While the world awaits a widely available COVID-19 vaccine, availability of testing is limited in many regions and can be further compounded by shortages of reagents, prolonged processing time and ...delayed results. One approach to rapid testing is to leverage the volatile organic compound (VOC) signature of SARS-CoV-2 infection. Detection dogs, a biological sensor of VOCs, were utilized to investigate whether SARS-CoV-2 positive urine and saliva patient samples had a unique odor signature. The virus was inactivated in all training samples with either detergent or heat treatment. Using detergent-inactivated urine samples, dogs were initially trained to find samples collected from hospitalized patients confirmed with SARS-CoV-2 infection, while ignoring samples collected from controls. Dogs were then tested on their ability to spontaneously recognize heat-treated urine samples as well as heat-treated saliva from hospitalized SARS-CoV-2 positive patients. Dogs successfully discriminated between infected and uninfected urine samples, regardless of the inactivation protocol, as well as heat-treated saliva samples. Generalization to novel samples was limited, particularly after intensive training with a restricted sample set. A unique odor associated with SARS-CoV-2 infection present in human urine as well as saliva, provides impetus for the development of odor-based screening, either by electronic, chemical, or biological sensing methods. The use of dogs for screening in an operational setting will require training with a large number of novel SARS-CoV-2 positive and confirmed negative samples.
During the first months of the COVID-19 pandemic, governments instituted a series of measures to control the spread of the virus. The measures were widely believed to increase women’s risk of violent ...victimization, most of which is by an intimate partner. We examined help-seeking during this period in a large U.S. city and used an interrupted time series analysis to assess the effects of three government interventions on domestic violence and sexual assault hotline calls and on “911” calls regarding domestic violence, assault, and rape. Declaration of an emergency appeared to reduce victim calls to the rape crisis hotline and the few “911” calls about rape. School closure was associated with a reduction in “911” calls about assault and rape and victim calls to the domestic violence hotline. Implementation of stay-at-home orders was associated with a gradual increase in domestic violence hotline calls. Although “911” calls regarding assault fell by nearly half, calls to police for domestic violence were unchanged. In sum, there was a decrease in help-seeking for sexual assault and assault in general but not for domestic violence during the initial phases of the COVID-19 outbreak. The analysis underscores the importance of distinguishing between the violence itself, calls to police, and calls to helplines when claims are made about changes over time in violence against women. The opportunities and constraints for each can differ widely under usual circumstances, circumstances that were altered by public health interventions related to the pandemic.
Regressions can be weighted by propensity scores in order to reduce bias. However, weighting is likely to increase random error in the estimates, and to bias the estimated standard errors downward, ...even when selection mechanisms are well understood. Moreover, in some cases, weighting will increase the bias in estimated causal parameters. If investigators have a good causal model, it seems better just to fit the model without weights. If the causal model is improperly specified, there can be significant problems in retrieving the situation by weighting, although weighting may help under some circumstances.
Research Summary: A substantial and powerful literature in statistics and computer science has clearly demonstrated that modern machine learning procedures can forecast more accurately than ...conventional parametric statistical models such as logistic regression. Yet, several recent studies have claimed that for criminal justice applications, forecasting accuracy is about the same. In this article, we address the apparent contradiction. Forecasting accuracy will depend on the complexity of the decision boundary. When that boundary is simple, most forecasting tools will have similar accuracy. When that boundary is complex, procedures such as machine learning, which proceed adaptively from the data, will improve forecasting accuracy, sometimes dramatically. Machine learning has other benefits as well, and effective software is readily available. Policy Implications: The complexity of the decision boundary will in practice be unknown, and there can be substantial risks to gambling on simplicity. Criminal justice decision makers and other stakeholders can be seriously misled with rippling effects going well beyond the immediate offender. There seems to be no reason for continuing to rely on traditional forecasting tools such as logistic regression. Adapted from the source document.
Arguably the most important decision at an arraignment is whether to release an offender until the date of his or her next scheduled court appearance. Under the Bail Reform Act of 1984, threats to ...public safety can be a key factor in that decision. Implicitly, a forecast of “future dangerousness” is required. In this article, we consider in particular whether usefully accurate forecasts of domestic violence can be obtained. We apply machine learning to data on over 28,000 arraignment cases from a major metropolitan area in which an offender faces domestic violence charges. One of three possible post‐arraignment outcomes is forecasted within two years: (1) a domestic violence arrest associated with a physical injury, (2) a domestic violence arrest not associated with a physical injury, and (3) no arrests for domestic violence. We incorporate asymmetric costs for different kinds of forecasting errors so that very strong statistical evidence is required before an offender is forecasted to be a good risk. When an out‐of‐sample forecast of no post‐arraignment domestic violence arrests within two years is made, it is correct about 90 percent of the time. Under current practice within the jurisdiction studied, approximately 20 percent of those released after an arraignment for domestic violence are arrested within two years for a new domestic violence offense. If magistrates used the methods we have developed and released only offenders forecasted not to be arrested for domestic violence within two years after an arraignment, as few as 10 percent might be arrested. The failure rate could be cut nearly in half. Over a typical 24‐month period in the jurisdiction studied, well over 2,000 post‐arraignment arrests for domestic violence perhaps could be averted.
In the United States and elsewhere, risk assessment algorithms are being used to help inform criminal justice decision-makers. A common intent is to forecast an offender’s “future dangerousness.” ...Such algorithms have been correctly criticized for potential unfairness, and there is an active cottage industry trying to make repairs. In this paper, we use counterfactual reasoning to consider the prospects for improved fairness when members of a disadvantaged class are treated by a risk algorithm as if they are members of an advantaged class. We combine a machine learning classifier trained in a novel manner with an optimal transport adjustment for the relevant joint probability distributions, which together provide a constructive response to claims of bias-in-bias-out. A key distinction is made between fairness claims that are empirically testable and fairness claims that are not. We then use confusion tables and conformal prediction sets to evaluate achieved fairness for estimated risk. Our data are a random sample of 300,000 offenders at their arraignments for a large metropolitan area in the United States during which decisions to release or detain are made. We show that substantial improvement in fairness can be achieved consistently with a Pareto improvement for legally protected classes.
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