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The theory that health behaviors spread through social groups implies that efforts to control COVID-19 through vaccination will succeed if people believe that others in their groups are getting vaccinated. But “others” can refer to many groups, including one’s family, neighbors, fellow city or state dwellers, or copartisans. One challenge to examining these understudied distinctions is that many factors may confound observed relationships between perceived social norms (what people believe others do) and intended behaviors (what people themselves will do), as there are plausible common causes for both. We address these issues using survey data collected in the United States during late fall 2020 ( n = 824) and spring 2021 ( n = 996) and a matched design that approximates pair-randomized experiments. We find a strong relationship between perceived vaccination social norms and vaccination intentions when controlling for real risk factors (e.g., age), as well as dimensions known to predict COVID-19 preventive behaviors (e.g., trust in scientists). The strength of the relationship declines as the queried social group grows larger and more heterogeneous. The relationship for copartisans is second in magnitude to that of family and friends among Republicans but undetectable for Democrats. Sensitivity analysis shows that these relationships could be explained away only by an unmeasured variable with large effects (odds ratios between 2 and 15) on social norms perceptions and vaccination intentions. In addition, a prediction from the “false consensus” view that intentions cause perceived social norms is not supported. We discuss the implications for public health policy and understanding social norms.

Water infrastructure in the United States is beginning to show its age, particularly through water main breaks. Main breaks cause major disruptions in everyday life for residents and businesses. Water main failures in Syracuse, N.Y. (as in most cities) are handled reactively rather than proactively. A barrier to proactive maintenance is the city's inability to predict the risk of failure on parts of its infrastructure. In response, we worked with the city to build a ML system to assess the risk of a water mains breaking. Using historical data on which mains have failed, descriptors of pipes, and other data sources, we evaluated several models' abilities to predict breaks three years into the future. Our results show that our system using gradient boosted decision trees performed the best out of several algorithms and expert heuristics, achieving precision at 1% (P@1) of 0.62. Our model outperforms a random baseline (P@1 of 0.08) and expert heuristics such as water main age (P@1 of 0.10) and history of past main breaks (P@1 of 0.48). e model is deployed in the City of Syracuse. We are running a pilot by calculating the risk of failure for each city block over the period 2016-2018 using data up to the end of 2015 and, as of the end of 2017, there have been 33 breaks on our riskiest 52 mains. is has been a successful initiative for the city of Syracuse in improving their infrastructure and we believe this approach can be applied to other cities. ACM Reference format: