Kristian Lum

Applications of Multiple Systems Estimation in Human Rights Research

Multiple systems estimation (MSE) is becoming an increasingly common approach for exploratory study of underreported events in the field of quantitative human rights. In this context, it is used to estimate the number of people who died as a result of political unrest when it is believed that many of those who died or disappeared were never reported. MSE relies upon several assumptions, each of which may be slightly or significantly violated in particular applications. This article outlines the evolution of the application of MSE to human rights research through the use of three case studies: Guatemala, Peru, and Colombia. Each of these cases presents distinct challenges to the MSE method. Motivated by these applications, we describe new methodology for assessing the impact of violated assumptions in MSE. Our approach uses simulations to explore the cumulative magnitude of errors introduced by violation of the model assumptions at each stage in the analysis.

Estimating human cases of avian influenza A(H7N9) from poultry exposure

In March 2013 an outbreak of avian influenza A(H7N9) was first recognized in China. To date there have been 130 cases in human, 47% of which are in men over the age of 55.The influenza strain is a novel subtype not seen before in humans; little is known about zoonotic transmission of the virus, but it is hypothesized that contact with poultry in live bird markets may be a source of exposure. The purpose of this study is to estimate the transmissibility of the virus from poultry to humans by estimating the amount of time shoppers, farmers, and live bird market retailers spend exposed to poultry each day. Results suggest that increased risk among older men is not due to greater exposure time at live bird markets.

Planning and response in the aftermath of a large crisis: an agent-based informatics framework

We present a synthetic information and modeling environment that can allow policy makers to study various counter-factual experiments in the event of a large human-initiated crisis. The specific scenario we consider is a ground detonation caused by an improvised nuclear device in a large urban region. In contrast to earlier work in this area that focuses largely on the prompt effects on human health and injury, we focus on co-evolution of individual and collective behavior and its interaction with the differentially damaged infrastructure. This allows us to study short term secondary and tertiary effects. The present environment is suitable for studying the dynamical outcomes over a two week period after the initial blast. A novel computing and data processing architecture is described; the architecture allows us to represent multiple co-evolving infrastructures and social networks at a highly resolved temporal, spatial, and individual scale. The representation allows us to study the emergent behavior of individuals as well as specific strategies to reduce casualties and injuries that exploit the spatial and temporal nature of the secondary and tertiary effects. A number of important conclusions are obtained using the modeling environment. For example, the studies decisively show that deploying ad hoc communication networks to reach individuals in the affected area is likely to have a significant impact on the overall casualties and injuries.

The contagious nature of imprisonment: an agent-based model to explain racial disparities in incarceration rates

We build an agent-based model of incarceration based on the susceptible–infected–suspectible (SIS) model of infectious disease propagation. Our central hypothesis is that the observed racial disparities in incarceration rates between Black and White Americans can be explained as the result of differential sentencing between the two demographic groups. We demonstrate that if incarceration can be spread through a social influence network, then even relatively small differences in sentencing can result in large disparities in incarceration rates. Controlling for effects of transmissibility, susceptibility and influence network structure, our model reproduces the observed large disparities in incarceration rates given the differences in sentence lengths for White and Black drug offenders in the USA without extensive parameter tuning. We further establish the suitability of the SIS model as applied to incarceration by demonstrating that the observed structural patterns of recidivism are an emergent property of the model. In fact, our model shows a remarkably close correspondence with California incarceration data. This work advances efforts to combine the theories and methods of epidemiology and criminology.

A Comparison of Marginal and Conditional Models for Capture-Recapture Data with Application to Human Rights Violations Data

Human rights data presents challenges for capture-recapture methodology. Lists of violent acts provided by many different groups create large, sparse tables of data for which saturated models are difficult to fit and for which simple models may be misspecified. We analyze data on killings and disappearances in Casanare, Colombia during years 1998 to 2007. Our estimates differ whether we choose to model marginal reporting probabilities and odds ratios, versus modeling the full reporting pattern in a conditional (log-linear) model. With 2629 observed killings, a marginal model we consider estimates over 9000 killings, while conditional models we consider estimate 6000-7000 killings. The latter agree with previous estimates, also from a conditional model. We see a twofold difference between the high sample coverage estimate of over 10,000 killings and low sample coverage lower bound estimate of 5200 killings. We use a simulation study to compare marginal and conditional models with at most two-way interactions and sample coverage estimators. The simulation results together with model selection criteria lead us to believe the previous estimates of total killings in Casanare may have been biased downward, suggesting that the violence was worse than previously thought. Model specification is an important consideration when interpreting population estimates from capture recapture analysis and the Casanare data is a protypical example of how that manifests.

Addressing the Race Gap in Incarceration Rates: An Agent Based Model

ABSTRACT Using an agent-based model of incarceration, the authors conduct a series of simulation experiments testing the efficacy of policy interventions designed to address racial disparities in incarceration rates. The first experiment eliminates race-based sentencing disparities, and additional experiments eliminate race-based sentencing disparities and disrupt the clustering of incarcerated individuals in their social networks. Findings suggest that eliminating race-based sentencing disparities will not result in a substantial closing of the incarceration gap because the clustering of ex-offenders contributes to offender recidivism and disparities in incarceration rates. The model suggests that addressing sentencing biases combined with interventions designed to disrupt the clustering of incarcerated individuals would reduce race-based incarceration gaps. Policy implications based on these findings are discussed.