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

Historical early warning systems

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The use of climate data for predicting outbreaks of infectious diseases dates back to work by Gill and others in India. Gill (1923) developed an EWS for malaria based on rainfall, prevalence of enlarged spleens, economic conditions (price of food grains) and epidemic potential (the coefficient of variation of fever mortality during October for the period 1828-1921). A response mechanism also existed which could be initiated within time to avert the worst impact. The model itself was used to predict epidemics from 1921-1942 in 29 districts of the Punjab; although the author believed that warnings in the first two years were issued too late (both in late September when the malaria season occurs in October). Formal assessment of the model’s predictions for 1923-42 indicated that accuracy was significantly better than would have been obtained by chance (Swaroop 1949). However, the model’s exact accuracy is difficult to assess as there is no indication of the number of epidemics correctly predicted. Gill’s approach demonstrates how an EWS can be constructed from relatively few vari-ables although this method can be very demanding in data requirements. Another problem with this analysis is that there is no indication of how an epidemic was defined.

Rogers (1923, 1925, 1926) described associations between climatic variables such as temperature, rainfall, humidity and winds, and the incidence of diseases such as pneumonia, smallpox, leprosy and tuberculosis in India and elsewhere. Although Rogers’ inferences were made on a visual rather than statistical basis, these studies highlighted the potential utility of long-term datasets. The leprosy data used, for example, represented 30 years of annual incidence data for the whole of India in combination with meteorological records from over 2 000 sites (Rogers 1923). Based on his conclusions, it was recommended that climatic variables be used for forecasting epidemics of TB, smallpox and pneumonia and for mapping worldwide incidence of leprosy. However, such systems were never implemented on a wide scale.

These historical studies demonstrate the usefulness of long-term historical or current datasets in predicting present and future patterns of disease. They also suggest that it is possible to construct an EWS based on overall associations of climate variables with disease incidence, without necessarily relying on complete knowledge of the effects of climate on all components of the disease transmission cycle.

The health sector is now in a much stronger position to explore the utility of EWS. Firstly, standardization of disease diagnosis and networked computerized reporting potentially allow accurate and rapid monitoring of disease incidence (although undermined by patchy and often deteriorating surveillance systems in many parts of the world). Secondly, a wide variety of environmental monitoring data from satellite and ground-based systems are easily accessible at no or low cost, facilitating the investigation of potential links to climate. Thirdly, advances in statistical and epidemiological modelling allow apparent associations to be tested explicitly, rather than relying on visual inspection.

Despite the renewed interest in EWS within the health sector, there has been little operational activity to date. This contrasts with other sectors: most notably, a large amount of research and development effort has been focused on the development of famine early warning systems (FEWS) following widespread famine in Africa in the early 1980s. A FEWS has been defined by Davies et al. (1991) as “a system of data collection to monitor people’s access to food, in order to provide timely notice when a food crisis threatens and, thus, to elicit appropriate response.”

FEWS operate at various geographical levels (Table 1), with food availability being predicted using risk indicators such as market export prices, pest infestations, war and conflict, nutritional indices and climate and vegetation variables. The Food and Agriculture Organization of the United Nations (FAO) has established the Africa Real Time Environmental Monitoring Information System (ARTEMIS) which uses Meteosat remotely sensed images to monitor crop seasons and rainfall. These can be used to assess environmental conditions during the current growing season relative to previous years.

Table 1. Examples of FEWS and their geographical coverage


Level Early warning system
Global Global Information and Early Warning System (GIEWS)
Regional Southern African Development Community (SADC) Comité Permanent Interétats de Lutte contre la Sécheresse dans le Sahel (CILSS)
National USAID Famine early warning system information network (FEWS NET)
Sub-national Save the Children Fund (SCF-UK), Darfur, Sudan
Local Suivi Alimentaire Delta Seno (SADS), Mopti, Mali

A critical point in Davies’ definition of a FEWS is the inclusion of an ‘appropriate response’, which suggests that an EWS should be part of a wider, integrated system designed to respond to a crisis. The importance of a response will be discussed below with particular reference to infectious diseases, but it is the phase following the early warning (i.e. mitigation and response) which so far has been crucial in determining the success of FEWS. The message from numerous studies is that EWS are of little use if the capacity to respond is not present – i.e. the resources to react promptly and effectively must be included within the EWS. For instance, the 1990-91 drought in southern Africa was the worst of the twentieth century, placing approximately 40 million people at risk of starvation. A major famine was averted due to both the SADC Regional EWS warning in March 1991 of a substantial grain shortfall and extensive national and international government involvement in ordering and delivering food imports.

Experience elsewhere has shown that where decisions are predicated on signs that a crisis is already underway, relief is not delivered on time – as was the case in Sudan and Chad 1990-91. Additionally, political issues can have a significant impact on the timing of the response. In Ethiopia, for example, early warning information from national systems was ignored for years due to political instability (Buchanan-Smith et al. 1995).

In various instances the success of the FEWS approach has been limited by a number of organizational problems, the implications of which should not be overlooked in the health sector:

  • Climate is only one of many determinants which could be included in an EWS.
  • Early warning of a crisis is no guarantee of prevention.
  • Interest in preventing a crisis is part of a wider political, economic and social agenda. In many cases governments are not directly accountable to vulnerable popu-lations.
  • In most cases, the purpose of early warning is undermined as relief arrives too late due to poor organization at donor-level.

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

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