Using Bandit Algorithms to Maximize SARS-CoV-2 Case-Finding: Evaluation and Feasibility Study.

Background: The Flexible Adaptive Algorithmic Surveillance Testing (FAAST) program represents an innovative approach for improving the detection of new cases of infectious disease; it is deployed here to screen and diagnose SARS-CoV-2. With the advent of treatment for COVID-19, finding individuals infected with SARS-CoV-2 is an urgent clinical and public health priority. While these kinds of Bayesian search algorithms are used widely in other settings (eg, to find downed aircraft, in submarine recovery, and to aid in oil exploration), this is the first time that Bayesian adaptive approaches have been used for active disease surveillance in the field.

Sentinel node approach to monitoring online COVID-19 misinformation.

Understanding how different online communities engage with COVID-19 misinformation is critical for public health response. For example, misinformation confined to a small, isolated community of users poses a different public health risk than misinformation being consumed by a large population spanning many diverse communities. Here we take a longitudinal approach that leverages tools from network science to study COVID-19 misinformation on Twitter.

A Bayesian Spatiotemporal Nowcasting Model for Public Health Decision-Making and Surveillance.

As coronavirus disease 2019 (COVID-19) spread through the United States in 2020, states began to set up alert systems to inform policy decisions and serve as risk communication tools for the general public. Many of these systems included indicators based on an assessment of trends in numbers of reported cases. However, when cases are indexed by date of disease onset, reporting delays complicate the interpretation of trends.

Earth observation for public health: Biodiversity change and emerging disease surveillance.

is an emerging concept in the health sciences that approaches human, animal and environmental health from a single framework. This policy approach is grounded in the knowledge that approximately 70 percent of emerging diseases in humans originate from other species, and that this species crossover is precipitated by stresses to environmental systems such as habitat change and biodiversity loss. Remote sensing tools apply well to this approach due to the multitude of variables that can be measured across borders in real-time.