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Ambient audio sampling methods such as the Electronically Activated Recorder (EAR) have become increasingly prominent in clinical and social sciences research. These methods record snippets of naturalistically assessed audio from participants’ daily lives, enabling novel observational research about the daily social interactions, identities, environments, behaviors, and speech of populations of interest. In practice, these scientific opportunities are equaled by methodological challenges: researchers’ own cultural backgrounds and identities can easily and unknowingly permeate the collection, coding, analysis, and interpretation of social data from daily life. Ambient audio sampling poses unique and significant challenges to cultural humility, diversity, equity, and inclusivity (DEI) in scientific research that require systematized attention. Motivated by this observation, an international consortium of 21 researchers who have used ambient audio sampling methodologies created a workgroup with the aim of improving upon existing published guidelines. We pooled formally and informally documented challenges pertaining to DEI in ambient audio sampling from our collective experience on 40+ studies (most of which used the EAR app) in clinical and healthy populations ranging from children to older adults. This article presents our resultant recommendations and argues for the incorporation of community-engaged research methods in observational ambulatory assessment designs looking forward. We provide concrete recommendations across each stage typical of an ambient audio sampling study (recruiting and enrolling participants, developing coding systems, training coders, handling multi-linguistic participants, data analysis and interpretation, and dissemination of results) as well as guiding questions that can be used to adapt these recommendations to project-specific constraints and needs.

In many settings, ongoing measles transmission is maintained due to pockets of un- or under-vaccinated individuals even if the critical vaccination threshold is reached nationwide. Therefore, assessing the underlying gaps in measles susceptibility within a population is essential for vaccination programs and measles control efforts. Recently, there have been increased efforts to use geospatial and small area methods to estimate subnational measles vaccination coverage in high-burden settings, such as in Ethiopia. However, the distribution of remaining susceptible individuals, either unvaccinated or having never previously been infected, across age groups and subnational geographies is unknown. In this study, we developed a dynamic transmission model that incorporates geospatial estimates of routine measles vaccination coverage, available data on supplemental immunization activities, and reported cases to estimate measles incidence and susceptibility across time, age, and space. We use gridded population estimates and subnational estimates of routine and supplemental measles vaccination coverage. To account for mixing between age-groups, we used a synthetic contact matrix, and travel times via a friction surface were used in a modified gravity model to account for spatial movement. We explored model fitting using Ethiopia as a case study. To address data-related and statistical challenges, we investigated a range of model parameterization and possible fitting algorithms. The approach with the best performance was a model fitted to case notifications adjusted for case ascertainment by using maximum likelihood estimation with block coordinate descent. This strategy was chosen because many data observations (and likely presence of unquantified uncertainty) yielded a steep likelihood surface, which was challenging to fit using Bayesian approaches. We ran sensitivity analyses to explore variations in vaccine effectiveness and compared patterns of susceptibility across space, time, and age. Substantial heterogeneity in reported measles cases as well as susceptibility persists across ages and second-administrative units. These methods and estimates could contribute towards tailored subnational and local planning to reduce preventable measles burden. However, computational and data challenges would need to be addressed for these methods to be applied on a large scale.

We provide efficient constructions and tight bounds for shared memory systems accessed by n processes, up to t of which may exhibit Byzantine failures, in a model previously explored by Malkhi et al. [21]. We show that sticky bits are universal in the Byzantine failure model for n ≥ 3t + 1, an improvement over the previous result requiring n ≥ (2t + 1)(t + 1). Our result follows from a new strong consensus construction that uses sticky bits and tolerates t Byzantine failures among n processes for any n ≥ 3t + 1, the best possible bound on n for strong consensus. We also present tight bounds on the efficiency of implementations of strong consensus objects from sticky bits and similar primitive objects.

The paper has been withdrawn owing to erroneous inclusion of confidential information relating to a third party.