Highly dynamic animal contact network and implications on disease transmission

Chen, Shi; White, Brad J.; Sanderson, Michael W.; Amrine, David E.; Ilany, Amiyaal; Lanzas, Cristina

The maximum prevalence and its associated occurrence day in each condition for each set of parameters were recorded, along with outbreak size (n), duration of outbreak (T f ), and basic reproduction number (R 0 ). The R 0 was numerically computed from the secondary infections in the simulations. To compare the complete dynamics among these four conditions (C1-C4), we computed the time series of the difference (D) of any two conditions a and b. Thus a total of six new time series, D 12 , D 13 , D 14 , D 23 , D 24 , and D 34 were calculated. Each of the new time series was fit to an autoregressive integrated moving average (ARIMA) model, with three parameters (p, d, q). If the fitted three parameters were 0, 0, 0, it indicated the difference between the two time series a and b resembled white noise; hence, those two time series were assumed to be similar. Otherwise, the two time series were statistically different. Besides the dynamics of prevalence, we also investigated individual contribution to new infections by computing the Gini coefficient, a parameter that quantified the heterogeneity in the group of individuals for the new infection. A homogeneous population would have a zero Gini coefficient, while a more heterogeneous system would give a higher Gini coefficient.