A probabilistic census-travel model to predict introduction sites of exotic plant, animal and human pathogens

Authors: Gottwald, Timothy; LUO, WEIQI - North Carolina State University; POSNEY, DREW - North Carolina State University; RILEY, TIM - US Department Of Agriculture (USDA); LOUWS, FRANK - North Carolina State University

Submitted to: Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/8/2019
Publication Date: 5/20/2019
Citation: Gottwald, T.R., Luo, W., Posney, D., Riley, T., Louws, F. 2019. A probabilistic census-travel model to predict introduction sites of exotic plant, animal and human pathogens. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 374(1776). https://doi.org/10.1098/rstb.2018.0260.
DOI: https://doi.org/10.1098/rstb.2018.0260

Interpretive Summary: International travel inadvertently provides a pathway for introductions of exotic infectious pathogens of plants animal and humans. Traditionally we react to these introductions after they have occurred. A better approach is to predict where such introductions will occur and use these predictions to develop survey programs to find the infections as early as possible. If we can find them early, we have the best chance of mitigating or even eradicating the pathogens before they reach a level where it is impossible. In this paper we present a model called the census travel model, that uses international travel and US census data to predict the most likely locations (within census tracts which are very similar to zip codes) where pathogens will be introduced. We provide a means to calculate the risk of introduction. We also show how to use this quantitative risk information to generate a detection survey. The census-travel model is versatile and independent of the type of pathogen. Thus it can be used for plant, animal and plant pathogens. We show examples of each using citrus huanglongbing, citrus canker, plum pox virus, Ebola virus and dengue virus as examples. We have also developed an online, interactive, user-friendly interface to run the model with various scenarios chosen by the user. The research was funded by USDA, APHIS and is intended to be used by regulatory agencies to explore potential pathogen introductions and develop surveys.

Technical Abstract: International travel offers an extensive network for new and recurring human-mediated introductions of exotic infectious pathogens and biota, freeing geographical constraints. We present a predictive census-travel model that integrates international travel with endpoint census data and epidemiological characteristics to predict points of introduction. Population demographics, inbound and outbound travel patterns, and quantification of source strength by country are combined to estimate and rank risk of introduction at user scalable land parcel areas (e.g., state, county, zip code, census tract, gridded landscapes [1-mi sq, 5-km sq, etc.]). This risk ranking by parcel can be used to develop pathogen surveillance programs, and has been incorporated in multiple U.S. state/federal surveillance protocols. The census-travel model is versatile and independent of pathosystems, and applies a risk algorithm to generate risk maps for plant, human, and animal contagions at different spatial scales. An interactive, user-friendly interface is available online (https://censustravel.shinyapps.io/Census_Travel/) to provide ease-of-use for regulatory agencies for early detection of high-risk exotics. The interface allows users to parameterize and run the model without knowledge of background code and underpinning data.