At the height of the COVID-19 pandemic, an international team of mathematicians borrowed techniques from the geosciences to predict the complex and shifting dynamics of the virus’ spread. The technique known as data assimilation combines numerical model data with fresh observational data to deliver more accurate forecasts. Validating the method in eight distinct countries, the team demonstrated the potential to reasonably and accurately predict the short-term impacts of various reopening measures on virus transmission. This method can provide critical information to policymakers to make informed decisions and design effective policies to mitigate the pandemic’s impacts.

Natale Alberto Carrassi (2022). IMPROVING PANDEMIC FORECASTS - ASSIMILATING OBSERVATIONS AND SIMULATIONS. N/A : UNESCO.

IMPROVING PANDEMIC FORECASTS - ASSIMILATING OBSERVATIONS AND SIMULATIONS

Natale Alberto Carrassi
2022

Abstract

At the height of the COVID-19 pandemic, an international team of mathematicians borrowed techniques from the geosciences to predict the complex and shifting dynamics of the virus’ spread. The technique known as data assimilation combines numerical model data with fresh observational data to deliver more accurate forecasts. Validating the method in eight distinct countries, the team demonstrated the potential to reasonably and accurately predict the short-term impacts of various reopening measures on virus transmission. This method can provide critical information to policymakers to make informed decisions and design effective policies to mitigate the pandemic’s impacts.
2022
Mathematics for action: supporting science-based decision-making
9
10
Natale Alberto Carrassi (2022). IMPROVING PANDEMIC FORECASTS - ASSIMILATING OBSERVATIONS AND SIMULATIONS. N/A : UNESCO.
Natale Alberto Carrassi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/879655
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