Impact of Long-COVID in children: a large cohort study.

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.

The straight and narrow: A game theory model of broad- and narrow-spectrum empiric antibiotic therapy.

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.

Allergic to bureaucracy? Regulatory allergenicity assessments of novel food: Motivations, challenges, compromises, and possibilities.

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.

Clonal Expansion of a Streptococcus pneumoniae Serotype 3 Capsule Variant Sequence Type 700 With Enhanced Vaccine Escape Potential After 13-Valent Pneumococcal Conjugate Vaccine Introduction.

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.

Opioid trends and risk factors for sustained use among children and adolescents in Israel: a retrospective cohort study.

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.

Distinguishing Immunologic and Behavioral Effects of Vaccination.

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.

The metabolic, virulence and antimicrobial resistance profiles of colonising Streptococcus pneumoniae shift after PCV13 introduction in urban Malawi.

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.

False-positive HIV screening tests during the COVID-19 pandemic, a passing trend?

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.

Should I Rest or Should I Go Now? A Randomized Cross-Over Trial Comparing Fixed and Self-Selected Rest Durations in High-Intensity Interval Training Cycling Sessions.

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.

Risk averse reproduction numbers improve resurgence detection.

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.

Global transmission suitability maps for dengue virus transmitted by Aedes aegypti from 1981 to 2019.

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.

Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source.

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.

Antibiotics are drugs that kill some bacteria. Antibiotic resistant bacteria are bacteria that continue to grow despite the presence of an antibiotic drug. These bacteria are a major problem in healthcare, particularly if the bacteria are resistant to multiple drugs. Here, we study bacteria that are resistant to several antibiotics that are present in patients in hospital. We find that patterns of cross-resistance differ between the location bacteria were sampled from, such as blood or urine. Our results highlight the importance of considering sample sources when assessing the likelihood that bacteria is resistant to multiple antibiotics. The information and methods described in our study should enable further analysis and prediction of the presence of cross-resistant bacteria, enabling appropriate antibiotic treatments to be used.

Prediction of ciprofloxacin resistance in hospitalized patients using machine learning.

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.

Ciprofloxacin is an antibiotic commonly used to treat various infections. Due to the frequent use of ciprofloxacin, bacteria have developed high rates of resistance to it, which means they continue to grow, reducing the effectiveness of treatment. The aim of this study was to develop computer code to predict ciprofloxacin resistance in hospitalized patients. We used data from medical records and tests of whether particular bacteria could be killed by antibiotics from a large hospital in Israel to develop the computer code. The computational model accurately predicted resistance. This model could enable antibiotic treatment to be more appropriately targeted to patients that would benefit from it and reduce the amount of bacteria resistant to ciprofloxacin.

Polymorphisms predicting phylogeny in hepatitis B virus.

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 Association Between High Ambient Temperature and Mortality in the Mediterranean Basin: a Systematic Review and Meta-analysis.

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 (I2 = 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.

The time-varying association between previous antibiotic use and antibiotic resistance.

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.

Seeing Effort: Assessing Coaches' Prediction of the Number of Repetitions in Reserve Before Task-Failure.

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.

Assessing COVID-19 vaccination strategies in varied demographics using an individual-based model.

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.