## Abstract

Background: The current novel coronavirus outbreak appears to have originated from a point-source exposure event at Huanan seafood wholesale market in Wuhan, China. There is still uncertainty around the scale and duration of this exposure event. This has implications for the estimated transmissibility of the coronavirus and as such, these potential scenarios should be explored.

Methods: We used a stochastic branching process model, parameterised with available data where possible and otherwise informed by the 2002-2003 SARS outbreak, to simulate the Wuhan outbreak. We evaluated scenarios for the following parameters: the size, and duration of the initial transmission event, the serial interval, and the reproduction number (R0). We restricted model simulations based on the number of observed cases on the 25th of January, accepting samples that were within a 5% interval on either side of this estimate.

Results: Using a pre-intervention SARS-like serial interval suggested a larger initial transmission event and a higher R0 estimate. Using a SARs-like serial interval we found that the most likely scenario produced an R0 estimate between 2-2.7 (90% credible interval (CrI)). A pre-intervention SARS-like serial interval resulted in an R0 estimate between 2-3 (90% CrI). There were other plausible scenarios with smaller events sizes and longer duration that had comparable R0 estimates. There were very few simulations that were able to reproduce the observed data when R0 was less than 1.

Conclusions: Our results indicate that an R0 of less than 1 was highly unlikely unless the size of the initial exposure event was much greater than currently reported. We found that R0 estimates were comparable across scenarios with decreasing event size and increasing duration. Scenarios with a pre-intervention SARS-like serial interval resulted in a higher R0 and were equally plausible to scenarios with SARs-like serial intervals.

## Usage

### Set up

Set your working directory to the home directory of this project (or use the provided Rstudio project). Install the analysis and all dependencies with:

remotes::install_github("epiforecasts/WuhanSeedingVsTransmission", dependencies = TRUE)

### Run analysis

Run the analysis with the following:

Rscript inst/scripts/run_grid.R

See run_scenario_grid for additional scenario analysis details.

### Inspect results

Use vignettes/output.Rmd to inspect the results of the analysis interactively. See vignettes/output.md for a markdown version of the analysis containing all results. See vignettes/rendered_output for version of the analysis rendered in other formats.

### Render output

Render the output to all formats with the following:

Rscript inst/scripts/render_output.R

### Update the fitted reporting delay function

In order to refit the reporting delay from linelist date on cases in China use the following:

Rscript data-raw/fitted_delay_sample_func.R

It is then neccessary to either rebuild the package or pass the updated function to run_scenario_grid explicitly. In normal usage this should not be neccessary for users of this analysis.

## Docker

This analysis was developed in a docker container based on the tidyverse docker image.

To build the docker image run (from the WuhanSeedingVsTransmission directory):

docker build . -t wuhansvst

To run the docker image run:

docker run -d -p 8787:8787 --name wuhansvst -e USER=wuhansvst -e PASSWORD=wuhansvst wuhansvst

The rstudio client can be found on port :8787 at your local machines ip. The default username:password is wuhansvst:wuhansvst, set the user with -e USER=username, and the password with - e PASSWORD=newpasswordhere. The default is to save the analysis files into the user directory.

To mount a folder (from your current working directory - here assumed to be tmp) in the docker container to your local system use the following in the above docker run command (as given mounts the whole wuhansvst directory to tmp).

{bash, eval = FALSE} --mount type=bind,source=\$(pwd)/tmp,target=/home/wuhansvst

To access the command line run the following:

{bash, eval = FALSE} docker exec -ti wuhansvst bash

Alternatively the analysis environment can be accessed via binder.