Infectious disease specialists have long warned us about the pandemic potential of a novel respiratory virus jumping from wildlife to humans, possibly from wet markets.
Why was the international community taken by surprise when this finally occurred even when business leaders such as Bill Gates also raised alarms about this potential pandemic and how to prepare for it? Epidemiologists use a wide variety of models to analyze and forecast the spread of diseases
such as COVID-19.
At this point you have certainly heard modeling lingo such as R0 and social distancing, but it’s also likely you have read about SIR, SEIR, agent-based, network, and statistical models.
The epidemic modeling literature is rich and there are hundreds of well documented and thoroughly tested epidemic modeling apps in the public domain.
Why is there not just one “overall model” that can help our leadership make informed decisions?
In this webinar we will review different epidemic models and their applications to manage epidemics such as COVID with an eye on the importance of choosing the right type of
model, and acknowledging parameter and model uncertainty.
We will then extend this reasoning to non-COVID situations, and will use real-life case-studies to discuss modeling aspects such as:
Fit for purpose: importance of selecting the right technique and type of analytical model for the right problem.
Acknowledging limitations of models, including uncertainty: we should help decision-makers understand the strengths and limitations of models, and embrace uncertainty as a reality of any prediction that should be included rather than ignored
Mechanistic vs statistical modeling: this important distinction previously reserved only to us working in prescriptive analytics has become a matter of great political debate, as demonstrated by the diverging COVID-19 mortality predictions between groups such as U of Washington’s Institute
for Health Metrics and Evaluation (using statistical models) vs those from the Imperial College (mechanistic).