# Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China

## Aim

To estimate the level of overdispersion in COVID-19 transmission from the worldwide case count data.

## Methods summary

- We extracted the number of imported/local cases in the affected countries from the WHO situation report 38 published on 27 February 2020.
- Assuming that the offspring distribution (distribution of the number of secondary transmissions) for COVID-19 cases is an identically- and independently-distributed negative-binomial distribution, we estimated the parameters of the negative-binomial distribution (reproduction number
*R*0 and overdispersion*k*using the likelihood of observing the reported number of imported/local cases (outbreak size) of COVID-19 for each country. - The outbreak size may grow in countries with an ongoing outbreak; using the current outbreak size as the final size for such countries may introduce bias. We assumed that the growth of a cluster in a country had not been ceased if the latest reported cases were within 7 days before 27 February 2020, and adjusted the likelihood for these countries by using the condition that the final cluster size has to be at least as large as the currently observed number of cases.

## Key findings

- The offspring distribution of COVID-19 is highly overdispersed.
- For the likely range of
*R*0 of 2-3, the overdispersion parameter*k*was estimated to be around 0.1, suggesting that the majority of secondary transmission is caused by a very small fraction of individuals (80% of transmissions caused by ~10% of the total cases). - Joint estimation of
*R*0 and*k*indicated it is likely that*R*0 > 1.4 and*k*< 0.2. The current data and model did not provide evidence on the upper bound of*R*0.

Figure 1. MCMC estimates given assumed *R*0 values.
(A) Estimated overestimation parameter for various basic reproduction number *R*0. (B) Proportion of infected individuals responsible for 80% of the total secondary transmissions (*p*80%).
The black lines show the median estimates given fixed *R*0 values and the grey shaded areas indicate 95% CrIs. The regions corresponding to the likely range of *R*0 (2-3) are indicated by colour.

Figure 2. Possible offspring distributions of COVID-19.
(A) Offspring distribution corresponding to *R*0 = 2.5 and *k* = 0.1 (median estimate). (B) Offspring distribution corresponding to *R*0 = 2.5 and *k* = 0.05 (95% CrI lower bound), 0.2 (upper bound). The probability mass functions of negative-binomial distributions are shown.

Table 1. Credible intervals from a joint estimation

Parameter | Prior distribution | 95% lower bound | 95% upper bound |
---|---|---|---|

\(R_0\) | \(\mathcal N(\mu=3,\sigma=5)\) | 1.4 | 11.6 |

\(k\) | \(\mathrm{HalfNormal}(\sigma=10)\) for the reciprocal \(k^{-1}\) | 0.04 | 0.2 |

## Limitations

- We used the confirmed case counts reported to WHO and did not account for possible underreporting of cases.
- Reported cases whose site of infection classified as unknown, which should in principle be counted as either imported or local cases, were excluded from analysis.
- The distinction between countries with and without ongoing outbreak (7 days without any new confirmation of cases) was arbitrary and the results may be sensitive to this assumption.

## Detailed methods

Full details and the underlying scripts can be found on a Github page.

### Data source

We extracted the number of imported/local cases in the affected countries from the WHO situation report 38 published on 27 February 2020, which, at the time of writing, is the latest report of the number of imported/local cases in each country (from the situation report 40, WHO no longer reports the number of cases stratified by the site of infection). We defined imported cases as the cases whose likely site of infection is outside the reporting country, and the local cases as those whose likely site of infection is inside the reporting country. Those whose site of infection under investigation were excluded from the analysis. In Egypt and Iran, no imported cases have been confirmed which cause the likelihood value to be zero. Data in these two countries were excluded.

To distinguish between countries with and without an ongoing outbreak, we extracted daily case counts from an online resource (COVID2019.app) and determined the dates of the latest case confirmation for each country (as of 27 February).

### Final outbreak size

Assume that the offspring distribution for COVID-19 cases is an i.i.d. negative-binomial distribution. The probability mass function for the final cluster size resulting from *s* initial cases is, according to Blumberg et al., given by

\[ c(x;s)=P(X=x;s)=\frac{ks}{kx+x-s}\binom{kx+x-s}{x-s}\frac{\left(\frac{R_0} k\right)^{x-s}}{\left(1+\frac{R_0} k\right)^{kx+x-s}}. \]

If the observed case counts are part of an ongoing outbreak in a country, cluster sizes may grow in the future. To address this issue, we adjusted the likelihood corresponding those countries with ongoing outbreak by only using the condition that the final cluster size of such a country has to be larger than the currently observed number of cases. The corresponding likelihood function is

\[ c_\mathrm{o}(x;s)=P(X\geq x;s)=1-\sum_{m=0}^{x}c(m;s)+c(x;s) \]

### Defining countries with ongoing outbreak and total likelihood

\[ L(R_0,k)=\prod_{i\in A}P(X=x_i;s_i)\prod_{i\in B}P(X\geq x_i;s_i) \]

### Estimating overdispersion parameter

### Proportion responsible for 80% of transmissions

Following Grantz et al., we calculated the estimated proportion of infected individuals responsible for 80% of secondary transmissions caused. Such proportion *p*80% is given as

\[ 1-p_{80\%}=\int_0^{X}\mathrm{NB}\left(\lfloor x\rfloor;k,\frac{k}{R_0+k}\right)dx, \]

where (X) satisfies

\[ 1-0.8=\frac 1{R_0}\int_0^{X}\lfloor x\rfloor\mathrm{NB}\left(\lfloor x\rfloor;k,\frac{k}{R_0+k}\right)dx. \]

Note that

\[ \frac 1{R_0}\int_0^{X}\lfloor x\rfloor\mathrm{NB}\left(\lfloor x\rfloor;k,\frac{k}{R_0+k}\right)dx=\int_0^{X-1}\mathrm{NB}\left(\lfloor x\rfloor;k+1,\frac{k}{R_0+k}\right)dx. \]

We computed *p*80% for each MCMC sample to yield median and 95% CrIs.