**Introduction and Goals.** SARS-CoV-2 is transmitted both in the community and within households. Social distancing and lockdowns reduce community transmission but do not directly address household transmission. We provide quantitative measures of household transmission based on empirical data, and estimate the contribution of households to overall spread. We highlight policy implications from our analysis of household transmission, and more generally, of changes in contact patterns under social distancing. **Methods. ** We investigate the household secondary attack rate (SAR) for SARS-CoV-2, as well as R_h, which is the average number of within-household infections caused by a single index case. We identify previous works that estimated the SAR. We correct these estimates based on the false-negative rate of PCR testing and the failure to test asymptomatics. Results are pooled by a hierarchical Bayesian random-effects model to provide a meta-analysis estimate of the SAR. We estimate R_h using results from population testing in Vo', Italy and contact tracing data that we curate from Singapore. The code and data behind our analysis are publicly available https://github.com/andrewilyas/covid-household-transmission. **Results.** We identified nine studies of the household secondary attack rate. Our modeling suggests the SAR is heterogeneous across studies. The pooled central estimate of the SAR is 30% but with a posterior 95% credible interval of (0%, 67%) reflecting this heterogeneity. This corresponds to a posterior mean for the SAR of 30% (18%,43%) and a standard deviation of 15% (9%, 27%). If results are not corrected for false negatives and asymptomatics, the pooled central estimate for the SAR is 20% (0%, 43%). From the same nine studies, we estimate R_h to be 0.47 (0.13, 0.77). Using contact tracing data from Singapore, we infer an R_h value of 0.32 (0.22,0.42). Population testing data from Vo' yields an R_h estimate of 0.37 (0.34, 0.40) after correcting for false negatives and asymptomatics. **Interpretation.** Our estimates of R_h suggest that household transmission was a small fraction (5%-35%) of R before social distancing but a large fraction after (30%-55%). This suggests that household transmission may be an effective target for interventions. A remaining uncertainty is whether household infections actually contribute to further community transmission or are contained within households. This can be estimated given high-quality contact tracing data. More broadly, our study points to emerging contact patterns (i.e., increased time at home relative to the community) playing a role in transmission of SARS-CoV-2. We briefly highlight another instance of this phenomenon (differences in contact between essential workers and the rest of the population), provide coarse estimates of its effect on transmission, and discuss how future data could enable a more reliable estimate.
Recommender systems operate in an inherently dynamical setting. Past recommendations influence future behavior, including which data points are observed and how user preferences change. However, experimenting in production systems with real user dynamics is often infeasible, and existing simulation-based approaches have limited scale. As a result, many state-ofthe-art algorithms are designed to solve supervised learning problems, and progress is judged only by offline metrics. In this work we investigate the extent to which offline metrics predict online performance by evaluating eleven recommenders across six controlled simulated environments. We observe that offline metrics are correlated with online performance over a range of environments. However, improvements in offline metrics lead to diminishing returns in online performance. Furthermore, we observe that the ranking of recommenders varies depending on the amount of initial offline data available. We study the impact of adding exploration strategies, and observe that their effectiveness, when compared to greedy recommendation, is highly dependent on the recommendation algorithm. We provide the environments and recommenders described in this paper as RecLab: an extensible ready-to-use simulation framework at this URL: https://github.com/berkeley-reclab/RecLab.
Background With reduced community mobility, household infections may become increasingly important in SARS-CoV-2 transmission dynamics. Methods We investigate the intra-household transmission of COVID-19 through the secondary-attack rate (SAR) and household reproduction number (Rh). We estimate these using (i) data from 29 prior studies (February–August 2020), (ii) epidemiologically linked confirmed cases from Singapore (January–April 2020) and (iii) widespread-testing data from Vo’ (February–March 2020). For (i), we use a Bayesian random-effects model that corrects for reverse transcription–polymerase chain reaction (RT–PCR) test sensitivity and asymptomatic cases. We investigate the robustness of Rh with respect to community transmission rates and mobility patterns. Results The corrected pooled estimates from prior studies for SAR and Rh are 24% (20–28%) and 0.34 (0.30–0.38), respectively. Without corrections, the pooled estimates are: SAR = 18% (14–21%) and Rh = 0.28 (0.25–0.32). The corrected estimates line up with direct estimates from contact-tracing data from Singapore [Rh = 0.32 (0.22–0.42)] and population testing data from Vo’ [SAR = 31% (28–34%) and Rh = 0.37 (0.34–0.40)]. The analysis of Singapore data further suggests that the value of Rh (0.22–0.42) is robust to community-spread dynamics; our estimate of Rh stays constant whereas the fraction of infections attributable to household transmission (Rh/Reff) is lowest during outbreaks (5–7%) and highest during lockdowns and periods of low community spread (25–30%). Conclusions The three data-source types yield broadly consistent estimates for SAR and Rh. Our study suggests that household infections are responsible for a large fraction of infections and so household transmission may be an effective target for intervention.
Designing recommendation systems that serve content aligned with time varying preferences requires proper accounting of the feedback effects of recommendations on human behavior and psychological condition. We argue that modeling the influence of recommendations on people's preferences must be grounded in psychologically plausible models. We contribute a methodology for developing grounded dynamic preference models. We demonstrate this method with models that capture three classic effects from the psychology literature: Mere-Exposure, Operant Conditioning, and Hedonic Adaptation. We conduct simulation-based studies to show that the psychological models manifest distinct behaviors that can inform system design. Our study has two direct implications for dynamic user modeling in recommendation systems. First, the methodology we outline is broadly applicable for psychologically grounding dynamic preference models. It allows us to critique recent contributions based on their limited discussion of psychological foundation and their implausible predictions. Second, we discuss implications of dynamic preference models for recommendation systems evaluation and design. In an example, we show that engagement and diversity metrics may be unable to capture desirable recommendation system performance.
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