Publication | Closed Access
Predictive Mathematical Models of the COVID-19 Pandemic
343
Citations
2
References
2020
Year
Infectious Disease ModelingGranular Local DataInfectious Disease ModellingUncertainty QuantificationGlobal Health CrisisCovid-19 PandemicManagementRegular UpdatingCovid-19 EpidemiologyPublic HealthCovid-19Computational EpidemiologyStatisticsEpidemiologyData ModelingPredictive Mathematical Models
This Viewpoint discusses the challenges of accurately modeling the COVID-19 pandemic and reviews principles that will make some models more useful than others, such as use of granular local data when available, regular updating and revision, and specification of uncertainty around estimates.
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