Microsoft (Israel)

We propose a method to estimate the household secondary attack rate (hSAR) of COVID-19 in the United Kingdom based on activity on the social media platform X, formerly known as Twitter. Conventional methods of hSAR estimation are resource intensive, requiring regular contact tracing of COVID-19 cases. Our proposed framework provides a complementary method that does not rely on conventional contact tracing or laboratory involvement, including the collection, processing, and analysis of biological samples. We use a text classifier to identify reports of people tweeting about themselves and/or members of their household having COVID-19 infections. A probabilistic analysis is then performed to estimate the hSAR based on the number of self or household, and self and household tweets of COVID-19 infection. The analysis includes adjustments for a reluctance of Twitter users to tweet about household members, and the possibility that the secondary infection was not acquired within the household. Experimental results for the UK, both monthly and weekly, are reported for the period from January 2020 to February 2022. Our results agree with previously reported hSAR estimates, varying with the primary variants of concern, e.g. delta and omicron. The serial interval (SI) is based on the time between the two tweets that indicate a primary and secondary infection. Experimental results, though larger than the consensus, are qualitatively similar. The estimation of hSAR and SI using social media data constitutes a new tool that may help in characterizing, forecasting and managing outbreaks and pandemics in a faster, affordable, and more efficient manner.

A major problem in fair division is how to allocate a set of indivisible resources among agents fairly and efficiently. The goal of this work is to characterize the tradeoffs between two well-studied measures of fairness and efficiency --- envy freeness up to any item (EFX) for fairness, and Nash welfare for efficiency --- by saying, for given constants α and β, whether there exists an α-EFX allocation that guarantees a β-fraction of the maximum Nash welfare (β-MNW). For additive valuations, we show that for any α ∈ [0,1], there exists a partial allocation that is α-EFX and 1/(α+1)-MNW. This tradeoff turns out to be tight (for every α) as demonstrated by an impossibility result that we give. We also show that for α ∈ [0, φ-1 ≃ 0.618] these partial allocations can be turned into complete allocations where all items are assigned. Furthermore, for any α ∈ [0, 1/2], we show that the tight tradeoff of α-EFX and 1/(α+1)-MNW with complete allocations holds for the more general setting of subadditive valuations. Our results improve upon the current state of the art, for both additive and subadditive valuations, and match the best-known approximations of EFX under complete allocations, regardless of Nash welfare guarantees. Notably, our constructions for additive valuations also provide EF1 and constant approximations for maximin share guarantees.

Dependency hell is a well-known pain point in the development of large software projects and machine learning (ML) code bases are not immune from it. In fact, ML applications suffer from an additional form of dependency hell, namely, data source dependency hell. This term refers to the central role played by data and its unique quirks that often lead to unexpected failures of ML models which cannot be explained by code changes. In this paper, we present an automated data source dependency mapping framework that allows MLOps engineers to monitor the whole dependency map of their models in a fast paced engineering environment and thus mitigate ahead of time the consequences of any data source changes. Our system is based on a unified and generic approach, employing techniques from static analysis, from which data sources can be identified on a wide range of source artifacts. Our framework is currently deployed within Microsoft and used by Microsoft MLOps engineers in production.

Abstract Amyotrophic lateral sclerosis (ALS), a motor neuron disease, remains a clinical diagnosis with a diagnostic delay of over a year. Here we examine the possibility that interactions with an internet search engine could be used to help screen for ALS. We identified 285 anonymous Bing users whose queries indicated that they had been diagnosed with ALS and matched them to 1) 3276 control users and 2) 1814 users whose searches indicated they had ALS disease mimics. We tested whether the ALS group could be distinguished from controls and disease mimics based on search engine query data. Finally, we conducted a prospective validation from participants who provided access to their Bing search data. The model distinguished between the ALS group and controls with an area under the curve (AUC) of 0.81. Model scores for the ALS group differed from the disease mimics group (ranksum test, P<0.05 with Bonferrini correction). Mild cognitive impairment could not be distinguished from ALS (NS). In the prospective analysis, the model reached an AUC of 0.74. These results suggest that interactions with search engines could be used as a tool to assist in screening for ALS, to reduce diagnostic delay.

BackgroundRespiratory syncytial virus (RSV) is a major cause of respiratory infection in children. Despite usually following a consistent seasonal pattern, the 2020-2021 RSV season in many countries was delayed and changed in magnitude.ObjectiveThis study aimed to test if these changes can be attributed to nonpharmaceutical interventions (NPIs) instituted around the world to combat SARS-CoV-2.MethodsWe used the internet search volume for RSV, as obtained from Google Trends, as a proxy to investigate these abnormalities.ResultsOur analysis shows a breakdown of the usual correlation between peak latency and magnitude during the year of the pandemic. Analyzing latency and magnitude separately, we found that the changes therein are associated with implemented NPIs. Among several important interventions, NPIs affecting population mobility are shown to be particularly relevant to RSV incidence.ConclusionsThe 2020-2021 RSV season served as a natural experiment to test NPIs that are likely to restrict RSV spread, and our findings can be used to guide health authorities to possible interventions.

The problem of Next Basket Recommendation (NBR) addresses the challenge of recommending items for the next basket of a user, based on her sequence of prior baskets. In this paper, we focus on a variation of this problem in which we aim to predict repurchases, i.e. we wish to recommend a user only items she had purchased before. We coin this problem Next Basket Repurchase Recommendation (NBRR). Over the years, a variety of models have been proposed to address the problem of NBR, however, the problem of NBRR has been overlooked. Although being highly related problems, which are often solved by the same methods, the problem of repurchase recommendation calls for a different approach. In this paper, we share insights from our experience of facing the challenge of NBRR. In light of these insights, we propose a novel hyper-convolutional model to leverage the behavioral patterns of repeated purchases. We demonstrate the effectiveness of the proposed model on three publicly available datasets, where it is shown to outperform other existing methods across multiple metrics.

We consider the problem of fair allocation of indivisible items among $n$ agents with additive valuations, when agents have equal entitlements to the goods, and there are no transfers. Best-of-Both-Worlds (BoBW) fairness mechanisms aim to give all agents both an ex-ante guarantee (such as getting the proportional share in expectation) and an ex-post guarantee. Prior BoBW results have focused on ex-post guarantees that are based on the to one paradigm, such as envy-free up to one item (EF1). In this work we attempt to give every agent a high value ex-post, and specifically, a constant fraction of his maximin share (MMS). The up to one item paradigm fails to give such a guarantee, and it is not difficult to present examples in which previous BoBW mechanisms give agents only a $\frac{1}{n}$ fraction of their MMS. Our main result is a deterministic polynomial time algorithm that computes a distribution over allocations that is ex-ante proportional, and ex-post, every allocation gives every agent at least his proportional share up to one item, and more importantly, at least half of his MMS. Moreover, this last ex-post guarantee holds even with respect to a more demanding notion of a share, introduced in this paper, that we refer to as the truncated proportional share (TPS). Our guarantees are nearly best possible, in the sense that one cannot guarantee agents more than their proportional share ex-ante, and one cannot guarantee agents more than a $\frac{n}{2n-1}$ fraction of their TPS ex-post.

[This corrects the article DOI: 10.1057/s41599-022-01338-7.].