Keywords
Malaria; early warning; early detection; remote sensing; health informatics
Location
Session E2: Environmental Modeling of Human Health Effects from Global to Local Scale
Start Date
18-6-2014 9:00 AM
End Date
18-6-2014 10:20 AM
Abstract
Advance information about the timing and locations of malaria epidemics allows more effective targeting of resources for prevention, control, and treatment. However, these predictions must be accurate to ensure that potential outbreaks are not missed and resources are not wasted responding to predicted outbreaks that do not occur. Early warning systems based on environmental monitoring can identify critical risk factors before an epidemic actually starts, but their accuracy is constrained by the complex interrelationships of climatic variability, mosquito population dynamics, malaria transmission, and the resulting risk of human infection. In contrast, early detection of malaria epidemics based on epidemiological surveillance can be more reliable because it is conditioned on direct observations, but it offers limited lead time and is dependent on timely, accurate surveillance data. Ideally, a malaria forecasting system that integrates elements of both early warning and early detection should be able to leverage the strengths and minimize the limitations of each approach. One reason for this lack of integration is a dearth of suitable tools and techniques. In response, we have developed a conceptual framework for the Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) computer system. This framework includes definitions of system users and their associated use cases, identification of critical input data, and a scientific workflow for integrating these spatially and temporally heterogeneous data streams to yield predictions of malaria risk. The system will be tested by implementing it in the Amhara Region of Ethiopia in collaboration with local stakeholders in the NGO and public health sectors. This innovative translational approach to health informatics will enable us to assess the practical effectiveness of the tools and continually upgrade and improve the technologies.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
EPIDEMIA - An EcoHealth Informatics System for Integrated Forecasting of Malaria Epidemics
Session E2: Environmental Modeling of Human Health Effects from Global to Local Scale
Advance information about the timing and locations of malaria epidemics allows more effective targeting of resources for prevention, control, and treatment. However, these predictions must be accurate to ensure that potential outbreaks are not missed and resources are not wasted responding to predicted outbreaks that do not occur. Early warning systems based on environmental monitoring can identify critical risk factors before an epidemic actually starts, but their accuracy is constrained by the complex interrelationships of climatic variability, mosquito population dynamics, malaria transmission, and the resulting risk of human infection. In contrast, early detection of malaria epidemics based on epidemiological surveillance can be more reliable because it is conditioned on direct observations, but it offers limited lead time and is dependent on timely, accurate surveillance data. Ideally, a malaria forecasting system that integrates elements of both early warning and early detection should be able to leverage the strengths and minimize the limitations of each approach. One reason for this lack of integration is a dearth of suitable tools and techniques. In response, we have developed a conceptual framework for the Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) computer system. This framework includes definitions of system users and their associated use cases, identification of critical input data, and a scientific workflow for integrating these spatially and temporally heterogeneous data streams to yield predictions of malaria risk. The system will be tested by implementing it in the Amhara Region of Ethiopia in collaboration with local stakeholders in the NGO and public health sectors. This innovative translational approach to health informatics will enable us to assess the practical effectiveness of the tools and continually upgrade and improve the technologies.
