Cite
A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States.
MLA
Stella C Watson, et al. “A Bayesian Spatio-Temporal Model for Forecasting the Prevalence of Antibodies to Borrelia Burgdorferi, Causative Agent of Lyme Disease, in Domestic Dogs within the Contiguous United States.” PLoS ONE, vol. 12, no. 5, Jan. 2017, p. e0174428. EBSCOhost, https://doi.org/10.1371/journal.pone.0174428.
APA
Stella C Watson, Yan Liu, Robert B Lund, Jenna R Gettings, Shila K Nordone, Christopher S McMahan, & Michael J Yabsley. (2017). A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States. PLoS ONE, 12(5), e0174428. https://doi.org/10.1371/journal.pone.0174428
Chicago
Stella C Watson, Yan Liu, Robert B Lund, Jenna R Gettings, Shila K Nordone, Christopher S McMahan, and Michael J Yabsley. 2017. “A Bayesian Spatio-Temporal Model for Forecasting the Prevalence of Antibodies to Borrelia Burgdorferi, Causative Agent of Lyme Disease, in Domestic Dogs within the Contiguous United States.” PLoS ONE 12 (5): e0174428. doi:10.1371/journal.pone.0174428.