BackgroundThe impacts of interannual climate fluctuations on vector-borne diseases, especially malaria, have received considerable attention in the scientific literature. These effects can be significant in semi-arid and high-elevation areas such as the highlands of East Africa because cooler temperature and seasonally dry conditions limit malaria transmission. Many previous studies have examined short-term lagged effects of climate on malaria (weeks to months), but fewer have explored the possibility of longer-term seasonal effects.MethodsThis study assessed the interannual variability of malaria occurrence from 2001 to 2009 in the Amhara region of Ethiopia. We tested for associations of climate variables summarized during the dry (January–April), early transition (May–June), and wet (July–September) seasons with malaria incidence in the early peak (May–July) and late peak (September–December) epidemic seasons using generalized linear models. Climate variables included land surface temperature (LST), rainfall, actual evapotranspiration (ET), and the enhanced vegetation index (EVI).ResultsWe found that both early and late peak malaria incidence had the strongest associations with meteorological conditions in the preceding dry and early transition seasons. Temperature had the strongest influence in the wetter western districts, whereas moisture variables had the strongest influence in the drier eastern districts. We also found a significant correlation between malaria incidence in the early and the subsquent late peak malaria seasons, and the addition of early peak malaria incidence as a predictor substantially improved models of late peak season malaria in both of the study sub-regions.ConclusionsThese findings suggest that climatic effects on malaria prior to the main rainy season can carry over through the rainy season and affect the probability of malaria epidemics during the late malaria peak. The results also emphasize the value of combining environmental monitoring with epidemiological surveillance to develop forecasts of malaria outbreaks, as well as the need for spatially stratified approaches that reflect the differential effects of climatic variations in the different sub-regions.

BackgroundEarly indication of an emerging malaria epidemic can provide an opportunity for proactive interventions. Challenges to the identification of nascent malaria epidemics include obtaining recent epidemiological surveillance data, spatially and temporally harmonizing this information with timely data on environmental precursors, applying models for early detection and early warning, and communicating results to public health officials. Automated web-based informatics systems can provide a solution to these problems, but their implementation in real-world settings has been limited.MethodsThe Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) computer system was designed and implemented to integrate disease surveillance with environmental monitoring in support of operational malaria forecasting in the Amhara region of Ethiopia. A co-design workshop was held with computer scientists, epidemiological modelers, and public health partners to develop an initial list of system requirements. Subsequent updates to the system were based on feedback obtained from system evaluation workshops and assessments conducted by a steering committee of users in the public health sector.ResultsThe system integrated epidemiological data uploaded weekly by the Amhara Regional Health Bureau with remotely-sensed environmental data freely available from online archives. Environmental data were acquired and processed automatically by the EASTWeb software program. Additional software was developed to implement a public health interface for data upload and download, harmonize the epidemiological and environmental data into a unified database, automatically update time series forecasting models, and generate formatted reports. Reporting features included district-level control charts and maps summarizing epidemiological indicators of emerging malaria outbreaks, environmental risk factors, and forecasts of future malaria risk.ConclusionsSuccessful implementation and use of EPIDEMIA is an important step forward in the use of epidemiological and environmental informatics systems for malaria surveillance. Developing software to automate the workflow steps while remaining robust to continual changes in the input data streams was a key technical challenge. Continual stakeholder involvement throughout design, implementation, and operation has created a strong enabling environment that will facilitate the ongoing development, application, and testing of the system.

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meteorological agencies, space agencies and researchers; a governance structure that ensures data sharing between public and private agencies; a funding model that builds open-access climate databases; climate scientists focused on the delivery of quality products, tailored to user needs; health professionals trained to demand and use climate information; and evidence of the value of all this, relative to alternative investments in health.

Background Despite global intervention efforts, malaria remains a major public health concern in many parts of the world. Understanding geographic variation in malaria patterns and their environmental determinants can support targeting of malaria control and development of elimination strategies. Methods We used remotely sensed environmental data to analyze the influences of environmental risk factors on malaria cases caused by Plasmodium falciparum and Plasmodium vivax from 2014 to 2017 in two geographic settings in Ethiopia. Geospatial datasets were derived from multiple sources and characterized climate, vegetation, land use, topography, and surface water. All data were summarized annually at the sub-district (kebele) level for each of the two study areas. We analyzed the associations between environmental data and malaria cases with Boosted Regression Tree (BRT) models. Results We found considerable spatial variation in malaria occurrence. Spectral indices related to land cover greenness (NDVI) and moisture (NDWI) showed negative associations with malaria, as the highest malaria rates were found in landscapes with low vegetation cover and moisture during the months that follow the rainy season. Climatic factors, including precipitation and land surface temperature, had positive associations with malaria. Settlement structure also played an important role, with different effects in the two study areas. Variables related to surface water, such as irrigated agriculture, wetlands, seasonally flooded waterbodies, and height above nearest drainage did not have strong influences on malaria. Conclusion We found different relationships between malaria and environmental conditions in two geographically distinctive areas. These results emphasize that studies of malaria-environmental relationships and predictive models of malaria occurrence should be context specific to account for such differences.

Background Despite remarkable progress in the reduction of malaria incidence, this disease remains a public health threat to a significant portion of the world’s population. Surveillance, combined with early detection algorithms, can be an effective intervention strategy to inform timely public health responses to potential outbreaks. Our main objective was to compare the potential for detecting malaria outbreaks by selected event detection methods. Methods We used historical surveillance data with weekly counts of confirmed Plasmodium falciparum (including mixed) cases from the Amhara region of Ethiopia, where there was a resurgence of malaria in 2019 following several years of declining cases. We evaluated three methods for early detection of the 2019 malaria events: 1) the Centers for Disease Prevention and Control (CDC) Early Aberration Reporting System (EARS), 2) methods based on weekly statistical thresholds, including the WHO and Cullen methods, and 3) the Farrington methods. Results All of the methods evaluated performed better than a naïve random alarm generator. We also found distinct trade-offs between the percent of events detected and the percent of true positive alarms. CDC EARS and weekly statistical threshold methods had high event sensitivities (80–100% CDC; 57–100% weekly statistical) and low to moderate alarm specificities (25–40% CDC; 16–61% weekly statistical). Farrington variants had a wide range of scores (20–100% sensitivities; 16–100% specificities) and could achieve various balances between sensitivity and specificity. Conclusions Of the methods tested, we found that the Farrington improved method was most effective at maximizing both the percent of events detected and true positive alarms for our dataset (> 70% sensitivity and > 70% specificity). This method uses statistical models to establish thresholds while controlling for seasonality and multi-year trends, and we suggest that it and other model-based approaches should be considered more broadly for malaria early detection.

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