Recent & Upcoming Talks

Forecasting the trajectory of the US COVID-19 pandemic can support public health officials who make decisions with partial knowledge of how the virus will evolve over time. Computational models are capable of processing large, structured datasets.

A safe, effective vaccine is a key component to slowing the spread of SARS- CoV- 2. But a recent poll suggests less than 50% of the US public would volunteer for inoculation, highlighting uncertainty surrounding the safety and efficacy of a vaccine.

In the first of our new Speaker Series exploring the science of forecasting, biostatistician @tomcm39 will present new research on Consensus Forecasting Models for COVID-19 Vaccines and Treatments, supporting policy makers and the public.

Community-wide transmission of SARS-CoV-2 was reported in the US on February 26th. Since the virus was declared a pandemic by the WHO on March 11th, it has spread rapidly throughout the US and has caused more than 1.

Community-wide transmission of SARS-CoV-2 was reported in the US on February 26th. Since the virus was declared a pandemic by the WHO on March 11th, it has spread rapidly throughout the US and has caused more than 1.

We present three combination forecasting techniques. The FluSight Network (FSN) ensemble model is a multimodel ensemble; a convex combination of component predictive densities. The adaptive ensemble is a multimodel ensemble, but unlike the FSN ensemble, recomputes component model weights every week.

Seasonal Influenza infects an average 30 million people in the United States every year, overburdening hospitals during weeks of peak incidence.Named by the CDC as an important tool to fight the damaging effects of these epidemics, accurate forecasts of influenza and influenza like illness (ILI) forewarn public health officials about when, and where, seasonal influenza outbreaks will hit hardest.

Influenza prediction can better prepare public health officials for seasonal outbreaks. We propose an adaptive multi-model ensemble forecast method that changes model weights week-by-week throughout the flu season. Without historical training data or when models do not have a track-record, an adaptive model can enhance the public health impact of ensemble forecasts.

Seasonal Influenza infects an average 30 million people in the United States every year, overburdening hospitals during weeks of peak incidence.Named by the CDC as an important tool to fight the damaging effects of these epidemics, accurate forecasts of influenza and influenza like illness (ILI) forewarn public health officials about when, and where, seasonal influenza outbreaks will hit hardest.

Background: Left ventricular ejection fraction (LVEF) is the most commonly used clinical measure of systolic function and is a powerful predictor of outcomes. Current guidelines recommend using the biplane method of discs (MOD) (modified Simpson’s rule) to measure LVEF however the accuracy of this measurement is dependent on endocardial border detection and orthogonal, on-axis biplane imaging, and studies report both inter and intraobserver variability.

Identifying and communicating relationships between causes and effects is important for understanding our world, but is affected by language structure, cognitive and emotional biases, and the properties of the communication medium.