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1.  Poverty, Disease, and the Ecology of Complex Systems 
PLoS Biology  2014;12(4):e1001827.
Coupled models of ecology and economic growth can provide key insights into the formation of poverty traps that arise from complex interactions between biosocial and biophysical processes.
Understanding why some human populations remain persistently poor remains a significant challenge for both the social and natural sciences. The extremely poor are generally reliant on their immediate natural resource base for subsistence and suffer high rates of mortality due to parasitic and infectious diseases. Economists have developed a range of models to explain persistent poverty, often characterized as poverty traps, but these rarely account for complex biophysical processes. In this Essay, we argue that by coupling insights from ecology and economics, we can begin to model and understand the complex dynamics that underlie the generation and maintenance of poverty traps, which can then be used to inform analyses and possible intervention policies. To illustrate the utility of this approach, we present a simple coupled model of infectious diseases and economic growth, where poverty traps emerge from nonlinear relationships determined by the number of pathogens in the system. These nonlinearities are comparable to those often incorporated into poverty trap models in the economics literature, but, importantly, here the mechanism is anchored in core ecological principles. Coupled models of this sort could be usefully developed in many economically important biophysical systems—such as agriculture, fisheries, nutrition, and land use change—to serve as foundations for deeper explorations of how fundamental ecological processes influence structural poverty and economic development.
PMCID: PMC3972083  PMID: 24690902
2.  Clusters of poverty and disease emerge from feedbacks on an epidemiological network 
The distribution of health conditions is characterized by extreme inequality. These disparities have been alternately attributed to disease ecology and the economics of poverty. Here, we provide a novel framework that integrates epidemiological and economic growth theory on an individual-based hierarchically structured network. Our model indicates that, under certain parameter regimes, feedbacks between disease ecology and economics create clusters of low income and high disease that can stably persist in populations that become otherwise predominantly rich and free of disease. Surprisingly, unlike traditional poverty trap models, these localized disease-driven poverty traps can arise despite homogeneity of parameters and evenly distributed initial economic conditions.
PMCID: PMC3565726  PMID: 23256187
poverty traps; transmission networks; social epidemiology
3.  Persisting Viral Sequences Shape Microbial CRISPR-based Immunity 
PLoS Computational Biology  2012;8(4):e1002475.
Well-studied innate immune systems exist throughout bacteria and archaea, but a more recently discovered genomic locus may offer prokaryotes surprising immunological adaptability. Mediated by a cassette-like genomic locus termed Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), the microbial adaptive immune system differs from its eukaryotic immune analogues by incorporating new immunities unidirectionally. CRISPR thus stores genomically recoverable timelines of virus-host coevolution in natural organisms refractory to laboratory cultivation. Here we combined a population genetic mathematical model of CRISPR-virus coevolution with six years of metagenomic sequencing to link the recoverable genomic dynamics of CRISPR loci to the unknown population dynamics of virus and host in natural communities. Metagenomic reconstructions in an acid-mine drainage system document CRISPR loci conserving ancestral immune elements to the base-pair across thousands of microbial generations. This ‘trailer-end conservation’ occurs despite rapid viral mutation and despite rapid prokaryotic genomic deletion. The trailer-ends of many reconstructed CRISPR loci are also largely identical across a population. ‘Trailer-end clonality’ occurs despite predictions of host immunological diversity due to negative frequency dependent selection (kill the winner dynamics). Statistical clustering and model simulations explain this lack of diversity by capturing rapid selective sweeps by highly immune CRISPR lineages. Potentially explaining ‘trailer-end conservation,’ we record the first example of a viral bloom overwhelming a CRISPR system. The polyclonal viruses bloom even though they share sequences previously targeted by host CRISPR loci. Simulations show how increasing random genomic deletions in CRISPR loci purges immunological controls on long-lived viral sequences, allowing polyclonal viruses to bloom and depressing host fitness. Our results thus link documented patterns of genomic conservation in CRISPR loci to an evolutionary advantage against persistent viruses. By maintaining old immunities, selection may be tuning CRISPR-mediated immunity against viruses reemerging from lysogeny or migration.
Author Summary
Most microbes appear unculturable in the laboratory, limiting our knowledge of how virus and prokaryotic host evolve in natural systems. However, a genomic locus found in many prokaryotes, CRISPR, may offer cultivation-independent probes of virus-microbe coevolution. Utilizing nearby genes, CRISPR can serially incorporate short viral and plasmid sequences. These sequences bind and cleave cognate regions in subsequent viral and plasmid insertions, conferring adaptive anti-viral and anti-plasmid immunity. By incorporating sequences undirectionally, CRISPR also provides timelines of virus-prokaryote coevolution. Yet, CRISPR only incorporates 30–80 base-pair viral sequences, leaving incomplete coevolutionary recordings. To reconstruct the missing coevolutionary dynamics shaping natural CRISPRs, we combined metagenomic reconstructions with population-scale mathematical modeling. Capturing rare and rapid sweeps of CRISPR diversity by highly immune lines, mathematical modeling explains why naturally reconstructed CRISPR loci are often largely identical across a population. Both model and experiment further document surprising proliferations of old viral sequences against which hosts had preexisting CRISPR immunity. Due to these deadly blooms of ancestral viral elements, CRISPR's conservation of old immune sequences appears to confer a selective advantage. This may explain the striking immunological memory documented in CRISPR loci, which occurs despite rapid viral mutation and despite rapid deletions in prokaryotic genomes.
PMCID: PMC3330103  PMID: 22532794
4.  Inferring Social Network Structure from Bacterial Sequence Data 
PLoS ONE  2011;6(8):e22685.
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. Here, 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.
PMCID: PMC3148245  PMID: 21829645
5.  Health safety nets can break cycles of poverty and disease: a stochastic ecological model 
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. Here, 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.
PMCID: PMC3203484  PMID: 21593026
population ecology; epidemiology; ecological modelling; poverty traps; safety nets; economic development

Results 1-5 (5)