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Modelling Co-Infection with Malaria and Lymphatic Filariasis

Overview of attention for article published in PLoS Computational Biology, June 2013
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Title
Modelling Co-Infection with Malaria and Lymphatic Filariasis
Published in
PLoS Computational Biology, June 2013
DOI 10.1371/journal.pcbi.1003096
Pubmed ID
Authors

Hannah C. Slater, Manoj Gambhir, Paul E. Parham, Edwin Michael

Abstract

Malaria and lymphatic filariasis (LF) continue to cause a considerable public health burden globally and are co-endemic in many regions of sub-Saharan Africa. These infections are transmitted by the same mosquito species which raises important questions about optimal vector control strategies in co-endemic regions, as well as the effect of the presence of each infection on endemicity of the other; there is currently little consensus on the latter. The need for comprehensive modelling studies to address such questions is therefore significant, yet very few have been undertaken to date despite the recognised explanatory power of reliable dynamic mathematical models. Here, we develop a malaria-LF co-infection modelling framework that accounts for two key interactions between these infections, namely the increase in vector mortality as LF mosquito prevalence increases and the antagonistic Th1/Th2 immune response that occurs in co-infected hosts. We consider the crucial interplay between these interactions on the resulting endemic prevalence when introducing each infection in regions where the other is already endemic (e.g. due to regional environmental change), and the associated timescale for such changes, as well as effects on the basic reproduction number Râ‚€ of each disease. We also highlight potential perverse effects of vector controls on human infection prevalence in co-endemic regions, noting that understanding such effects is critical in designing optimal integrated control programmes. Hence, as well as highlighting where better data are required to more reliably address such questions, we provide an important framework that will form the basis of future scenario analysis tools used to plan and inform policy decisions on intervention measures in different transmission settings.

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Geographical breakdown

Country Count As %
United Kingdom 2 2%
United States 2 2%
Indonesia 1 <1%
India 1 <1%
Unknown 103 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 16%
Researcher 15 14%
Student > Master 12 11%
Student > Bachelor 12 11%
Student > Doctoral Student 9 8%
Other 21 19%
Unknown 23 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 21%
Mathematics 12 11%
Biochemistry, Genetics and Molecular Biology 10 9%
Medicine and Dentistry 10 9%
Environmental Science 7 6%
Other 22 20%
Unknown 25 23%