Self-Reported and Physiologic Reactions to Third BNT162b2 mRNA COVID-19 (Booster) Vaccine Dose.

Despite extensive technological advances in recent years, objective and continuous assessment of physiologic measures after vaccination is rarely performed. We conducted a prospective observational study to evaluate short-term self-reported and physiologic reactions to the booster BNT162b2 mRNA (Pfizer-BioNTech, https://www.pfizer.com) vaccine dose. A total of 1,609 participants were equipped with smartwatches and completed daily questionnaires through a dedicated mobile application. The extent of systemic reactions reported after the booster dose was similar to that of the second dose and considerably greater than that of the first dose. Analyses of objective heart rate and heart rate variability measures recorded by smartwatches further supported this finding. Subjective and objective reactions after the booster dose were more apparent in younger participants and in participants who did not have underlying medical conditions. Our findings further support the safety of the booster dose from subjective and objective perspectives and underscore the need for integrating wearables in clinical trials.

Country versus pharmaceutical company interests for hepatitis C treatment.

Hepatitis C virus (HCV) is one of the leading causes of liver disease and is responsible for massive health and economic burden worldwide. The disease is asymptomatic in its early stages, but it can progress over time to fatal end-stage liver disease. Thus, the majority of individuals infected with HCV are unaware of their chronic condition. Recent treatment options for HCV can completely cure the infection but are costly. We developed a game model between a pharmaceutical company (PC) and a country striving to maximize its citizens' utility. First, the PC determines the price of HCV treatment; then, the country responds with corresponding screening and treatment strategies. We employed an analytical framework to calculate the utility of the players for each selected strategy. Calibrated to detailed HCV data from Israel, we found that the PC will gain higher revenue by offering a quantity discount rather than using standard fixed pricing per treatment, by indirectly forcing the country to conduct more screening than it desired. By contrast, risk-sharing agreements, in which the country pays only for successful treatments are beneficial for the country. Our findings underscore that policy makers worldwide should prudently consider recent offers by PCs to increase screening either directly, via covering HCV screening, or indirectly, by providing discounts following a predetermined volume of sales. More broadly, our approach is applicable in other healthcare settings where screening is essential to determine treatment strategies.

Optimizing antiviral treatment for seasonal influenza in the USA: a mathematical modeling analysis.

BACKGROUND: Seasonal influenza remains a major cause of morbidity and mortality in the USA. Despite the US Centers for Disease Control and Prevention recommendation promoting the early antiviral treatment of high-risk patients, treatment coverage remains low. METHODS: To evaluate the population-level impact of increasing antiviral treatment timeliness and coverage among high-risk patients in the USA, we developed an influenza transmission model that incorporates data on infectious viral load, social contact, and healthcare-seeking behavior. We modeled the reduction in transmissibility in treated individuals based on their reduced daily viral load. The reduction in hospitalizations following treatment was based on estimates from clinical trials. We calibrated the model to weekly influenza data from Texas, California, Connecticut, and Virginia between 2014 and 2019. We considered in the baseline scenario that 2.7-4.8% are treated within 48 h of symptom onset while an additional 7.3-12.8% are treated after 48 h of symptom onset. We evaluated the impact of improving the timeliness and uptake of antiviral treatment on influenza cases and hospitalizations. RESULTS: Model projections suggest that treating high-risk individuals as early as 48 h after symptom onset while maintaining the current treatment coverage level would avert 2.9-4.5% of all symptomatic cases and 5.5-7.1% of all hospitalizations. Geographic variability in the effectiveness of earlier treatment arises primarily from variabilities in vaccination coverage and population demographics. Regardless of these variabilities, we found that when 20% of the high-risk individuals were treated within 48 h, the reduction in hospitalizations doubled. We found that treatment of the elderly population (> 65 years old) had the highest impact on reducing hospitalizations, whereas treating high-risk individuals aged 5-19 years old had the highest impact on reducing transmission. Furthermore, the population-level benefit per treated individual is enhanced under conditions of high vaccination coverage and a low attack rate during an influenza season. CONCLUSIONS: Increased timeliness and coverage of antiviral treatment among high-risk patients have the potential to substantially reduce the burden of seasonal influenza in the USA, regardless of influenza vaccination coverage and the severity of the influenza season.

Influenza vaccination should have no border: cost-effectiveness of cross-border subsidy.

BACKGROUND: Influenza is a substantial cause of morbidity and mortality for Israel and the Palestinian territory. Given the extensive interaction between the two populations, vaccination in one population may indirectly benefit the other via reduced transmission. Due to the mobility and extensive contacts, Palestinians employed in Israel could be a prime target for vaccination. METHODS: To evaluate the epidemiological and the economic benefits conferred by vaccinating Palestinians employed in Israel, we developed a model of influenza transmission within and between Israel and the West Bank. We parameterized the contact patterns underlying transmission by conducting a survey among Palestinians employed in Israel, and integrating survey results with traffic patterns and socio-demographic data. RESULTS: Vaccinating 50% of Palestinian workers is predicted to reduce the annual influenza burden by 28,745 cases (95% CI: 15,031-50,717) and 37.7 deaths (95% CI: 19·9-65·5) for the Israeli population, and by 32,9900 cases (95% CI: 14,379-51,531) and 20.2 deaths (CI 95%: 9·8-31·5) for the Palestinian population. Further, we found that as the indirect protection was so substantial, funding such a vaccination campaign would be cost-saving from the Israeli Ministry of Health perspective. CONCLUSIONS: Offering influenza vaccination to Palestinians employed in Israel could efficiently reduce morbidity and mortality within both Israel and the Palestinian territory.

Human mobility and poverty as key drivers of COVID-19 transmission and control.

BACKGROUND: Applying heavy nationwide restrictions is a powerful method to curtail COVID-19 transmission but poses a significant humanitarian and economic crisis. Thus, it is essential to improve our understanding of COVID-19 transmission, and develop more focused and effective strategies. As human mobility drives transmission, data from cellphone devices can be utilized to achieve these goals. METHODS: We analyzed aggregated and anonymized mobility data from the cell phone devices of> 3 million users between February 1, 2020, to May 16, 2020 - in which several movement restrictions were applied and lifted in Israel. We integrated these mobility patterns into age-, risk- and region-structured transmission model. Calibrated to coronavirus incidence in 250 regions covering Israel, we evaluated the efficacy and effectiveness in decreasing morbidity and mortality of applying localized and temporal lockdowns (stay-at-home order). RESULTS: Poorer regions exhibited lower and slower compliance with the restrictions. Our transmission model further indicated that individuals from impoverished areas were associated with high transmission rates. Considering a horizon of 1-3 years, we found that to reduce COVID-19 mortality, school closure has an adverse effect, while interventions focusing on the elderly are the most efficient. We also found that applying localized and temporal lockdowns during regional outbreaks reduces the overall mortality and morbidity compared to nationwide lockdowns. These trends were consistent across vast ranges of epidemiological parameters, and potential seasonal forcing. CONCLUSIONS: More resources should be devoted to helping impoverished regions. Utilizing cellphone data despite being anonymized and aggregated can help policymakers worldwide identify hotspots and apply designated strategies against future COVID-19 outbreaks.

Predicting and improving patient-level antibiotic adherence.

Low adherence to prescribed medications causes substantial health and economic burden. We analyzed primary data from electronic medical records of 250,000 random patients from Israel's Maccabi Healthcare services from 2007 to 2017 to predict whether a patient will purchase a prescribed antibiotic. We developed a decision model to evaluate whether an intervention to improve purchasing adherence is warranted for the patient, considering the cost of the intervention and the cost of non-adherence. The best performing prediction model achieved an average area under the receiver operating characteristic curve (AUC) of 0.684, with 82% accuracy in detecting individuals who had less than 50% chance of purchasing a prescribed drug. Using the decision model, an adherence intervention targeted to patients whose predicted purchasing probability is below a specified threshold can increase the number of prescriptions filled while generating significant savings compared to no intervention - on the order of 6.4% savings and 4.0% more prescriptions filled for our dataset. We conclude that analysis of large-scale patient data from electronic medical records can help predict the probability that a patient will purchase a prescribed antibiotic and can provide real-time predictions to physicians, who can then counsel the patient about medication importance. More broadly, in-depth analysis of patient-level data can help shape the next generation of personalized interventions.

Personal and social patterns predict influenza vaccination decision.

BACKGROUND: Seasonal influenza vaccination coverage remains suboptimal in most developed countries, despite longstanding recommendations of public health organizations. The individual's decision regarding vaccination is located at the core of non-adherence. We analyzed large-scale data to identify personal and social behavioral patterns for influenza vaccination uptake, and develop a model to predict vaccination decision of individuals in an upcoming influenza season. METHODS: We analyzed primary data from the electronic medical records of a retrospective cohort of 250,000 individuals between the years 2007 and 2017, collected from 137 clinics. Individuals were randomly sampled from the database of Maccabi Healthcare Services. Maccabi's clients are representative of the Israeli population, reflect all demographic, ethnic, and socioeconomic groups and levels. We used several machine-learning models to predict whether a patient would get vaccinated in the future. Models' performance was evaluated based on the area under the ROC curve. RESULTS: The vaccination decision of an individual can be explained in two dimensions, Personal and social. The personal dimension is strongly shaped by a "default" behavior, such as vaccination timing in previous seasons and general health consumption, but can also be affected by temporal factors such as respiratory illness in the prior year. In the social dimension, a patient is more likely to become vaccinated in a given season if at least one member of his family also became vaccinated in the same season. Vaccination uptake was highly assertive with age, socioeconomic score, and geographic location. An XGBoost-based predictive model achieved an ROC-AUC score of 0.91 with accuracy and recall rates of 90% on the test set. Prediction relied mainly on the patient's individual and household vaccination status in the past, age, number of encounters with the healthcare system, number of prescribed medications, and indicators of chronic illnesses. CONCLUSIONS: Our ability to make an excellent prediction of the patient's decision sets a major step toward personalized influenza vaccination campaigns, and will help shape the next generation of targeted vaccination efforts.

Vaccination strategies against respiratory syncytial virus.

Respiratory syncytial virus (RSV) is the most common cause of US infant hospitalization. Additionally, RSV is responsible for 10,000 deaths annually among the elderly across the United States, and accounts for nearly as many hospitalizations as influenza. Currently, several RSV vaccine candidates are under development to target different age groups. To evaluate the potential effectiveness of age-specific vaccination strategies in averting RSV incidence, we developed a transmission model that integrates data on daily infectious viral load and changes of behavior associated with RSV symptoms. Calibrating to RSV weekly incidence rates in Texas, California, Colorado, and Pennsylvania, we show that in all states considered, an infected child under 5 y of age is more than twice as likely as a person over 50 y of age to transmit the virus. Geographic variability in the effectiveness of a vaccination program across states arises from interplay between seasonality patterns, population demography, vaccination uptake, and vaccine mechanism of action. Regardless of these variabilities, our analysis showed that allocating vaccine to children under 5 y of age would be the most efficient strategy per dose to avert RSV in both children and adults. Furthermore, due to substantial indirect protection, the targeting of children is even predicted to reduce RSV in the elderly more than directly vaccinating the elderly themselves. Our results can help inform ongoing clinical trials and future recommendations on RSV vaccination.

Cost-Effectiveness of Rotavirus Vaccination in France-Accounting for Indirect Protection.

BACKGROUND: Vaccination against rotavirus has shown great potential for reducing the primary cause of severe childhood gastroenteritis. Previous economic evaluations of rotavirus vaccination in France have not modeled the potential impact of vaccines on disease burden via reduced transmission. OBJECTIVE: To determine the cost-effectiveness of the introduction of pentavalent rotavirus vaccination into the French infant vaccination schedule. METHODS: We developed an age-structured model of rotavirus transmission calibrated to 6 years of French gastroenteritis incidence and vaccine clinical trial data. We evaluated the cost-effectiveness of pentavalent rotavirus vaccination considering that 75% of infants would receive the three-dose vaccine course. RESULTS: Our model predicts that rotavirus vaccination will decrease rotavirus gastroenteritis incidence and associated clinical outcomes in vaccinated and unvaccinated individuals, delay the seasonal peak of infection, and increase the age of infection. From the societal perspective, our base-case scenario predicts that vaccination coverage would be cost-effective at €115 or €135 per vaccine course at €28,500 and €39,500/quality-adjusted life-year (QALY) gained, respectively, and suggests that almost 95% of the financial benefits will be recouped within the first 5 years following vaccination implementation. From the third-party payer perspective, incremental cost-effectiveness ratios ranged from €12,500 to €20,000/QALY, respectively. Our uncertainty analysis suggests that findings were sensitive to various assumptions including the number of hospitalizations, outpatient visits, and the extent of QALY losses per rotavirus episode. CONCLUSIONS: Introducing pentavalent rotavirus vaccination into the French infant vaccination schedule would significantly reduce the burden of rotavirus disease in children, and could be cost-effective under plausible conditions.

Effect of Ebola progression on transmission and control in Liberia.

BACKGROUND: The Ebola outbreak that is sweeping across West Africa is the largest, most volatile, and deadliest Ebola epidemic ever recorded. Liberia is the most profoundly affected country, with more than 3500 infections and 2000 deaths recorded in the past 3 months. OBJECTIVE: To evaluate the contribution of disease progression and case fatality on transmission and to examine the potential for targeted interventions to eliminate the disease. DESIGN: Stochastic transmission model that integrates epidemiologic and clinical data on incidence and case fatality, daily viral load among survivors and nonsurvivors evaluated on the basis of the 2000-2001 outbreak in Uganda, and primary data on contacts of patients with Ebola in Liberia. SETTING: Montserrado County, Liberia, July to September 2014. MEASUREMENTS: Ebola incidence and case-fatality records from 2014 Liberian Ministry of Health and Social Welfare. RESULTS: The average number of secondary infections generated throughout the entire infectious period of a single infected case, R, was estimated as 1.73 (95% CI, 1.66 to 1.83). There was substantial stratification between survivors (RSurvivors), for whom the estimate was 0.66 (CI, 0.10 to 1.69), and nonsurvivors (RNonsurvivors), for whom the estimate was 2.36 (CI, 1.72 to 2.80). The nonsurvivors had the highest risk for transmitting the virus later in the course of disease progression. Consequently, the isolation of 75% of infected individuals in critical condition within 4 days from symptom onset has a high chance of eliminating the disease. LIMITATION: Projections are based on the initial dynamics of the epidemic, which may change as the outbreak and interventions evolve. CONCLUSION: These results underscore the importance of isolating the most severely ill patients with Ebola within the first few days of their symptomatic phase. PRIMARY FUNDING SOURCE: National Institutes of Health.

Epidemiological and viral genomic sequence analysis of the 2014 ebola outbreak reveals clustered transmission.

Using Ebolavirus genomic and epidemiological data, we conducted the first joint analysis in which both data types were used to fit dynamic transmission models for an ongoing outbreak. Our results indicate that transmission is clustered, highlighting a potential bias in medical demand forecasts, and provide the first empirical estimate of underreporting.

An innovative influenza vaccination policy: targeting last season's patients.

Influenza vaccination is the primary approach to prevent influenza annually. WHO/CDC recommendations prioritize vaccinations mainly on the basis of age and co-morbidities, but have never considered influenza infection history of individuals for vaccination targeting. We evaluated such influenza vaccination policies through small-world contact networks simulations. Further, to verify our findings we analyzed, independently, large-scale empirical data of influenza diagnosis from the two largest Health Maintenance Organizations in Israel, together covering more than 74% of the Israeli population. These longitudinal individual-level data include about nine million cases of influenza diagnosed over a decade. Through contact network epidemiology simulations, we found that individuals previously infected with influenza have a disproportionate probability of being highly connected within networks and transmitting to others. Therefore, we showed that prioritizing those previously infected for vaccination would be more effective than a random vaccination policy in reducing infection. The effectiveness of such a policy is robust over a range of epidemiological assumptions, including cross-reactivity between influenza strains conferring partial protection as high as 55%. Empirically, our analysis of the medical records confirms that in every age group, case definition for influenza, clinical diagnosis, and year tested, patients infected in the year prior had a substantially higher risk of becoming infected in the subsequent year. Accordingly, considering individual infection history in targeting and promoting influenza vaccination is predicted to be a highly effective supplement to the current policy. Our approach can also be generalized for other infectious disease, computer viruses, or ecological networks.

Prediction and detection of side effects severity following COVID-19 and influenza vaccinations: utilizing smartwatches and smartphones.

Vaccines stand out as one of the most effective tools in our arsenal for reducing morbidity and mortality. Nonetheless, public hesitancy towards vaccination often stems from concerns about potential side effects, which can vary from person to person. As of now, there are no automated systems available to proactively warn against potential side effects or gauge their severity following vaccination. We have developed machine learning (ML) models designed to predict and detect the severity of post-vaccination side effects. Our study involved 2111 participants who had received at least one dose of either a COVID-19 or influenza vaccine. Each participant was equipped with a Garmin Vivosmart 4 smartwatch and was required to complete a daily self-reported questionnaire regarding local and systemic reactions through a dedicated mobile application. Our XGBoost models yielded an area under the receiver operating characteristic curve (AUROC) of 0.69 and 0.74 in predicting and detecting moderate to severe side effects, respectively. These predictions were primarily based on variables such as vaccine type (influenza vs. COVID-19), the individual's history of side effects from previous vaccines, and specific data collected from the smartwatches prior to vaccine administration, including resting heart rate, heart rate, and heart rate variability. In conclusion, our findings suggest that wearable devices can provide an objective and continuous method for predicting and monitoring moderate to severe vaccine side effects. This technology has the potential to improve clinical trials by automating the classification of vaccine severity.

Utilizing direct and indirect information to improve the COVID-19 vaccination booster scheduling.

Current global COVID-19 booster scheduling strategies mainly focus on vaccinating high-risk populations at predetermined intervals. However, these strategies overlook key data: the direct insights into individual immunity levels from active serological testing and the indirect information available either through sample-based sero-surveillance, or vital demographic, location, and epidemiological factors. Our research, employing an age-, risk-, and region-structured mathematical model of disease transmission-based on COVID-19 incidence and vaccination data from Israel between 15 May 2020 and 25 October 2021-reveals that a more comprehensive strategy integrating these elements can significantly reduce COVID-19 hospitalizations without increasing existing booster coverage. Notably, the effective use of indirect information alone can considerably decrease COVID-19 cases and hospitalizations, without the need for additional vaccine doses. This approach may also be applicable in optimizing vaccination strategies for other infectious diseases, including influenza.

Changes in behavior and biomarkers during the diagnostic decision period for COVID-19, influenza, and group A streptococcus (GAS): a two-year prospective cohort study in Israel.

Background: Limited knowledge exists regarding behavioral and biomarker shifts during the period from respiratory infection exposure to testing decisions (the diagnostic decision period), a key phase affecting transmission dynamics and public health strategy development. This study aims to examine the changes in behavior and biomarkers during the diagnostic decision period for COVID-19, influenza, and group A streptococcus (GAS). Methods: We analyzed data from a two-year prospective cohort study involving 4795 participants in Israel, incorporating smartwatch data, self-reported symptoms, and medical records. Our analysis focused on three critical phases: the digital incubation period (from exposure to physiological anomalies detected by smartwatches), the symptomatic incubation period (from exposure to onset of symptoms), and the diagnostic decision period for influenza, COVID-19, and GAS. Findings: The delay between initial symptom reporting and testing was 39 [95% confidence interval (CI): 34-45] hours for influenza, 53 [95% CI: 49-58] hours for COVID-19, and 38 [95% CI: 32-46] hours for GAS, with 73 [95% CI: 67-78] hours from anomalies in heart measures to symptom onset for influenza, 23 [95% CI: 18-27] hours for COVID-19, and 62 [95% CI: 54-68] hours for GAS. Analyzing the entire course of infection of each individual, the greatest changes in heart rates were detected 67.6 [95% CI: 62.8-72.5] hours prior to testing for influenza, 64.1 [95% CI: 61.4-66.7] hours prior for COVID-19, and 58.2 [95% CI: 52.1-64.2] hours prior for GAS. In contrast, the greatest reduction in physical activities and social contacts occurred after testing. Interpretation: These findings highlight the delayed response of patients in seeking medical attention and reducing social contacts and demonstrate the transformative potential of smartwatches for identifying infection and enabling timely public health interventions. Funding: This work was supported by the European Research Council, project #949850, the Israel Science Foundation (ISF), grant No. 3409/19, within the Israel Precision Medicine Partnership program, and a Koret Foundation gift for Smart Cities and Digital Living.

Safety of the fourth COVID-19 BNT162b2 mRNA (second booster) vaccine: a prospective and retrospective cohort study.

BACKGROUND: The effectiveness of the second BNT162b2 (Pfizer-BioNTech) mRNA COVID-19 booster vaccine dose (ie, fourth inoculation) is well established, but its safety has yet to be fully understood. The absence of sufficient vaccine safety information is one of the key contributors to vaccine hesitancy. In this study, we aimed to evaluate the safety profile of the second BNT162b2 mRNA COVID-19 booster vaccine using data from a retrospective cohort and a prospective cohort. METHODS: To evaluate the safety profile of the second booster vaccine, we analysed its short-term effects and compared them to those of the first booster by using data from, first, a retrospective cohort of 250 000 random members of the second-largest health-care organisation in Israel (Maccabi Healthcare Services) and, second, a prospective cohort (the PerMed study) of 4698 participants from all across Israel. Individuals who were aged 18 years or older who received the second BNT162b2 mRNA COVID-19 vaccine booster during the vaccination campaign, from Dec 30, 2021, to July 22, 2022, were eligible for inclusion in the retrospective cohort analysis. To be included in the PerMed study, participants needed to be 18 years or older, members of Maccabi Healthcare Services at the time of enrolment, using their own smartphone, and be able to give informed consent by themselves. Participants from the prospective cohort received smartwatches, downloaded a dedicated mobile application, and granted access to their medical records. The smartwatches continuously monitored several physiological measures, including heart rate. For analysis of the prospective cohort data, we used the Kruskal-Wallis test to compare heart rate levels observed before and after vaccination. The mobile application collected daily self-reported questionnaires on local and systemic reactions. Medical records of the retrospective cohort were accessed to examine the occurrence of 25 potential adverse events, and we evaluated the risk differences between 42 days in the periods before and after vaccination in a pairwise method using non-parametric percentile bootstrap. FINDINGS: The retrospective cohort included 94 169 participants who received the first booster and 17 814 who received the second booster. Comparing the 42 days before and after vaccination, the second booster was not associated with any of the 25 adverse events investigated, including myocardial infarction (risk difference, 2·25 events per 10 000 individuals [95% CI -3·93 to 8·98]) and Bell's Palsy (-1·68 events [-5·61 to 2·25]). None of the individuals was diagnosed with myocarditis or pericarditis following vaccination with the second booster. The prospective cohort included 1785 participants who received the first booster and 699 who received the second booster. We found no significant differences after inoculation with the first booster compared with the second booster (heart rate: day 2 [p=0·3], day 6 [p=0·89]; extent of self-reported reactions [p=0·06]). We found a significant increase in mean heart rate relative to that observed during the week before vaccination (baseline) levels during the first 3 days following the second booster (p<0·0001), peaking on day 2 (mean difference of 1·61 bpm [1·07 to 2·16] compared with baseline). Mean heart rate values returned to baseline levels by day 6 (-0·055 bpm [-0·56 to 0·45] compared with baseline). INTERPRETATION: Both our retrospective and prospective analyses support the safety of the second booster, with our findings reflecting physicians' diagnoses, patients' objective physiological measures, and patients' subjective reactions. We believe this study provides safety assurances to the global population who are eligible to receive an additional COVID-19 booster inoculation. These assurances can help increase the number of high-risk individuals who opt to receive this booster vaccine and thereby prevent severe outcomes associated with COVID-19. FUNDING: European Research Council (ERC).

Real-time sensing of war's effects on wellbeing with smartphones and smartwatches.

BACKGROUND: Modern wars have a catastrophic effect on the wellbeing of civilians. However, the nature of this effect remains unclear, with most insights gleaned from subjective, retrospective studies. METHODS: We prospectively monitored 954 Israelis (>40 years) from two weeks before the May 2021 Israel-Gaza war until four weeks after the ceasefire using smartwatches and a dedicated mobile application with daily questionnaires on wellbeing. This war severely affected civilians on both sides, where over 4300 rockets and missiles were launched towards Israeli cities, and 1500 aerial, land, and sea strikes were launched towards 16,500 targets in the Gaza Strip. RESULTS: We identify considerable changes in all the examined wellbeing indicators during missile attacks and throughout the war, including spikes in heart rate levels, excessive screen-on time, and a reduction in sleep duration and quality. These changes, however, fade shortly after the war, with all affected measures returning to baseline in nearly all the participants. Greater changes are observed in individuals living closer to the battlefield, women, and younger individuals. CONCLUSIONS: The demonstrated ability to monitor objective and subjective wellbeing indicators during crises in real-time is pivotal for the early detection of and prompt assistance to populations in need.

This study investigated the impact of the May 2021 Israel-Gaza war on the wellbeing of Israeli civilians. To do so, 954 Israelis over the age of 40 were monitored for six weeks before and after the war using smartwatches and a mobile application that asked daily wellbeing questions. The researchers found that during the war, people experienced spikes in heart rate, decreased sleep quality and duration, and increased screen time. These changes were more significant in people living closer to the battlefield, women, and younger individuals. However, after the ceasefire, wellbeing indicators returned to baseline levels. The study shows that monitoring wellbeing in real-time during crises can help identify and assist populations in need.

Safety of monovalent and bivalent BNT162b2 mRNA COVID-19 vaccine boosters in at-risk populations in Israel: a large-scale, retrospective, self-controlled case series study.

BACKGROUND: COVID-19 continues to be a major health threat, particularly among at-risk groups, including individuals aged 60 years or older and people with particular medical conditions. Nevertheless, the absence of sufficient vaccine safety information is one of the key contributors to vaccine refusal. We aimed to assess the short-term safety profile of the BNT162b2 mRNA COVID-19 vaccine booster doses. METHODS: In this self-controlled case series study, we used a database of members of the largest health-care organisation in Israel. We analysed the medical records of individuals at risk of COVID-19 complications who had received two doses of the monovalent BNT162b2 mRNA COVID-19 vaccine (tozinameran, Pfizer-BioNTech) as their primary course of vaccination and then also received BNT162b2 mRNA COVID-19 vaccine boosters between July 30, 2021, and Nov 28, 2022, as a monovalent first or second booster, or as a bivalent first, second, or third booster, or a combination of these. We included individuals who had active membership of the health-care organisation and who were alive (excluding COVID-19 deaths) throughout the entire study period. We excluded individuals who, during the study period, were either not active Clalit Health Services members or died of non-COVID-19 causes, and those who were infected with COVID-19 during the 7-day period after vaccination. Individuals' at-risk status was assessed on the day before the baseline period started. The primary outcome was non-COVID-19 hospitalisation for 29 adverse events that might be associated with vaccination. For each adverse event, we compared the risk difference of hospitalisation during a 28-day pre-vaccination baseline period versus during a 28-day post-vaccination period, using a non-parametric percentile bootstrap method. FINDINGS: Of the 3 574 243 members of the health-care organisation, 1 073 110 received a first monovalent booster, 394 251 received a second monovalent booster, and 123 084 received a bivalent first, second, or third booster. Overall, we found no indication of an elevated risk of non-COVID-19 hospitalisation following administration of any of the booster vaccines (risk difference in events per 100 000 individuals: first monovalent booster -37·1 [95% CI -49·8 to -24·2]; second monovalent booster -37·8 [-62·2 to -13·2]; and bivalent booster -18·7 [-53·6 to 15·4]). Except for extremely rare elevated risks after the first monovalent booster-of myocarditis (risk difference 0·7 events per 100 000 individuals [95% CI 0·3-1·3]), seizures (2·2 [0·4-4·1]), and thrombocytopenia (2·6 [0·7-4·7])-we found no safety signals in other adverse events, including ischaemic stroke. INTERPRETATION: This study provides the necessary vaccine safety assurances for at-risk populations to receive timed roll-out booster vaccinations. These assurances could reduce vaccine hesitancy and increase the number of at-risk individuals who opt to become vaccinated, and thereby prevent the severe outcomes associated with COVID-19. FUNDING: Israel Science Foundation and Israel Precision Medicine Partnership programme.

Early Oxygen Treatment Measurements Can Predict COVID-19 Mortality: A Preliminary Study.

Halting the rapid clinical deterioration, marked by arterial hypoxemia, is among the greatest challenges clinicians face when treating COVID-19 patients in hospitals. While it is clear that oxygen measures and treatment procedures describe a patient's clinical condition at a given time point, the potential predictive strength of the duration and extent of oxygen supplementation methods over the entire course of hospitalization for a patient death from COVID-19 has yet to be assessed. In this study, we aim to develop a prediction model for COVID-19 mortality in hospitals by utilizing data on oxygen supplementation modalities of patients. We analyzed the data of 545 patients hospitalized with COVID-19 complications admitted to Assuta Ashdod Medical Center, Israel, between 7 March 2020, and 16 March 2021. By solely analyzing the daily data on oxygen supplementation modalities in 182 random patients, we could identify that 75% (9 out of 12) of individuals supported by reservoir oxygen masks during the first two days died 3-30 days following hospital admission. By contrast, the mortality rate was 4% (4 out of 98) among those who did not require any oxygenation supplementation. Then, we combined this data with daily blood test results and clinical information of 545 patients to predict COVID-19 mortality. Our Random Forest model yielded an area under the receiver operating characteristic curve (AUC) score on the test set of 82.5%, 81.3%, and 83.0% at admission, two days post-admission, and seven days post-admission, respectively. Overall, our results could essentially assist clinical decision-making and optimized treatment and management for COVID-19 hospitalized patients with an elevated risk of mortality.