MERS-CoV spillover at the camel-human interface

Gytis Dudas; Luiz Max Carvalho; Andrew Rambaut; Trevor Bedford; Ali M. Somily; Mazin Barry; Sarah S. Al Subaie; Abdulaziz A. BinSaeed; Fahad A. Alzamil; Waleed Zaher; Theeb Al Qahtani; Khaldoon Al Jerian; Scott J.N. McNabb; Imad A. Al-Jahdali; Ahmed M. Alotaibi; Nahid A. Batarfi; Matthew Cotten; Simon J. Watson; Spela Binter; Paul Kellam

Although we find evidence of seasonality in zoonotic spillover timing, no such relationship exists for sizes of human sequence clusters ( Figure 2B ). This is entirely expected, since little seasonality in human behaviour that could facilitate MERS-CoV transmission is expected following an introduction. Similarly, we do not observe any trend in human sequence cluster sizes over time ( Figure 2B , Spearman ρ = 0.06, p = 0.68), suggesting that MERS-CoV outbreaks in humans are neither growing nor shrinking in size. This is not surprising either, since MERS-CoV is a camel virus that has to date, experienced little-to-no selective pressure to improve transmissibility between humans. behaviour metric that does not require expertise in reading phylogenies. The parameter R 0 determines expected number of secondary cases in a single infections as well as the distribution of total cases that result from a single introduction event into the human population (Equation 1, Methods). We estimate R 0 along with other relevant parameters via Monte Carlo simulation in two steps. First, we simulate case counts across multiple outbreaks totaling 2000 cases using Equation 1 and then we subsample from each case cluster to simulate sequencing of a fraction of cases. Sequencing simulations are performed via a multivariate hypergeometric distribution, where the probability of sequencing from a particular cluster depends on available sequencing capacity (number of trials), numbers of cases in the cluster (number of successes) and number of cases outside the cluster (number of failures). In addition, each hypergeometric distribution used to simulate sequencing is concentrated via a bias parameter, that enriches for large sequence clusters at the expense of smaller ones (for its effects see Figure S5 ). This is a particularly pressing issue, since a priori we expect large hospital outbreaks of MERS to be overrepresented in sequence data, whereas sequences from primary cases will be sampled exceedingly rarely. We record the number, mean, standard deviation and skewness (third standardised moment) of sequence cluster sizes in each simulation (left-hand panel in Figure 3 ) and extract the subset of Monte Carlo simulations in which these summary statistics fall within the 95% highest posterior density observed in the empirical MERS-CoV data from structured coalescent analyses. We record R 0 values, as well as the number of case clusters (equivalent to number of zoonotic introductions), for these empirically matched simulations. A schematic of this Monte Carlo procedure is shown in Figure S6 . Since the total number of cases is fixed at 2000, higher R 0 results in fewer larger transmission clusters, while lower R 0 results in many smaller transmission clusters. shows the distribution of individual Monte Carlo simulation sequence cluster size statistics (mean and skewness) coloured by the R 0 value used for the simulation. The dotted rectangle identifies the 95% highest posterior density bounds for sequence cluster size mean and skewness observed for empirical MERS-CoV data. The distribution of R 0 values that fall within 95% HPDs for sequence cluster size mean, standard deviation, skewness and number of introductions, is shown in the middle, on the same y-axis. Bins falling inside the 95% percentiles are coloured by R 0 , as in the leftmost scatter plot. The distribution of total number of introductions associated with simulations matching MERS-CoV sequence clusters is