UC Berkeley

Infectious disease modeling has an untapped potential to provide insight into how disease burden is distributed in human populations. Here, we apply the techniques of infectious disease modeling to applications ranging from health economics to molecular epidemiology.

The persistence of extreme poverty is increasingly attributed to dynamic interactions between biophysical processes and economics, though there remains a dearth of integrated theoretical frameworks that can inform policy. In Chapter 1, we present a stochastic model of disease-driven poverty traps. Whereas deterministic models can result in poverty traps that can only be broken by substantial external changes to the initial conditions, in the stochastic model there is always some probability that a population will leave or enter a poverty trap. We show that a `safety net', defined as an externally enforced minimum level of health or economic conditions, can guarantee ultimate escape from a poverty trap, even if the safety net is set

within the basin of attraction of the poverty trap, and even if the safety net is only in the form of a public health measure. Whereas the deterministic model implies that small improvements in initial conditions near the poverty-trap equilibrium are futile, the stochastic model suggests that the impact of changes in the location of the safety net on the rate of development may be strongest near the poverty-trap equilibrium.

In Chapter 2, we show that the same feedbacks between health and income explored in the first chapter, when applied to an individual level (rather than population level), can lead to persistent poverty and high levels of disease among certain individuals in a population, even when the population overall has high income and low disease. This suggests that disease-induced poverty might be a compelling mechanistic explanation for the persistence of health and wealth disparities. Using an individual-based network model with community structure, we show that the structure of the disease transmission network is crucial for the formation of clusters of high poverty and high disease, further highlighting the importance of population structure in studying issues of human health, a topic of increasing importance in both infectious disease modeling as well as social epidemiology.

In Chapter 3, we show how network structure could potentially be measured, using standard molecular epidemiology techniques. Using DNA sequence data from pathogens to infer transmission networks has traditionally been done in the context of epidemics and outbreaks. Sequence data could analogously be applied to cases of ubiquitous commensal bacteria; however, instead of inferring chains of transmission to track the spread of a pathogen, sequence data for bacteria circulating in an endemic equilibrium could be used to infer information about host contact networks. We show - using simulated data - that multilocus DNA sequence data, based on multilocus sequence typing schemes (MLST), from isolates of commensal bacteria can be used to infer both local and global properties of the contact networks of the populations being sampled. Specifically, for MLST data simulated from small-world networks, the small world parameter controlling the degree of structure in the contact network can robustly be estimated. Moreover, we show that pairwise distances in the network - degrees of separation - correlate with genetic distances between isolates, so that how far apart two individuals in the network are can be inferred from MLST analysis of their commensal bacteria. This result has important consequences, and we show an example from epidemiology - how this result could be used to test for infectious origins of diseases of unknown etiology.