Once an outbreak of pandemic potential has been identified, models have enormous potential to improve the effectiveness of the response. Models can be used to synthesize data to provide enhanced situational awareness, predict the future course of the pandemic, and to plan mitigation strategies.
The idea that major cities and their airports are important drivers of worldwide contagion makes intuitive sense, and it’s supported by mathematical models. But models don’t always reflect reality.
A forecasting tool that uses data collected by state health departments could be used to forecast disease in the event of a bioterrorist attack or influenza pandemic.
Researchers have identified features of the influenza virus genome that affect how well the virus multiplies these findings may help health experts know which strains to watch.
Health researchers are looking to become more proactive by using machine learning to identify potential hot spots and predict future outbreaks.
Many in the global health field assert that progress toward a more vigilant global health ecosystem still hasn’t come to grips with a crucial common denominator: animal health.
Researchers suggest that more accurate pandemic virus predictions may come from studying ecological fault lines of human–animal interface.
Ambitious initiatives are underway to pinpoint the next big viral threats, but some virologists say the task is too difficult.
To address the major health and economic impacts of zoonotic diseases, researchers developed a system-dynamics approach to capture the impact of future climate, land use and human population change on Ebola.
Researechers found that cross-strain immunity may delay emergence of pandemics during height of flu season.