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Using climate to predict disease outbreaks: a review: Previous pag: Conceptual framework for developing climate-based EWS for infectious disease | 1,2,3,4,5,6,7,8,9,10,11,12

Identifying candidate diseases for early warning systems


As described in previous sections, a number of preliminary steps are necessary in order to assess the viability of climate-based EWS for a given disease. Table 2 has been constructed by following each of the preliminary steps presented in the framework proposed above. It comprises a list of the most important infectious diseases from the WHO global burden of disease assessment (WHO 2002a), in descending order by global burden, i.e. disability adjusted life years (DALYs). Each disease has been assessed for inclusion in this review according to its associated disease burden, evidence of interannual variability and climate sensitivity. A detailed discussion of the various diseases in the context of this report is given in Appendix 1.

Table 2 indicates that the evidence for climate sensitivity of a range of epidemic-prone infectious diseases varies both in terms of the number of studies undertaken

and the rigour with which apparent associations have been tested. Although outbreaks of many infectious diseases have an apparent climate link, still there is a lack of solid statistical support to back up historical anecdotes on the occurrence of epidemics. On the basis of the evidence presented in the table, the following diseases have been selected for further examination in this report:

  • Cholera
  • Malaria
  • Meningococcal meningitis
  • Dengue/dengue haemorrhagic fever
  • Yellow fever
  • Japanese and St. Louis encephalitis
  • Rift Valley fever
  • Leishmaniasis
  • African trypanosomiasis
  • West Nile virus
  • Murray Valley fever and Ross River virus
- Table 2 - Common communicable diseases, their distribution, epidemic potential and sensitivity to climate [pdf 49kb]

Using climate to predict disease outbreaks: a review: 1,2,3,4,5,6,7,8,9,10,11,12 | Next page: Climate-based early warning systems for infectious diseases

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