Mathematical modeling for public health policy in resource limited settings
Abstract/Contents
- Abstract
- The goal of the research presented in this dissertation is to present a data driven policy making approach in three different settings. In the first setting, we look at using data to inform policies to supply supplementary food aid for undernourished children in Guatemala. We fit a trivariate model of weight-for-age z score (WAZ), height-for-age z score (HAZ) and diarrhea status to data from an observational study of supplemen- tary feeding in 17 Guatemalan communities. We estimate how the effect of supplementary food on WAZ, HAZ and diarrhea status varies with a child's age. We find that the effect of supplementary food on all 3 metrics decreases linearly with age from 6 to 20 mo and has little effect after 20 mo. We derive 2 food allocation policies that myopically (i.e., looking ahead 2 mo) minimize either the underweight or stunting severity -- i.e., the sum of squared WAZ or HAZ scores for all children with WAZ or HAZ < 0. A simulation study based on the statistical model predicts that in a low-dose (100 kCal/day) supplementary feeding setting in Guatemala, allocating food primarily to 6-12 mo infants can reduce the severity of underweight and stunting. In the second setting, we look at analyzing how targeting interventions to the most under- nourished would perform in reducing the malaria burden amongst children in sub-Saharan Africa. We construct a malaria model with superinfection and heterogeneous susceptibility, where a portion of this susceptibility is due to undernutrition (as measured by weight-for-age z scores); so as to isolate the impact of supplementary food on malaria from the influence of confounding factors, we estimate the portion of the total susceptibility that is due to undernutrition from a large randomized trial of supplementary feeding. A simulation study based on the malaria model suggests that targeting insecticide-treated bed nets to undernutritioned children leads to fewer malaria deaths than the random distribution of bed nets in the hypoendemic and mesoendemic settings. In the third setting, we look to quantify Tuberculosis (TB) transmission risk within and outside cells using high-resolution social contact data of prisoner in a large prison in the state of Mato Grosso do Sul, Brazil. We then develop and parameterize a model of TB natural history and transmission to evaluate the impact of TB control interventions in prisons on TB incidence amongst the prisoners. A simulation study using this model suggests that any single intervention (improved case detection, decrowding or active case detection) would be insufficient to bring the TB incidence back to the TB incidence levels of the early 2000s but a combination of interventions would be necessary. The results of these studies demonstrate that mathematical modeling can be a powerful tool in understanding the interplaying factors behind the policy in question and help policy makers make informed decisions.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Lakkam, Milinda | |
|---|---|---|
| Associated with | Stanford University, Institute for Computational and Mathematical Engineering. | |
| Primary advisor | Wein, Lawrence | |
| Thesis advisor | Wein, Lawrence | |
| Thesis advisor | Andrews, Jason | |
| Thesis advisor | Gerritsen, Margot (Margot G.) | |
| Advisor | Andrews, Jason | |
| Advisor | Gerritsen, Margot (Margot G.) |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Milinda Lakkam. |
|---|---|
| Note | Submitted to the Institute for Computational and Mathematical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Milinda Lakkam
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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