Bayesian clustering of multiple zero-inflated outcomes

Several applications involving counts present a large proportion of zeros (excess-of-zeros data). A popular model for such data is the hurdle model, which explicitly models the probability of a zero count, while assuming a sampling distribution on the positive integers. We consider data from multiple count processes. In this context, it is of interest to study the patterns of counts and cluster the subjects accordingly. We introduce a novel Bayesian approach to cluster multiple, possibly related, zero-inflated processes. We propose a joint model for zero-inflated counts, specifying a hurdle model for each process with a shifted Negative Binomial sampling distribution. Conditionally on the model parameters, the different processes are assumed independent, leading to a substantial reduction in the number of parameters as compared with traditional multivariate approaches. The subject-specific probabilities of zero-inflation and the parameters of the sampling distribution are flexibly modelled via an enriched finite mixture with random number of components. This induces a two-level clustering of the subjects based on the zero/non-zero patterns (outer clustering) and on the sampling distribution (inner clustering). Posterior inference is performed through tailored Markov chain Monte Carlo schemes. We demonstrate the proposed approach on an application involving the use of the messaging service WhatsApp. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'

Understanding public support for COVID-19 pandemic mitigation measures over time:Does it wear out?

Background: COVID-19 mitigation measures intend to protect public health, but their adverse psychological, social, and economic effects weaken public support. Less favorable trade-offs may especially weaken support for more restrictive measures. Support for mitigation measures may also differ between population subgroups who experience different benefits and costs, and decrease over time, a phenomenon termed “pandemic fatigue.” Methods: We examined self-reported support for COVID-19 mitigation measures in the Netherlands over 12 consecutives waves of data collection between April 2020 and May 2021 in an open population cohort study. Participants were recruited through community panels of the 25 regional public health services, and through links to the online surveys advertised on social media. The 54,010 unique participants in the cohort study on average participated in 4 waves of data collection. Most participants were female (65%), middle-aged [57% (40–69 years)], highly educated (57%), not living alone (84%), residing in an urban area (60%), and born in the Netherlands (95%). Results: COVID-19 mitigation measures implemented in the Netherlands remained generally well-supported over time [all scores >3 on 5-point scale ranging 1 (low)−5 (high)]. During the whole period studied, support was highest for personal hygiene measures, quarantine and wearing face masks, high but somewhat lower for not shaking hands, testing and self-isolation, and restricting social contacts, and lowest for limiting visitors at home, and not traveling abroad. Women and higher educated people were more supportive of some mitigation measures than men and lower educated people. Older people were more supportive of more restrictive measures than younger people, and support for more socially restrictive measures decreased most over time in higher educated people or in younger people. Conclusions: This study found no support for pandemic fatigue in terms of a gradual decline in support for all mitigation measures in the first year of the pandemic. Rather, findings suggest that support for mitigation measures reflects a balancing of benefits and cost, which may change over time, and differ between measures and population subgroups