resistant malaria in Africa and Southeast Asia and with a dramatic change in the local epidemiology of malaria in Pakistan (Verdrager, 1986; Thimasarn et al., 1995; Kazmi and Pandit, 2001). In Kenya, the seasonal movement of many workers from lowlands to highland tea plantations has triggered epidemics (Malakooti et al., 1998; Shanks et al., 2000). Seasonal influx of migrant farm workers from Central and South America has produced local outbreaks of malaria in the United States (Zucker, 1996).
Similar data link malaria outbreaks with political upheaval and migration. In Southeast Asia, malaria was the leading cause of morbidity and mortality among Cambodian refugees upon arrival in eastern Thailand (Glass et al., 1980) and the Karen refugees in western Thailand (Luxemburger et al., 1996). In Central Asia, civil war and the collapse of the health care infrastructure led to a reemergence of malaria in Tajikistan (Pitt et al., 1998). In East Timor and Afghanistan, recent relief efforts have been complicated by malaria (Ezard, 2001; Sharp et al., 2002b). Africa’s examples are most numerous. To name just a few: malaria was the leading cause of death among Mozambican refuges in Malawi and Ethiopian refugees in eastern Sudan, whereas fever due to malaria was second only diarrheal disease as the leading cause of morbidity and mortality among Rwandan refugees entering eastern Zaire (now Democratic Republic of Congo) (Bloland and Williams, 2002).
Following a mass migration into a malarious region, added risk factors for malaria outbreaks include: substandard housing and environmental protection from nighttime anopheline bites, deliberate relocation near open water, overcrowding, proximity of livestock, low socioeconomic status, poor nutritional status, a lack of immunity to malaria, destruction or over-burdening of existing infrastructure, and scarce to non-existent health care services (Bloland and Williams, 2002).
One pillar of Roll Back Malaria’s efforts to reduce Africa’s malaria burden is the application of malaria early warning systems (MEWS) to speed responses to epidemics (WHO, 2000; WHO, 2001). Specifically, the Abuja target aims to identify, and respond to 60 percent of malaria epidemics in Africa within 2 weeks of onset and detection (WHO, 2001). The main tools of MEWS are:
Forecasting (usually refers to seasonal climate forecasts)
Early warning (monitoring meteorological conditions such as rainfall and temperature)
Early detection (based on routine clinical surveillance)