Surveillance is a key public health function to enable early detection of infectious disease events and inform public health action. Data linkage may improve the depth of data for response to infectious disease events. This study aimed to describe the uses of linked data for infectious disease events. A systematic review was conducted using Pubmed, CINAHL and Web of Science. Studies were included if they used data linkage for an acute infectious disease event (e.g. outbreak of disease). We summarised the event, study aims and designs; data sets; linkage methods; outcomes reported; and benefits and limitations. Fifty-four studies were included. Uses of linkage for infectious disease events included assessment of severity of disease and risk factors; improved case finding and contact tracing; and vaccine uptake, safety and effectiveness. The ability to conduct larger scale population level studies was identified as a benefit, in particular for rarer exposures, risk factors or outcomes. Limitations included timeliness, data quality and inability to collect additional variables. This review demonstrated multiple uses of data linkage for infectious disease events. As infectious disease events occur without warning, there is a need to establish pre-approved protocols and the infrastructure for data-linkage to enhance information available during an event.

Under the influence of γ-quanta (60Co, P = 9.276 rad/s, T = 300 K), the amount, formation rate, and radiation-chemical yield of molecular hydrogen obtained from the radiolysis process that changes the mass of water (m = 0.0001 ÷ 0.8 g) have been defined in the created nano-SiO2/H2O system with m = 0.2 g mass and d = 20 nm particle size. It was determined that the radiation-chemical yield of molecular hydrogen obtained from the water radiolysis process in the nano-SiO2/H2O system created by the adsorption of water on the nanoparticle surface had a low value. In systems created with the addition of water, the radiation-chemical yield of molecular hydrogen obtained from its radiolysis increased in direct proportion to the water mass. This proves that due to ionizing rays, the yield of electrons emitted from the nanoparticle surface into the water and solvated there increases. Therefore, the radiation-chemical yield of molecular hydrogen is higher than that of the adsorbed system.

Oral rotavirus vaccine efficacy estimates from randomised controlled trials are highly variable across settings. Although the randomised study design increases the likelihood of internal validity of findings, results from trials may not always apply outside the context of the study due to differences between trial participants and the target population. Here, we used a weight-based method to transport results from a monovalent rotavirus vaccine clinical trial conducted in Malawi between 2005 and 2008 to a target population of all trial-eligible children in Malawi, represented by data from the 2015–2016 Malawi Demographic and Health Survey (DHS). We reweighted trial participants to reflect the population characteristics described by the Malawi DHS. Vaccine efficacy was estimated for 1008 trial participants after applying these weights such that they represented trial-eligible children in Malawi. We also conducted subgroup analyses to examine the heterogeneous treatment effects by stunting and tuberculosis vaccination status at enrolment. In the original trial, the estimates of one-year vaccine efficacy against severe rotavirus gastroenteritis and any-severity rotavirus gastroenteritis in Malawi were 49.2% (95% CI 15.6%–70.3%) and 32.1% (95% CI 2.5%–53.1%), respectively. After weighting trial participants to represent all trial-eligible children in Malawi, vaccine efficacy increased to 62.2% (95% CI 35.5%–79.0%) against severe rotavirus gastroenteritis and 38.9% (95% CI 11.4%–58.5%) against any-severity rotavirus gastroenteritis. Rotavirus vaccine efficacy may differ between trial participants and target populations when these two populations differ. Differences in tuberculosis vaccination status between the trial sample and DHS population contributed to varying trial and target population vaccine efficacy estimates.

The resurgence and outbreaks of mumps occur frequently in many countries worldwide in recent years, even in countries with high vaccination coverage. In this study, a descriptive and spatiotemporal clustering analysis at the township level was conducted to explore the dynamic spatiotemporal aggregation and epidemiological characteristics of mumps in Wuhan. During 2005 and 2019, there were 40 685 cases reported in Wuhan, with an average annual morbidity of 28.11 per 100 000 populations. The morbidity showed a fluctuating tendency, and peaked in 2010 and 2018. Bimodal seasonality was found, with a large peak between May and July, and a mild peak from November to January in the following year. Male students aged 5–9-year-old were the main risk group of mumps infection. Significant global spatial auto-correlation was detected except in 2007, 2009 and 2015. The spatial and temporal scan statistics indicated that the hot-spots mainly located at the western and southern areas of Wuhan with variations almost every year. Our findings could assist the public health authorities to develop and improve targeted health strategies, and allocate health resources rationally.

Imagine, you enter a grocery store to buy food. How many people do you overlap with in this store? How much time do you overlap with each person in the store? In this paper, we answer these questions by studying the overlap times between customers in the infinite server queue. We compute in closed form the steady-state distribution of the overlap time between a pair of customers and the distribution of the number of customers that an arriving customer will overlap with. Finally, we define a residual process that counts the number of overlapping customers that overlap in the queue for at least $\delta$ time units and compute its distribution.

We consider the asset price as the weak solution to a stochastic differential equation driven by both a Brownian motion and the counting process martingale whose predictable compensator follows shot-noise and Hawkes processes. In this framework, we discuss the Esscher martingale measure where the conditions for its existence are detailed. This generalizes certain relationships not yet encountered in the literature.

The tail index is an important parameter that measures how extreme events occur. In many practical cases, this tail index depends on covariates. In this paper,we assume that it takes a finite number of values over a partition of the covariate space. This article proposes a tail index partition-based rules extraction method that is able to construct estimates of the partition subsets and estimates of the tail index values. The method combines two steps: first an additive tree ensemble based on the Gamma deviance is fitted, and second a hierarchical clustering with spatial constraints is used to estimate the subsets of the partition. We also propose a global tree surrogate model to approximate the partition-based rules while providing an explainable model from the initial covariates. Our procedure is illustrated on simulated data. A real case study on wind property damages caused by tornadoes is finally presented.

This article proposes a continuous time mortality model based on calendar years. Mortality rates belong to a mean-reverting random field indexed by time and age. In order to explain the improvement of life expectancies, the reversion level of mortality rates is the product of a deterministic function of age and of a decreasing jump-diffusion process driving the evolution of longevity. We provide a general closed-form expression for survival probabilities and develop it when the mean reversion level of mortality rates is proportional to a Gompertz–Makeham law. We develop an econometric estimation method and validate the model on the Belgian population.

The association between time to positivity (TTP) of blood culture and the clinical prognosis of patients with Klebsiella pneumoniae bloodstream infection (BSI) remains unclear. A retrospective study of 148 inpatients with BSI caused by K. pneumoniae was performed at Shanghai Tongji Hospital, China, from October 2016–2020. The total in-hospital fatality rate was 32%. The median TTP was 11.0 (7.7–16.1) h and the optimal cutoff for prediction of in-hospital mortality was 9.4 h according to the ROC curve. Early TTP (<9.4 h) was a risk factor for in-hospital mortality by univariate analysis (OR = 2.5, 95% CI 1.2–5.0, P = 0.01), but not by multivariate analysis (OR = 2.7, 95% CI 1.0–7.4, P = 0.06). Old age, serum creatinine, white blood cells, and C-reactive protein values were risk factors for in-hospital mortality by multivariate analysis. Early TTP was not a risk factor for septic shock (OR = 1.8, 95% CI 0.6–5.1, P = 0.27) or ICU admission (OR = 1.0, 95% CI 1.0–1.0, P = 0.32). In conclusion, the in-hospital fatality rate of patients with K. pneumoniae BSI was relatively high and associated with an early TTP of blood cultures. However, no increased risk of mortality, septic shock or ICU admission was evident in early TTP patients.

We study an optimal reinsurance problem for a diffusion model, in which the drift of the claim follows an Ornstein–Uhlenbeck process. The aim of the insurer is to maximize the expected exponential utility of its terminal wealth. We consider two cases: full information and partial information. Full information occurs when the insurer directly observes the drift; partial information occurs when the insurer observes only its claims. By applying stochastic control and by solving the corresponding Hamilton–Jacobi–Bellman equations, we find the value function and the optimal reinsurance strategy under both full and partial information. We determine a relationship between the value function and reinsurance strategy under full information with the value function and reinsurance strategy under partial information.

Our study population consisted of all children and adolescents, with laboratory-confirmed SARS-Co-V-2 infection, hospitalised from February 2020 through February 2022, among residents of the Tel Aviv (TA) District, Israel. There were 491 children and adolescents hospitalised with Sars-CoV-2 infection. Among them, 281 (57%) admitted with coronavirus disease 2019 (COVID-19) as the primary cause of admission (rate of 39 per 100 000). Among all children and adolescents in the TA District, the highest hospitalisation rates were observed among infants and children below the age of 4 years (rate of 311 per 100 000 population). Severe disease was observed mostly among children with multiple underlying medical conditions. Admission rates were also elevated among residents of the ultra-orthodox community (rate ratio (RR) compared to the rest of the district; 95% confidence interval (CI) 2.38–3.82). Admission rates with COVID-19 as primary cause of admission were higher during Omicron compared to Delta predominance period (RR 1.7; 95% CI 1.22–2.32). Targeted social and public health policies should be put in place when rates of disease start to increase, such as encouraging vaccine uptake for eligible children and social distancing when necessary, taking into account already existing social and learning gaps, in order to reduce the burden of disease.

Explainability is highly desired in machine learning (ML) systems supporting high-stakes policy decisions in areas such as health, criminal justice, education, and employment. While the field of explainable ML has expanded in recent years, much of this work has not taken real-world needs into account. A majority of proposed methods are designed with generic explainability goals without well-defined use cases or intended end users and evaluated on simplified tasks, benchmark problems/datasets, or with proxy users (e.g., Amazon Mechanical Turk). We argue that these simplified evaluation settings do not capture the nuances and complexities of real-world applications. As a result, the applicability and effectiveness of this large body of theoretical and methodological work in real-world applications are unclear. In this work, we take steps toward addressing this gap for the domain of public policy. First, we identify the primary use cases of explainable ML within public policy problems. For each use case, we define the end users of explanations and the specific goals the explanations have to fulfill. Finally, we map existing work in explainable ML to these use cases, identify gaps in established capabilities, and propose research directions to fill those gaps to have a practical societal impact through ML. The contribution is (a) a methodology for explainable ML researchers to identify use cases and develop methods targeted at them and (b) using that methodology for the domain of public policy and giving an example for the researchers on developing explainable ML methods that result in real-world impact.

Commodity spot prices tend to revert to some long-term mean level and most commodity derivatives are based on futures prices, not on spot prices. So, we consider spread options on futures instead of spot or spot index, where the log spot price follows a mean-reverting process. The volatility of the mean-reverting process is driven by two different (fast and slow) scale factors. We use asymptotic analysis to obtain a closed-form approximation of the futures prices and a closed-form formula for the approximate prices of spread options on the futures. The overall improvement of our analytic formula over the classical Kirk–Bjerksund–Sternsland (KBS) formula is discussed via numerical experiments.

We developed a mechanism model which allows for simulating the novel coronavirus (COVID-19) transmission dynamics with the combined effects of human adaptive behaviours and vaccination, aiming at predicting the end time of COVID-19 infection in global scale. Based on the surveillance information (reported cases and vaccination data) between 22 January 2020 and 18 July 2022, we validated the model by Markov Chain Monte Carlo (MCMC) fitting method. We found that (1) if without adaptive behaviours, the epidemic could sweep the world in 2022 and 2023, causing 3.098 billion of human infections, which is 5.39 times of current number; (2) 645 million people could be avoided from infection due to vaccination; and (3) in current scenarios of protective behaviours and vaccination, infection cases would increase slowly, levelling off around 2023, and it would end completely in June 2025, causing 1.024 billion infections, with 12.5 million death. Our findings suggest that vaccination and the collective protection behaviour remain the key determinants against the global process of COVID-19 transmission.

The purpose of this study was to analyse the clinical characteristics of patients with severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) PCR re-positivity after recovering from coronavirus disease 2019 (COVID-19). Patients (n = 1391) from Guangzhou, China, who had recovered from COVID-19 were recruited between 7 September 2021 and 11 March 2022. Data on epidemiology, symptoms, laboratory test results and treatment were analysed. In this study, 42.7% of recovered patients had re-positive result. Most re-positive patients were asymptomatic, did not have severe comorbidities, and were not contagious. The re-positivity rate was 39%, 46%, 11% and 25% in patients who had received inactivated, mRNA, adenovirus vector and recombinant subunit vaccines, respectively. Seven independent risk factors for testing re-positive were identified, and a predictive model was constructed using these variables. The predictors of re-positivity were COVID-19 vaccination status, previous SARs-CoV-12 infection prior to the most recent episode, renal function, SARS-CoV-2 IgG and IgM antibody levels and white blood cell count. The predictive model could benefit the control of the spread of COVID-19.

Given partially ordered sets (posets) $(P, \leq _P\!)$ and $(P^{\prime}, \leq _{P^{\prime}}\!)$, we say that $P^{\prime}$ contains a copy of $P$ if for some injective function $f\,:\, P\rightarrow P^{\prime}$ and for any $X, Y\in P$, $X\leq _P Y$ if and only if $f(X)\leq _{P^{\prime}} f(Y)$. For any posets $P$ and $Q$, the poset Ramsey number $R(P,Q)$ is the least positive integer $N$ such that no matter how the elements of an $N$-dimensional Boolean lattice are coloured in blue and red, there is either a copy of $P$ with all blue elements or a copy of $Q$ with all red elements. We focus on a poset Ramsey number $R(P, Q_n)$ for a fixed poset $P$ and an $n$-dimensional Boolean lattice $Q_n$, as $n$ grows large. We show a sharp jump in behaviour of this number as a function of $n$ depending on whether or not $P$ contains a copy of either a poset $V$, that is a poset on elements $A, B, C$ such that $B\gt C$, $A\gt C$, and $A$ and $B$ incomparable, or a poset $\Lambda$, its symmetric counterpart. Specifically, we prove that if $P$ contains a copy of $V$ or $\Lambda$ then $R(P, Q_n) \geq n +\frac{1}{15} \frac{n}{\log n}$. Otherwise $R(P, Q_n) \leq n + c(P)$ for a constant $c(P)$. This gives the first non-marginal improvement of a lower bound on poset Ramsey numbers and as a consequence gives $R(Q_2, Q_n) = n + \Theta \left(\frac{n}{\log n}\right)$.

In this paper, we study the estimation of a scale parameter from a sample of lifetimes of coherent systems with a fixed structure. We assume that the components are independent and identically distributed having a common distribution which belongs to a scale parameter family. Some results are obtained as well for dependent (exchangeable) components. To this end, we will use the representations for the distribution function of a coherent system based on signatures. We prove that the efficiency of the estimators depends on the structure of the system and on the scale parameter family. In the dependence case, it also depends on the baseline copula function.

This study aimed to assess human papillomavirus (HPV) vaccine effectiveness (VE) against both vaccine-type and nonvaccine-type high-risk HPV (hrHPV) infection, and duration of protection in United States. The study population was female participants aged 18–35 years with an HPV vaccination history and genital testing for HPV from the National Health and Nutrition Examination Survey, 2007–2016. Participants vaccinated before sexual debut were assessed against 13 nonvaccine-type hrHPV infection including 31/33/35/39/45/51/52/56/58/59/68/73/82. Multivariable logistic regression was used to estimate VE overall, by age at diagnosis, time since vaccination and lifetime sexual partners. A total of 3866 women were included in the analysis, with 23.3% (95% CI 21.3%–25.4%) having been vaccinated (≥1 dose). VE against vaccine-type HPV18/16/11/6 infection was 58% overall, which was mainly driven by those aged 18–22 years (VE = 64%) and 23–27 years (65%). Among participants aged 18–22 years vaccinated before sexual debut, the VE was 47% (23%–64%) against 13 nonvaccine-type hrHPV and 61% (95% CI 36%–77%) against 5 selected nonvaccine-type hrHPV35/39/52/58/59. Both direct effectiveness and cross-protection maintained effective for 5–10 years post vaccination. We also found the prevalence of ever diagnosed cervical cancer among vaccinated was significantly lower (0.46%, 4/874) than that among unvaccinated participants (1.27%, 38/2992). These findings highlight the potential of significant reduction of cervical cancer following the universal HPV vaccination programme.