This paper extends previous research on using quantum computers for risk management to a substantial, real-world challenge: constructing a quantum internal model for a medium-sized insurance company. Leveraging the author’s extensive experience as the former Head of Internal Model at a prominent UK insurer, we closely examine the practical bottlenecks in developing and maintaining quantum internal models. Our work seeks to determine whether a quadratic speedup, through quantum amplitude estimation can be realised for problems at an industrial scale. It also builds on previous work that explores the application of quantum computing to the problem of asset liability management in an actuarial context. Finally, we identify both the obstacles and the potential opportunities that emerge from applying quantum computing to the field of insurance risk management.

Extraintestinal pathogenic Escherichia coli (ExPEC) causes invasive E. coli disease (IED), including bacteraemia and (uro)sepsis, resulting in a high disease burden, especially among older adults. This study describes the epidemiology of IED in England (2013–2017) by combining laboratory surveillance and clinical data. A total of 191 612 IED cases were identified. IED incidence increased annually by 4.4–8.2% across all ages and 2.8–7.6% among adults ≥60 years of age. When laboratory-confirmed urosepsis cases without a positive blood culture were included, IED incidence in 2017 reached 149.4/100 000 person-years among all adults and 368.4/100 000 person-years among adults ≥60 years of age. Laboratory-confirmed IED cases were identified through E. coli-positive blood samples (55.3%), other sterile site samples (26.3%), and urine samples (16.6%), with similar proportions observed among adults ≥60 years of age. IED-associated case fatality rates ranged between 11.8–13.2% among all adults and 13.1–14.7% among adults ≥60 years of age. This study reflects the findings of other published studies and demonstrates IED constitutes a major and growing global health concern disproportionately affecting the older adult population. The high case fatality rates observed despite available antibiotic treatments emphasize the growing urgency for effective intervention strategies. The burden of urosepsis due to E. coli is likely underestimated and requires additional investigation.

With the ongoing emergence of SARS-CoV-2 variants, there is a need for standard approaches to characterize the risk of vaccine breakthrough. We aimed to estimate the association between variant and vaccination status in case-only surveillance data. Included cases were symptomatic adult laboratory-confirmed COVID-19 cases, with onset between January 2021 and April 2022, reported by five European countries (Estonia, Ireland, Luxembourg, Poland, and Slovakia) to The European Surveillance System. Associations between variant and vaccination status were estimated using conditional logistic regression, within strata of country and calendar date, and adjusting for age and sex. We included 80,143 cases including 20,244 Alpha (B.1.1.7), 152 Beta (B.1.351), 39,900 Delta (B.1.617.2), 361 Gamma (P.1), 10,014 Omicron BA.1, and 9,472 Omicron BA.2. Partially vaccinated cases were more likely than unvaccinated cases to be Beta than Alpha (adjusted odds ratio [aOR] 2.48, 95% CI 1.29–4.74), and Delta than Alpha (aOR 1.75, 1.31–2.34). Fully vaccinated cases were relative to unvaccinated cases more frequently Beta than Alpha (aOR 4.61, 1.89–11.21), Delta than Alpha (aOR 2.30, 1.55–3.39), and Omicron BA.1 than Delta (aOR 1.91, 1.60–2.28). We found signals of increased breakthrough infections for Delta and Beta relative to Alpha, and Omicron BA.1 relative to Delta.

The basic question in perturbation analysis of Markov chains is: how do small changes in the transition kernels of Markov chains translate to chains in their stationary distributions? Many papers on the subject have shown, roughly, that the change in stationary distribution is small as long as the change in the kernel is much less than some measure of the convergence rate. This result is essentially sharp for generic Markov chains. We show that much larger errors, up to size roughly the square root of the convergence rate, are permissible for many target distributions associated with graphical models. The main motivation for this work comes from computational statistics, where there is often a tradeoff between the per-step error and per-step cost of approximate MCMC algorithms. Our results show that larger perturbations (and thus less-expensive chains) still give results with small error.

Group B streptococcus (GBS) is a major global cause of neonatal, infant, and maternal infections. In Japan, national guidelines based on Centers for Disease Control and Prevention recommendations mandate culture-based screening and intrapartum antibiotic prophylaxis (IAP) for GBS-positive pregnant women. Despite initial reductions in GBS infections, the incidence has plateaued, and there are notable limitations in current prevention methods. Approximately 15% of pregnant women are not screened for GBS, and intermittent colonization undermines screening accuracy, contributing to early-onset disease. IAP does not prevent late-onset disease, the incidence of which is increasing in Japan. This study reviewed maternal and neonatal GBS colonization using polymerase chain reaction, evaluated capsular type distributions, and explored late-onset disease infection routes. Among 525 mother-neonate pairs, the study found a higher detection rate of GBS via polymerase chain reaction compared to culture methods and identified significant discrepancies between antepartum and intrapartum colonization. GBS was detected in 3.5% of neonates from initially negative mothers at 4 days of age. Capsular types varied between mothers and neonates, indicating potential horizontal transmission. This study underscores the need for improved rapid diagnostic tests and highlights the potential of maternal GBS vaccination as a future prevention strategy.

The problem of reservation in a large distributed system is analyzed via a new mathematical model. The target application is car-sharing systems. This model is motivated by the large station-based car-sharing system in France called Autolib’. This system can be described as a closed stochastic network where the nodes are the stations and the customers are the cars. The user can reserve a car and a parking space. We study the evolution of the system when the reservation of parking spaces and cars is effective for all users. The asymptotic behavior of the underlying stochastic network is given when the number N of stations and the fleet size M increase at the same rate. The analysis involves a Markov process on a state space with dimension of order $N^2$. It is quite remarkable that the state process describing the evolution of the stations, whose dimension is of order N, converges in distribution, although not Markov, to a non-homogeneous Markov process. We prove this mean-field convergence. We also prove, using combinatorial arguments, that the mean-field limit has a unique equilibrium measure when the time between reserving and picking up the car is sufficiently small. This result extends the case where only the parking space can be reserved.