Assessing biases in phylodynamic inferences in the presence of super-spreaders

• Published in: Veterinary research (Paris) Vol. 50; no. 1; p. 74
• Main Authors: Hidano, Arata, Gates, M Carolyn
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 27.09.2019; BioMed Central; BMC
• Subjects: Accuracy; Animal diseases; Animals; Computer Simulation; Disease transmission; Distribution; Epidemics; Epidemics - veterinary; Epidemiology; Evaluation; Foot and mouth disease; Foot-and-Mouth Disease - epidemiology; Foot-and-Mouth Disease - virology; Foot-and-Mouth Disease Virus - genetics; Gene mutation; Genetic aspects; Infectious diseases; Life Sciences; Livestock; Models, Statistical; New Zealand; Phylogenetics; Phylogeny; Population; Regression analysis; Research bias; Researchers; Sampling Studies; Simulation; Statistics; Studies; Veterinary research; Veterinary virology; New Zealand
• ISSN: 1297-9716; 0928-4249; 1297-9716
• DOI: 10.1186/s13567-019-0692-5

Phylodynamic analyses using pathogen genetic data have become popular for making epidemiological inferences. However, many methods assume that the underlying host population follows homogenous mixing patterns. Nevertheless, in real disease outbreaks, a small number of individuals infect a disproportionately large number of others (super-spreaders). Our objective was to quantify the degree of bias in estimating the epidemic starting date in the presence of super-spreaders using different sample selection strategies. We simulated 100 epidemics of a hypothetical pathogen (fast evolving foot and mouth disease virus-like) over a real livestock movement network allowing the genetic mutations in pathogen sequence. Genetic sequences were sampled serially over the epidemic, which were then used to estimate the epidemic starting date using Extended Bayesian Coalescent Skyline plot (EBSP) and Birth-death skyline plot (BDSKY) models. Our results showed that the degree of bias varies over different epidemic situations, with substantial overestimations on the epidemic duration occurring in some occasions. While the accuracy and precision of BDSKY were deteriorated when a super-spreader generated a larger proportion of secondary cases, those of EBSP were deteriorated when epidemics were shorter. The accuracies of the inference were similar irrespective of whether the analysis used all sampled sequences or only a subset of them, although the former required substantially longer computational times. When phylodynamic analyses need to be performed under a time constraint to inform policy makers, we suggest multiple phylodynamics models to be used simultaneously for a subset of data to ascertain the robustness of inferences.