title: Optimal sampling strategies for detecting zoonotic disease epidemics.
abstract: The early detection of disease epidemics reduces the chance of successful introductions into new locales, minimizes the number of infections, and reduces the financial impact. We develop a framework to determine the optimal sampling strategy for disease detection in zoonotic host-vector epidemiological systems when a disease goes from below detectable levels to an epidemic. We find that if the time of disease introduction is known then the optimal sampling strategy can switch abruptly between sampling only from the vector population to sampling only from the host population. We also construct time-independent optimal sampling strategies when conducting periodic sampling that can involve sampling both the host and the vector populations simultaneously. Both time-dependent and -independent solutions can be useful for sampling design, depending on whether the time of introduction of the disease is known or not. We illustrate the approach with West Nile virus, a globally-spreading zoonotic arbovirus. Though our analytical results are based on a linearization of the dynamical systems, the sampling rules appear robust over a wide range of parameter space when compared to nonlinear simulation models. Our results suggest some simple rules that can be used by practitioners when developing surveillance programs. These rules require knowledge of transition rates between epidemiological compartments, which population was initially infected, and of the cost per sample for serological tests.
authors: Department of Biology, University of Florida, Gainesville, Florida, United States of America.
authors: Jake M
authors: JM
authors: Ferguson
authors: Department of Mathematics, University of Florida, Gainesville, Florida, United States of America.
authors: Jessica B
authors: JB
authors: Langebrake
authors: Department of Biology, University of Florida, Gainesville, Florida, United States of America.
authors: Vincent L
authors: VL
authors: Cannataro
authors: Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America; Department of Geography, University of Florida, Gainesville, Florida, United States of America.
authors: Andres J
authors: AJ
authors: Garcia
authors: Department of Biology, University of Florida, Gainesville, Florida, United States of America.
authors: Elizabeth A
authors: EA
authors: Hamman
authors: Department of Mathematics, University of Florida, Gainesville, Florida, United States of America.
authors: Maia
authors: M
authors: Martcheva
authors: Department of Biology, University of Florida, Gainesville, Florida, United States of America.
authors: Craig W
authors: CW
authors: Osenberg
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D004199:Disease Vectors; D058872:Epidemics; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D011159:Population Surveillance; D014901:West Nile Fever / Q000453:epidemiology / Q000635:transmission; D014902:West Nile virus; D015047:Zoonoses / Q000453:epidemiology* / Q000635:transmission*