Mapping imported malaria in Bangladesh using parasite genetic and human mobility data
Abstract
For countries aiming for malaria elimination, travel of infected individuals between endemic areas undermines local interventions. Quantifying parasite importation has therefore become a priority for national control programs. We analyzed epidemiological surveillance data, travel surveys, parasite genetic data, and anonymized mobile phone data to measure the spatial spread of malaria parasites in southeast Bangladesh. We developed a genetic mixing index to estimate the likelihood of samples being local or imported from parasite genetic data and inferred the direction and intensity of parasite flow between locations using an epidemiological model integrating the travel survey and mobile phone calling data. Our approach indicates that, contrary to dogma, frequent mixing occurs in low transmission regions in the southwest, and elimination will require interventions in addition to reducing imported infections from forested regions. Unlike risk maps generated from clinical case counts alone, therefore, our approach distinguishes areas of frequent importation as well as high transmission.
Data availability
All genetic data are included in Supplementary file 4 and all travel matrices are included in Supplementary file 5.
Article and author information
Author details
Funding
National Institute of General Medical Sciences (U54GM088558)
- Hsiao-Han Chang
Burroughs Wellcome Fund
- Amy Wesolowski
Bill and Melinda Gates Foundation (CPT000390)
- Ipsita Sinha
- Sazid Ibna Zaman
- Richard J Maude
Medical Research Council (G0600718)
- Christopher G Jacob
- Eleanor Drury
- Sonia Gonçalves
- Mihir Kekre
- Dominic Kwiatkowski
National Institute of General Medical Sciences (R35GM124715-02)
- Caroline Buckee
Bill and Melinda Gates Foundation (OPP1118166)
- Christopher G Jacob
- Olivo Miotto
- Caroline Buckee
Bill and Melinda Gates Foundation (OPP1129596)
- Ipsita Sinha
- Sazid Ibna Zaman
- Richard J Maude
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2019, Chang et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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