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BackgroundIsraeli has vaccinated over 80% of its adult population, with two doses of the Pfizer BNT162b2 vaccine. This intervention has been highly successful in curtailing the coronavirus 2 outbreak. One major concern is the ability of the virus to mutate which potentially can cause SARS-CoV-2 to partially escape from the immune system. Here we evaluate the efficacy of the Pfizer vaccine against the B.1.351 variant.MethodsThe Ministry of Health, initiated sequencing of selected positive swab samples identified as being of interest. We used logistic regression, with variant type as the dependent variable, vaccination status as the main explanatory variable, controlling for age, sex, subpopulation, place of residence and time of sample, to estimate the odds ratio for a vaccinated case to have the B. 1.351 versus the B.1.1.7 variant, within vaccinated and unvaccinated persons who tested positive.FindingsThere were 19 cases of B.1.351 variant (3.2%) among those vaccinated more than 14 days before the positive sample and 88 (3.5%) among the unvaccinated. The estimated odds ratio was 1.29 [95% CI: 0.66-2.50]. From this result, assuming the efficacy against the B.1.1.7 variant to be 95%, the estimated efficacy against the B.1.351 variant was 94% [95% CI: 87-97%].InterpretationDespite the concerns caused by the B.1.351 variant, the BNT162b2 vaccine seems to provide substantial immunity against both that variant and the B.1.1.7. Our results suggest that from 14 days following the second vaccine dose the efficacy of BNT162b2 vaccine is at most marginally affected by the B.1.351 variant.FundingNo funding

Grid-based location prediction algorithms are widely researched and evaluated. These algorithms usually integrate the speed and the direction during the learning process as regular contextual features, for example, like the time of the day or the day of the week. Unfortunately, the way speed and direction are currently used does not fulfill their potential. In this paper, we propose an alternative approach for integrating the user's current speed and direction in a post-processing mechanism that highly improves the algorithms' accuracy. We dynamically update the probabilities of the predictions provided by the existing (base) algorithms by dividing the surface into four areas while boosting the probabilities in some areas and reducing the probabilities in others. We evaluated our method on three well-known grid-based location prediction algorithms and two different datasets and were able to show that our method improves the predictions made solely by the algorithms. Our improvement was stable during the entire experiment for long-term predictions and for greater prediction distances, particularly in the cold start phase which is considered more difficult to improve.

Background: SARS-CoV-2 variant Beta (B.1.351) was designated as a Variant of Concern (VoC) after becoming the dominant strain in South Africa and spreading internationally. BNT162b2 showed lower levels of neutralizing antibodies against Beta than against other strains raising concerns about effectiveness of vaccines against infections caused by Beta. We estimated BNT162b2 effectiveness against Beta infections in Israel, a country with high vaccine uptake. Methods: The Ministry of Health (MoH) identified Beta cases through mandatory reporting of SARS-CoV-2 cases and whole genome sequencing (WGS) of specimens from vaccination-breakthrough infections, reinfections, arriving international travelers, and a selection of other infected persons. A cohort analysis was conducted of exposure events of contacts of primary Beta cases. WGS was conducted on available PCR-positive specimens collected from contacts. VE estimates with 95% confidence intervals (CIs) against confirmed and probable Beta infections were determined by comparing infection risk between unvaccinated and fully-vaccinated ( > 7 days after the second dose) contacts, and between unvaccinated and partially-vaccinated ( 16 years, were identified. 343/552 (62%) contacts were interviewed and tested. 71/343 (21%) contacts were PCR-positive. WGS was performed on 7/71 (10%) PCR-positive specimens; all were Beta. Among SARS-CoV-2-infected contacts, 48/71 (68%) were symptomatic, 10/71 (14%) hospitalized, and 2/71 (3%) died. Fully-vaccinated VE against confirmed or probable Beta infections was 72% (95% CI -5 - 97%; p =0.04) and against symptomatic confirmed or probable Beta infections was 100% (95% CI 19 - 100%; p =0.01). There was no evidence of protection in partially-vaccinated contacts. Interpretation: Two doses of BNT162b2 conferred protection against Beta infections and disease. Introductions of Beta did not interrupt control of the pandemic in Israel. Funding: None to declare. Declaration of Interest: Frederick Angulo, David Swerdlow, John McLaughlin, Farid Khan, Gabriel Mircus, Kaijie Pan, Jo Southern, and Luis Jodar hold stock and stock options in Pfizer Inc. Marc Lipsitch has provided advice on COVID-19 free of charge to Janssen, Astra-Zeneca, Pfizer, and COVAXX (United Biomedical), as well as to the nonprofit One Day Sooner and has received consulting income or honoraria from Merck, Pfizer, Bristol Meyers Squibb, Janssen, and Sanofi, and institutional research support from Pfizer. He is on the Scientific Advisory Committee of the Coalition for Epidemic Preparedness and Innovations (CEPI). All other authors report no conflicts. Ethical Approval: This study was approved by MoH’s Institutional Review Board (CoR-MoH-080-2021).

Background: SARS-CoV-2 variant Beta (B.1.351) was designated as a Variant of Concern (VoC) after becoming the dominant strain in South Africa and spreading internationally. BNT162b2 showed lower levels of neutralizing antibodies against Beta than against other strains raising concerns about effectiveness of vaccines against infections caused by Beta. We estimated BNT162b2 vaccine effectiveness (VE) against Beta infections in Israel, a country with high vaccine uptake. Methods: The Ministry of Health (MoH) identified Beta cases through mandatory reporting of SARS-CoV-2 cases and whole genome sequencing (WGS) of specimens from vaccination-breakthrough infections, reinfections, arriving international travelers, and a selection of other infected persons. A cohort analysis was conducted of exposure events of contacts of primary Beta cases. WGS was conducted on available PCR-positive specimens collected from contacts. VE estimates with 95% confidence intervals (CIs) against confirmed and probable Beta infections were determined by comparing infection risk between unvaccinated and fully-vaccinated (≥7 days after the second dose) contacts, and between unvaccinated and partially-vaccinated (

Grid-based location prediction algorithms for predicting future locations of mobile device users have been proposed and evaluated. Most of the studies, however, ignore the fact that prediction accuracy is highly dependent on the user’s current behavior (i.e., whether current user behavior complies with previous behavior patterns), the type of predicted location (e.g., home, shopping center, work), the confidence in the prediction, and the error of the location sensor. In this paper we propose four methods for providing a dynamic and effective confidence area for each predicted location provided by any grid-based location prediction algorithm. We define confidence area as the extended area defined by radius around the predicted location (cell) that the user might be in, and confidence as the level of assurance that the user will be in the predicted area. We applied the proposed radius prediction methods on the output of three representative location prediction algorithms (frequent cells, Markov chain model and matrix factorization) using three different datasets, and compared the methods with the previously proposed fixed radius approach. Our results demonstrate the ability to dynamically determine a confidence radius that increases prediction accuracy while maintaining a small average radius.

In recent years, keystroke dynamics has gained popularity as a reliable means of verifying user identity in remote systems. Due to its high performance in verification and the fact that it does not require additional effort from the user, keystroke dynamics has become one of the most preferred second factor of authentication. Despite its prominence, it has one major limitation: keystroke dynamics algorithms are good at fitting a model to one user and one user only. When such algorithms try to fit a model to more than one user, the verification accuracy decreases dramatically. However, in real-world applications it is common practice for two or more users to use the same credentials, such as in shared bank accounts, shared social media profiles, and shared streaming licenses which allow multiple users in one account. In these cases, keystroke dynamics solutions become unreliable. To address this limitation, we propose a method that can leverage existing keystroke dynamics algorithms to automatically determine the number of users sharing the account and accurately support accounts that are shared with multiple users. We evaluate our method using eight state-of-the-art keystroke dynamics algorithms and three public datasets, with up to five different users in one model, achieving an average improvement in verification of 9.2% for the AUC and 8.6% for the EER in the multi-user cases, with just a negligible reduction of 0.2% for the AUC and 0.3% for the EER in the one-user cases.

Keystroke dynamics is one of the most widely adopted identity verification techniques in remote systems. It is based on modeling users’ specific patterns of typing on the keyboard. When utilized in conjunction with the commonly used passwords, the use of keystroke dynamics can dramatically increase the level of security without interfering with the user experience. However, aspects of keystroke dynamics that applied on passwords, such as processing keystroke events and storing feature vectors or user models, can expose users to identity theft and a new set of privacy risks, thus questioning the added value of keystroke dynamics. In addition, common encryption techniques will be unable to mitigate these threats, since the user's behavior changes from one session to another. In this paper, we suggest key grouping as an obfuscation method to ensure keystroke dynamics privacy. When applied on the keystroke events, the key grouping dramatically reduces the possibility of password theft. To perform the key grouping optimally, we present a novel method which produces groups that can integrated with any keystroke dynamics algorithm. Our method divides the keys into groups using hierarchical clustering with dedicated statistical heuristics algorithm. We tested our method's key grouping output on five keystroke dynamics algorithms using a public dataset and managed to show a consistent improvement of up to 7% in the AUC over other, more intuitive key groupings and random key groupings.

Keystroke dynamics is a viable behavioral biometric technique for identity verification based on users’ keyboard interaction traits. Keystroke dynamics can help prevent credentials from being abused in case of theft or leakage. But what happens if the keystroke events are eavesdropped and being replayed? Attackers that intercept keystroke dynamics authentication sessions of benign users can easily replay them from other sources unchanged or with minor changes and gain illegitimate privileges. Hence, even with its major security advantages, keystroke dynamics can still expose authentication mechanisms to replay attacks. Although replay attack is one of the oldest techniques to manipulate authentication systems, keystroke dynamics does not help preventing it. We suggest a new protocol for dynamics exchange based on choosing a subset of real and fake information snippets shared between the client and service providers to lure potential attackers. We evaluated our method on four state-of-the-art keystroke dynamics algorithms and three publicly available datasets and showed that we can dramatically reduce the possibility of replay attacks while preserving highly accurate user verification.