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Understanding poverty and malaria dynamics

Overview

Coupled models of socio-economic and epidemiological components of poverty can provide key insights into the formation of disease-driven poverty traps (vicious cycles of disease and poverty) that arise from complex interactions between environmental and biosocial processes, insights that can also inform public health and economic development policies. However, modelers of epidemiological and ecological systems often neglect socio-economic aspects and focus exclusively on epidemiological and ecological factors.

This group will 1) couple a malaria transmission model with an economic growth model, 2) test the coupled system with statistical analyses on data from Democratic Republic of Congo, 3) explore whether the coupled system can give rise to complex emergent properties such as multi-stable states, and virtuous versus vicious cycles, and 4) estimate the economic burden of disease on populations.

Things to consider

• This group is recommended for:
  • Participants who are interested in malaria and infectious diseases of the poor
  • Participants who are interested in economic growth theory
  • Participants who are interested in interactions between diseases and poverty
  • Participants who are interested in fitting dynamical models to disease and economic data
• This group will have the opportunity to engage in any of the following:
  • Couple disease and economic models together
  • Fit coupled models to data from Democratic Republic Congo
  • Conduct global uncertainty and sensitivity analyses
• Potential group members are encouraged to review the following sessions before before Week 2:
  • Introduction to dynamic modeling of infectious diseases (Monday AM)
  • Introduction to infectious disease data (Track A, Monday PM)
  • Foundations of dynamic modeling (Track B, Monday PM)
  • Introduction to Likelihood (Thursday PM)
  • Likelihood fitting and dynamic models I (Friday AM)
  • MLE fitting of an SIR model to prevalence data (Friday PM)

Background

Understanding why some human populations remain poor is a significant challenge for both the natural and social sciences. The extremely poor are generally reliant on their immediate environment and natural resource base for subsistence and suffer from high burdens of disease. Thus, two primary characteristics of underdeveloped economies are the role of subsistence agriculture as the primary form of economic activity, and high morbidity and mortality due to infectious diseases. An understanding of the dynamics of poverty from an epidemiological/multidisciplinary perspective can have significant implications for public health policy and basic research at the interface between the epidemiological, mathematical, and social sciences.

Data

This project will rely on the following publicly available datasets:

• GDP data
• Malaria data
• DALYs (Malaria)

Resources

References

• Ngonghala, CN, MM Plucinski, MB Murray, PE Farmer, CB Barrett, DC Keenan, & MH Bonds. (2014) Poverty, disease, and the ecology of complex systems. PLoS Biology 12 (4), e100182
• Bonds MH, Keenan DC, Rohani P, & Sachs JD (2010) Poverty trap formed by the ecology of infectious diseases. Proceedings of Royal Society B: Biological Sciences 277 (1685):1185-92
• Rist, CL, CN Ngonghala, A Garchitorena, CE Brook, R Ramananjato, AC Miller, M Randrianarivelojosia, PC Wright, TR Gillespie, & MH Bonds (2015) Modeling the burden of poultry disease on the rural poor in Madagascar. One Health 1, 60–65
• Rist, CL, A Garchitorena, CN Ngonghala, TR Gillespie, & MH Bonds (2015) The burden of livestock parasites on the poor. Trends in Parasitology 31 (11), 527–530
• Garchitorena, A, CN Ngonghala, J-F Guegan, G Texier, M Bellanger, B Roche, & MH Bonds. (2015) Economic inequality caused by feedbacks between poverty and the dynamics of a rare tropical disease: the case of Buruli ulcer in sub-Saharan Africa. Proceedings of Royal Society B: Biological Sciences 282 (1818), 20151426
• Plucinski, MM, CN Ngonghala, W Getz, & MH Bonds (2013) Clusters of poverty and disease emerge in epidemiological networks with community structure. Journal of The Royal Society Interface 10 (80), 20120656
• Plucinski, MM, CN Ngonghala, & MH Bonds (2011) Health safety nets can break cycles of poverty and disease: a stochastic ecological model. Journal of The Royal Society Interface 8 (65), 1796–1803

Tutorials

• Lab 1: ODE models in R
• Lab 5: Introduction to Likelihood