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.

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.

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.

Detrimental network effects in privacy: A graph-theoretic model for node-based intrusions.

Despite proportionality being one of the tenets of data protection laws, we currently lack a robust analytical framework to evaluate the reach of modern data collections and the network effects at play. Here, we propose a graph-theoretic model and notions of node- and edge-observability to quantify the reach of networked data collections. We first prove closed-form expressions for our metrics and quantify the impact of the graph's structure on observability. Second, using our model, we quantify how (1) from 270,000 compromised accounts, Cambridge Analytica collected 68.0M Facebook profiles; (2) from surveilling 0.01% of the nodes in a mobile phone network, a law enforcement agency could observe 18.6% of all communications; and (3) an app installed on 1% of smartphones could monitor the location of half of the London population through close proximity tracing. Better quantifying the reach of data collection mechanisms is essential to evaluate their proportionality.

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).

Higher sensitivity monitoring of reactions to COVID-19 vaccination using smartwatches.

More than 12 billion COVID-19 vaccination shots have been administered as of August 2022, but information from active surveillance about vaccine safety is limited. Surveillance is generally based on self-reporting, making the monitoring process subjective. We study participants in Israel who received their second or third Pfizer BioNTech COVID-19 vaccination. All participants wore a Garmin Vivosmart 4 smartwatch and completed a daily questionnaire via smartphone. We compare post-vaccination smartwatch heart rate data and a Garmin-computed stress measure based on heart rate variability with data from the patient questionnaires. Using a mixed effects panel regression to remove participant-level fixed and random effects, we identify considerable changes in smartwatch measures in the 72 h post-vaccination even among participants who reported no side effects in the questionnaire. Wearable devices were more sensitive than questionnaires in determining when participants returned to baseline levels. We conclude that wearable devices can detect physiological responses following vaccination that may not be captured by patient self-reporting. More broadly, the ubiquity of smartwatches provides an opportunity to gather improved data on patient health, including active surveillance of vaccine safety.

Beyond preferential attachment: falling of stars and survival of superstars.

Numerous studies over the past decades established that real-world networks typically follow preferential attachment and detachment principles. Subsequently, this implies that degree fluctuations monotonically increase while rising up the 'degree ladder', causing high-degree nodes to be prone for attachment of new edges and for detachment of existing ones. Despite the extensive study of node degrees (absolute popularity), many domains consider node ranks (relative popularity) as of greater importance. This raises intriguing questions-what dynamics are expected to emerge when observing the ranking of network nodes over time? Does the ranking of nodes present similar monotonous patterns to the dynamics of their corresponding degrees? In this paper, we show that surprisingly the answer is not straightforward. By performing both theoretical and empirical analyses, we demonstrate that preferential principles do not apply to the temporal changes in node ranking. We show that the ranking dynamics follows a non-monotonous curve, suggesting an inherent partition of the nodes into qualitatively distinct stability categories. These findings provide plausible explanations to observed yet hitherto unexplained phenomena, such as how superstars fortify their ranks despite massive fluctuations in their degrees, and how stars are more prone to rank instability.

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.

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.

Utilizing wearable sensors for continuous and highly-sensitive monitoring of reactions to the BNT162b2 mRNA COVID-19 vaccine.

Background: Clinical trial guidelines for assessing the safety of vaccines, are primarily based on self-reported questionnaires. Despite the tremendous technological advances in recent years, objective, continuous assessment of physiological measures post-vaccination is rarely performed. Methods: We conducted a prospective observational study during the mass vaccination campaign in Israel. 160 participants >18 years who were not previously found to be COVID-19 positive and who received the BNT162b2 COVID-19 (Pfizer BioNTech) vaccine were equipped with an FDA-approved chest-patch sensor and a dedicated mobile application. The chest-patch sensor continuously monitored 13 different cardiovascular, and hemodynamic vitals: 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 skin temperature. The mobile application collected daily self-reported questionnaires on local and systemic reactions. Results: We identify continuous and significant changes following vaccine administration in nearly all vitals. Markedly, these changes are observed even in presumably asymptomatic participants who did not report any local or systemic reaction. Changes in vitals are more apparent at night, in younger participants, and in participants following the second vaccine dose. Conclusion: the considerably higher sensitivity of wearable sensors can revolutionize clinical trials by enabling earlier identification of abnormal reactions with fewer subjects.

A multi-layer model for the early detection of COVID-19.

Current COVID-19 screening efforts mainly rely on reported symptoms and the potential exposure to infected individuals. Here, we developed a machine-learning model for COVID-19 detection that uses four layers of information: (i) sociodemographic characteristics of the individual, (ii) spatio-temporal patterns of the disease, (iii) medical condition and general health consumption of the individual and (iv) information reported by the individual during the testing episode. We evaluated our model on 140 682 members of Maccabi Health Services who were tested for COVID-19 at least once between February and October 2020. These individuals underwent, in total, 264 516 COVID-19 PCR tests, out of which 16 512 were positive. Our multi-layer model obtained an area under the curve (AUC) of 81.6% when evaluated over all the individuals in the dataset, and an AUC of 72.8% when only individuals who did not report any symptom were included. Furthermore, considering only information collected before the testing episode-i.e. before the individual had the chance to report on any symptom-our model could reach a considerably high AUC of 79.5%. Our ability to predict early on the outcomes of COVID-19 tests is pivotal for breaking transmission chains, and can be used for a more efficient testing policy.

Cost-Effectiveness of Pertussis Vaccination Schedule in Israel.

Pertussis is a highly contagious bacterial disease that primarily affects infants. To optimize the pertussis vaccination schedule in Israel and evaluate the cost-effectiveness of alternative strategies that add or remove booster doses, we developed an age-structured model for pertussis transmission. Our model was calibrated using 16 years of data from laboratory-confirmed pertussis cases in Israel. Costs and quality-adjusted life years (QALYs) projected by the model within 12 years from the implementation of the considered interventions were compared with the current vaccination schedule. We found that by using the same number of vaccines administered today, the targeting of children at the age of six instead of seven would be predicted to be the optimal schedule to decrease both outpatient visits and hospitalizations. We also found that any increase in maternal vaccination coverage is likely to be cost-effective, with an incremental cost-effectiveness ratio of $77,000-$97,000 per QALY. By contrast, the contribution of the second booster dose is limited, with a probability of only 0.6 to be cost-effective at $110,000/QALY saved. Additional effort should be invested to encourage maternal vaccination against pertussis. We recommend moving the first booster to age six and prudently considering the necessity of the second booster dose.

Differential effects of COVID-19 lockdowns on well-being: interaction between age, gender and chronotype.

The unprecedented restrictions imposed due to the COVID-19 pandemic altered our daily habits and severely affected our well-being and physiology. The effect of these changes is yet to be fully understood. Here, we analysed highly detailed data on 169 participants for two to six months, before and during the second COVID-19 lockdown in Israel. We extracted 12 well-being indicators from sensory data of smartwatches and from self-reported questionnaires, filled daily using a designated mobile application. We found that, in general, lockdowns resulted in significant changes in mood, sleep duration, sport duration, social encounters, resting heart rate and number of steps. Examining subpopulations, we found that younger participants (aged 20-40 years) suffered from a greater decline in mood and number of steps than older participants (aged 60-80 years). Likewise, women suffered from a higher increase in stress and reduction in social encounters than men. Younger early chronotypes did not increase their sleep duration and exhibited the highest drop in mood. Our findings underscore that while lockdowns severely impacted our well-being and physiology in general, greater damage has been identified in certain subpopulations. Accordingly, special attention should be given to younger people, who are usually not in the focus of social support, and to women.

Bayesian collective learning emerges from heuristic social learning.

Researchers across cognitive science, economics, and evolutionary biology have studied the ubiquitous phenomenon of social learning-the use of information about other people's decisions to make your own. Decision-making with the benefit of the accumulated knowledge of a community can result in superior decisions compared to what people can achieve alone. However, groups of people face two coupled challenges in accumulating knowledge to make good decisions: (1) aggregating information and (2) addressing an informational public goods problem known as the exploration-exploitation dilemma. Here, we show how a Bayesian social sampling model can in principle simultaneously optimally aggregate information and nearly optimally solve the exploration-exploitation dilemma. The key idea we explore is that Bayesian rationality at the level of a population can be implemented through a more simplistic heuristic social learning mechanism at the individual level. This simple individual-level behavioral rule in the context of a group of decision-makers functions as a distributed algorithm that tracks a Bayesian posterior in population-level statistics. We test this model using a large-scale dataset from an online financial trading platform.

State-based targeted vaccination.

Vaccination has become one of the most prominent measures for preventing the spread of infectious diseases in modern times. However, mass vaccination of the population may not always be possible due to high costs, severe side effects, or shortage. Therefore, identifying individuals with a high potential of spreading the disease and targeted vaccination of these individuals is of high importance. While various strategies for identifying such individuals have been proposed in the network epidemiology literature, the vast majority of them rely solely on the network topology. In contrast, in this paper, we propose a novel targeted vaccination strategy that considers both the static network topology and the dynamic states of the network nodes over time. This allows our strategy to find the individuals with the highest potential to spread the disease at any given point in time. Extensive evaluation that we conducted over various real-world network topologies, network sizes, vaccination budgets, and parameters of the contagion model, demonstrates that the proposed strategy considerably outperforms existing state-of-the-art targeted vaccination strategies in reducing the spread of the disease. In particular, the proposed vaccination strategy further reduces the number of infected nodes by 23-99%, compared to a vaccination strategy based on Betweenness Centrality.

Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming.

In this paper, we propose a comprehensive analytics framework that can serve as a decision support tool for HR recruiters in real-world settings in order to improve hiring and placement decisions. The proposed framework follows two main phases: a local prediction scheme for recruitments' success at the level of a single job placement, and a mathematical model that provides a global recruitment optimization scheme for the organization, taking into account multilevel considerations. In the first phase, a key property of the proposed prediction approach is the interpretability of the machine learning (ML) model, which in this case is obtained by applying the Variable-Order Bayesian Network (VOBN) model to the recruitment data. Specifically, we used a uniquely large dataset that contains recruitment records of hundreds of thousands of employees over a decade and represents a wide range of heterogeneous populations. Our analysis shows that the VOBN model can provide both high accuracy and interpretability insights to HR professionals. Moreover, we show that using the interpretable VOBN can lead to unexpected and sometimes counter-intuitive insights that might otherwise be overlooked by recruiters who rely on conventional methods. We demonstrate that it is feasible to predict the successful placement of a candidate in a specific position at a pre-hire stage and utilize predictions to devise a global optimization model. Our results show that in comparison to actual recruitment decisions, the devised framework is capable of providing a balanced recruitment plan while improving both diversity and recruitment success rates, despite the inherent trade-off between the two.

Network Dynamics of a Financial Ecosystem.

Global financial crises have led to the understanding that classical econometric models are limited in comprehending financial markets in extreme conditions, partially since they disregarded complex interactions within the system. Consequently, in recent years research efforts have been directed towards modeling the structure and dynamics of the underlying networks of financial ecosystems. However, difficulties in acquiring fine-grained empirical financial data, due to regulatory limitations, intellectual property and privacy control, still hinder the application of network analysis to financial markets. In this paper we study the trading of cryptocurrency tokens on top of the Ethereum Blockchain, which is the largest publicly available financial data source that has a granularity of individual trades and users, and which provides a rare opportunity to analyze and model financial behavior in an evolving market from its inception. This quickly developing economy is comprised of tens of thousands of different financial assets with an aggregated valuation of more than 500 Billion USD and typical daily volume of 30 Billion USD, and manifests highly volatile dynamics when viewed using classic market measures. However, by applying network theory methods we demonstrate clear structural properties and converging dynamics, indicating that this ecosystem functions as a single coherent financial market. These results suggest that a better understanding of traditional markets could become possible through the analysis of fine-grained, abundant and publicly available data of cryptomarkets.

Tdap vaccination during pregnancy interrupts a twenty-year increase in the incidence of pertussis.

Pertussis incidence in developed countries, including Israel, has increased over the past two decades despite the addition of two booster doses in children. However, as pertussis is characterized by a multi-annual periodicity, and since clinical diagnosis can miss cases, determining disease trends at the population level is challenging. To bridge this gap, we developed a simple statistical model to capture the temporal patterns of pertussis incidence in Israel. Our model was calibrated and tested using laboratory-confirmed cases of pertussis for the Israeli population between 1998 and 2019. The model identifies a clear four-year periodicity of pertussis incidence over the past two decades that is identical to the one observed in the pre-vaccine era. Accounting for this periodicity, the model shows a 325% increase in pertussis incidence from 2002 to 2014. These multi-year trends were interrupted shortly after the introduction of routine immunization of Tdap vaccine in pregnancy in 2015, after which we found a 59.7% (95% CI: 57.7-61.6%)decline in pertussis incidence and a 49.5% (36.0-61.6%) decline in hospitalizations compared to the model's projection. While this sharp decline cannot be fully attributed to the newly introduced vaccination policy, sharper reductions of 71.2% (65.6-76.1%) in incidence and 58.4% (39.6-72.7%) in hospitalizations, have been observed in infants of age two months and below - young infants that have yet to become vaccinated and are more likely to be protected by maternal vaccination. Our work suggests that Tdap vaccination during pregnancy is a promising policy for controlling pertussis. Furthermore, due to the stable periodicity of pertussis, public health decision-makers should invest continuous efforts in the implementation of this strategy with additional reinforcement in expected peak years.

Structural Entropy: Monitoring Correlation-Based Networks Over Time With Application To Financial Markets.

The concept of "Structural Diversity" of a network refers to the level of dissimilarity between the various agents acting in the system, and it is typically interpreted as the number of connected components in the network. This key property of networks has been studied in multiple settings, including diffusion of ideas in social networks and functional diversity of regions in brain networks. Here, we propose a new measure, "Structural Entropy", as a revised interpretation to "Structural Diversity". The proposed measure relies on the finer-grained network communities (in contrast to the network's connected components), and takes into consideration both the number of communities and their sizes, generating a single representative value. We then propose an approach for monitoring the structure of correlation-based networks over time, which relies on the newly suggested measure. Finally, we illustrate the usefulness of the new approach, by applying it to the particular case of emergent organization of financial markets. This provides us a way to explore their underlying structural changes, revealing a remarkably high linear correlation between the new measure and the volatility of the assets' prices over time.

Second opinion utilization by healthcare insurance type in a mixed private-public healthcare system: a population-based study.

OBJECTIVES: To evaluate the utilisation (overall and by specialty) and the characteristics of second-opinion seekers by insurance type (either health fund or supplementary insurance) in a mixed private-public healthcare. DESIGN: An observational study. SETTING: Secondary care visits provided by a large public health fund and a large supplementary health insurance in Israel. PARTICIPANTS: The entire sample included 1 392 907 patients aged 21 years and above who visited at least one specialist over an 18 months period, either in the secondary care or privately via the supplementary insurance. OUTCOMES MEASURES: An algorithm was developed to identify potential second-opinion instances in the dataset using visits and claims data. Multivariate logistic regression was used to identify characteristics of second-opinion seekers by the type of insurance they used. RESULTS: 143 371 (13%) out of 1 080 892 patients who had supplementary insurance sought a single second opinion, mostly from orthopaedic surgeons. Relatively to patients who sought second opinion via the supplementary insurance, second-opinion seekers via the health fund tended to be females (OR=1.2, 95% CI 1.17 to 1.23), of age 40-59 years (OR=1.36, 95% CI 1.31 to 1.42) and with chronic conditions (OR=1.13, 95% CI 1.08 to 1.18). In contrast, second-opinion seekers via the supplementary insurance tended to be native-born and established immigrants (OR=0.79, 95% CI 0.76 to 0.84), in a high socioeconomic level (OR=0.39, 95% CI 0.37 to 0. 4) and living in central areas (OR=0.88, 95% CI 0.85 to 0.9). CONCLUSIONS: Certain patient profiles tended to seek second opinions via the supplementary insurance more than others. People from the centre of the country and with a high socioeconomic status tended to do so, as medical specialists tend to reside in central urban areas. Further research is recommended to examine the availability of medical specialists by specialty and residence.