Safety of the fourth COVID-19 BNT162b2 mRNA (second booster) vaccine

COVID-19 remains a global concern due to vaccine protection waning and the emergence of immuneevasive variants. While the effectiveness of a second booster vaccine dose (i.e., fourth inoculation) is well proven, its safety has yet to be fully understood, and vaccine compliance remains low. We conducted a prospective observational study to compare the short-term effects of the first and second BNT162b2 mRNA COVID-19 vaccine booster doses. 2,019 participants received smartwatches and filled in a daily questionnaire on systemic reactions to the vaccine. We found substantial changes from baseline levels in the 72 hours post-vaccination with the second booster in both self-reported and physiological reactions measured by the smartwatches. However, no significant difference in reactions was observed between the first and second boosters. We also found that participants who experienced more severe reactions to the first booster tended to likewise experience more severe reactions to the second booster. Our work supports the safety of the second booster from both subjective (self-reported questionnaires) and objective (physiological measurements) perspectives.

Self-reported and physiological reactions to the third BNT162b2 mRNA COVID-19 (booster) vaccine dose

BackgroundThe rapid rise in hospitalizations associated with the Delta-driven COVID-19 resurgence, and the imminent risk of hospital overcrowding, led the Israeli government to initialize a national third (booster) COVID-19 vaccination campaign in early August 2021, offering the BNT162b2 mRNA vaccine to individuals who received their second dose over five months ago. However, the safety of the third (booster) dose has not been fully established yet. ObjectiveEvaluate the short-term, self-reported and physiological reactions to the third BNT162b2 mRNA COVID-19 (booster) vaccine dose. DesignA prospective observational study, in which participants are equipped with a smartwatch and fill in a daily questionnaire via a dedicated mobile application for a period of 21 days, starting seven days before the vaccination. SettingAn Israel-wide third (booster) vaccination campaign. ParticipantsA group of 1,609 (18+ years of age) recipients of at least one dose of the BNT162b2 vaccine between December 20, 2020, and September 15, 2021, out of a larger cohort of 2,912 prospective study participants. 1,344 of the participants were recipients of the third vaccine dose. MeasurementsDaily self-reported questionnaires regarding local and systemic reactions, mood level, stress level, sport duration, and sleep quality. Heart rate, heart rate variability and blood oxygen saturation level were continuously measured by Garmin Vivosmart 4 smartwatches. ResultsThe extent of systemic reactions reported following the third (booster) dose administration is similar to that reported following the second dose (p-value=0.305) and considerably greater than that reported following the first dose (p-value<0.001). Our analyses of self-reported well-being indicators as well as the objective heart rate and heart rate variability measures recorded by the smartwatches further support this finding. Focusing on the third dose, reactions were more apparent in younger participants (p-value<0.01), in women (p-value<0.001), and in participants with no underlying medical conditions (p-value<0.001). Nevertheless, reported reactions and changes in physiological measures returned to their baseline levels within three days from inoculation with the third dose. LimitationsParticipants may not adequately represent the vaccinated population in Israel and elsewhere. ConclusionOur work further supports the safety of a third COVID-19 BNT162b2 mRNA (booster) vaccine dose from both a subjective and an objective perspective, particularly in individuals 65+ years of age and those with underlying medical conditions. Primary funding sourceEuropean Research Council (ERC) project #949850

Early Detection of COVID-19 Outbreaks Using Human Mobility Data

BackgroundContact mixing plays a key role in the spread of COVID-19. Thus, mobility restrictions of varying degrees up to and including nationwide lockdowns have been implemented in over 200 countries. To appropriately target the timing, location, and severity of measures intended to encourage social distancing at a country level, it is essential to predict when and where outbreaks will occur, and how widespread they will be. MethodsWe analyze aggregated, anonymized health data and cell phone mobility data from Israel. We develop predictive models for daily new cases and the test positivity rate over the next 7 days for different geographic regions in Israel. We evaluate model goodness of fit using root mean squared error (RMSE). We use these predictions in a five-tier categorization scheme to predict the severity of COVID-19 in each region over the next week. We measure magnitude accuracy (MA), the extent to which the correct severity tier is predicted. ResultsModels using mobility data outperformed models that did not use mobility data, reducing RMSE by 17.3% when predicting new cases and by 10.2% when predicting the test positivity rate. The best set of predictors for new cases consisted of 1-day lag of past 7-day average new cases, along with a measure of internal movement within a region. The best set of predictors for the test positivity rate consisted of 3-days lag of past 7-day average test positivity rate, along with the same measure of internal movement. Using these predictors, RMSE was 4.812 cases per 100,000 people when predicting new cases and 0.79% when predicting the test positivity rate. MA in predicting new cases was 0.775, and accuracy of prediction to within one tier was 1.0. MA in predicting the test positivity rate was 0.820, and accuracy to within one tier was 0.998. ConclusionsUsing anonymized, macro-level data human mobility data along with health data aids predictions of when and where COVID-19 outbreaks are likely to occur. Our method provides a useful tool for government decision makers, particularly in the post-vaccination era, when focused interventions are needed to contain COVID-19 outbreaks while mitigating the collateral damage of more global restrictions.

Short-term effects of BNT162b2 mRNA COVID-19 vaccination on physiological measures: a prospective study

BackgroundClinical trial guidelines for assessing the safety of vaccines, including the FDA criteria, are primarily based on subjective, self-reported questionnaires. Despite the tremendous technological advances in recent years, objective, continuous assessment of physiological measures post-vaccination is rarely performed. MethodsTo evaluate the short-term effects of the BNT162b2 COVID-19 vaccine on physiological measures, we conducted a prospective observational study during the mass vaccination campaign in Israel. 160 individuals >18 years who were not previously found to be COVID-19 positive and who received the second dose of the COVID-19 vaccine between 1 January, 2021, and 13 March, 2021 were equipped with a chest-patch sensor and a dedicated mobile application. The chest-patch sensor continuously measured 13 physiological vitals one day before the inoculation (baseline), for four days: heart rate, blood oxygen saturation, respiratory rate, systolic and diastolic blood pressure, pulse pressure, mean arterial pressure, heart rate variability, stroke volume, cardiac output, cardiac index, systemic vascular resistance, and body temperature. The mobile application collected daily self-reported questionnaires starting one day before the inoculation, for 15 days on local and systemic reactions, sleep quality, stress levels, physical activity, and mood levels. FindingsWithin the first 48 hours post-vaccination, we identified significant changes (p-value <0.05) in nearly all 13 chest-patch indicators compared to their baseline levels. 48.5% (n=78) reported no local or systemic reaction. Nevertheless, we identified considerable changes in chest-patch indicators during the first 48 hours post-vaccination also in this group of presumably asymptomatic participants. Within three days from vaccination, these measures returned to baseline levels in both groups, further supporting the safety of the vaccine. InterpretationOur work underscores the importance of obtaining objective physiological data in addition to self-reported questionnaires when performing clinical trials, particularly in ones conducted in very short time frames. FundingThe European Research Council (ERC) project #949850.

A Multilayer Model for Early Detection of COVID-19

Current efforts for COVID-19 screening mainly rely on reported symptoms and potential exposure to infected individuals. Here, we developed a machine-learning model for COVID-19 detection that utilizes four layers of information: 1) sociodemographic characteristics of the tested individual, 2) spatiotemporal patterns of the disease observed near the testing episode, 3) medical condition and general health consumption of the tested individual over the past five years, and 4) information reported by the tested individual during the testing episode. We evaluated our model on 140,682 members of Maccabi Health Services, tested for COVID-19 at least once between February and October 2020. These individuals had 264,516 COVID-19 PCR-tests, out of which 16,512 were found positive. Our multilayer model obtained an area under the curve (AUC) of 81.6% when tested over all individuals, and of 72.8% when tested over individuals who did not report any symptom. Furthermore, considering only information collected before the testing episode - that is, before the individual may had the chance to report on any symptom - our model could reach a considerably high AUC of 79.5%. Namely, most of the value contributed by the testing episode can be gained by earlier information. Our ability to predict early the outcomes of COVID-19 tests is pivotal for breaking transmission chains, and can be utilized for a more efficient testing policy.

The rise of SARS-CoV-2 variant B.1.1.7 in Israel intensifies the role of surveillance and vaccination in elderly

Since the emergence of the SARS-CoV-2 pandemic various generic variants have been described. Of specific interest is a new variant, which was observed in England during December 2020 and is now termed B.1.1.7. This variant is now associated with increased infectivity and therefore its spread within the community is of great importance. The Israeli government established three noteworthy programs namely, mass PCR testing, focused protection of the elderly and more recently an unparalleled prioritized vaccination program. In this study we analyzed primary data of >300,000 RT-PCR samples collected throughout December 6th 2020 until February 10th 2021 in the general community and nursing homes. We identified that within a period of six weeks, the B.1.1.7 variant was capable of out competing the wildtype SARS-CoV-2 strain to become the main strain. Furthermore, we show that the transmission of B.1.1.7 in the 60+ population reached a near complete halt, due to an ongoing surveillance testing program in nursing homes and the vaccination program of Israel. Thus, proactive protection programs such as routine surveillance and monitoring of populations at risk combined with prioritized vaccination, is achievable and will result in a reduction of severe illness and subsequent death.

Human mobility and poverty as key factors in strategies against COVID-19

BackgroundApplying 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 cell phone devices can be utilized to achieve these goals. MethodsWe 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 mortality of applying localized and temporal lockdowns (stay-at-home order). ResultsPoorer regions exhibited lower and slower compliance with the restrictions. Our transmission model further indicated that individuals from poverty areas were associated with high transmission rates. Model projections suggested that, counterintuitively, school closure has an adverse effect and increases COVID-19 mortality in the long run, while interventions focusing on the elderly are the most efficient. We also found that applying localized and temporal lockdowns during regional outbreaks reduce mortality compared to nationwide lockdowns. These trends were consistent across vast ranges of epidemiological parameters, possible seasonal forcing, and even when we assumed that vaccination would be commercially available in 1-3 years. ConclusionsMore 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.