%0 Journal Article %1 wesolowski2018mapping %A Wesolowski, Amy %A Taylor, Aimee %A Chang, Hsiao-Han %A Verity, Robert %A Tessema, Sofonias %A Bailey, Jeffrey %A Perkins, T. Alex %A Neafsey, Daniel %A Greenhouse, Bryan %A Buckee, Caroline O %D 2018 %I Cold Spring Harbor Laboratory %J bioRxiv %K epidemiology malaria malaria_control mosquitos spatial_epidemiology spatial_spread %R 10.1101/288506 %T Mapping malaria by combining parasite genomic and epidemiologic data %U https://www.biorxiv.org/content/early/2018/03/26/288506 %X Recent global progress in scaling up malaria control interventions has revived the goal of complete elimination in many countries. Decreasing transmission intensity generally leads to increasingly patchy spatial patterns of malaria transmission, however, and control programs must accurately identify remaining foci in order to target interventions efficiently. In particular, mosquito control interventions like bed nets and insecticide spraying are best targeted to transmission hotspots, and the role of connectivity between different pockets of local transmission becomes increasingly important since humans are able to move parasites beyond the limits of mosquito dispersal and re-introduce parasites to previously malaria-free regions. Quantifying the connectivity between regions due to human travel, measuring malaria transmission intensity in different areas, and monitoring parasite spatial spread are therefore key issues for policy-makers because they underpin the feasibility of elimination and inform the path to its attainment. To this end, recent efforts have been made to develop new approaches to incorporating human mobility into spatial epidemiological models, for example using mobile phone data, and there has been a surge of interest in collecting spatially informative parasite samples to measure the genomic signatures of parasite connectivity. Due to their complicated life-cycles, Plasmodium parasites pose unique challenges to researchers in this respect and new methods that move beyond traditional phylogenetic and population genetic tools must be developed to harness genetic information effectively. Here, we discuss the spatial epidemiology of malaria in the context of transmission-reduction interventions, and the challenges and promising directions for the development of integrated mapping, modeling, and genomic approaches that leverage disparate data sets to measure both connectivity and transmission.

@article{wesolowski2018mapping, abstract = {Recent global progress in scaling up malaria control interventions has revived the goal of complete elimination in many countries. Decreasing transmission intensity generally leads to increasingly patchy spatial patterns of malaria transmission, however, and control programs must accurately identify remaining foci in order to target interventions efficiently. In particular, mosquito control interventions like bed nets and insecticide spraying are best targeted to transmission hotspots, and the role of connectivity between different pockets of local transmission becomes increasingly important since humans are able to move parasites beyond the limits of mosquito dispersal and re-introduce parasites to previously malaria-free regions. Quantifying the connectivity between regions due to human travel, measuring malaria transmission intensity in different areas, and monitoring parasite spatial spread are therefore key issues for policy-makers because they underpin the feasibility of elimination and inform the path to its attainment. To this end, recent efforts have been made to develop new approaches to incorporating human mobility into spatial epidemiological models, for example using mobile phone data, and there has been a surge of interest in collecting spatially informative parasite samples to measure the genomic signatures of parasite connectivity. Due to their complicated life-cycles, Plasmodium parasites pose unique challenges to researchers in this respect and new methods that move beyond traditional phylogenetic and population genetic tools must be developed to harness genetic information effectively. Here, we discuss the spatial epidemiology of malaria in the context of transmission-reduction interventions, and the challenges and promising directions for the development of integrated mapping, modeling, and genomic approaches that leverage disparate data sets to measure both connectivity and transmission.}, added-at = {2018-04-07T05:31:16.000+0200}, author = {Wesolowski, Amy and Taylor, Aimee and Chang, Hsiao-Han and Verity, Robert and Tessema, Sofonias and Bailey, Jeffrey and Perkins, T. Alex and Neafsey, Daniel and Greenhouse, Bryan and Buckee, Caroline O}, biburl = {https://www.bibsonomy.org/bibtex/2580ffbfad8277cc6093014898411ba86/peter.ralph}, doi = {10.1101/288506}, eprint = {https://www.biorxiv.org/content/early/2018/03/26/288506.full.pdf}, interhash = {4f01670ba1289008b0e0f1953a8f4a91}, intrahash = {580ffbfad8277cc6093014898411ba86}, journal = {bioRxiv}, keywords = {epidemiology malaria malaria_control mosquitos spatial_epidemiology spatial_spread}, publisher = {Cold Spring Harbor Laboratory}, timestamp = {2018-04-07T05:31:16.000+0200}, title = {Mapping malaria by combining parasite genomic and epidemiologic data}, url = {https://www.biorxiv.org/content/early/2018/03/26/288506}, year = 2018 }