An Exploration of the Multiplicative Effect of "Other People" and Other Environmental Effects on Violence in the Night-Time Environment

BACKGROUND

The characteristics of night-time environments (NTEs) in which alcohol is consumed and that contribute to violence are poorly described. We explore competing explanations for violence in the NTE, with a particular focus on the number of patrons and its association with assault-related visits to a hospital emergency department. Other environmental features including the weather and notable events were also considered. The primary aim was to stimulate debate around the causal mechanisms responsible for violence.

METHODS

Assault-related ED visits occurring between 8 pm and 4 am were recorded at the University Hospital of Wales, the single Emergency Department (ED) serving Cardiff, Wales, United Kingdom. Footfall was derived from the total number of unique MAC addresses recorded per hour collected from ten wireless fidelity monitoring tools located in the city centre. A narrative review of the literature concerning alcohol and violence informed exploratory analyses into the association between night-time footfall, sporting events, the weather, and other potential predictors of assault-related visits to the ED. We developed analytic methods from formal accounts of queueing.

RESULTS

International rugby matches at home, the weather (temperature), national holidays, the day of the week, and number of patrons in the NTE predicted assault-related injury (R² = 0.70), with footfall yielding a positive non-linear exponential association consistent with predictions derived from mathematical models of queueing.

DISCUSSION

Assault-related visits to the ED have a non-linear association with the number of people socialising in the night-time environment and are further influenced by the weather and notable events. Opportunities for further research that might inform policy and interventions aimed at better managing NTEs are discussed.

Mitigating long queues and waiting times with service resetting

What determines the average length of a queue, which stretches in front of a service station? The answer to this question clearly depends on the average rate at which jobs arrive at the queue and on the average rate of service. Somewhat less obvious is the fact that stochastic fluctuations in service and arrival times are also important, and that these are a major source of backlogs and delays. Strategies that could mitigate fluctuations-induced delays are, thus in high demand as queue structures appear in various natural and man-made systems. Here, we demonstrate that a simple service resetting mechanism can reverse the deleterious effects of large fluctuations in service times, thus turning a marked drawback into a favorable advantage. This happens when stochastic fluctuations are intrinsic to the server, and we show that service resetting can then dramatically cut down average queue lengths and waiting times. Remarkably, this strategy is also useful in extreme situations where the variance, and possibly even mean, of the service time diverge-as resetting can then prevent queues from "blowing up." We illustrate these results on the M/G/1 queue in which service times are general and arrivals are assumed to be Markovian. However, the main results and conclusions coming from our analysis are not specific to this particular model system. Thus, the results presented herein can be carried over to other queueing systems: in telecommunications, via computing, and all the way to molecular queues that emerge in enzymatic and metabolic cycles of living organisms.

On evolution model for SARS-Cov-2-infected population: the case of New Zealand

The work proposes a mathematical model of the process of COVID-19 epidemic as it evolved in New Zealand. The model uses a system of differential equations which emanate from natural assumptions on some probability measure and evolution of this measure on evolving family of simplexes. The authors tried to create the model which, at one hand, is simple and easy to follow. and, at the other hand, reflects the observed epidemic process correctly. The practical aim was to come to justifiable estimations of important parameters like the rate of infection as function of time, thus quantifying effectiveness of the Government measures. Another parameters estimated were the probability distribution of detection times and recovery times.

Assessing risk in the retail environment during the COVID-19 pandemic

The COVID-19 pandemic has caused unprecedented disruption, particularly in retail. Where essential demand cannot be fulfilled online, or where more stringent measures have been relaxed, customers must visit shop premises in person. This naturally gives rise to some risk of susceptible individuals (customers or staff) becoming infected. It is essential to minimize this risk as far as possible while retaining economic viability of the shop. We therefore explore and compare the spread of COVID-19 in different shopping situations involving person-to-person interactions: (i) free-flowing, unstructured shopping; (ii) structured shopping (e.g. a queue). We examine which of (i) or (ii) may be preferable for minimizing the spread of COVID-19 in a given shop, subject to constraints such as the geometry of the shop; compliance of the population to local guidelines; and additional safety measures which may be available to the organizers of the shop. We derive a series of conclusions, such as unidirectional free movement being preferable to bidirectional shopping, and that the number of servers should be maximized as long as they can be well protected from infection.

Doctors' response to queues: Evidence from a Portuguese emergency department

We evaluate how doctors in an emergency department react to the number of patients waiting for treatment. Our outcomes reflect the time spent with the patient, the intensity of treatment, and discharge destination. Using visit-level data in a Lisbon-area hospital, we use a fixed effects model to exploit variation in the queue size while addressing endogeneity using the number of arrivals to the hospital in the previous 60 min as an instrumental variable. Furthermore, we estimate doctors' reactions separately for patients with different degrees of urgency, as measured by the Manchester triage system. Results show that doctors discharge patients more rapidly as queues become longer, and this effect is stronger for patients that do not have life-threatening conditions. We also find that the intensity of diagnosis/treatment procedures decreases when patients face longer queues, driven by the extensive margin. Finally, doctors are less likely to admit patients to inpatient care. We interpret the results in the light of the doctors' incentives literature, explaining how these agents behave in the context of a National Health Service, with no financial incentives.

Nonfixed Retirement Age for University Professors: Modeling Its Effects on New Faculty Hires

We model the set of tenure-track faculty members at a university as a queue, where "customers" in queue are faculty members in active careers. Arrivals to the queue are usually young, untenured assistant professors, and departures from the queue are primarily those who do not pass a promotion or tenure hurdle and those who retire. There are other less-often-used ways to enter and leave the queue. Our focus is on system effects of the elimination of mandatory retirement age. In particular, we are concerned with estimating the number of assistant professor slots that annually are no longer available because of the elimination of mandatory retirement. We start with steady-state assumptions that require use of Little's Law of Queueing, and we progress to a transient model using system dynamics. We apply these simple models using available data from our home university, the Massachusetts Institute of Technology.

Is follow-up capacity the current NHS bottleneck?

Capacity and demand theory suggests that the presence of a queue is not necessarily an indication of a shortage of capacity in a system. It is much more likely that either there is a demand and capacity variation that creates queues or there is a delay designed into the system. A shortage of capacity is only really indicated where a backlog is not stable and continues to grow. In this article, data are taken from one NHS trust that provides evidence for a continually growing backlog for follow-up outpatient services. It is believed that these data are representative of most locations within the NHS in England and therefore suggest an immediate shortage in effective follow-up capacity. To avoid compromise to patient care, the problem will have to be addressed before the situation becomes unmanageable. The paper highlights options to reduce or deflect demand or to increase effective capacity.