Malaria Stratification, Climate, and Epidemic Early Warning in Eritrea

Abstract

Eritrea has a successful malaria control program, but it is still susceptible to devastating malaria epidemics. Monthly data on clinical malaria cases from 242 health facilities in 58 subzobas (districts) of Eritrea from 1996 to 2003 were used in a novel stratification process using principal component analysis and nonhierarchical clustering to define five areas with distinct malaria intensity and seasonality patterns, to guide future interventions and development of an epidemic early warning system. Relationships between monthly clinical malaria incidence by subzoba and monthly climate data from several sources, and with seasonal climate forecasts, were investigated. Remotely sensed climate data were averaged over the same subzoba geographic administrative units as the malaria cases. Although correlation was good between malaria anomalies and actual rainfall from ground stations (lagged by 2 months), the stations did not have sufficiently even coverage to be widely useful. Satellite derived rainfall from the Climate Prediction Center Merged Analysis of Precipitation was correlated with malaria incidence anomalies, with a lead time of 2-3 months. NDVI anomalies were highly correlated with malaria incidence anomalies, particularly in the semi-arid north of the country and along the northern Red Sea coast, which is a highly epidemic-prone area. Eritrea has 2 distinct rainy seasons in different parts of the country. The seasonal forecasting skill from Global Circulation Models for the June/July/August season was low except for the Eastern border. For the coastal October/November/December season, forecasting skill was good only during the 1997-1998 El Niño event. For epidemic control, shorter-range warning based on remotely sensed rainfall estimates and an enhanced epidemic early-detection system based on data derived for this study are needed.

The Normalized Difference Vegetation Index (NDVI): unforeseen successes in animal ecology

“…NDVI was used to generate risk and distribution maps for diseases with arthropod vectors (Kalluri et al 2007), as well as for temporal models of disease outbreaks (Chretien et al 2007). Malaria and other mosquito-borne diseases such as West Nile virus seem to receive the most frequent attention and be particularly amenable to modeling efforts involving spatiotemporal data such as NDVI, due to the close causal link between rainfall and both mosquito abundance and NDVI (Brooker et al 2006, Gemperli et al 2006, Ceccato et al 2007, Britch et al 2008. But other zoonoses like Hantaan virus (Yan et al 2007), schistosomiasis (Clements et al 2008, Wang et al 2008, Ebola (Pinzon et al 2004a), bubonic plague (Kausrud et al 2007), leishmaniasis (Werneck et al 2007), Rift Valley fever (Anyamba et al 2009) and others have been found to have spatial and/or temporal components associated with NDVI variability.…”

Difficulties in organizing first indoor spray programme against malaria in Angola under the President’s Malaria Initiative

“…Seldom has a large spray programme been so hurried that pretreatment evaluation was omitted. [10][11][12][13] This mistake might be due to lack of experience of the PMI leadership with the complexities of malaria control in Africa. It might also have been wiser to start PMI in a country with a stronger commitment to malaria control.…”

“…Normally several methods are used in an integrated strategy. [6][7][8][9][10][11][12] At least six key components were included in large malaria control efforts by WHO in Nigeria and the Sudan in previous decades. 13 In an effort to rush initiation of the PMI, one of the consultants hired by USAID -the technical director -was sent to Angola in August 2005, within a month of PMI being announced.…”

Bridging critical gaps in climate services and applications in africa

“…As a World Health Organisation Collaborating Center for malaria early warning and other climate sensitive diseases, IRI has particularly focused on the use of climate information in malaria risk mapping (Ceccato et al 2007), early warning (Thomson et al 2006) and impact assessment -see below.…”