Forecasting, warning and detection of malaria emergencies: a case study.

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http://news.bmn.com/magazine/researchupdate?uid=UPDATE.Cox0406200372

Forecasting, warning and detection of malaria emergencies: a case study.
Hay S.I. et al.
The Lancet 2003 361:1705-1706
Full Text   
4 June 2003
Commentary by Jonathan Cox jonathan.cox@lshtm.ac.uk  and Tarekegn Abeku tarekegn.abeku@lshtm.ac.uk 
Increasing preparedness for malaria epidemics is a central tenet of the Roll Back Malaria initiative. This translates to specific goals of detecting 60% of malaria epidemics within two weeks of onset and responding to 60% of epidemics within two weeks of detection. Malaria early warning systems (MEWS) are widely advocated as a mechanism to achieve this preparedness in sub-Saharan Africa. Hay et al.'s paper in The Lancet, which summarizes a more detailed UNICEF report, evaluates various indicators for epidemic forecasting, warning and detection using the case of the 2002 malaria emergency in Kenya. Results indicated that only rainfall monitoring [through remotely sensed rainfall estimates (RFE)] offered any sort of value in terms of early warning (although the utility of other weather variables was not evaluated). Questions concerning the predictive skill and applicability of MEWS across a range of malaria endemicities remain, but few would argue with Hay et al.'s notion that simple analyses of rainfall are informative in areas of unstable malaria transmission.



 
We may soon have the opportunity to prospectively test the reliability of rainfall monitoring data as an early warning indicator. Recent RFE data indicate that many parts of East Africa, including epidemic prone areas of Uganda, Kenya and southern Ethiopia have experienced abnormally high rainfall since March of this year (http://edcw2ks21.cr.usgs.gov/adds/). Although Hay et al. are sceptical about the value of seasonal climate forecasts, it is worth pointing out that current forecasts for the area predict a 40% chance of higher than normal precipitation and a 55% chance of above average temperatures for May-July (the chance of near-normal conditions being 35 and 30% respectively) (http://iri.columbia.edu/). With warm and wet conditions likely to coincide with the traditional peak malaria transmission season, the occurrence of malaria epidemics in the coming months would not be unexpected. We would echo Hay et al.'s call for increased epidemic preparedness - and particularly the need to ensure adequate supplies of antimalarial drugs. In addition to RFE data, it would be prudent to also track ground-based meteorological data for epidemic prone areas.

Hay et al.'s results suggest that monitoring meteorological variables can provide planners with an objective, if relatively rough, means of epidemic early warning. However, for effective intervention this approach needs to be coupled with a surveillance system that can locate incipient epidemics in space and time in a sufficiently timely manner. In developing such a system, a balance needs to be reached in which it is possible to collect sufficiently specific malaria data on a weekly basis without over-burdening already stretched health facilities with increased administration and form-filling. In East Africa, the HIMAL project - a collaboration between the Kenyan and Ugandan Ministries of Health (www.HIMAL.uk.net) is currently testing a system for epidemic early detection (based on a network of sentinel health facilities) and a prediction system in a number of epidemic prone districts. Results from HIMAL and similar studies should help shed more light on precipitating factors for epidemics in African highlands and allow us to improve on the rather rudimentary approaches currently being touted.


 
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