Does mass drug administration for community-based scabies control works? The experience in Ethiopia.

INTRODUCTION: After a scabies outbreak in Amhara Region, Ethiopia in 2015/2016, the Regional Health Bureau performed an extensive Mass Drug Administration (MDA). In May 2017, we collected data to assess the impact of the treatment on the scabies control. METHODOLOGY: We retrieved baseline data from the 2015/16 burden assessment: campaign organization and administration information. We did a community based cross-sectional study using a structured questionnaire on disease and treatment history plus the presence or absence of active scabies in three Zones. We selected households using stratified random sampling deployed 7581 questionnaires and performed key informant interviews. RESULTS: 46.3% had a previous scabies diagnosis in the last 2 years of which 86.1% received treatment, and the cure rate was 90.6%. Fifteen months after intervention the scabies prevalence was 21.0 % (67.3% new cases and 32.7% recurrences). The highest burden of new cases (93.1%) was found in the North Gondar zone. The likelihood of treatment failure was higher for treatments offered in clinics (12.2%) as opposed to via the campaign (7.9%). Failure to follow the guidelines, shortage of medicine and lack of leadership prioritization were identified as reasons for resurgence of the disease. CONCLUSIONS: We demonstrated that community engagement is essential in the success of scabies MDA, alongside strong political commitment, and guideline adherence. Effectiveness and sustainability of the MDA was compromised by the failing of proper contact treatment, surveillance and case management.

A genetic algorithm for identifying spatially-varying environmental drivers in a malaria time series model.

Time series models of malaria cases can be applied to forecast epidemics and support proactive interventions. Mosquito life history and parasite development are sensitive to environmental factors such as temperature and precipitation, and these variables are often used as predictors in malaria models. However, malaria-environment relationships can vary with ecological and social context. We used a genetic algorithm to optimize a spatiotemporal malaria model by aggregating locations into clusters with similar environmental sensitivities. We tested the algorithm in the Amhara Region of Ethiopia using seven years of weekly Plasmodium falciparum data from 47 districts and remotely-sensed land surface temperature, precipitation, and spectral indices as predictors. The best model identified six clusters, and the districts in each cluster had distinctive responses to the environmental predictors. We conclude that spatial stratification can improve the fit of environmentally-driven disease models, and genetic algorithms provide a practical and effective approach for identifying these clusters.

Integrating malaria surveillance with climate data for outbreak detection and forecasting: the EPIDEMIA system.

BACKGROUND: Early 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. METHODS: The 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. RESULTS: The 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. CONCLUSIONS: Successful 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.