Your browser doesn't support javascript.
Early dynamics of transmission and control of 2019-nCoV: a mathematical modelling study (preprint)
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.01.31.20019901
ABSTRACT

Background:

An outbreak of the novel coronavirus SARS-CoV-2 has led to 46,997 confirmed cases as of 13th February 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas.

Methods:

We combined a stochastic transmission model with data on cases of novel coronavirus disease (COVID-19) in Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January and February 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas.

Findings:

We estimated that the median daily reproduction number, Rt , declined from 2.35 (95% CI 1.15-4.77) one week before travel restrictions were introduced on 23rd January to 1.05 (95% CI 0.413-2.39) one week after. Based on our estimates of Rt,we calculated that in locations with similar transmission potential as Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population.

Interpretation:

Our results show that COVID-19 transmission likely declined in Wuhan during late January 2020, coinciding with the introduction of control measures. As more cases arrive in international locations with similar transmission potential to Wuhan pre-control, it is likely many chains of transmission will fail to establish initially, but may still cause new outbreaks eventually.
Subject(s)

Full text: Available Collection: Preprints Database: medRxiv Main subject: Coronavirus Infections / COVID-19 Language: English Year: 2020 Document Type: Preprint

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: Preprints Database: medRxiv Main subject: Coronavirus Infections / COVID-19 Language: English Year: 2020 Document Type: Preprint