This seems like one of the hardest things to measure: how many people *actually* have coronavirus, given your estimate? It's wildly dependent upon testing strategy and delays in the system.

- This paper uses simulations from Wubei to derive a maximum likelihood reporting rate of
**0.14**- This is the most up-to-date estimate we've seen, so we're
**currently using this**

- This is the most up-to-date estimate we've seen, so we're
- This paper used sampling from evacuated Japanese individuals to place the rate at ~
**0.1**- We used this in the app before 19th March 2020

We use the SIR model, a standard epidemiological model, to estimate the disease dynamics.

Compartmental models in epidemiology

The specific flavour we use is the Kermack-McKendrick model:

https://mathworld.wolfram.com/Kermack-McKendrickModel.html

The crucial factor governing disease spread is R0 (the basic reproduction rate), which is the **average number of people somebody with the disease infects.** This is a function of the number of susceptible people, the infection rate β and the recovery rate γ.

You can decompose this into how many people an infected person contacts a day, and the transmission probability from a given contact:

**β = Probability of transmission x Number of contacts**

We allow you to control **Number of Contacts per day** in the webapp. We have estimated Probability of Transmission from the following numbers:

**Number of infectious days**from https://github.com/midas-network/COVID-19/tree/master/parameter_estimates/2019_novel_coronavirus, and https://www.medrxiv.org/content/10.1101/2020.03.05.20030502v1**Number of contacts per day**and**R0**from**Table 1 and 2**of https://www.mdpi.com/2077-0383/9/2/462/htm

We can then formulate R0 as:

**R0 = Probability of transmission x Number of Contacts per day x Number of infectious days**

Subbing in our numbers:

```
**2.5 = Probability of Transmission x 14 x 10**
```

we solve for Probability of transmission, which comes out as ~0.018, or 1.8%.