Disease Surveillance Using Molecular and Geographic Methods in the Congo

Project Summary

Infectious diseases are still the leading cause of disability and death in poor countries. But poor countries also have poor public health infrastructures. As a result, we can make only educated guesses about the distributions and burdens of important endemic tropical diseases like malaria, trypanosomiasis, leishmaniasis, and filariasis on the global, national, and subnational levels. This lack of clarity impedes effective resource allocation, implementation of control measures, and the monitoring and evaluation of interventions. In addition, new infectious diseases continually emerge, often in poorer countries. They pose serious threats to global public health. Better surveillance for emerging infections could lead to earlier and more effective global responses.

Our long-term goal is to expand the mission of MEASURE DHS to include gathering prevalence data on endemic and emerging infectious diseases using high-throughput molecular diagnostics. Demographic Health Surveys (DHSs) are well-respected, time-tested sources of population-based data on demography, reproductive health, and HIV. By leveraging the DHS infrastructure, we aim (1) to acquire global population-based data for multiple infectious diseases. The DHS data will be used to calculate national disease prevalence, (2) to map the spatial distribution of the disease prevalence, and (3) to develop methods that use resulting geographic information systems (GIS) maps of prevalence for ecological mapping and analysis.

The prevalences of malaria, drug-resistant malaria, and African trypanosomiasis in the Democratic Republic of the Congo (DRC) will be mapped. This data will be provided to the DRC Ministry of Health to help guide control programs. Prevalence maps will be integrated with population-level factors in order to identify population and environmental factors that are related to disease prevalence. This ecological analysis can then be used to estimate potential prevalence where DBSs are unavailable or surveillance programs are not in place. This extrapolation will be done by initially overlaying population and environmental ecosystem variables that are hypothesized to be related to African trypanosomiasis and malaria with the prevalence maps. For instance, tsetse fly distribution is a limiting factor for African trypanosomiasis prevalence, and the United Nations Food and Agriculture Organization has mapped the distribution of the 23 species. Other factors (i.e., GIS layers) that will be included in the analysis will include maps of land use-land cover, socio-economic status, and population and demographic variables available through both the DHS demographic information and other sources such as the Gridded Population of the World (GPWv3) data.

Project Team Members

Michael Emch
Janey Messina
Sophia Giebultowicz

Funding

Meshnick, S. (PI), Emch, M.E. (Co-PI), and Miller, M. (Co-PI). Center for Accurate Data on Endemic and Emerging Infectious Diseases in Developing Countries, Gillings Innovation Labs, UNC School of Public Health, $504,452. 2008-10.