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

We performed a smaller set of simulations with 2500 replicates and twice the number of cases, i.e. K i=1 C i = 4000, to explore a dramatically underreported epidemic. Additionally, we performed additional smaller set of simulations on a rougher grid of R 0 values (23 values, 0.50-1.05), with 5 values of dispersion parameter ω (0.002, 0.04, 0.1, 0.5, 1.0) and 3 levels of bias (1, 2, 3) to justify our choice of dispersion parameter ω that was fixed to 0.1 in the main analyses ( Figure S15 ). Draw a sequence cluster size s i from array of potential sequence cluster sizes (0, 1, . . . , c i ) according to p; end Add remaining sequences to last sequence cluster c K = M − s K−1 ; Algorithm 1: Multivariate hypergeometric sampling scheme. Pseudocode describes the multivariate hypergeometric sampling scheme that simulates sequencing. Probability of sequencing a given number of cases from a case cluster depends on cluster size and sequences left (i.e. "sequencing capacity"). The bias parameter determines how probability mass function of the hypergeometric distribution is concentrated.