Quantifying the impact of prevalence-dependent adaptive behavior on COVID-19 transmission: A modeling case study in Maryland.

The COVID-19 pandemic highlighted the need for robust epidemic forecasts, projecting health burden over short- and medium-term time horizons. Many COVID-19 forecasting models incorporate information on infection transmission, disease progression, and the effects of interventions, but few combine information on how individuals change their behavior based on altruism, fear, risk perception, or personal economic circumstances. Moreover, early models of COVID-19 produced under- and over-estimates, failing to consider the complexity of human responses to disease threat and prevention measures.

Incorporating endogenous human behavior in models of COVID-19 transmission: A systematic scoping review.

Background: During the COVID-19 pandemic there was a plethora of dynamical forecasting models created, but their ability to effectively describe future trajectories of disease was mixed. A major challenge in evaluating future case trends was forecasting the behavior of individuals. When behavior was incorporated into models, it was primarily incorporated exogenously (e.

A novel therapeutic vaccine targeting the soluble TNFα receptor II to limit the progression of cardiovascular disease: AtheroVax™.

The burden of atherosclerotic cardiovascular disease contributes to a large proportion of morbidity and mortality, globally. Vaccination against atherosclerosis has been proposed for over 20 years targeting different mediators of atherothrombosis; however, these have not been adequately evaluated in human clinical trials to assess safety and efficacy. Inflammation is a driver of atherosclerosis, but inflammatory mediators are essential components of the immune response.