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.

ObjectiveTo evaluate the importance of external quality assessment program on malaria microscopic diagnosis.ResultsA total of 3148 slides were collected in 4 consecutive external quality assessment rounds and blindly rechecked at Amhara Public Health Institute. The average agreement between health facility and APHI slide readers was 96.6%. The percent agreement for parasite detection and species identification for P. falciparum became improved in four consecutive EQA rounds from 93.88 to 99.24% and 92.67 to 97.35% respectively. The rates of false positive and false negative were also dramatically decreased in each round from 10.5 to 0.79% and 2.14 to 0.74% respectively. Therefore, we recommend that malaria EQA program should maintain and expand in all malaria diagnostic health facilities in the region to provide accurate and reliable malaria microscopic service.

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.

Hysteria described for more than 600 years in a variety of cultures and settings for significant adverse of public health consequences and economic implications. The aim of this study was to investigate the outbreak, determine possible risk factors and guide intervention measures. In December 25, 2012, district health office notified to Regional Health Bureau about a suspected hysteria outbreak at Kombolcha General primary school. We investigated all 50 cases and compared with 100 matched controls. For the study, detailed discussions were also undertaken with school principal, teachers, students' parents, district health officers and administrators about the event. Then data was analyzed using Epi Info version 7. Fifty cases and no death were identified. The mean age of all cases and controls was 13 with a range 9-16 year. All were girls, and mostly friends (75%). The overall attack rate of the cases were 32 per 1000 populations in all age group. Using multivariate analysis, illness were remained as risk factors, perceive evil devil force (Adjusted Odds Ratio (AOR) 5.3 with 95% CI 2.3-12), psycho stress (AOR) 2.6, 95% CI 1.14-5.72) and seeing the affected students (AOR) 2.9; 95% CI 1.1-7.78). Knowledge of modes of transmission illness (AOR) 0.48, 95% CI 0.24-0.96) and separation of girls from the environment at least for 1-2 weeks (AOR) 0.49, (95% CI 0.22-0.98) were remained as protective factors for the illness. The study confirmed the hysteria outbreak in Kombolcha Town of school girls at General Primary school. And threatening situation was associated with a socio-cultural belief with psycho stress. We recommended conducting immediate reassurance, separate therapy, create community awareness about the illness and counseling at the school could be possible to manage events.

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.

While malaria is mostly an endemic disease in Ethiopia. It may also occur as outbreaks in areas with low seasonal transmission because of an increase in vector breeding sites, migration of infected people into a vector-rich area populated with susceptible individuals, breakdown of vector control measures and resistance of the vectors to insecticides. The aim of this study was to verify, investigate the outbreak epidemiologically and guide intervention measures. We reviewed data from health post records from 2007 to 2011 and screened randomly 50 fevers cases from the community members by Rapid Diagnostic Test/RDT/ to determine the baseline incidence of malaria in Ankesha District. We defined a confirmed malaria cases as any person with fever or fever with headache with plasmodium parasites by confirmed microscopy and RDT. We conduct descriptive epidemiological study and Entomological survey. A total of 2,061 confirmed malaria cases and one death (CFR=0.05%) were identified. Among the species distributions, plasmodium falciparum was accounted 1484 (72%). The alert threshold (Quartile for 5 th year's data/doubling the last year data) had been reached and crossed in 24 th March, 2012 in all health facilities. It showed highest peak between the epidemiological weeks of 17-27 in 2012. The median age was 17 years with a range 0.2 to 83 years. The highest age specific attack rate was in 5-14yr, 17.9 per1000 (642) population. Among sex distribution male comprise 53.3%. The attack rate was high in Azena Kebele which was 165per 1000(883) population in under-fives and 132 per 1000(745) population in>5 years old. Environmental assessment revealed the presence of multiple breeding sits, which was favorable for larvae of Anopheles mosquito. Arbitrarily 53 visited households were observed the LLIN bed net utilization, only 17 (32.1%) households were hanging in their ceiling directly to the bedding. And also IRS was not sprayed for last one years in all affected kebeles. The outbreak was caused by the breakage of Ayehu river formed high larva density, improper usage of LLIN's bed net and impediment / low coverage of IRS operation. Factors such as delayed detection and notification and inadequate surveillance were also instrumental for the outbreak. We recommended creates awareness giving health education, strengthen the surveillance system and implement a community based program on the LLINs utilization, IRS and environmental management.

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. We used multiple sources of remotely-sensed environmental data to analyze the influences of environmental risk factors on malaria cases caused by Plasmodium falciparum and Plasmodium vivax in two geographic settings in Ethiopia. Results We found considerable spatial variation in malaria proportion and the distribution of malaria hot spots. Spectral indices were related to land cover greenness (NDVI) and moisture (NDWI) showed an association between malaria and dry landscapes. Climatic factors, including precipitation and land surface temperature, had positive associations with malaria occurrence. Settlement structure also played an important role, with opposing relationships between settlement density and malaria for the two study areas. Variables related to land surface water, such as irrigated agriculture, wetlands, seasonally flooded waterbodies and height above nearest drainage did not influence malaria proportion. 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 occurence 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 early detection methods to detect 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 algorithms.Results All of the methods and parameters 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 (83% sensitivity, 51% 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.