Designing pandemic-resilient voting systems.

The 2020 general election occurred while many parts of the nation were under emergency orders related to the COVID-19 pandemic. This led to new requirements and considerations for voting systems. We introduce a model of the voting process to capture pandemic-related changes. Using a discrete event simulation case study of Milwaukee, WI, we study how to design in-person voting systems whose performance are robust to pandemic conditions, such as protective measures implemented during the COVID-19 pandemic. We assess various voting system designs on the voter wait times, voter sojourn times, line lengths at polling locations, voter time spent inside, and the number of voters inside. The analysis indicates that poll worker shortages, social distancing, and personalized protective equipment usage and sanitation measures can lead to extremely long voter wait times. We consider several design choices for mitigating the impact of pandemic-related changes on voting metrics. The case study suggests that long wait times can be avoided by staffing additional check-in locations, expanding early voting, and avoiding consolidated polling locations. Additionally, the analysis suggests that implementing a priority queue discipline has the potential to reduce waiting times for vulnerable populations at increased susceptibility to health risks associated with in-person voting.

Impact of a community-policing initiative promoting substance use disorder treatment over criminal charges on arrest recidivism.

BACKGROUND: Overdose deaths, addiction, and drug-related crime have increased in the United States over the past decade. Treatment improves outcomes, including reducing crime, but few individuals with addiction receive treatment. Here, we determine whether the Madison Addiction Recovery Initiative (MARI), a community policing program implemented by the City of Madison (Wisconsin) Police Department (MPD) that diverts adults who committed a non-violent, drug use-related crime from criminal prosecution to addiction treatment, reduces the risk of recidivism (i.e., an arrest) in the 6-month period following the index crime. METHODS: Observational data were collected by the MPD for 12 months before through 6 months after an index crime from participants in the MARI program (n = 263) who referred to MARI between September 1, 2017 and August 31, 2020 and a Historical Comparison group (n = 52) who committed a comparable crime between September 1, 2015 and August 31, 2016. Average effects were estimated using intention-to-treat (ITT), a per-protocol, and a complier average causal effects (CACE) analyses, adjusted for covariates. RESULTS: ITT analysis did not show that MARI assignment lowered adjusted odds of 6-month recidivism (aOR = 0.59 [0.32, 1.12], p = 0.11). Per-protocol analysis showed that completing MARI lowered the adjusted odds of 6-month recidivism (aOR = 0.23 [0.10, 0.52], p < 0.001). CACE analysis indicated that assignment to MARI among individuals who would complete the MARI program if assigned to the program lowered the adjusted odds of 6-month recidivism (aOR = 0.85 [0.80, 0.90], p < 0.001). CONCLUSIONS: Diverting adults who committed a non-violent, drug use-related crime from criminal prosecution to addiction treatment may reduce 6-month recidivism.

A Robust Approach for Mitigating Risks in Cyber Supply Chains.

In recent years, there have been growing concerns regarding risks in federal information technology (IT) supply chains in the United States that protect cyber infrastructure. A critical need faced by decisionmakers is to prioritize investment in security mitigations to maximally reduce risks in IT supply chains. We extend existing stochastic expected budgeted maximum multiple coverage models that identify "good" solutions on average that may be unacceptable in certain circumstances. We propose three alternative models that consider different robustness methods that hedge against worst-case risks, including models that maximize the worst-case coverage, minimize the worst-case regret, and maximize the average coverage in the ( 1 - α ) worst cases (conditional value at risk). We illustrate the solutions to the robust methods with a case study and discuss the insights their solutions provide into mitigation selection compared to an expected-value maximizer. Our study provides valuable tools and insights for decisionmakers with different risk attitudes to manage cybersecurity risks under uncertainty.

An expected coverage model with a cutoff priority queue.

Emergency medical services provide immediate care to patients with various types of needs. When the system is congested, the response to urgent emergency calls can be delayed. To address this issue, we propose a spatial Hypercube approximation model with a cutoff priority queue that estimates performance measures for a system where some servers are reserved exclusively for high priority calls when the system is congested. In the cutoff priority queue, low priority calls are not immediately served-they are either lost or entered into a queue-whenever the number of busy ambulances is equal to or greater than the cutoff. The spatial Hypercube approximation model can be used to evaluate the design of public safety systems that employ a cutoff priority queue. A mixed integer linear programming model uses the Hypercube model to identify deployment and dispatch decisions in a cutoff priority queue paradigm. Our computational study suggests that the improvement in the expected coverage is significant when the cutoff is imposed, and it elucidates the tradeoff between the coverage improvement and the cost to low-priority calls that are "lost" when using a cutoff. Finally, we present a method for selecting the cutoff value for a system based on the relative importance of low-priority calls to high-priority calls.