*Contributors: Adam Kucharski, Tim Russell, Charlie Diamond, Yang Liu, CMMID nCoV working group, John Edmunds, Sebastian Funk, Rosalind Eggo.*

*Note: this is preliminary analysis and has not yet been peer-reviewed. *

To understand how human-to-human transmission varied in Wuhan during the early stages of the 2019-2020 nCoV outbreak and project forward based on current trends.

• To estimate the early dynamics of transmission in Wuhan, we fitted a mathematical model to multiple available datasets on international exported cases from Wuhan and cases in Wuhan. Fitting to multiple data sources rather than a single dataset (or data point) is particularly useful for estimates in real-time, because some datasets may be unreliable.

• Transmission was a random process in the model, and could vary over time – this means the model can uncover fluctuations in transmission during the early stages of the outbreak. Our group previously used a similar analysis to understand the dynamics of Ebola in Liberia.

• We assumed that the chance of cases being exported from Wuhan to other countries depended on the number of cases in Wuhan, the number of outbound travellers (accounting for travel restrictions after 23rd January), and the relative connectivity of different countries. We considered the 30 countries outside China most at risk of exported cases in the analysis. The model accounts for delays in symptom onset and reporting (see methods below).

• **We estimated that the median effective basic reproduction number, Rt, had likely been fluctuating between 1.5-4.5 prior to travel restrictions being introduced on 23rd Jan (Figure 1E)**. (The effective reproduction number is the average number of secondary cases generated by a typical infectious individual at a given point in time).

• **If Rt continues to vary as it has in Wuhan, we projected that the outbreak would peak in mid-to-late-February (Figure 1C-D).** There is substantial uncertainty about what the exact height and timing of the peak might be - currently the model predicts the peak as a result of susceptibility declining to the point where transmission cannot be sustained. As we get more data in the coming days, we will be able to refine these projections.

• **Based on the median reproduction number observed during January before travel restrictions were introduced, we estimated that a single introduction of 2019-nCoV with SARS-like or MERS-like individual-level variation in transmission would have a 20–30% probability of causing a large outbreak, assuming Wuhan-like transmission**. Assuming SARS-like individual variation in transmission, we estimated that once more than three infections have been independently introduced into a new location with Wuhan-like transmission, there is an over 50% chance that an outbreak will occur. We have made an online tool so that users can explore scenarios further.

• We assume that the proportion of cases reported during December and January is consistent, but detection may well have changed over time, potentially explaining the differences between model predictions and observed data in Figures 1C and 1F.

• We used biological parameters from current papers, but these may change as we get better data. We also made assumptions about the proportion of people who travel following the methods in the J-IDEA reports. However, jointly fitting to multiple datasets will reduce the influence that one single dataset has on results.

• We are currently using connectivity estimates from MOBS lab for exported cases, but obtained similar results when we fitted to WorldPop risk estimates.

• The model is flexible, and this page will be updated as we incorporate new information and data.

We used a stochastic SEIR model implemented using the Euler-Maruyama algorithm with a 6hr timestep, with transmission rate following geometric brownian motion (i.e. dlog(beta) = s dBt, where s is the volatility of transmission over time). We assume no travel out of Wuhan occurs after 23rd Jan, when restrictions were put in place. Cases that travel were distributed among other countries based on risk inferred from connectivity to those countries.