On the use of growth models to understand epidemic outbreaks with application to COVID-19 data.
PLoS One
; 15(10): e0240578, 2020.
Article
in English
| MEDLINE | ID: covidwho-881157
ABSTRACT
The initial phase dynamics of an epidemic without containment measures is commonly well modelled using exponential growth models. However, in the presence of containment measures, the exponential model becomes less appropriate. Under the implementation of an isolation measure for detected infectives, we propose to model epidemic dynamics by fitting a flexible growth model curve to reported positive cases, and to infer the overall epidemic dynamics by introducing information on the detection/testing effort and recovery and death rates. The resulting modelling approach is close to the Susceptible-Infectious-Quarantined-Recovered model framework. We focused on predicting the peaks (time and size) in positive cases, active cases and new infections. We applied the approach to data from the COVID-19 outbreak in Italy. Fits on limited data before the observed peaks illustrate the ability of the flexible growth model to approach the estimates from the whole data.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Quarantine
/
Models, Statistical
/
Coronavirus Infections
/
Containment of Biohazards
/
Pandemics
/
Betacoronavirus
Type of study:
Observational study
/
Prognostic study
Limits:
Humans
Country/Region as subject:
Europa
Language:
English
Journal:
PLoS One
Journal subject:
Science
/
Medicine
Year:
2020
Document Type:
Article
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