Carceral Amplification of COVID-19: Impacts for Community, Corrections Officer, and Incarcerated Population Risks.

COVID-19 is challenging many societal institutions, including our criminal justice systems. Some have proposed or enacted (e.g., the State of New Jersey) reductions in the jail and/or prison populations. We present a mathematical model to explore the epidemiologic impact of such interventions in jails and contrast them with the consequences of maintaining unaltered practices. We consider infection risk and likely in-custody deaths, and estimate how within-jail dynamics lead to spill-over risks, not only affecting incarcerated people but increasing exposure, infection, and death rates for both corrections officers and the broader community beyond the justice system. We show that, given a typical jail-community dynamic, operating in a business-as-usual way results in substantial, rapid, and ongoing loss of life. Our results are consistent with the hypothesis that large-scale reductions in arrest and speeding of releases are likely to save the lives of incarcerated people, jail staff, and the wider community.

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.

Erratum: Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics.

[This corrects the article DOI: 10.1016/j.patter.2022.100568.].

Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics.

The harmful impacts of algorithmic decision systems have recently come into focus, with many examples of machine learning (ML) models amplifying societal biases. In this paper, we propose adapting income inequality metrics from economics to complement existing model-level fairness metrics, which focus on intergroup differences of model performance. In particular, we evaluate their ability to measure disparities between exposures that individuals receive in a production recommendation system, the Twitter algorithmic timeline. We define desirable criteria for metrics to be used in an operational setting by ML practitioners. We characterize engagements with content on Twitter using these metrics and use the results to evaluate the metrics with respect to our criteria. We also show that we can use these metrics to identify content suggestion algorithms that contribute more strongly to skewed outcomes between users. Overall, we conclude that these metrics can be a useful tool for auditing algorithms in production settings.

Characterizing patterns in police stops by race in Minneapolis from 2016 to 2021.

The murder of George Floyd centered Minneapolis, Minnesota, in conversations on racial injustice in the US. We leverage open data from the Minneapolis Police Department to analyze individual, geographic, and temporal patterns in more than 170,000 police stops since 2016. We evaluate person and vehicle searches at the individual level by race using generalized estimating equations with neighborhood clustering, directly addressing neighborhood differences in police activity. Minneapolis exhibits clear patterns of disproportionate policing by race, wherein Black people are searched at higher rates compared to White people. Temporal visualizations indicate that police stops declined following the murder of George Floyd. This analysis provides contemporary evidence on the state of policing for a major metropolitan area in the United States.

Removing the influence of group variables in high-dimensional predictive modelling.

In many application areas, predictive models are used to support or make important decisions. There is increasing awareness that these models may contain spurious or otherwise undesirable correlations. Such correlations may arise from a variety of sources, including batch effects, systematic measurement errors, or sampling bias. Without explicit adjustment, machine learning algorithms trained using these data can produce poor out-of-sample predictions which propagate these undesirable correlations. We propose a method to pre-process the training data, producing an adjusted dataset that is statistically independent of the nuisance variables with minimum information loss. We develop a conceptually simple approach for creating an adjusted dataset in high-dimensional settings based on a constrained form of matrix decomposition. The resulting dataset can then be used in any predictive algorithm with the guarantee that predictions will be statistically independent of the group variable. We develop a scalable algorithm for implementing the method, along with theory support in the form of independence guarantees and optimality. The method is illustrated on some simulation examples and applied to two case studies: removing machine-specific correlations from brain scan data, and removing race and ethnicity information from a dataset used to predict recidivism. That the motivation for removing undesirable correlations is quite different in the two applications illustrates the broad applicability of our approach.

Remission of type 2 diabetes after Roux-en-Y gastric bypass is associated with greater weight loss.

BACKGROUND: Physiologic studies in rodents and preliminary human studies have suggested that Roux-en-Y gastric bypass (RYGB) improves type 2 diabetes mellitus (T2DM) by way of metabolic changes, long before the bariatric or weight loss effects occur, leading to the concept of "metabolic surgery." To test this hypothesis, we studied patients with insulin-dependent T2DM who underwent RYGB to determine whether T2DM remission in this treatment-resistant subgroup occurred independent of weight loss. METHODS: Of all the patients undergoing RYGB from 2000 to 2006 (n = 1546) with >/=12 months of follow-up, 318 had T2DM (21%), and 75 (24%) of these were insulin dependent. Of the 75 patients, 4 were found to have T1DM (5.3%) and were excluded, leaving a study population of 71 patients. The patients who achieved remission, defined as a cessation of diabetic medications with a hemoglobin A1c level of <7%, were compared with those who did not achieve remission. Statistical significance was set at P < .05, using the Student t test, chi-square test, and logistic regression analysis, as appropriate. RESULTS: After RYGB, all 71 patients with insulin-dependent T2DM had achieved a reduction in the dose and/or number of medications at 29.6 +/- 17.0 months. Of these 71 patients, 35 (49%) demonstrated a remission of T2DM. The preoperative body mass index, age, number of medications, and hemoglobin A1c level did not differentiate between those who attained remission and those who still required diabetic medication. From the multivariate analysis, the significant factors associated with remission were the preoperative insulin dose and the percentage of excess weight loss. The percentage of excess weight loss was greater in the remission patients as early as 3 months postoperatively (P = .04) and also at 6, 12, 18, and 24 months. CONCLUSION: RYGB uniformly improved the medication requirements of patients with insulin-dependent T2DM. Although physiologic mechanisms likely contributed, early rapid weight loss was associated with the remission of T2DM.

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.

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.

Spontaneous resolution of a 13-mm Chiari malformation Type I in relation to differential growth of the posterior fossa volume.

The case of a 3-year-old patient with tuberous sclerosis and a 13-mm Chiari malformation Type I that spontaneously disappeared over the course of 4 years is presented. Using morphometric measurements of the posterior fossa and cerebellum in this patient, the authors show that the volume of the posterior fossa at the time of initial evaluation was consistent with that reported as normal in the literature (180.24 cm3; normal volume 132-198 cm3). Moreover, the patient showed a normal rate of growth of his posterior fossa over the period of observation (201.05 cm3; normal range 153-230 cm3). Cerebellar volumes were found to increase only minimally during this time period, which is compatible with observations in healthy controls. The posterior fossa volume, on the other hand, was shown to increase significantly more than that of the cerebellum (p=0.0185). This differential growth may permit the tonsils to ascend back up into the posterior fossa. Therefore, pediatric patients with normal posterior fossa volumes and normal development may have a spontaneous resolution of their asymptomatic Chiari malformation Type I.