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The Challenges of Forecasting the Spread and Mortality of COVID-19

April 15, 2020

Like climate models, epidemiological models used to project COVID-19 infections and deaths are grounded in assumptions, many of which about there is currently little knowledge.

From the study:

While statistical models can be useful tools for tracking COVID-19, they are only as accurate as the input assumptions, which depend on continually changing data.

Widespread, randomized testing is critical to generating credible forecasts and developing a full understanding of the disease.

To make well-informed decisions, policymakers must use a range of forecasts and fully understand how sensitive models are to the smallest changes in assumptions.

Models projecting climate responses to greenhouse gas concentrations and other factors suffer from similar limits and weaknesses.

Author
Kevin D. Dayaratna is Senior Statistician and Research Programmer in The Heritage Foundation’s Center for Data Analysis (CDA) and a Policy Advisor with the Heartland Institute.