Lassa fever (LF) virus (LASV) is endemic in Sierra Leone (SL) and poses a significant public health threat to the region; however, no risk factors for clinical LF have been reported in SL. The objective of this study was to identify the risk factors for clinical LF in an endemic community in SL. We conducted a case–control study by enrolling 37 laboratory-confirmed LF cases identified through the national LF surveillance system in SL and 140 controls resided within a one-kilometre radius of the case household. We performed a conditional multiple logistic regression analysis to identify the risk factors for clinical LF. Of the 37 cases enrolled, 23 died (62% case fatality rate). Cases were younger than controls (19.5 years vs 28.9 years, p < 0.05) and more frequently female (64.8% vs 52.8%). Compared to the controls, clinical LF cases had higher contact with rodents (rats or mice) in their households in the preceding three weeks (83.8% vs 47.8%). Households with a cat reported a lower presence of rodents (73% vs 38%, p < 0.01) and contributed to a lower rate of clinical LF (48.6% vs 55.7%) although not statistically significant (p = 0.56). The presence of rodents in the households (matched adjusted odds ratio (mAOR): 11.1) and younger age (mAOR: 0.99) were independently associated with clinical LF.

Rodent access to households and younger age were independently associated with clinical LF. Rodent access to households is likely a key risk factor for clinical LF in rural SL and potentially in other countries within the West African region. Implementing measures to control rodents and their access to households could potentially decrease the number of clinical LF cases in rural SL and West Africa.

SARS-CoV-2 transmission dynamics within households involving children are complex. We examined the association between paediatric index case (PIC) age and subsequent household SARS-CoV-2 transmission among cases reported to the Minnesota Department of Health between March 2021 and February 2022. In our primary analysis, we used logistic regression to estimate odds ratios adjusted for race/ethnicity, sex, geographic region, and disease severity among households with an unvaccinated PIC. We performed a secondary analysis among households where the PIC was eligible for vaccination adjusting for the same covariates plus time since the last vaccination. Both analyses were stratified by variant wave. During the Alpha wave, PICs of all age groups had similar odds of subsequent transmission. During Delta and Omicron waves, PICs aged 16–17 had higher odds of subsequent transmission than PICs aged 0–4 (Delta OR, 1.32; [95% CI, 1.16–1.51], Omicron OR, 4.21; [95% CI, 3.25–5.45]). In the secondary analysis, unvaccinated PICs had higher odds of subsequent transmission than vaccinated PICs (Delta OR 2.89 [95% CI, 2.18–3.84], Omicron OR 1.35 [95% CI, 1.21–1.50]). Enhanced preventative measures, especially for 12–17-year-olds, may limit SARS-CoV-2 transmission within households involving children.

Foodborne diseases are ongoing and significant public health concerns. This study analysed data obtained from the Foodborne Outbreaks Surveillance System of Wenzhou to comprehensively summarise the characteristics of foodborne outbreaks from 2012 to 2022. A total of 198 outbreaks were reported, resulting in 2,216 cases, 208 hospitalisations, and eight deaths over 11 years. The findings suggested that foodborne outbreaks were more prevalent in the third quarter, with most cases occurring in households (30.8%). Outbreaks were primarily associated with aquatic products (17.7%) as sources of contamination. The primary transmission pathways were accidental ingestion (20.2%) and multi-pathway transmission (12.1%). Microbiological aetiologies (46.0%), including Vibrio parahaemolyticus, Salmonella ssp., and Staphylococcus aureus, were identified as the main causes of foodborne outbreaks. Furthermore, mushroom toxins (75.0%), poisonous animals (12.5%), and poisonous plants (12.5%) were responsible for deaths from accidental ingestion. This study identified crucial settings and aetiologies that require the attention of both individuals and governments, thereby enabling the development of effective preventive measures to mitigate foodborne outbreaks, particularly in coastal cities.

We prove that any bounded degree regular graph with sufficiently strong spectral expansion contains an induced path of linear length. This is the first such result for expanders, strengthening an analogous result in the random setting by Draganić, Glock, and Krivelevich. More generally, we find long induced paths in sparse graphs that satisfy a mild upper-uniformity edge-distribution condition.

We study the problem of identifying a small number $k\sim n^\theta$, $0\lt \theta \lt 1$, of infected individuals within a large population of size $n$ by testing groups of individuals simultaneously. All tests are conducted concurrently. The goal is to minimise the total number of tests required. In this paper, we make the (realistic) assumption that tests are noisy, that is, that a group that contains an infected individual may return a negative test result or one that does not contain an infected individual may return a positive test result with a certain probability. The noise need not be symmetric. We develop an algorithm called SPARC that correctly identifies the set of infected individuals up to $o(k)$ errors with high probability with the asymptotically minimum number of tests. Additionally, we develop an algorithm called SPEX that exactly identifies the set of infected individuals w.h.p. with a number of tests that match the information-theoretic lower bound for the constant column design, a powerful and well-studied test design.

Our study aims to enhance future pandemic preparedness by leveraging insights from historical pandemics, focusing on the multidimensional analysis of past outbreaks. In this study, we digitised and analysed for the first time aggregated mortality and morbidity data series from the Russian flu in Switzerland in 1889/1890 and subsequent years to assess its comprehensive impact. The strongest effects were observed in January 1890, showing significant monthly excess mortality from all causes compared to the preceding five years (58.9%, 95% CI 36.6 to 81.0). Even though the whole of Switzerland was affected, the impact varied regionally due to ecological variations. Deaths from other conditions such as tuberculosis and heart disease also increased during this period. A significant drop in birth occurred 9 months later, in the autumn of 1890. Morbidity estimates by physicians suggest that around 60% of the Swiss population fell ill, with regional discrepancies and earlier outbreaks among postal workers (1–2 weeks earlier than the rest of the population). A subsequent spike in all-cause excess and influenza mortality was recorded in January 1894 but more localized than in 1890. Our findings show no cross-protection between the 1890 and 1894 outbreaks.

Current fault diagnosis (FD) methods for heating, ventilation, and air conditioning (HVAC) systems do not accommodate for system reconfigurations throughout the systems’ lifetime. However, system reconfiguration can change the causal relationship between faults and symptoms, which leads to a drop in FD accuracy. In this paper, we present Fault-Symptom Brick (FSBrick), an extension to the Brick metadata schema intended to represent information necessary to propagate system configuration changes onto FD algorithms, and ultimately revise FSRs. We motivate the need to represent FSRs by illustrating their changes when the system reconfigures. Then, we survey FD methods’ representation needs and compare them against existing information modeling efforts within and outside of the HVAC sector. We introduce the FSBrick architecture and discuss which extensions are added to represent FSRs. To evaluate the coverage of FSBrick, we implement FSBrick on (i) the motivational case study scenario, (ii) Building Automation Systems’ representation of FSRs from 3 HVACs, and (iii) FSRs from 12 FD method papers, and find that FSBrick can represent 88.2% of fault behaviors, 92.8% of fault severities, 67.9% of symptoms, and 100% of grouped symptoms, FSRs, and probabilities associated with FSRs. The analyses show that both Brick and FSBrick should be expanded further to cover HVAC component information and mathematical and logical statements to formulate FSRs in real life. As there is currently no generic and extensible information model to represent FSRs in commercial buildings, FSBrick paves the way to future extensions that would aid the automated revision of FSRs upon system reconfiguration.

The extended $3/2$ short rate model is a mean reverting model of the short rate which, for suitably chosen parameters, permits a sensible term structure of bond yields and closed-form valuation formulae of zero-coupon bonds and options. This article supplies proofs of the formulae for the expected present values of future cash flows under the real-world probability measure, known as actuarial valuation. Finally, we give formulae for asymptotic levels of bond yields and formulae for bond option prices for the extended $3/2$ model, under particular conditions on its parameters.

The COVID-19 pandemic underscored the critical need for timely data and information to aid interventions and decision-making. Efforts by different actors resulted in various data-driven initiatives, constituting experiences of deploying data in the COVID-19 response and valuable lessons that can advance the sharing and use of data for social good beyond COVID-19. This commentary highlights key case studies detailing the experiences and lessons of those who implemented data science solutions for the COVID-19 response, as well as findings from 74 data-centric COVID-19 interventions. These interventions demonstrated successful data access strategies, productive intervention processes, and effective stakeholder engagement, all of which present potential pathways to overcoming data access obstacles across Africa. Additionally, this study also briefly explores three areas for action (i.e., institutions, people, and platforms) that can inform future policy development to increase data sharing for societal benefit in the long term.

We report a foodborne outbreak of the previously undetected Cryptosporidium parvum gp60 subtype IIγA11. In December 2023, notifications of cryptosporidiosis cases increased in Sweden, prompting the initiation of a national outbreak investigation, and a case–control study was performed to identify the source. We identified 60 cases between 15 December 2023 and 1 January 2024. The median age was 44 years (range: 16–81), and 73% were women. Controls were recruited from a national random pool; frequency was matched by age group and sex. Compared to controls, cases were more likely to have consumed items from salad bars in grocery stores (8% vs. 85%; adjusted odds ratios [aOR]: 58; 95% confidence interval [CI]: 22–186). In regards to food items from the salad bars, cases were more likely to have consumed kale mix salad compared to controls (62% vs. 32%; aOR: 3.6; 95%CI: 1.2–12). Trace-back investigations identified kale producers from Sweden, Belgium, and Spain, but no particular grower was identified, and no food samples were available for microbiological analysis. Our investigation indicates that leafy greens such as kale may contain Cryptosporidium spp. and cause outbreaks and it is important to understand how the contamination occurs to prevent future outbreaks and apply adequate preventive measures.

Predicting epidemic trends of coronavirus disease 2019 (COVID-19) remains a key public health concern globally today. However, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection rate in previous studies of the transmission dynamics model was mostly a fixed value. Therefore, we proposed a meta-Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) model by adding a time-varying SARS-CoV-2 reinfection rate to the transmission dynamics model to more accurately characterize the changes in the number of infected persons. The time-varying reinfection rate was estimated using random-effect multivariate meta-regression based on published literature reports of SARS-CoV-2 reinfection rates. The meta-SEIRS model was constructed to predict the epidemic trend of COVID-19 from February to December 2023 in Sichuan province. Finally, according to the online questionnaire survey, the SARS-CoV-2 infection rate at the end of December 2022 in Sichuan province was 82.45%. The time-varying effective reproduction number in Sichuan province had two peaks from July to December 2022, with a maximum peak value of about 15. The prediction results based on the meta-SEIRS model showed that the highest peak of the second wave of COVID-19 in Sichuan province would be in late May 2023. The number of new infections per day at the peak would be up to 2.6 million. We constructed a meta-SEIRS model to predict the epidemic trend of COVID-19 in Sichuan province, which was consistent with the trend of SARS-CoV-2 positivity in China. Therefore, a meta-SEIRS model parameterized based on evidence-based data can be more relevant to the actual situation and thus more accurately predict future trends in the number of infections.

Residual blood specimens provide a sample repository that could be analyzed to estimate and track changes in seroprevalence with fewer resources than household-based surveys. We conducted parallel facility and community-based cross-sectional serological surveys in two districts in India, Kanpur Nagar District, Uttar Pradesh, and Palghar District, Maharashtra, before and after a measles-rubella supplemental immunization activity (MR-SIA) from 2018 to 2019. Anonymized residual specimens from children 9 months to younger than 15 years of age were collected from public and private diagnostic laboratories and public hospitals and tested for IgG antibodies to measles and rubella viruses. Significant increases in seroprevalence were observed following the MR SIA using the facility-based specimens. Younger children whose specimens were tested at a public facility in Kanpur Nagar District had significantly lower rubella seroprevalence prior to the SIA compared to those attending a private hospital, but this difference was not observed following the SIA. Similar increases in rubella seroprevalence were observed in facility-based and community-based serosurveys following the MR SIA, but trends in measles seroprevalence were inconsistent between the two specimen sources. Despite challenges with representativeness and limited metadata, residual specimens can be useful in estimating seroprevalence and assessing trends through facility-based sentinel surveillance.

Since the beginning of mass vaccination campaign for COVID-19 in Italy (December 2020) and following the rapidly increasing vaccine administration, sex differences have been emphasized. Nevertheless, incomplete and frequently incoherent sex-disaggregated data for COVID-19 vaccinations are currently available, and vaccines clinical studies generally do not include sex-specific analyses for safety and efficacy. We looked at sex variations in the COVID-19 vaccine’s effectiveness against infection and severe disease outcomes. We conducted a nationwide retrospective cohort study on Italian population, linking information on COVID-19 vaccine administrations obtained through the Italian National Vaccination Registry, with the COVID-19 integrated surveillance system, held by the Istituto Superiore di Sanità. The results showed that, in all age groups, vaccine effectiveness (VE) was higher in the time-interval ≤120 days post-vaccination. In terms of the sex difference in vaccination effectiveness, men and women were protected against serious illness by vaccination in a comparable way, while men were protected against infection to a somewhat greater extent than women. To fully understand the mechanisms underlying the sex difference in vaccine response and its consequences for vaccine effectiveness and development, further research is required. The sex-related analysis of vaccine response may contribute to adjust vaccination strategies, improving overall public health programmes.

By coupling long-range polymerase chain reaction, wastewater-based epidemiology, and pathogen sequencing, we show that adenovirus type 41 hexon-sequence lineages, described in children with hepatitis of unknown origin in the United States in 2021, were already circulating within the country in 2019. We also observed other lineages in the wastewater, whose complete genomes have yet to be documented from clinical samples.

The design of gas turbine combustors for optimal operation at different power ratings is a multifaceted engineering task, as it requires the consideration of several objectives that must be evaluated under different test conditions. We address this challenge by presenting a data-driven approach that uses multiple probabilistic surrogate models derived from Gaussian process regression to automatically select optimal combustor designs from a large parameter space, requiring only a few experimental data points. We present two strategies for surrogate model training that differ in terms of required experimental and computational efforts. Depending on the measurement time and cost for a target, one of the strategies may be preferred. We apply the methodology to train three surrogate models under operating conditions where the corresponding design objectives are critical: reduction of NOx emissions, prevention of lean flame extinction, and mitigation of thermoacoustic oscillations. Once trained, the models can be flexibly used for different forms of a posteriori design optimization, as we demonstrate in this study.

We consider the problem of parameter estimation for the superposition of square-root diffusions. We first derive the explicit formulas for the moments and auto-covariances based on which we develop our moment estimators. We then establish a central limit theorem for the estimators with the explicit formulas for the asymptotic covariance matrix. Finally, we conduct numerical experiments to validate our method.

Motivated by the impact of emerging technologies on (toll) parks, this paper studies a problem of customers’ strategic behavior, social optimization, and revenue maximization for infinite-server queues. More specifically, we assume that a customer’s utility consists of a positive reward for receiving service minus a cost caused by the other customers in the system. In the observable setting, we show the existence, uniqueness, and expressions of the individual equilibrium threshold, the socially optimal threshold, and the optimal revenue threshold, respectively. Then, we prove that the optimal revenue threshold is smaller than the socially optimal threshold, which is smaller than the individual one. Furthermore, we also extend the cost functions to any finite polynomial function with nonnegative coefficients. In the unobservable setting, we derive the joining probabilities of individual equilibrium and optimal revenue. Finally, using numerical experiments, we complement our results and compare the social welfare and the revenue under these two information levels.

In this paper, we define weighted failure rate and their means from the stand point of an application. We begin by emphasizing that the formation of n independent component series system having weighted failure rates with sum of weight functions being unity is same as a mixture of n distributions. We derive some parametric and non-parametric characterization results. We discuss on the form invariance property of baseline failure rate for a specific choice of weight function. Some bounds on means of aging functions are obtained. Here, we establish that weighted increasing failure rate average (IFRA) class is not closed under formation of coherent systems unlike the IFRA class. An interesting application of the present work is credited to the fact that the quantile version of means of failure rate is obtained as a special case of weighted means of failure rate.

This paper investigates the precise large deviations of the net loss process in a two-dimensional risk model with consistently varying tails and dependence structures, and gives some asymptotic formulas which hold uniformly for all x varying in t-intervals. The study is among the initial efforts to analyze potential risk via large deviation results for the net loss process of the two-dimensional risk model, and can provide a novel insight to assess the operation risk in a long run by fully considering the premium income factors of the insurance company.