BackgroundSevere fever with thrombocytopenia syndrome (SFTS) was first reported in China in 2011. Human-to-human transmission of the virus occurred occasionally in family clusters. However, pneumonia as an onset syndrome was not common in most SFTS cases. Our aim is to report a family cluster of SFTS with clinical manifestation of pneumonia in Shanghai.MethodsEpidemiologic investigations were conducted when a family cluster of severe fever with thrombocytopenia syndrome virus (SFTSV) infection was identified in Shanghai in June 2016. Samples were collected from two secondary cases and two close contacts with fever. SFTSV was detected by Real-Time reverse transcription polymerase chain reaction (RT-PCR).ResultsThere were two confirmed STFS cases and one potential index case. The potential index case became ill on 21 May and died on 31 May. Case A had onset from 4 to 23 June and case B from 8 June to 25 June. All the three cases experienced pneumonia at the early stage of SFTSV infection. Three (3) out of thirty two (32) close contacts had symptoms of fever or cough but were detected STFSV negative by real-time RT-PCR. According to epidemiologic investigations, the potential index case had outdoor activities on a nearby hill. A tick bite could have been the reason for the SFTSV infection in the potential index case as ticks were found both in grassland or shrubs on the hill and also found on mice caught in her house. Both cases A and B had provided bedside care for the potential index case without any protection and had contacted with blood and other body fluids.ConclusionIt was a family cluster of SFTSV infection imported from Jiangsu province located in the east of China. We suggested to become alert to atypical SFTSV infected cases.

A/B tests have been widely adopted across industries as the golden rule that guides decision making. However, the long-term true north metrics we ultimately want to drive through A/B test may take a long time to mature. In these situations, a surrogate metric which predicts the long-term metric is often used instead to conclude whether the treatment is eective. However, because the surrogate rarely predicts the true north perfectly, a regular A/B test based on surrogate metrics tends to have high false positive rate and the treatment variant deemed favorable from the test may not be the winning one. In this paper, we discuss how to adjust the A/B testing comparison to ensure experiment results are trustworthy. We also provide practical guidelines on the choice of good surrogate metrics. To provide a concrete example of how to leverage surrogate metrics for fast decision making, we present a case study on developing and evaluating the predicted conrmed hire surrogate metric in LinkedIn job marketplace.

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For many classification problems, in addition to providing accurate classification results, very often it is equally important to determine which predictors are contributing to the results, and by how much. This motivates this article, where a new sensitivity analysis framework is developed for classification problems to address this issue. The effectiveness of this framework is illustrated through simulation studies and application to real data. Notice that this framework can be coupled with different classification methods. However, in practice it is recommended to pair it with a classification method called Bayesian smoothing spline ANOVA probit regression (BSSANOVA). When compared with other existing methods through numerical experiments, BSSANOVA performs extremely well for both classification and sensitivity analysis.