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The yellow fever virus (YFV) epidemic in Brazil is the largest in decades. The recent discovery of YFV in Brazilian Aedes species mosquitos highlights a need to monitor the risk of reestablishment of urban YFV transmission in the Americas. We use a suite of epidemiological, spatial, and genomic approaches to characterize YFV transmission. We show that the age and sex distribution of human cases is characteristic of sylvatic transmission. Analysis of YFV cases combined with genomes generated locally reveals an early phase of sylvatic YFV transmission and spatial expansion toward previously YFV-free areas, followed by a rise in viral spillover to humans in late 2016. Our results establish a framework for monitoring YFV transmission in real time that will contribute to a global strategy to eliminate future YFV epidemics.

Abstract Background Antibiotic resistance in bacterial infections is a growing threat to public health. Recent evidence shows that when exposed to stressful conditions, some bacteria perform higher rates of horizontal gene transfer and mutation, and thus acquire antibiotic resistance more rapidly. Methods We incorporate this new notion into a mathematical model for the emergence of antibiotic multi-resistance in a hospital setting. Results We show that when stress has a considerable effect on genetic variation, the emergence of antibiotic resistance is dramatically affected. A strategy in which patients receive a combination of antibiotics (combining) is expected to facilitate the emergence of multi-resistant bacteria when genetic variation is stress-induced. The preference between a strategy in which one of two effective drugs is assigned randomly to each patient (mixing), and a strategy where only one drug is administered for a specific period of time (cycling) is determined by the resistance acquisition mechanisms. We discuss several features of the mechanisms by which stress affects variation and predict the conditions for success of different antibiotic treatment strategies. Conclusions These findings should encourage research on the mechanisms of stress-induced genetic variation and establish the importance of incorporating data about these mechanisms when considering antibiotic treatment strategies.

Abstract Background Homing endonucleases (HEases) are a large and diverse group of site-specific DNAases. They reside within self-splicing introns and inteins, and promote their horizontal dissemination. In recent years, HEases have been the focus of extensive research due to their promising potential use in gene targeting procedures for the treatment of genetic diseases and for the genetic engineering of crop, animal models and cell lines. Results Using mathematical analysis and computational modeling, we present here a novel account for the evolution and population dynamics of HEase genes (HEGs). We describe HEGs as paradoxical selfish elements whose long-term persistence in a single population relies on low transmission rates and a positive correlation between transmission efficiency and toxicity. Conclusion Plausible conditions allow HEGs to sustain at high frequency through long evolutionary periods, with the endonuclease frequency being either at equilibrium or periodically oscillating. The predictions of our model may prove important not only for evolutionary theory but also for gene therapy and bio-engineering applications of HEases.

Purpose: C-reactive protein (CRP) is a common proxy of inflammation, but accurate characterizations of its dynamics during acute infections are scant. The goal of this study was to examine C-reactive protein (CRP) trajectories in hospitalized patients with viral infections, confirmed bacteremia (stratified by Gram-negative or Gram-positive bacteria), and non-bacteremic infections/inflammations, considering antibiotic treatment., Methods: Electronic medical records from Tel Aviv Sourasky Medical Center (July 2007-May 2023) were analyzed. Patients with blood cultures or positive viral tests were included. CRP levels were modeled using generalized additive mixed-effects models (GAMMs) and observed up to 150 h after initial infection diagnosis. Patients with initial CRP levels > 31.9 were excluded, to remove individuals already in a highly active inflammatory process. The shapes of the CRP curves were characterized and peak CRP as well as area under the CRP curve were the primary variables of interest., Results: Viral infections had the lowest and flattest CRP curves. Non-bacteremic infections showed intermediate levels, while bacteremia (especially Gram-negative under antibiotic treatment) had the highest CRP peaks. For instance, peak CRP ranged from 15.4 mg/L in viral infections without antibiotics to 140.9 mg/L in Gram-negative bacteremia with antibiotics., Conclusions: CRP trajectories significantly differ based on infection type and antibiotic treatment. Frequent CRP measurement could be a valuable diagnostic and risk stratification tool in hospitalized patients., (© 2024. The Author(s).)

Background: Opioid misuse in the paediatric population is understudied. This study aimed to develop a machine learning classifier to differentiate between occasional and sustained opioid users among children and adolescents in outpatient settings., Methods: Data for 29,335 patients under 19 yr with recorded opioid purchases were collected from medical records. Machine learning methods were applied to predict sustained opioid use within 1, 2, or 3 yr after first opioid use, using sociodemographic information, medical history, and healthcare usage variables collected near the time of first prescription fulfilment. The models' performance was evaluated with classification and calibration metrics, and a decision curve analysis. An online tool was deployed for model self-exploration and visualisation., Results: The models demonstrated good performance, with a 1-yr follow-up model achieving a sensitivity of 0.772, a specificity of 0.703, and an ROC-AUC of 0.792 on an independent test set, with calibration intercept and slope of 0.00 and 1.02, respectively. Decision curve analysis revealed the clinical benefit of using the model relative to other strategies. SHAP analysis (SHapley Additive exPlanations) identified influential variables, including the number of diagnoses, medical images, laboratory tests, and type of opioid used., Conclusions: Our model showed promising performance in predicting sustained opioid use among paediatric patients. The online risk prediction tool can facilitate compliance to such tools by clinicians. This study presents the potential of machine learning in identifying at-risk paediatric populations for sustained opioid use, potentially contributing to secondary prevention of opioid abuse., (Copyright © 2024 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.)

Background: Streptococcus pneumoniae serotype 3 remains a problem globally. Malawi introduced 13-valent pneumococcal conjugate vaccine (PCV13) in 2011, but there has been no direct protection against serotype 3 carriage. We explored whether vaccine escape by serotype 3 is due to clonal expansion of a lineage with a competitive advantage., Methods: The distribution of serotype 3 Global Pneumococcal Sequence Clusters (GPSCs) and sequence types (STs) globally was assessed using sequences from the Global Pneumococcal Sequencing Project. Whole-genome sequences of 135 serotype 3 carriage isolates from Blantyre, Malawi (2015-2019) were analyzed. Comparative analysis of the capsule locus, entire genomes, antimicrobial resistance, and phylogenetic reconstructions were undertaken. Opsonophagocytosis was evaluated using serum samples from vaccinated adults and children., Results: Serotype 3 GPSC10-ST700 isolates were most prominent in Malawi. Compared with the prototypical serotype 3 capsular polysaccharide locus sequence, 6 genes are absent, with retention of capsule polysaccharide biosynthesis. This lineage is characterized by increased antimicrobial resistance and lower susceptibility to opsonophagocytic killing., Conclusions: A serotype 3 variant in Malawi has genotypic and phenotypic characteristics that could enhance vaccine escape and clonal expansion after post-PCV13 introduction. Genomic surveillance among high-burden populations is essential to improve the effectiveness of next-generation pneumococcal vaccines., Competing Interests: Potential conflicts of interest. All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Abstract: Despite growing global concern over opioids, little is known about the epidemiology of opioid use in children and adolescents. This retrospective study investigated opioid use trends and identified risk factors associated with sustained opioid use among outpatient children and adolescents in Israel. Electronic health records of 110,955 children and adolescents were used to establish opioid purchase trends in outpatient settings between 2003 and 2021. Of these, data from 2012 to 2021, n = 32,956, were included in a Cox proportional hazards analysis to identify demographic, clinical, and pharmacological risk factors for sustained opioid use. An increase in opioid use was observed, with a notable rise among strong opioids, peripheral areas, and noncancer patients. Prevalence of sustained opioid users was approximately 2.5%. Risk factors with significant adjusted hazard ratios for sustained use included history of frequent doctor visits 1.82 (95% CI [1.50-2.22]) and drug purchases 1.30 (95% CI [1.07-1.58]), malignancy 1.50 (95% CI [1.07-2.09]), history of cardiovascular (1.44 (95% CI [1.04-1.98]) and pain-related conditions 1.34 (95% CI [1.14-1.58]), and different opioid substances (relative to codeine use): tramadol 2.38 (95% CI [1.73-3.27]), oxycodone 4.29 (95% CI [3.00-6.16]), and "other strong opioids" 6.05 (95% CI [3.59-10.2]). Awareness of observed increase in opioid purchases is crucial for doctors and public health practitioners. Additional monitoring and secondary prevention of children and adolescents possessing the identified risk factors should facilitate where appropriate reducing sustained opioid use when it is unnecessary., (Copyright © 2024 International Association for the Study of Pain.)

Physicians prescribe empiric antibiotic treatment when definitive knowledge of the pathogen causing an infection is lacking. The options of empiric treatment can be largely divided into broad- and narrow-spectrum antibiotics. Prescribing a broad-spectrum antibiotic increases the chances of covering the causative pathogen, and hence benefits the current patient's recovery. However, prescription of broad-spectrum antibiotics also accelerates the expansion of antibiotic resistance, potentially harming future patients. We analyse the social dilemma using game theory. In our game model, physicians choose between prescribing broad and narrow-spectrum antibiotics to their patients. Their decisions rely on the probability of an infection by a resistant pathogen before definitive laboratory results are available. We prove that whenever the equilibrium strategies differ from the socially optimal policy, the deviation is always towards a more excessive use of the broad-spectrum antibiotic. We further show that if prescribing broad-spectrum antibiotics only to patients with a high probability of resistant infection is the socially optimal policy, then decentralization of the decision making may make this policy individually irrational, and thus sabotage its implementation. We discuss the importance of improving the probabilistic information available to the physician and promoting centralized decision making., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: The impact of long-term Coronavirus disease 2019 (COVID-19) on the pediatric population is still not well understood. This study was designed to estimate the magnitude of COVID-19 long-term morbidity 3-6 months after the date of diagnosis., Methods: A retrospective study of all Clalit Health Services members in Israel aged 1-16 years who tested positive for SARS-CoV-2 between April 1, 2020 and March 31, 2021. Controls, who had no previous diagnosis of COVID-19, were one-to-one matched to 65,548 COVID-19-positive children and teens, and were assigned the infection dates of their matches as their index date. Matching included age, sex, socio-economic score, and societal sector. Individuals were excluded from the study if they had severe medical conditions before the diagnosis such as cancer, diabetes, chronic respiratory diseases, and/or abnormal physiological development. Generalized Estimating Equations were used to estimate the associations between COVID-19 and the use of medical services. The analysis focused on the 3-6 months after the infection date. Adjustments were made for demographics and for the use of medical services 6-12 and 3-6 months before the infection date. The latter was necessary because of observed disparities in medical service utilization between the groups before the COVID-19 diagnosis, despite the matching process., Results: Statistically significant differences were only found for referrals for mental health services [adjusted relative-risk (RR) 1·51, 95%CI 1·15 - 1·96; adjusted risk-difference (RD) 0·001, 95%CI 0·0006 - 0·002], and medication prescriptions of any kind (RR 1·03, 95%CI 1·01-1·06; RD 0·01 95%CI 0·004 - 0·02)., Conclusions: The significant increase in medication prescriptions and mental health service referrals support the hypothesis that COVID-19 is associated with long-lasting morbidities in children and adolescents aged 1-16 years. However, the risk difference in both instances was small, suggesting a minor impact on medical services., (© 2024. The Author(s).)

New sources of proteins are essential to meet the demands of the growing world population and evolving food trends. Assessing the allergenicity of proteins in novel food (NF) poses a significant food safety regulatory challenge. The Codex Alimentarius Commission presented an allergenicity assessment protocol for genetically modified (GM) foods, which can also be adapted for NF. Since no single laboratory test can adequately predict the allergenic potential of NF, the protocol follows a weight-of-evidence approach, evaluated by experts, as part of a risk management process. Regulatory bodies worldwide have adopted this safety protocol, which, among other things, promotes global harmonization. This review unravels the reliability and various motivations, terms, concepts, and approaches of allergenicity assessments, aiming to enhance understanding among manufacturers and the public. Health Canada, Food Safety Commission JAPAN, and Food Standards Australia New Zealand were surveyed, focusing on the European Food Safety Authority and the US Food Safety Administration for examples of scientific opinions regarding allergenicity assessments for novel and GM foods, from 2019 to 2023. According to our findings, current regulatory allergenicity assessments for NF approval primarily rely on literature reviews. Only a few of the NF assessments proactively presented additional tests. We recommend conducting bioinformatic analyses on NF when a panel of experts deems that there is insufficient prior scientific research., (© 2024 The Authors. Comprehensive Reviews in Food Science and Food Safety published by Wiley Periodicals LLC on behalf of Institute of Food Technologists.)

The interpretation of vaccine efficacy estimands is subtle, even in randomized trials designed to quantify the immunologic effects of vaccination. In this article, we introduce terminology to distinguish between different vaccine efficacy estimands and clarify their interpretations. This allows us to explicitly consider the immunologic and behavioral effects of vaccination, and establish that policy-relevant estimands can differ substantially from those commonly reported in vaccine trials. We further show that a conventional vaccine trial allows the identification and estimation of different vaccine estimands under plausible conditions if one additional post-treatment variable is measured. Specifically, we utilize a "belief variable" that indicates the treatment an individual believed they had received. The belief variable is similar to "blinding assessment" variables that are occasionally collected in placebo-controlled trials in other fields. We illustrate the relations between the different estimands, and their practical relevance, in numerical examples based on an influenza vaccine trial., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

False-positive fourth-generation HIV screening tests are rare and are usually associated with various infections and autoimmune diseases. SARS-CoV-2 infection and vaccination were recently linked with false-positive HIV screening test results. However, little is known about false-positives in people who performed HIV screening tests after outbreaks of different SARS-CoV-2 strains and vaccination campaigns. Here, we examined the false-positive rates in samples collected by the Israeli AIDS Task Force in 2018--2022, with respect to such factors., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Streptococcus pneumoniae causes substantial mortality among children under 5-years-old worldwide. Polysaccharide conjugate vaccines (PCVs) are highly effective at reducing vaccine serotype disease, but emergence of non-vaccine serotypes and persistent nasopharyngeal carriage threaten this success. We investigated the hypothesis that following vaccine, adapted pneumococcal genotypes emerge with the potential for vaccine escape. We genome sequenced 2804 penumococcal isolates, collected 4-8 years after introduction of PCV13 in Blantyre, Malawi. We developed a pipeline to cluster the pneumococcal population based on metabolic core genes into "Metabolic genotypes" (MTs). We show that S. pneumoniae population genetics are characterised by emergence of MTs with distinct virulence and antimicrobial resistance (AMR) profiles. Preliminary in vitro and murine experiments revealed that representative isolates from emerging MTs differed in growth, haemolytic, epithelial infection, and murine colonisation characteristics. Our results suggest that in the context of PCV13 introduction, pneumococcal population dynamics had shifted, a phenomenon that could further undermine vaccine control and promote spread of AMR., (© 2023. The Author(s).)

The effective reproduction number R is a prominent statistic for inferring the transmissibility of infectious diseases and effectiveness of interventions. R purportedly provides an easy-to-interpret threshold for deducing whether an epidemic will grow (R>1) or decline (R<1). We posit that this interpretation can be misleading and statistically overconfident when applied to infections accumulated from groups featuring heterogeneous dynamics. These groups may be delineated by geography, infectiousness or sociodemographic factors. In these settings, R implicitly weights the dynamics of the groups by their number of circulating infections. We find that this weighting can cause delayed detection of outbreak resurgence and premature signalling of epidemic control because it underrepresents the risks from highly transmissible groups. Applying E-optimal experimental design theory, we develop a weighting algorithm to minimise these issues, yielding the risk averse reproduction number E. Using simulations, analytic approaches and real-world COVID-19 data stratified at the city and district level, we show that E meaningfully summarises transmission dynamics across groups, balancing bias from the averaging underlying R with variance from directly using local group estimates. An E>1generates timely resurgence signals (upweighting risky groups), while an E<1ensures local outbreaks are under control. We propose E as an alternative to R for informing policy and assessing transmissibility at large scales (e.g., state-wide or nationally), where R is commonly computed but well-mixed or homogeneity assumptions break down., Competing Interests: We declare no competing interests., (Copyright: © 2023 Parag, Obolski. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: In high-intensity interval training (HIIT), the rest durations between intervals are commonly prescribed using a fixed approach (e.g., 30 s between intervals). An alternative is the self-selected (SS) approach, in which trainees select their resting durations. Studies comparing the two approaches report mixed results. However, in these studies, trainees in the SS condition rested for as little or as long as they wished, leading to dissimilar total rest durations between conditions. Here, for the first time, we compare the two approaches while controlling for total rest duration., Methods: Twenty-four amateur adult male cyclists completed a familiarization session, followed by two counterbalanced cycling HIIT sessions. Each session was composed of nine, 30-s intervals, in which the goal was to accumulate as many watts as possible on an SRM ergometer. In the fixed condition, cyclists rested for 90 s between intervals. In the SS condition, cyclists had 720 s (i.e., 8  × 90 s) of rest to allocate in any way they wished. We measured and compared watts, heart rate, electromyography of the knee flexors and extensors, rating of perceived effort and fatigue, perception of autonomy and enjoyment. Additionally, a subsample of ten cyclists completed a retest of the SS condition., Results: With the exception of perception of autonomy, which was higher in the SS condition, outcomes were highly similar in both conditions. For example, the average aggregated differences were: 0.57 (95% CI - 8.94, 10.09) for watts; - 0.85 (95% CI - 2.89, 1.18) for heart rate; and 0.01 (95% CI - 0.29, 0.30) for rating of perceived effort (on a 0-10 scale). Additionally, the retest of the SS condition resulted in a similar rest allocation pattern across the intervals and in similar outcomes., Conclusion: Given the similarities in performance, physiological and psychological outcomes between the fixed and SS conditions, both can be equally utilized based on coaches' and cyclists' preferences and training goals., (© 2023. The Author(s).)

Mosquito-borne viruses increasingly threaten human populations due to accelerating changes in climate, human and mosquito migration, and land use practices. Over the last three decades, the global distribution of dengue has rapidly expanded, causing detrimental health and economic problems in many areas of the world. To develop effective disease control measures and plan for future epidemics, there is an urgent need to map the current and future transmission potential of dengue across both endemic and emerging areas. Expanding and applying Index P, a previously developed mosquito-borne viral suitability measure, we map the global climate-driven transmission potential of dengue virus transmitted by Aedes aegypti mosquitoes from 1981 to 2019. This database of dengue transmission suitability maps and an R package for Index P estimations are offered to the public health community as resources towards the identification of past, current and future transmission hotspots. These resources and the studies they facilitate can contribute to the planning of disease control and prevention strategies, especially in areas where surveillance is unreliable or non-existent., (© 2023. The Author(s).)

Background: Antimicrobial resistance is a major healthcare burden, aggravated when it extends to multiple drugs. While cross-resistance is well-studied experimentally, it is not the case in clinical settings, and especially not while considering confounding. Here, we estimated patterns of cross-resistance from clinical samples, while controlling for multiple clinical confounders and stratifying by sample sources., Methods: We employed additive Bayesian network (ABN) modelling to examine antibiotic cross- resistance in five major bacterial species, obtained from different sources (urine, wound, blood, and sputum) in a clinical setting, collected in a large hospital in Israel over a 4-year period. Overall, the number of samples available were 3525 for E coli, 1125 for K pneumoniae, 1828 for P aeruginosa, 701 for P mirabilis, and 835 for S aureus., Results: Patterns of cross-resistance differ across sample sources. All identified links between resistance to different antibiotics are positive. However, in 15 of 18 instances, the magnitudes of the links are significantly different between sources. For example, E coli exhibits adjusted odds ratios of gentamicin-ofloxacin cross-resistance ranging from 3.0 (95%CI [2.3,4.0]) in urine samples to 11.0 (95%CI [5.2,26.1]) in blood samples. Furthermore, we found that for P mirabilis, the magnitude of cross-resistance among linked antibiotics is higher in urine than in wound samples, whereas the opposite is true for K pneumoniae and P aeruginosa., Conclusions: Our results highlight the importance of considering sample sources when assessing likelihood of antibiotic cross-resistance. The information and methods described in our study can refine future estimation of cross-resistance patterns and facilitate determination of antibiotic treatment regimens., (© 2023. The Author(s).)

Background: Ciprofloxacin is a widely used antibiotic that has lost efficiency due to extensive resistance. We developed machine learning (ML) models that predict the probability of ciprofloxacin resistance in hospitalized patients., Methods: Data were collected from electronic records of hospitalized patients with positive bacterial cultures, during 2016-2019. Susceptibility results to ciprofloxacin (n = 10,053 cultures) were obtained for Escherichia coli, Klebsiella pneumoniae, Morganella morganii, Pseudomonas aeruginosa, Proteus mirabilis and Staphylococcus aureus. An ensemble model, combining several base models, was developed to predict ciprofloxacin resistant cultures, either with (gnostic) or without (agnostic) information on the infecting bacterial species., Results: The ensemble models' predictions are well-calibrated, and yield ROC-AUCs (area under the receiver operating characteristic curve) of 0.737 (95%CI 0.715-0.758) and 0.837 (95%CI 0.821-0.854) on independent test-sets for the agnostic and gnostic datasets, respectively. Shapley additive explanations analysis identifies that influential variables are related to resistance of previous infections, where patients arrived from (hospital, nursing home, etc.), and recent resistance frequencies in the hospital. A decision curve analysis reveals that implementing our models can be beneficial in a wide range of cost-benefits considerations of ciprofloxacin administration., Conclusions: This study develops ML models to predict ciprofloxacin resistance in hospitalized patients. The models achieve high predictive ability, are well calibrated, have substantial net-benefit across a wide range of conditions, and rely on predictors consistent with the literature. This is a further step on the way to inclusion of ML decision support systems into clinical practice., (© 2023. The Author(s).)

Objectives: The objective of the study was to estimate how the time elapsed from previous antibiotic use is associated with antibiotic resistance., Methods: Data comprised electronic medical records of all patients in an Israeli hospital who had a positive bacterial culture from 2016 to 2019. These included susceptibility testing results and clinical and demographic data. Mixed-effects time-varying logistic models were fitted to estimate the association between the time elapsed since the last use of aminoglycosides and gentamicin resistance (n = 13 095), cephalosporins and ceftazidime resistance (n = 13 051), and fluoroquinolones and ciprofloxacin resistance (n = 15 364) while adjusting for multiple covariates., Results: For all examined antibiotics, previous antibiotic use had a statistically significant association with resistance (p < 0.001). These associations exhibited a clear decreasing pattern over time, which we present as a flexible function of time. Nonetheless, previous antibiotic use remained a significant risk factor for resistance for at least 180 days when the adjusted ORs were 1.94 (95% CI, 1.40-2.69), 1.33 (95% CI, 1.10-1.61), and 2.25 (95% CI, 1.49-3.41) for gentamicin, ceftazidime, and ciprofloxacin, respectively., Discussion: The association between prior antibiotic use and resistance decreases over time. Commonly used cut-offs for prior antibiotic use can either misclassify patients still at higher risk when too recent or provide a diluted estimate of the effects of antibiotic use on future resistance when too distant. Hence, prior antibiotic use should be considered a time-dependent risk factor for resistance in both epidemiological research and clinical practice., (Copyright © 2022 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.)

Purpose of Review: The Mediterranean basin is highly vulnerable to climate change. This study is aimed at quantifying the risk of mortality associated with exposure to high ambient temperature in the Mediterranean basin in the general population and in vulnerable sub-populations., Recent Findings: We retrieved effect estimates from studies linking temperature and mortality in the Mediterranean basin, between 2000 and 2021. In a meta-analysis of 16 studies, we found an increased risk of all-cause mortality due to ambient heat/high temperature exposure in the Mediterranean basin, with a pooled RR of 1.035 (95%CI 1.028-1.041) per 1 °C increase in temperature above local thresholds (I 2  = 79%). Risk was highest for respiratory mortality (RR = 1.063, 95% CI 1.052-1.074) and cardiovascular mortality (RR = 1.046, 95% CI 1.036-1.057). Hot ambient temperatures increase the mortality risk across the Mediterranean basin. Further studies, especially in North African, Asian Mediterranean, and eastern European countries, are needed to bolster regional preparedness against future heat-related health burdens., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Hepatitis B viruses (HBVs) are compact viruses with circular genomes of ∼3.2 kb in length. Four genes ( HBx, Core, Surface, and Polymerase ) generating seven products are encoded on overlapping reading frames. Ten HBV genotypes have been characterised (A-J), which may account for differences in transmission, outcomes of infection, and treatment response. However, HBV genotyping is rarely undertaken, and sequencing remains inaccessible in many settings. We set out to assess which amino acid (aa) sites in the HBV genome are most informative for determining genotype, using a machine learning approach based on random forest algorithms (RFA). We downloaded 5,496 genome-length HBV sequences from a public database, excluding recombinant sequences, regions with conserved indels, and genotypes I and J. Each gene was separately translated into aa, and the proteins concatenated into a single sequence (length 1,614 aa). Using RFA, we searched for aa sites predictive of genotype and assessed covariation among the sites with a mutual information-based method. We were able to discriminate confidently between genotypes A-H using ten aa sites. Half of these sites (5/10) sites were identified in Polymerase (Pol), of which 4/5 were in the spacer domain and one in reverse transcriptase. A further 4/10 sites were located in Surface protein and a single site in HBx. There were no informative sites in Core. Properties of the aa were generally not conserved between genotypes at informative sites. Among the highest co-varying pairs of sites, there were fifty-five pairs that included one of these 'top ten' sites. Overall, we have shown that RFA analysis is a powerful tool for identifying aa sites that predict the HBV lineage, with an unexpectedly high number of such sites in the spacer domain, which has conventionally been viewed as unimportant for structure or function. Our results improve ease of genotype prediction from limited regions of HBV sequences and may have future applications in understanding HBV evolution., (© The Author(s) 2023. Published by Oxford University Press.)

In August 2021, a major wave of the SARS-CoV-2 Delta variant erupted in the highly vaccinated population of Israel. The transmission advantage of the Delta variant enabled it to replace the Alpha variant in approximately two months. The outbreak led to an unexpectedly large proportion of breakthrough infections (BTI)-a phenomenon that received worldwide attention. Most of the Israeli population, especially those aged 60+, received their second dose of the vaccination four months before the invasion of the Delta variant. Hence, either the vaccine induced immunity dropped significantly or the Delta variant possesses immunity escaping abilities, or both. In this work, we model data obtained from the Israeli Ministry of Health, to help understand the epidemiological factors involved in the outbreak. We propose a mathematical model that captures a multitude of factors, including age structure, the time varying vaccine efficacy, time varying transmission rate, BTIs, reduced susceptibility and infectivity of vaccinated individuals, protection duration of the vaccine induced immunity, and the vaccine distribution. We fitted our model to COVID-19 cases among the vaccinated and unvaccinated, for <60 and 60+ age groups, and quantified the transmission rate, the vaccine efficacy over time and the impact of the third dose booster vaccine. The peak transmission rate of the Delta variant was found to be 2.14 times higher than that of the Alpha variant. The two-dose vaccine efficacy against infection dropped significantly from >90% to ~40% over 6 months. We further performed model simulations and quantified counterfactual scenarios examining what would happen if the booster had not been rolled out. We estimated that approximately 4.03 million infective cases (95%CI 3.19, 4.86) were prevented by vaccination overall, and 1.22 million infective cases (95%CI 0.89, 1.62) averted by the booster., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Feng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: A key role of resistance training (RT) coaches is to personalize programs based on their trainees' abilities and goals. Specifically, coaches often assess how many repetitions in reserve (RIR) their trainees have until task-failure. Coaches can then modify the number of repetitions assigned per set accordingly. However, coaches' ability to predict the number of RIR is unknown., Methods: Certified RT coaches (n = 259) were randomly assigned to watch a video of one of eight trainees. The trainees performed two sets of barbell squats and preacher biceps-curls, using 70% or 80% of their 1RM, to task-failure. The coaches predicted trainees' RIR at 33%, 66%, and 90% of the total number of repetitions the trainees completed in each set. We fitted a linear mixed model with various predictors to the prediction errors as the outcomes (i.e., signed and unsigned values of the predicted minus actual repetitions to task-failure)., Results: The overall average number of repetitions completed by the trainees was 13.9. The average absolute errors were 4.8, 2.0, and 1.2 repetitions for the 33%, 66%, and 90% time-points, respectively. The absolute prediction error increased for the biceps-curl compared to the squat (1.43, 95% CI [1.13, 1.74]), but decreased for heavier loads (- 1.17, 95% CI [- 2.16, - 0.19]), and in the second set of each exercise (- 1.20, 95% CI [- 1.38, - 1.02]). Surprisingly, coaches' years of experience had a negligible effect on the absolute error (- 0.020, 95% CI [- 0.039, - 0.0007]). Finally, coaches underpredicted the RIR at early time-points but reverted to slight overprediction at later time-points., Conclusions: Prior coaching experience seems to play a minor role in RIR predictions. However, even short-term exposures to new trainees performing different exercises can substantially improve coaches' RIR predictions., (© 2022. The Author(s).)

Background: New variants of SARS-CoV-2 are constantly discovered. Administration of COVID-19 vaccines and booster doses, combined with the application of non-pharmaceutical interventions (NPIs ) , is often used to prevent outbreaks of emerging variants. Such outbreak dynamics are further complicated by the population's behavior and demographic composition. Hence, realistic simulations are needed to estimate the efficiency of proposed vaccination strategies in conjunction with NPIs., Methods: We developed an individual-based model of COVID-19 dynamics that considers age-dependent parameters such as contact matrices, probabilities of symptomatic and severe disease, and households' age distribution. As a case study, we simulate outbreak dynamics under the demographic compositions of two Israeli cities with different household sizes and age distributions. We compare two vaccination strategies: vaccinate individuals in a currently prioritized age group, or dynamically prioritize neighborhoods with a high estimated reproductive number. Total infections and hospitalizations are used to compare the efficiency of the vaccination strategies under the two demographic structures, in conjunction with different NPIs., Results: We demonstrate the effectiveness of vaccination strategies targeting highly infected localities and of NPIs actively detecting asymptomatic infections. We further show that different optimal vaccination strategies exist for each sub-population's demographic composition and that their application is superior to a uniformly applied strategy., Conclusion: Our study emphasizes the importance of tailoring vaccination strategies to subpopulations' infection rates and to the unique characteristics of their demographics (e.g., household size and age distributions). The presented simulation framework and findings can help better design future responses against the following emerging variants., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer RT declared a past co-authorship with the author UO to the handling editor., (Copyright © 2022 Ben-Zuk, Daon, Sasson, Ben-Adi, Huppert, Nevo and Obolski.)

The role of immune responses to previously seen endemic coronavirus epitopes in severe acute respiratory coronavirus 2 (SARS-CoV-2) infection and disease progression has not yet been determined. Here, we show that a key characteristic of fatal outcomes with coronavirus disease 2019 (COVID-19) is that the immune response to the SARS-CoV-2 spike protein is enriched for antibodies directed against epitopes shared with endemic beta-coronaviruses and has a lower proportion of antibodies targeting the more protective variable regions of the spike. The magnitude of antibody responses to the SARS-CoV-2 full-length spike protein, its domains and subunits, and the SARS-CoV-2 nucleocapsid also correlated strongly with responses to the endemic beta-coronavirus spike proteins in individuals admitted to an intensive care unit (ICU) with fatal COVID-19 outcomes, but not in individuals with nonfatal outcomes. This correlation was found to be due to the antibody response directed at the S2 subunit of the SARS-CoV-2 spike protein, which has the highest degree of conservation between the beta-coronavirus spike proteins. Intriguingly, antibody responses to the less cross-reactive SARS-CoV-2 nucleocapsid were not significantly different in individuals who were admitted to an ICU with fatal and nonfatal outcomes, suggesting an antibody profile in individuals with fatal outcomes consistent with an "original antigenic sin" type response.

Background: Quantitative estimates of collateral resistance induced by antibiotic use are scarce., Objectives: To estimate the effects of treatment with amoxicillin/clavulanate or cefazolin, compared with cefuroxime, on future resistance to ceftazidime among hospitalized patients., Methods: A retrospective analysis of patients with positive bacterial cultures hospitalized in an Israeli hospital during 2016-19 was conducted. Patients were restricted to those treated with amoxicillin/clavulanate, cefazolin or cefuroxime and re-hospitalized with a positive bacterial culture during the following year. Matching was performed using exact, Mahalanobis and propensity score matching. Each patient in the amoxicillin/clavulanate and cefazolin groups was matched to a single patient from the cefuroxime group, yielding 185:185 and 298:298 matched patients. Logistic regression and the g-formula (standardization) were used to estimate the OR, risk difference (RD) and number needed to harm (NNH)., Results: Cefuroxime induced significantly higher resistance to ceftazidime than amoxicillin/clavulanate or cefazolin; the marginal OR was 1.76 (95% CI = 1.16-2.83) compared with amoxicillin/clavulanate and 1.98 (95% CI = 1.41-2.8) compared with cefazolin and the RD was 0.118 (95% CI = 0.031-0.215) compared with amoxicillin/clavulanate and 0.131 (95% CI = 0.058-0.197) compared with cefazolin. We also estimated the NNH; replacing amoxicillin/clavulanate or cefazolin with cefuroxime would yield ceftazidime resistance in 1 more patient for every 8.5 (95% CI = 4.66-32.14) or 7.6 (95% CI = 5.1-17.3) patients re-hospitalized in the following year, respectively., Conclusions: Our results indicate that treatment with amoxicillin/clavulanate or cefazolin is preferable to cefuroxime, in terms of future collateral resistance. The results presented here are a first step towards quantitative estimations of the ecological damage caused by different antibiotics., (© The Author(s) 2022. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: During Feb-Apr. 2020, many countries implemented non-pharmaceutical interventions (NPIs), such as school closures and lockdowns, to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. Overall, these interventions seem to have reduced the spread of the pandemic. We hypothesized that the official and effective start dates of NPIs can be noticeably different, for example, due to slow adoption by the population, and that these differences can lead to errors in the estimation of the impact of NPIs., Methods: SEIR models were fitted to case data from 12 regions to infer the effective start dates of interventions and compare these with the official dates. The impact of NPIs was estimated from the inferred model parameters., Results: We infer mostly late effective start dates of interventions. For example, Italy implemented a lockdown on Mar 11, but we infer the effective start date on Mar 17 (+3.05-2.01 days 95% CI). Moreover, we find that the impact of NPIs can be underestimated if it is assumed they start on their official date., Conclusions: Differences between the official and effective start of NPIs are likely. Neglecting such differences can lead to underestimation of the impact of NPIs, which could cause decision-makers to escalate interventions and guidelines., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The study aim was to examine the incidence and risk factors of respiratory syncytial virus (RSV) bronchiolitis hospitalisations and disease severity among infants. We compared demographic and health characteristics of children aged 0-23 hospitalised for RSV bronchiolitis (cases, n = 1227) during 2008-2018 and control children (n = 554) of the same age admitted for non-respiratory disease. RSV antigen was detected in nasal swabs by immunochromatography. Multiple logistic regression models were applied. The average annual incidence of hospitalisation for RSV bronchiolitis was 12.6 per 1000 and 1.7 per 1000 (P < 0.001) among infants and toddlers, respectively, with winter seasonality (November-March). The risk of hospitalisation for RSV bronchiolitis increased among children aged 0-5 months (OR 7.66; 95% CI 5.61-10.45) and 6-11 months (OR 12.88, 95% CI 8.48-19.55), compared to those aged 12-23 months. Additional risk factors were living in low vs. higher socio-economic status towns (OR 1.49; 95% CI 1.14-1.95), having chronic medical conditions (OR 2.75; 95% CI 1.61-4.70), birth month (October-January vs. June-September) (OR 2.19; 95% CI 1.60-2.99) and history of stay in neonatal intensive care unit at birth (OR 2.37; 95% CI 1.27-4.41). Male children and those who had pneumonia were more likely to have severe RSV bronchiolitis. In conclusion, the burden of hospitalisations for RSV bronchiolitis is high, especially in young infants. Effective preventive measures such as RSV active vaccines can reduce the risk of hospitalisations for RSV bronchiolitis among these vulnerable groups.

It is unclear whether West Nile virus (WNV) circulates endemically in Portugal. Despite the country's adequate climate for transmission, Portugal has only reported four human WNV infections so far. We performed a review of WNV-related data (1966-2020), explored mosquito (2016-2019) and land type distributions (1992-2019), and used climate data (1981-2019) to estimate WNV transmission suitability in Portugal. Serological and molecular evidence of WNV circulation from animals and vectors was largely restricted to the south. Land type and climate-driven transmission suitability distributions, but not the distribution of WNV-capable vectors, were compatible with the North-South divide present in serological and molecular evidence of WNV circulation. Our study offers a comprehensive, data-informed perspective and review on the past epidemiology, surveillance and climate-driven transmission suitability of WNV in Portugal, highlighting the south as a subregion of importance. Given the recent WNV outbreaks across Europe, our results support a timely change towards local, active surveillance., (© 2022. The Author(s).)

Objectives: Respiratory syncytial virus (RSV) is a leading cause of respiratory tract infections, especially in young children. Antibiotics are often unnecessarily prescribed for the treatment of RSV. Such treatments affect antibiotic resistance in future bacterial infections of treated patients and the general population. This study aimed to understand risk factors for and patterns of unnecessary antibiotic prescription in children with RSV., Methods: In a single-centre, retrospective study in Israel, we obtained data for children aged ≤2 years (n = 1016) hospitalised for RSV bronchiolitis during 2008-2018 and ascertained not to have bacterial co-infections. Antibiotic misuse was defined as prescription of antibiotics during hospitalisation of the study population. Demographic and clinical variables were assessed as predictors of unnecessary antibiotic treatment in a multivariable logistic regression model., Results: The unnecessary antibiotic treatment rate of children infected with RSV and ascertained not to have a bacterial co-infection was estimated at 33.4% (95% CI 30.5-36.4%). An increased likelihood of antibiotic misuse was associated with drawing bacterial cultures and with variables indicative of a severe patient status such as lower oxygen saturation, higher body temperature, tachypnoea and prior recent emergency room visit. Older age and female sex were also associated with an increased likelihood of unnecessary antibiotic treatment., Conclusions: Unnecessary antibiotic treatment in RSV patients was very common and may be largely attributed to physicians' perception of patients' severity. Improving prescription guidelines, implementing antibiotic stewardship programmes and utilising decision support systems may help achieve a better balance between prescribing and withholding antibiotic treatment., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objective: Testing individuals for the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen causing the coronavirus disease 2019 (COVID-19), is crucial for curtailing transmission chains. Moreover, rapidly testing many potentially infected individuals is often a limiting factor in controlling COVID-19 outbreaks. Hence, pooling strategies, wherein individuals are grouped and tested simultaneously, are employed. Here, we present a novel pooling strategy that builds on the Bayesian D-optimal experimental design criterion., Materials and Methods: Our strategy, called DOPE (D-Optimal Pooling Experimental design), is built on a novel Bayesian formulation of pooling. DOPE defines optimal pooled tests as those maximizing the mutual information between data and infection states. We estimate said mutual information via Monte-Carlo sampling and employ a discrete optimization heuristic to maximize it., Results: We compare DOPE to other, commonly used pooling strategies, as well as to individual testing. DOPE dominates the other strategies as it yields lower error rates while utilizing fewer tests. We show that DOPE maintains this dominance for a variety of infection prevalence values., Discussion: DOPE has several additional advantages over common pooling strategies: it provides posterior distributions of the probability of infection rather than only binary classification outcomes; it naturally incorporates prior information of infection probabilities and test error rates; and finally, it can be easily extended to include other, newly discovered information regarding COVID-19., Conclusion: DOPE can substantially improve accuracy and throughput over current pooling strategies. Hence, DOPE can facilitate rapid testing and aid the efforts of combating COVID-19 and other future pandemics., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Pooling is a method of simultaneously testing multiple samples for the presence of pathogens. Pooling of SARS-CoV-2 tests is increasing in popularity, due to its high testing throughput. A popular pooling scheme is Dorfman pooling: test N individuals simultaneously, if the test is positive, each individual is then tested separately; otherwise, all are declared negative. Most analyses of the error rates of pooling schemes assume that including more than a single infected sample in a pooled test does not increase the probability of a positive outcome. We challenge this assumption with experimental data and suggest a novel and parsimonious probabilistic model for the outcomes of pooled tests. As an application, we analyse the false-negative rate (i.e. the probability of a negative result for an infected individual) of Dorfman pooling. We show that the false-negative rates under Dorfman pooling increase when the prevalence of infection decreases. However, low infection prevalence is exactly the condition when Dorfman pooling achieves highest throughput efficiency. We therefore urge the cautious use of pooling and development of pooling schemes that consider correctly accounting for tests' error rates., (© 2021 The Authors.)