Early dynamics of transmission and control of COVID-19: a mathematical modelling study

By A. Kucharski, T. Russell, Charlie Diamond, Yang Liu, J. Edmunds, Sebastian Funk, R. Eggo. medRxiv, 2020

2,469 citations recorded by the source corpus.

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.

Subfields: Infectious Diseases, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/3fde1bab7aa31dc3e0129833a422cc811ea25c80

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Citations in the corpus

Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures (2020, background): “3–6, 13, 14, 25, 26, and 39.”
Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study (2020, background): “We found that the epidemic was self-sustained for short periods of time only (no more than 3 weeks) in provinces outside Hubei that reported a large number of cases, 32 and estimated that, since the end of January, 2020, R t has been below the epidemic threshold in all studied provinces.”
Modelling transmission and control of the COVID-19 pandemic in Australia (2020, methodology): “t studies estimated that before travel restrictions were introduced in Wuhan on 23 January 2020, the median daily reproduction number R 0 in Wuhan was 2:35, with 95% condence interval of (1:15 4:77) [53]. An agent-based model of the Imperial College COVID-19 Response Team used a baseline assumption ”
Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy (2020, background): “observed transmission dynamics to estimate clinical severity [25]; stochastic transmission models have been also considered, see for instance [26, 27].”
Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis (2020, neutral): “For these five countries he outbreaks of COVID-19 started almost from the same timeline nd the epidemic curves still not showing the sharp diminishing ature, just like China.”
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, neutral): “In [7] , a simulation has been performed based on a trained ordinary differential equation model to describe the effect of an imported escaper and asymptomatic patients on conﬁrmed Covid-19 infections in China’s Heilongjiang Province.”
Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China (2020, result): “3 for the discussed provinces, consistent with estimates found in previous early assessment studies (4, 8, 21, 22).”
To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic (2020, neutral): “Most mathematical models of the COVID-19 pandemic can broadly be divided into either population-based, SIR (Kermack-McKendrick)type models, driven by (potentially stochastic) differential equations [38, 20, 34, 22, 21, 23, 31, 26, 32, 24, 33], or agent-based models [39, 28, 25, 27, 30], in which ind”
Networks beyond pairwise interactions: structure and dynamics (2020, background): “[443] Adam J Kucharski, Timothy W Russell, Charlie Diamond, Yang Liu, John Edmunds, Sebastian Funk, Rosalind M Eggo, Fiona Sun, Mark Jit, James D Munday, et al.”
The challenges of modeling and forecasting the spread of COVID-19 (2020, neutral): “Bulletin of Mathematical 542 Biology 74(9).”