Biology subjects

Jones, J. H.

Publications and source records attributed to Jones, J. H..

6 recordsLinked to original sources

bioRxiv2020

End-to-end Bayesian analysis of 14C dates reveals new insights into lowland Maya demography

ABSTRACTArchaeologists and demographers increasingly employ aggregations of published radiocarbon (14C) dates as demographic proxies summarizing changes in human activity in past societies. Presently, summed probability densities (SPDs) of calibrated radiocarbon dates are the dominant method of using 14C dates to reconstruct demographic trends. Unfortunately, SPDs are incapable of converging on their true generating distributions even as the number of observations gets large. To overcome this problem, we propose a more principled alternative that combines finite mixture models and Bayesian inference to identify the generating distribution of a set of radiocarbon dates. Numerical simulations and an assessment of the statistical identifiability of our method demonstrate that it correctly converges on the generating distribution. We apply this novel end-to-end Bayesian approach to reconstruct prehistoric Maya demographic growth using a recently compiled Mesoamerican radiocarbon database. Our results show that the Maya Lowlands experienced a century of rapid growth rates (1%) during the Late Classic, followed by a rapid decrease in population during the Terminal Classic, and a subsequent more-modest resurgence in population during the Postclassic. Additionally, a detailed population reconstruction of the important political center of Tikal verifies that slow population growth between the Preclassic and Early Classic gave pace to rapid growth starting around AD 500 and peaking at the beginning of the eight century. Our proposed method verifies previous reconstructions based on settlement patterns and ceramics, but with far more precise time-resolution and characterization of uncertainty than has been possible.Competing Interest StatementThe authors have declared no competing interest.View Full Text

ecology↗

bioRxiv2020

Coupled Dynamics of Behavior and Disease Contagion Among Antagonistic Groups

Disease transmission and behavior change are both fundamentally social phenomena. Behavior change can have profound consequences for disease transmission, and epidemic conditions can favor the more rapid adoption of behavioral innovations. We analyze a simple model of coupled behavior-change and infection in a structured population characterized by homophily and outgroup aversion. Outgroup aversion slows the rate of adoption and can lead to lower rates of adoption in the later-adopting group or even behavioral divergence between groups when outgroup aversion exceeds positive ingroup influence. When disease dynamics are coupled to the behavior-adoption model, a wide variety of outcomes are possible. Homophily can either increase or decrease the final size of the epidemic depending on its relative strength in the two groups and on R0 for the infection. For example, if the first group is homophilous and the second is not, the second group will have a larger epidemic. Homophily and outgroup aversion can also produce dynamics suggestive of a "second wave" in the first group that follows the peak of the epidemic in the second group. Our simple model reveals dynamics that are suggestive of the processes currently observed under pandemic conditions in culturally and/or politically polarized populations such as the United States.

ecology↗

bioRxiv2020

Complex dynamics induced by delayed adaptive behavior during outbreaks

The COVID-19 pandemic has posed a significant dilemma for governments across the globe. The public health consequences of inaction are catastrophic; but the economic consequences of drastic action are likewise catastrophic. Governments must therefore strike a balance in the face of these trade-offs. But with critical uncertainty about how to find such a balance, they are forced to experiment with their interventions and await the results of their experimentation. Models have proved inaccurate because behavioral response patterns are either not factored in or are hard to predict. One crucial behavioral response in a pandemic is adaptive social contact: potentially infectious contact between people is deliberately reduced either individually or by fiat; and this must be balanced against the economic cost of having fewer people in contact and therefore active in the labor force. We develop a model for adaptive optimal control of the effective social contact rate within a Susceptible-Infectious-Susceptible (SIS) epidemic model using a dynamic utility function with delayed information. This utility function trades off the population-wide contact rate with the expected cost and risk of increasing infections. Our analytical and computational analysis of this simple discrete-time deterministic model reveals the existence of a non-zero equilibrium, oscillatory dynamics around this equilibrium under some parametric conditions, and complex dynamic regimes that shift under small parameter perturbations. These results support the supposition that infectious disease dynamics under adaptive behavior-change may have an indifference point, may produce oscillatory dynamics without other forcing, and constitute complex adaptive systems with associated dynamics. Implications for COVID-19 include an expectation of fluctuations, for a considerable time, around a quasi-equilibrium that balances public health and economic priorities, that shows multiple peaks and surges in some scenarios, and that implies a high degree of uncertainty in mathematical projections. Author summaryEpidemic response in the form of social contact reduction, such as has been utilized during the ongoing COVID-19 pandemic, presents inherent tradeoffs between the economic costs of reducing social contacts and the public health costs of neglecting to do so. Such tradeoffs introduce an interactive, iterative mechanism which adds complexity to an infectious disease system. Consequently, infectious disease modeling typically has not included dynamic behavior change that must address such a tradeoff. Here, we develop a theoretical model that introduces lost or gained economic and public health utility through the adjustment of social contact rates with delayed information. We find this model produces an equilibrium, a point of indifference where the tradeoff is neutral, and at which a disease will be endemic for a long period of time. Under small perturbations, this model exhibits complex dynamic regimes, including oscillatory behavior, runaway exponential growth, and eradication. These dynamics suggest that for epidemic response that relies on social contact reduction, secondary waves and surges with accompanied business re-closures and shutdowns may be expected, and that accurate projection under such circumstances is unlikely.

systems biology↗

bioRxiv2018

Genome-wide patterns of gene expression in a wild primate indicate species-specific mechanisms associated with tolerance to natural simian immunodeficiency virus infection

Over 40 species of nonhuman primates host simian immunodeficiency viruses (SIVs). In natural hosts, infection is generally assumed to be nonpathogenic due to a long coevolutionary history between host and virus, although pathogenicity is difficult to study in wild nonhuman primates. We used whole-blood RNA-seq and SIV prevalence from 29 wild Ugandan red colobus (Piliocolobus tephrosceles) to assess the effects of SIV infection on host gene expression in wild, naturally SIV-infected primates. We found no evidence for chronic immune activation in infected individuals, suggesting that SIV is not immunocompromising in this species, in contrast to HIV in humans. Notably, an immunosuppressive gene, CD101, was upregulated in infected individuals. This gene has not been previously described in the context of nonpathogenic SIV infection. This expands the known variation associated with SIV infection in natural hosts, and may suggest a novel mechanism for tolerance of SIV infection in the Ugandan red colobus.

genomics↗

bioRxiv2017

A general goodness-of-fit test for survival analysis

Existing goodness-of-fit tests for survival data are either exclusively graphical in nature or only test specific model assumptions, such as the proportional hazards assumption. We describe a flexible, parameter-free goodness-of-fit test that provides a simple numerical assessment of a models suitability regardless of the structure of the underlying model. Intuitively, the goodness-of-fit test utilizes the fact that for a good model early event occurrence is predicted to be just as likely as late event occurrence, whereas a bad model has a bias towards early or late events. Formally, the goodness-of-fit test is based on a novel generalized Martingale residual which we call the martingale survival residual. The martingale survival residual has a uniform probability density function defined on the interval -0.5 to +0.5 if censoring is either absent or accounted for as one outcome in a competing hazards framework. For a good model, the set of calculated residuals is statistically indistinguishable from the uniform distribution, which is tested using the Kolmogorov-Smirnov statistic.

epidemiology↗

bioRxiv2016

Hierarchical Evolutionary Preferences Explain Discrepancies in Expected Utility Theory

The standard axiomatic theory of rationality posits that agents order preferences according to the average utilities associated with different choices. Expected Utility Theory has repeatedly failed as a predictive theory of choice behavior, as reflected in a growing literature in behavioral economics. Evolutionary theorists have suggested that seemingly irrational behaviors in contemporary contexts may have once served important functions, but there has been little attempt to formalize the relationship between evolutionary fitness and choice behavior. Biological agents should optimize fitness, but fitness itself is not a reasonable value function for decision-making since its time-scale exceeds the lifespan of the decision-maker. Consequently, organisms use proximate motivational systems that work on appropriate time-scales and are amenable to feedback and learning. We develop an evolutionary principal-agent model in which individuals maximize a set of proximal choice variables (age-specific demographic rates), the interests of which are aligned with fitness. The solution to our model yields pessimistic probability weightings compatible with the Rank-Dependent Expected Utility family of choice models. The pessimistic probability weighting characteristic of these models emerges naturally in an evolutionary framework because of extreme intolerance to zeros in multiplicative growth processes.

evolutionary biology↗