Malaria Infection
Sixth annual Clinic on Meaningful Modeling of Epidemiological Data
June 1-12, 2015, African Institute for Mathematical Sciences, Muizenberg, Cape Town, RSA
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Overview
An important question in understanding malaria dynamics is how infection plays out at the individual level, and particularly how it interacts with clinical immunity.
Understanding the relative importance of clinically immune individuals in transmission has profound impacts for malaria interventions.
This group will analyze individual-level data and evaluate patterns of parasite load through time.
Things to consider
- This group is recommended for:
- Participants who are interested in malaria
- Participants who are interested in within-host dynamics, and implications for population-level patterns
- Participants who are interested in engaging, interpreting and analyzing published data
- Participants who are interested in statistical analyses
- Participants who would be a good fit for this group.
- This group will have the opportunity to engage in any of the following:
- Obtain and clean data from published papers and reproducible research repositories
- Make statistical models of parasite load in individuals through time
- If desired: make dynamical models of parasite load in individuals
- If desired: use individual-level results to parameterize population-level models
Background
Clinical immunity to malaria is an important medical, dynamical and public health phenomenon. The importance of transmission from clinically immune individuals – people who carry malaria parasites but are not clinically ill – in population-level dynamics is poorly understood.
Data
- The Garki project
- The Gonoa study
Resources
References
- Águas et al, 2008 suggested that transmission from clinically immune individuals could lead to “backwards bifurcations”; this issue is also addressed by the Chiyaka 2013 review paper.
- Short review and experimental data on Plasmodium falciparum - This comes from a book on Primate Malarias in the 1970s. It’s an old text but full of interesting (and horrifying) data.
The ICI3D program is supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R25GM102149.