Identifying Malaria Transmission Foci for Elimination Using Human Mobility Data

Nick W. Ruktanonchai, Patrick DeLeenheer, Andrew J. Tatem, Victor A. Alegana, T. Trevor Caughlin, Elisabeth zu Erbach-Schoenberg, Christopher Lourenço, Corrine W. Ruktanonchai, David L. Smith

Research output: Contribution to journalArticle

Abstract

Humans move frequently and tend to carry parasites among areas with endemic malaria and into areas where local transmission is unsustainable. Human-mediated parasite mobility can thus sustain parasite populations in areas where they would otherwise be absent. Data describing human mobility and malaria epidemiology can help classify landscapes into parasite demographic sources and sinks, ecological concepts that have parallels in malaria control discussions of transmission foci. By linking transmission to parasite flow, it is possible to stratify landscapes for malaria control and elimination, as sources are disproportionately important to the regional persistence of malaria parasites. Here, we identify putative malaria sources and sinks for pre-elimination Namibia using malaria parasite rate (PR) maps and call data records from mobile phones, using a steady-state analysis of a malaria transmission model to infer where infections most likely occurred. We also examined how the landscape of transmission and burden changed from the pre-elimination setting by comparing the location and extent of predicted pre-elimination transmission foci with modeled incidence for 2009. This comparison suggests that while transmission was spatially focal pre-elimination, the spatial distribution of cases changed as burden declined. The changing spatial distribution of burden could be due to importation, with cases focused around importation hotspots, or due to heterogeneous application of elimination effort. While this framework is an important step towards understanding progressive changes in malaria distribution and the role of subnational transmission dynamics in a policy-relevant way, future work should account for international parasite movement, utilize real time surveillance data, and relax the steady state assumption required by the presented model.

Original languageEnglish (US)
Article numbere1004846
JournalPLoS Computational Biology
Volume12
Issue number4
DOIs
StatePublished - Apr 1 2016
Externally publishedYes

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ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Modeling and Simulation
  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Molecular Biology
  • Ecology
  • Cellular and Molecular Neuroscience

Cite this

Ruktanonchai, N. W., DeLeenheer, P., Tatem, A. J., Alegana, V. A., Caughlin, T. T., zu Erbach-Schoenberg, E., Lourenço, C., Ruktanonchai, C. W., & Smith, D. L. (2016). Identifying Malaria Transmission Foci for Elimination Using Human Mobility Data. PLoS Computational Biology, 12(4), [e1004846]. https://doi.org/10.1371/journal.pcbi.1004846