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Using climate to predict disease outbreaks: a review: Previous page | 1,2,3,4,5,6,7,8,9,10,11,12

Executive summary


It is commonly accepted that climate plays a role in the transmission of many infectious diseases, of which some are among the most important causes of mortality and morbidity in developing countries. Often these diseases occur as epidemics which may be triggered by variability in climatic conditions that favour higher transmission rates. With increasing demand for operational disease early warning systems (EWS), recent advances in the availability of climate and environmental data and increased use of geographical information systems (GIS) and remote sensing make climate-based EWS increasingly feasible from a technical point of view.

This report presents a framework for developing disease EWS and, following steps within it, reviews the degree to which individual infectious diseases are sensitive to climate variability. This is used as a basis for identifying diseases for which climate-based prediction offers most potential for disease control. Subsequent sections review the current state of development of EWS for specific diseases and assess their likelihood of success.

This report demonstrates that there is considerable on-going research activity identifying climate-epidemic links. Of the 18 diseases meeting defined criteria for the potential for climate-based EWS, few (African trypanosomiasis, leishmaniasis, yellow fever and Murray Valley encephalitis) are not associated with some sort of EWS development activity. For others (St. Louis encephalitis and West Nile virus in the United States of America) operational and effective warning systems have been developed which rely solely on viral activity detection (also the strategy employed for early detection and prediction of influenza outbreaks). It remains unclear whether the addition of climatic predictors would improve the predictive accuracy or lead-time of these systems. For the remaining diseases (cholera, malaria, meningitis, dengue, Japanese encephalitis, Rift Valley fever and Ross River virus), research projects have demonstrated a temporal link between climatic factors and variations in disease rates. In some of these cases the power to predict epidemics has been tested, although the tests are preliminary and usually based on either limited data or inadequate description of the methods used. From the published literature so far, there is little evidence to suggest that any of these systems currently are being used to influence disease control decisions.

This report suggests a number of likely explanations for this:

  • Affordable and accessible data and analytical tools have become widespread only recently, so that the field is at a relatively early stage of development. Many more studies should be available in the next two to three years as systems are completed and tested in other locations.
  • Few studies have been published, so there are no generally agreed criteria for assessing predictive accuracy (for example, it is seldom clear how an epidemic year is defined). As a consequence it is often difficult to judge the utility of existing systems.
  • Most research projects have had relatively limited resources and therefore not been tested in locations outside the original study area.
  • Most studies in this area focus solely on climatic factors and do not explicitly test other explanations for variations in disease rates through time.
  • Many studies are undertaken as ‘pure research’ therefore neither the extent to which they address specific control decisions nor their utility for planning public health interventions is clear.

This report concludes that a number of steps could be taken to begin to address these issues. These include:

  • Maintaining and strengthening disease surveillance systems for monitoring incidence of epidemic diseases. High quality, long-term disease data are essential for generating and refining models relating climate to infectious disease; lack of disease data is a more common limiting factor than lack of climate data. In some cases existing approaches to surveillance may generate disease data appropriate for use within an EWS – in others it may be necessary to either modify existing systems or build completely new systems. The introduction of computer hardware and software at appropriate levels within the surveillance system may facilitate timely collation and analysis of incoming disease data. Widespread introduction of GIS tools, the WHO Healthmapper software for example, may allow surveillance data to be stored and accessed in a disaggregated form, allowing detailed analysis of spatial and temporal distributions. Consideration should be given to integrating such monitoring into single systems (e.g. by combining disease and famine EWS) to facilitate data access and maximize comparability.
  • Clarifying definitions of terminology and methods for assessing predictive accuracy. For instance, the definition of an epidemic (i.e. number of cases in a specific population over a specified time) should be determined before the modelling process is carried out. The accuracy of the system could be measured using standard epidemiological measures (e.g. sensitivity, specificity, positive and negative predictive value, and kappa statistics). The accuracy of predictive models for incidence numbers or rates could be measured as the root mean square error, or as correlation coefficients between observed and predicted case numbers – always against independent data (i.e. not included in the original model building process).
  • Testing for non-climatic influences (e.g. population immunity, migration rates, drug resistance etc.) on disease fluctuations is desirable. This should avoid disease variations being attributed incorrectly to climate. Theoretically, measurements of all relevant factors for which data are available should allow more accurate predictive models, although this is not always feasible in practice.
  • Including health policy-makers in all stages of system design (e.g. involvement of local control personnel in defining an epidemic and determining the most appropriate warning lead-time). These discussions should relate to specific control decisions and consider local (particularly resource) constraints on the implementation of the EWS. Experience with famine EWS in the 1990s showed the effectiveness of predictions to depend less on their accuracy, more on political factors.
  • Basing final recommendations on EWS implementation on thorough cost-effectiveness analysis. This should measure the value of collecting data on the various climatic and non-climatic influences for predicting the occurrence, timing and scale of epidemics. In some situations, for example, adding climatic information to an EWS may give only a small increase in predictive power and therefore effectiveness of control, however if sufficiently cheap and simple to collect it justifies inclusion. Economic evaluation of EWS should recognise the opportunity costs involved in diverting scarce resources from other strata of disease transmission.

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