tetano
Editor, Senior Moderator
Proc Natl Acad Sci U S A
. 2020 Jul 2;202006520.
doi: 10.1073/pnas.2006520117. Online ahead of print.
The Challenges of Modeling and Forecasting the Spread of COVID-19
Andrea L Bertozzi[SUP] 1 2 [/SUP], Elisa Franco[SUP] 2 3 [/SUP], George Mohler[SUP] 4 [/SUP], Martin B Short[SUP] 5 [/SUP], Daniel Sledge[SUP] 6 [/SUP]
AffiliationsExpand
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.
Keywords: COVID-19; branching process; compartmental models; pandemic.
. 2020 Jul 2;202006520.
doi: 10.1073/pnas.2006520117. Online ahead of print.
The Challenges of Modeling and Forecasting the Spread of COVID-19
Andrea L Bertozzi[SUP] 1 2 [/SUP], Elisa Franco[SUP] 2 3 [/SUP], George Mohler[SUP] 4 [/SUP], Martin B Short[SUP] 5 [/SUP], Daniel Sledge[SUP] 6 [/SUP]
AffiliationsExpand
- PMID: 32616574
- DOI: 10.1073/pnas.2006520117
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.
Keywords: COVID-19; branching process; compartmental models; pandemic.