Effectiveness of Community-Engaged Research Strategies in Increasing COVID-19 Vaccinations in Targeted Communities: The Community Engagement Alliance (CEAL) Regional Teams.

Objectives. To explore a key outcome of interest for the Community Engagement Alliance (CEAL) Regional Teams by examining COVID-19 vaccinations over time in US counties where CEAL teams operated and comparing them to demographically similar counties in the same state. Methods. Our evaluation used a nonequivalent control group design. Each county where a CEAL team operated was matched to a unique non-CEAL county in the same state. Components of the Centers for Disease Control and Prevention's Social Vulnerability Index were used as the matching criteria. COVID-19 vaccinations (county-level percentage of residents aged 18 years or older who are fully vaccinated) for CEAL and matched counties were analyzed over time. Results. The mean percentage of vaccinated adults was significantly higher in CEAL counties than in matched non-CEAL counties. Sensitivity analyses confirmed conclusions did not change depending on the CEAL cohort or closeness of matches. Conclusions. Our findings support CEAL team efforts to increase COVID-19 vaccinations in target communities and employ community-engaged research more broadly within public health contexts. (Am J Public Health. 2024;114(S1):S124-S127. https://doi.org/10.2105/AJPH.2023.307517).

Machine learning for modeling animal movement.

Animal movement drives important ecological processes such as migration and the spread of infectious disease. Current approaches to modeling animal tracking data focus on parametric models used to understand environmental effects on movement behavior and to fill in missing tracking data. Machine Learning and Deep learning algorithms are powerful and flexible predictive modeling tools but have rarely been applied to animal movement data. In this study we present a general framework for predicting animal movement that is a combination of two steps: first predicting movement behavioral states and second predicting the animal's velocity. We specify this framework at the individual level as well as for collective movement. We use Random Forests, Neural and Recurrent Neural Networks to compare performance predicting one step ahead as well as long range simulations. We compare results against a custom constructed Stochastic Differential Equation (SDE) model. We apply this approach to high resolution ant movement data. We found that the individual level Machine Learning and Deep Learning methods outperformed the SDE model for one step ahead prediction. The SDE model did comparatively better at simulating long range movement behaviour. Of the Machine Learning and Deep Learning models the Long Short Term Memory (LSTM) individual level model did best at long range simulations. We also applied the Random Forest and LSTM individual level models to model gull migratory movement to demonstrate the generalizability of this framework. Machine Learning and deep learning models are easier to specify compared to traditional parametric movement models which can have restrictive assumptions. However, machine learning and deep learning models are less interpretable than parametric movement models. The type of model used should be determined by the goal of the study, if the goal is prediction, our study provides evidence that machine learning and deep learning models could be useful tools.