28(3):251-272.
43. Crawley MJ. Statistical Computing: An Introduction to Data Analysis Using S-Plus. John Wiley & Sons Ltd: London, 2002. 1-761.
44. Mabaso MLH, Vounatsou P, Midzi S, DaSilva J, Smith T. Spatio-temporal analysis of the role of climate in inter-annual variation of malaria incidence in Zimbabwe. International Journal of Health Geographics 2006; 5(20):1-9.
45. McCullagh P, Nelder JA. Generalized Linear Models, 2nd edition. Chapman Hall: London, 1989. 1-536.
46. Venables WN, Ripley BD. Modern Applied Statistics With S. Springer: New York, 2002. 1-495.
47. Lowe R. Spatio-temporal modelling of climate-sensitive disease risk: towards an early warning system for dengue in brazil. PhD Thesis, University of Exeter, 2010.
48. Wu PC, Guo HR, Lung SC, Lin CY, Su HJ. Weather as an effective predictor for occurrence of dengue fever in Taiwan. Acta Tropica 2007; 103(1):50-57.
49. Johansson MA, Dominici F, Glass GE. Local and global effects of climate on dengue transmission in Puerto Rico. PLoS Neglected Tropical Diseases 2009; 3(2):e382; 1-5.
50. Zhou G, Minakawa N, Githeko AK, Yan G. Association between climate variability and malaria epidemics in the East African highlands. Proceedings of the National Academy of Sciences of the United States of America 2004; 101(8):2375-2380.
51. Gomez-Elipe A, Otero A, VanHerp M, Aguirre-Jaime A. Forecasting malaria incidence based on monthly case reports and environmental factors in Karuzi, Burundi, 1997-2003. Malaria Journal 2007; 6(129):1-10.
52. Lawson AB. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. Chapman & Hall/CRC: BocaRaton, FL, 2008. 1-344.
53. Mollie A. Bayesian mapping of disease. Markov Chain Monte Carlo in Practice 1996; 1:359-379.
54. Besag J, Green P, Higdon D, Mengersen K. Bayesian computation and stochastic systems. Statistical Science 1995; 10(1):3-41.
55. Best N, Arnold R, Thomas A, Waller L, Conlon E. Bayesian models for spatially correlated disease and exposure data. Bayesian Statistics 1999; 6:131-156.
56. Gilks WR, Richardson S, Spiegelhalter DJ. Markov Chain Monte Carlo in Practice. Chapman & Hall/CRC: Boca Raton, FL, 1996. 1-486.
57. Wakefield JC, Best NG, Waller L. Bayesian approaches to disease mapping. Spatial Epidemiology: Methods and Applications 2000:104-127.
58. Lunn DJ, Thomas A, Best N, Spiegelhalter D. WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing 2000; 10(4):325-337.
59. Sturtz S, Ligges U, Gelman A.R2WinBUGS: a package for running WinBUGS from R. Journal of Statistical Software 2005; 12(3):1-16.
60. Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Statistical Science 1992; 7(4):457-472.
61. Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian Data Analysis, 2edn. Chapman & Hall/CRC: Boca Raton, FL, 2004. 1-668.
62. Gelman A, Meng XL, Stern H. Posterior predictive assessment of model fitness via realized discrepancies. Statistica Sinica 1996; 6:733-759.
63. Mason I. On reducing probability forecasts to yes/no forecasts. Monthly Weather Review 1979; 107(2):207-211.
64. Mason IB. Binary events. In Forecast Verification: A Practitioners Guide in Atmospheric Science, Joliffe IT, Stephenson DB (eds). Wiley: Hoboken, 2003.
65. Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters 2006; 27(8):861-874.
66. Mason SJ, Graham NE. Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: statistical significance and interpretation. Quarterly Journal of the Royal Meteorological Society 2002; 128(584):2145-2166.
67. Pepe MS. The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press: USA, 2004. 1-302.
68. Jupp TE, Lowe R, Coelho CAS, Stephenson DB. On the visualization, verification and recalibration of ternary probabilistic forecasts. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 2012; 370(1962):1100-1120.
69. Coelho CAS, Stephenson DB, Balmaseda M, Doblas-Reyes FJ, van Oldenborgh GJ. Toward an integrated seasonal forecasting system for South America. Journal of Climate 2006; 19(15):3704-3721.