Found this report circulating on twitter from a junior doctor in Australia.
Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions
> Key findings:
> ● We estimate the basic reproduction number of the infection (𝑅𝑅0) to be significantly greater than one. We estimate it to be between 3.6 and 4.0, indicating that 72-75% of transmissions must be prevented by control measures for infections to stop increasing.
> ● We estimate that only 5.1% (95%CI, 4.8–5.5) of infections in Wuhan are identified, indicating a large number of infections in the community, and also reflecting the difficulty in detecting cases of this new disease. Surveillance for this novel pathogen has been launched very quickly by public health authorities in China, allowing for rapid assessment of the speed of increase of cases in Wuhan and other areas.
> ● If no change in control or transmission happens, then we expect further outbreaks to occur in other Chinese cities, and that infections will continue to be exported to international destinations at an increasing rate. In 14 days’ time (4 February 2020), our model predicts the number of infected people in Wuhan to be greater than 190 thousand (prediction interval, 132,751 to 273,649). We predict the cities with the largest outbreaks elsewhere in China to be Shanghai, Beijing, Guangzhou, Chongqing and Chengdu. We also predict that by 4 Feb 2020, the countries or special administrative regions at greatest risk of importing infections through air travel are Thailand, Japan, Taiwan, Hong Kong, and South Korea.
> ● Our model suggests that travel restrictions from and to Wuhan city are unlikely to be effective in halting transmission across China; with a 99% effective reduction in travel, the size of the epidemic outside of Wuhan may only be reduced by 24.9% on 4 February.
> ● There are important caveats to the reliability of our model predictions, based on the assumptions underpinning the model as well as the data used to fit the model. These should be considered when interpreting our findings.