The global malaria burden remains high despite recent advances in malaria control efforts. The goal of this project is to develop data-driven tool to enable malaria control programs to target the malaria transmission reservoir. We will use samples from the Democratic Republic of the Congo, one of countries with the highest malaria burden in the world. Dried blood spots will be obtained a large cross-sectional study, the 2014 Demographic and Health Survey (DHS), and from a new prospective study of 1000 individuals in 4 sites with varying levels of endemicity. Gametocytemia will be measured by reverse transcriptase PCR.
Our first aim i s to map the gametocyte reservoir and determine how individual, community-level, and ecological factors determine an individual's risk for gametocyte carriage.
Our second aim i s to identify barriers and corridors of gene flow. The structure of the P. falciparum at each cluster in the DHS will be assessed using neutral microsatellites. The genetic distances between parasite subpopulations will be calculated using population genetic metrics (Rst, Gst, Nm) and used to infer corridors of, and barriers to, gene flow.
Our third aim i s to use the results from aims 1 and 2 to parameterize and improve an existing mathematical spatial model for malaria transmission. This latter model will be developed into a user-friendly freely available PC-based tool to allow the impact of targeted control to be explored in DRC and the surrounding region. This project represents a unique multidisciplinary collaboration between molecular epidemiologists, medical geographers and mathematical modelers. By incorporating results from molecular and spatial epidemiological studies into mathematical models, we hope to develop a useful and practical tool for malaria control.

Public Health Relevance

Malaria represents a huge global health problem, especially in the Democratic Republic of the Congo (DRC) where 1/3 of adults may be infected. Malaria control efforts need to target the transmission reservoir, people bearing the gametocyte lifecycle stage. The aim of this proposal is to develop maps and tools to enable malaria control programs to predict how malaria spreads over space and how to target hotspots of transmission

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
1R01AI107949-01A1
Application #
8676238
Study Section
Infectious Diseases, Reproductive Health, Asthma and Pulmonary Conditions Study Section (IRAP)
Program Officer
Rao, Malla R
Project Start
2014-04-01
Project End
2019-03-31
Budget Start
2014-04-01
Budget End
2015-03-31
Support Year
1
Fiscal Year
2014
Total Cost
$594,403
Indirect Cost
$181,280
Name
University of North Carolina Chapel Hill
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
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Janko, Mark M; Irish, Seth R; Reich, Brian J et al. (2018) The links between agriculture, Anopheles mosquitoes, and malaria risk in children younger than 5 years in the Democratic Republic of the Congo: a population-based, cross-sectional, spatial study. Lancet Planet Health 2:e74-e82
Verity, Robert; Hathaway, Nicholas J; Waltmann, Andreea et al. (2018) Plasmodium falciparum genetic variation of var2csa in the Democratic Republic of the Congo. Malar J 17:46
Janko, Mark M; Churcher, Thomas S; Emch, Michael E et al. (2018) Strengthening long-lasting insecticidal nets effectiveness monitoring using retrospective analysis of cross-sectional, population-based surveys across sub-Saharan Africa. Sci Rep 8:17110
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Parr, Jonathan B; Verity, Robert; Doctor, Stephanie M et al. (2017) Pfhrp2-Deleted Plasmodium falciparum Parasites in the Democratic Republic of the Congo: A National Cross-sectional Survey. J Infect Dis 216:36-44
Mwandagalirwa, Melchior Kashamuka; Levitz, Lauren; Thwai, Kyaw L et al. (2017) Individual and household characteristics of persons with Plasmodium falciparum malaria in sites with varying endemicities in Kinshasa Province, Democratic Republic of the Congo. Malar J 16:456
Miller, Robin H; Hathaway, Nicholas J; Kharabora, Oksana et al. (2017) A deep sequencing approach to estimate Plasmodium falciparum complexity of infection (COI) and explore apical membrane antigen 1 diversity. Malar J 16:490
Watson, Oliver J; Slater, Hannah C; Verity, Robert et al. (2017) Modelling the drivers of the spread of Plasmodium falciparum hrp2 gene deletions in sub-Saharan Africa. Elife 6:

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