Seasonal forecasting and health impact models: Challenges and opportunities

Annals of the New York Academy of Sciences

Volumn 1382, Issue 1, 2016, Pages 8-20

  Ballester, Joan ^a   Lowe, Rachel ^a   Diggle, Peter J ^b   Rodó, Xavier ^a,c  

^a Institut Catalá de Ciéncies del Clima IC3   (Spain)

^b LANCASTER UNIVERSITY   (United Kingdom)

^c INSTITUCIÓ CATALANA DE RECERCA I ESTUDIS AVANÇATS ICREA   (Spain)

Author keywords

Climate services; Climate variability; Health impacts; Seasonal forecasting; Statistical models

Indexed keywords

ARTICLE; CLIMATE CHANGE; FORECASTING; INFORMATION PROCESSING; PREDICTION; HEALTH IMPACT ASSESSMENT; HUMAN; PROCEDURES; SEASON; THEORETICAL MODEL; TRENDS; WEATHER; MODEL;

CLIMATE CHANGE; FORECASTING; HEALTH IMPACT ASSESSMENT; HUMANS; MODELS, THEORETICAL; SEASONS; WEATHER;

EID: 84978524218     PISSN: 00778923     EISSN: 17496632     Source Type: Book Series    
DOI: 10.1111/nyas.13129     Document Type: Article

References (99)

1. Patz, J.A., D. Campbell-Lendrum, T. Holloway & J.A. Foley. 2005. Impact of regional climate change on human health. Nature 438: 310–317.
2. Frumkin, H., J. Hess, G. Luber, et al. 2008. Climate change: the public health response. Am. J. Public Health 98: 435.
3. Watts, N. et al. 2015. Health and climate change: policy responses to protect public health. Lancet 386: 1861–1914.
4. Cash, B.A., X. Rodó, J. Ballester, et al. 2013. Malaria epidemics highlight influence of the tropical South Atlantic on the Indian monsoons. Nat. Clim. Change 3: 502–507.
5. McMichael, A.J., R.E. Woodruff & S. Hales. 2006. Climate change and human health: present and future risks. Lancet 367: 859–869.
6. Hsieh, Y.-H. & C. Chen. 2009. Turning points, reproduction number, and impact of climatological events formulti-wave dengue outbreaks. Trop. Med. Int. Health 14: 628–638.
7. Banu, S., W. Hu, C. Hurst & S. Tong. 2011. Dengue transmission in the Asia-Pacific region: impact of climate change and socio-environmental factors. Trop. Med. Int. Health 16: 598–607.
8. McMichael, A.J. & R.E. Woodruff. 2011. Climate change and infectious diseases. Soc. Ecol. Infect. Dis. 14: 378.
9. Johansson, M.A. 2015. Chikungunya on the move. Trends Parasitol. 31: 43–45.
10. Semenza, J.C. et al. 2014. International dispersal of dengue through air travel: importation risk for Europe. PLoS Negl. Trop. Dis. 8: e3278.
11. Confalonieri, U.E., C. Margonari & A.F. Quintão. 2014. Environmental change and the dynamics of parasitic diseases in the Amazon. Acta Trop. 129: 33–41.
12. da Silva-Nunes, M. et al. 2012. Amazonian malaria: asymptomatic human reservoirs, diagnostic challenges, environmentally driven changes in mosquito vector populations, and the mandate for sustainable control strategies. Acta Trop. 121: 281–291.
13. Keesing, F. et al. 2010. Impacts of biodiversity on the emergence and transmission of infectious diseases. Nature 468: 647–652.
14. Felzemburgh, R.D. et al. 2014. Prospective study of leptospirosis transmission in an urban slum community: role of poor environment in repeated exposures to the Leptospira agent. PLoS Negl. Trop. Dis. 8: e2927.
15. Weiss, R.A. & A.J. McMichael. 2004. Social and environmental risk factors in the emergence of infectious diseases. Nat. Med. 10: S70–S76.
16. Rodó, X. et al. 2013. Climate change and infectious diseases: can we meet the needs for better prediction? Clim. Change 118: 625–640.
17. Costello, A. et al. 2009. Managing the health effects of climate change: Lancet and University College London Institute for Global Health Commission. Lancet 373: 1693–1733.
18. Ostro, B., J. Barrera-Gómez, J. Ballester, et al. 2012. The impact of future summer temperature on public health in Barcelona and Catalonia, Spain. Int. J. Biometeorol. 56: 1135–1144.
19. Jancloes, M. et al. 2014. Climate services to improve public health. Int. J. Environ. Res. Public Health 11: 4555–4559.
20. Vaughan, C. & S. Dessai. 2014. Climate services for society: origins, institutional arrangements, and design elements for an evaluation framework. Wiley Interdiscip. Rev. Clim. Change 5: 587–603.
21. Hewitt, C., S. Mason & D. Walland. 2012. The global framework for climate services. Nat. Clim. Change 2: 831–832.
22. Gelfand, A.E., P. Diggle, P. Guttorp & M. Fuentes. 2010. Handbook of Spatial Statistics. Boca Raton: CRC Press.
23. Diggle, P. & P.J. Ribeiro. 2007. Model-Based Geostatistics. New York: Springer Science + Business Media.
24. Anderson, R.M. & R.M. May. 1979. Population biology of infectious diseases: part I. Nature 280: 361–367.
25. May, R.M. & R.M. Anderson. 1979. Population biology of infectious diseases: part II. Nature 280: 455–461.
26. Keeling, M.J. & P. Rohani. 2008. Modelling InfectiousDiseases in Humans and Animals. Princeton: Princeton University Press.
27. Keeling, M.J., M.E.J. Woolhouse, D.J. Shaw, et al. 2001. Dynamics of the 2001UKfoot and mouth epidemic: stochastic dispersal in a heterogeneous landscape. Science 294: 813–817.
28. Cox, D.R. 1972. Regression models and life tables. J. R. Stat. Soc. B 34: 187–220.
29. Lindgren, F., H. Rue & J. Lindström. 2011. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. J. R. Stat. Soc. B 73: 423–498.
30. Lowe, R. & X. Rodó. 2016. “Modelling climate-sensitive disease risk: a decision support tool for public health services.” In Communicating Climate-Change and Natural Hazard Risk and Cultivating Resilience. J.L. Drake, Y.Y. Kontar, J.C. Eichelberger, T.S. Rupp & K.M. Taylor, Eds.: 115–130. Springer.
31. Pascual, M., M.J. Bouma, A. Dobson. 2002. Cholera and climate: revisiting the quantitative evidence. Microb. Infect. 4: 237–245.
32. Diggle, P.J., P. Moraga, B. Rowlingson, et al. 2013. Spatial and spatio-temporal log-Gaussian Cox processes: extending the geostatistical paradigm. Stat. Sci. 28: 542–563.
33. Bivand, R.S., E.J. Pebesma, V. Gomez-Rubio & E.J. Pebesma. 2008. Applied Spatial Data Analysis with R. New York: Springer.
34. Lowe, R. et al. 2011. Spatio-temporal modeling of climatesensitive disease risk: towards an early warning system for dengue in Brazil. Comput. Geosci. 37: 371–381.
35. Besag, J., P. Green, D. Higdon & K. Mengersen. 1995. Bayesian computation and stochastic systems. Stat. Sci. 10: 3–41.
36. Jones, A.E. & A.P. Morse. 2012. Skill ofENSEMBLESseasonal re-forecasts for malaria prediction in West Africa. Geophys. Res. Lett. 39: L23707.
37. Lauderdale, J.M., C. Caminade, A.E. Heath, et al. 2014. Towards seasonal forecasting of malaria in India. Malar. J. 13: 310.
38. MacLeod, D.A., A. Jones, F. DiGiuseppe, et al. 2015. Demonstration of successful malaria forecasts for Botswana using an operational seasonal climate model. Environ. Res. Lett. 10. doi: 10.1088/1748-9326/10/4/044005.
39. Blench, R. 1999. Seasonal climatic forecasting: who can use it and how should it be disseminated? Nat. Resour. Perspect. 47: 1–4.
40. Thomson, M., R. Garcia-Herrera & M. Beniston. 2008. Seasonal Forecasts, Climatic Change and Human Health: Health and Climate. New York: Springer-Verlag.
41. Thomson, M. et al. 2006. Malaria early warnings based on seasonal climate forecasts from multi-model ensembles. Nature 439: 576–579.
42. Anyamba, A. et al. 2009. Prediction of a rift valley fever outbreak. Proc. Natl. Acad. Sci. U.S.A. 106: 955–959.
43. Stockdale, T.N., D.L.T. Anderson, J.O.S. Alves & M.A. Balmasada. 1998. Global seasonal rainfall forecasts using a coupled ocean–atmosphere model. Nature 392: 370–373.
44. Coelho, C., D. Stephenson, M. Balmaseda, et al. 2006. Toward an integrated seasonal forecasting system for South America. J. Clim. 19: 3704–3721.
45. Lowe, R. et al. 2013. 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.
46. Lowe, R. et al. 2014. Dengue outlook for the World Cup in Brazil: an early warning model framework driven by realtime seasonal climate forecasts. Lancet Infect. Dis. 14: 619–626.
47. Jupp, T.E., R. Lowe, C.A. Coelho & D.B. Stephenson. 2012. Onthe visualization, verification and recalibration of ternary probabilistic forecasts. Philos. Trans. A Math. Phys. Eng. Sci. 370: 1100–1120.
48. ECDC. 2014. Risk assessment Brazil 2014 FIFA World Cup, 12 June–13 July 2014. Accessed June 2016. http://www. ecdc.europa.eu/en/publications/Publications/world-cup-Brazil-mass-gathering-risk-assessment-28-may-2014.pdf.
49. Palmer, T. & R. Hagedorn. 2006. Predictability of Weather and Climate. Cambridge: Cambridge University Press.
50. Shukla, J. 1998. Predictability in the midst of chaos: a scientific basis for climate forecasting. Science 282: 728–731.
51. Ballester, J. et al. 2013. Kawasaki disease and ENSO-driven wind circulation. Geophys. Res. Lett. 40: 2284–2289.
52. Shukla, J. & J. Kinter, III. 2006. Predictability of Seasonal Climate Variations: A Pedagogical Review. Cambridge: Cambridge University Press.
53. Rodó, X., M.A. Rodríguez-Arias & J. Ballester. 2006. The role of ENSO in fostering teleconnection patterns between the tropical north Atlantic and the western Mediterranean Basin. CLIVAR Exch. 11: 26–27.
54. Doblas-Reyes, F.J., J. García-Serrano, F. Lienert, et al. 2013. Seasonal climate predictability and forecasting: status and prospects. Wiley Interdiscip. Rev. Clim. Change 4: 245–268.
55. Petrova, D., S.J. Koopman, J. Ballester & X. Rodó. 2016. Improving the long-lead predictability of El Niño using a novel forecasting scheme based on a dynamic components model. Clim. Dyn. 1–28.
56. Ballester, J., S. Bordoni, D. Petrova & X. Rodó. 2016. Heat advection processes leading to El Niño events as depicted by an ensemble of ocean assimilation products. J. Geophys. Res. Oceans. doi: 10.1002/2016JC011718.
57. Goddard, L. & S. Mason. 2002. Sensitivity of seasonal climate forecasts to persisted SST anomalies. Clim.Dyn. 19: 619–632.
58. Ballester, J., M.A. Rodríguez-Arias & X. Rodó. 2011. A new extratropical tracer describing the role of the western Pacific in the onset of El Niño: implications for ENSO understanding and forecasting. J. Clim. 24: 1425–1437.
59. McPhaden, M.J., S.E. Zebiak & M.H. Glantz. 2006. ENSO as an integrating concept in earth science. Science 314: 1740–1745.
60. Ballester, J., S. Bordoni, D. Petrova & X. Rodó. 2015. On the dynamical mechanisms explaining the western Pacific subsurface temperature buildup leading to ENSO events. Geophys. Res. Lett. 42: 2961–2967.
61. Ropelewski, C.F. & M.S. Halpert. 1987. Global and regional scale precipitation patterns associated with the El Niño/southern oscillation. Mon. Weather Rev. 115: 1606–1626.
62. Lyon, B. & A.G. Barnston. 2005. ENSO and the spatial extent of interannual precipitation extremes in tropical land areas. J. Clim. 18: 5095–5109.
63. Ballester, J., S. Bordoni, D. Petrova & X. Rodó. 2016. El Niño dynamics and long lead climate forecasts. Climanosco. In press.
64. Kovats, R.S., M.J. Bouma, S. Hajat, et al. 2003. El Niño and health. Lancet 362: 1481–1489.
65. Stewart-Ibarra, A.M. & R. Lowe. 2013. Climate and nonclimate drivers of dengue epidemics in southern coastal Ecuador. Am. J. Trop. Med. Hyg. 88: 971–981.
66. Pascual, M., X. Rodó, S.P. Ellner, et al. 2000. Cholera dynamics and El Nino—southern oscillation. Science 289: 1766–1769.
67. Rodó, X., M. Pascual, G. Fuchs & A. Faruque. 2002. ENSO and cholera: a nonstationary link related to climate change? Proc. Natl. Acad. Sci. U.S.A. 99: 12901–12906.
68. Rodó, X. et al. 2011. Association of Kawasaki disease with tropospheric wind patterns. Sci. Rep. 1: 152.
69. Rodó, X. et al. 2014. Tropospheric winds from northeastern China carry the etiologic agent of Kawasaki disease from its source to Japan.Proc. Natl. Acad. Sci. U.S.A. 111: 7952–7957.
70. Connor, S.J. & G.C. Mantilla. 2008. “Integration of seasonal forecasts into early warning systems for climate-sensitive diseases such as malaria and dengue.” In Seasonal Forecasts, Climatic Change and Human Health. M.C. Thomson, R. Garcia-Herrera&M.Beniston, Eds.: 71–84. theNetherlands: Springer.
71. Ballester, J., F. Giorgi & X. Rodó. 2010. Changes in European temperature extremes can be predicted fromchanges in PDF central statistics. Clim. Change Lett. 98: 277–284.
72. Seneviratne, S.I., T. Corti, E.L. Davin, et al. 2010. Investigating soilmoisture–climate interactions in a changing climate: a review. Earth Sci. Rev. 99: 125–161.
73. Ballester, J., H. Douville & F. Chauvin. 2009. Present-day climatology and projected changes of warm and cold days in the CNRM–CM3 global climate model. Clim. Dyn. 32: 1432-0894.
74. Ballester, J., X. Rodó & F. Giorgi. 2010. Future changes in Central Europe heat waves expected to mostly follow summer mean warming. Clim. Dyn. 35: 1191–1205.
75. Weisheimer, A., F.J. Doblas-Reyes, T. Jung & T.N. Palmer. 2011. On the predictability of the extreme summer 2003 over Europe. Geophys. Res. Lett. 38: L05704.
76. Hirschi, M. et al. 2010. Observational evidence for soil– moisture impact on hot extremes in southeastern Europe. Nat. Geosci. 4: 17–21.
77. Quesada, B., R. Vautard, P. Yiou, et al. 2012. Asymmetric European summer heat predictability from wet and dry southernwinters and springs. Nat. Clim. Change 2: 736–741.
78. Hewitt, C., C. Buontempo & P. Newton. 2013. Using climate predictions to better serve society’s needs. Eos Trans. Am. Geophys. Union 94: 105–107.
79. Lowe, R. et al. 2016. Evaluation of an early-warning system for heat wave-related mortality in Europe: implications for seasonal forecasting and climate services. Int. J. Environ. Res. Public Health 13: 206.
80. Ballester, J., J.-M. Robine, F.R. Herrmann & X. Rodó. 2011. Long-term projections and acclimatization scenarios of temperature-related mortality in Europe. Nat. Commun. 2: 358.
81. Lowe, R. et al. 2015. Evaluating the performance of a climatedriven mortality model during heat waves and cold spells in Europe. Int. J. Environ. Res. Public Health 12: 1279–1294.
82. Wu, R. & B. Wang. 2010. Acontrast of the EastAsian summer monsoon–ENSO relationship between 1962–77 and 1978– 93. J. Clim. 15: 3266–3279.
83. Ashok, K., S.K. Behera, S.A. Rao, et al. 2007. ElNiñoModoki and its possible teleconnection. J. Geophys. Res. Oceans 112: C11007.
84. Ashok, K. & T. Yamagata. 2009. Climate change: the El Niño with a difference. Nature 461: 481–484.
85. Yeh, S.W., J.S. Kug, B. Dewitte, et al. 2009. El Niño in a changing climate. Nature 461: 511–514.
86. Rodó, X. & M.A. Rodriguez-Arias. 2006. Detecting transitory processes inthe climate systemwith the scale-dependent correlation (SDC) analysis: an application to remote ElNiño forcings. Clim. Dyn. 27: 441–458.
87. Sack, D., R. Sack, G. Nair & A. Siddique. 2004. Cholera. Lancet 363: 223–33.
88. de Magny, G.C. et al. 2008. Environmental signatures associated with cholera epidemics. Proc. Natl. Acad. Sci. U.S.A. 105: 17676–17681.
89. Reyburn, R. et al. 2011. Climate variability and the outbreaks of cholera in Zanzibar, East Africa: a time series analysis. Am. J. Trop. Med. Hyg. 84: 862–869.
90. Mandal, S., R.R. Sarkar & S. Sinha. 2011. Mathematical models of malaria—a review. Malar. J. 10: 10–1186.
91. Donaldson, G., S. Ermakov, Y. Komarov, et al. 1998. Cold related mortalities and protection against cold in Yakutsk, eastern Siberia: observation and interview study. BMJ 317: 978–982.
92. Fouillet, A. et al. 2008. Has the impact of heat waves on mortality changed in France since the European heat wave of summer 2003? A study of the 2006 heat wave. Int. J. Epidemiol. 37: 309–317.
93. Boeckmann, M. & I. Rohn. 2014. Is planned adaptation to heat reducing heat-related mortality and illness? A systematic review. BMC Public Health 14: 1112.
94. Hajat, S., L.T. Barnard, C. Butler, et al. 2014. Heat-related and cold-related mortality and morbidity. Clim. Change Glob. Health 21: 21–37.
95. Christidis, N., G.C. Donaldson & P.A. Stott. 2010. Causes for the recent changes in cold- and heat-related mortality in England andWales. Clim. Change 102: 539–553.
96. Todd, N. & A.J. Valleron. 2015. Space–time covariation of mortality with temperature: a systematic study of deaths in France, 1968–2009. Environ. Health Perspect. 123: 659–664.
97. Ballester, J., X. Rodó, J.M. Robine & F.R. Herrmann. 2016. European seasonal mortality and influenza incidence due to winter temperature variability. Nat. Clim. Change. DOI: 10.1038/NCLIMATE3070.
98. Shumake-Guillemot, J. et al. 2014. Health exemplar to the user interface platform of the global framework for climate services. World Meteorological Organization, Geneva.
99. Davis, M., R. Lowe, S. Steffen, et al. 2016. “Barriers to using climate information: challenges in communicating probabilistic forecasts to decision-makers.” In Communicating Climate-Change and Natural Hazard Risk and Cultivating Resilience. J.L. Drake, Y.Y. Kontar, J.C. Eichelberger, T.S. Rupp & K.M. Taylor, Eds.: 95–113. Springer.