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

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 mi², 5 km², etc.)). This risk ranking by parcel can be used to develop pathogen surveillance programmes, and has been incorporated in multiple US 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://epi-models.shinyapps.io/Census_Travel/) to provide ease-of-use for regulatory agencies for early detection of high-risk exotics. The interface allows users to parametrize and run the model without knowledge of background code and underpinning data. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

Keywords: census-travel model; contagion; exotic; introductions.

Figure 1.

Schematic diagram to represent the conceptual pathway model for international disease spread from 176 potential pathogen-originating countries. The connectivity between known source countries, suspected neighbouring countries and the potential traveller endpoints with consideration of source strength, travel volume and endpoint demographics represents a simplification of the real global travel network structure. The census-travel model integrates all of these components to estimate risk of introduction.

Figure 2.

Census-travel model introduction risk for citrus HLB in Central and South Florida citrus-producing regions, comparing retrospective and recent risk estimates (note that HLB spread within/between USA is not explicitly considered). (a,d) Source CRIs for the top 10 countries of highest risk contribution in 2000 and 2010, respectively. Note, over time, Brazil emerged as the largest contributor of HLB risk of introduction for Florida. (b,e) Introduction risk estimates by census tract for 2000 and 2010. (c,f) Higher resolution metropolitan Miami demonstrating the shift in risk distribution for introduction points.

Figure 3.

Procedure for multiple pathogen/disease survey using census-travel model risk output. (a) Global disease distribution for citrus black spot, citrus HLB and citrus canker. (b–d) Individual risk maps for Florida introduction in 2010. (e) Expert opinion scores on suitability for spread, vector prevalence (if applicable), reproduction rate (R₀), detection and confirmation inefficiency, control cost(s) and yield reduction/crop damage for each disease (score ranking: 1, low; 5, high). (f) Combined overall risk map used for survey prioritization. For visualization, risks are summarized at 1 mi² to meet US regulatory agency survey implementation requirements.

Figure 4.

(a) Annual travel volume by year into the USA from West African countries, and (b) aggregated Ebola risk distribution by state (indicated by state abbreviation). Census-travel model output for 2015 Ebola (EVD) risk introduction predictions for the entire USA (c), Texas (d) and the Dallas metropolitan area (e) by land parcel (census tract) using the log transformation of number of cases from infected West African countries. Actual introduction site (apartment complex; green triangle and arrow) and attending hospital (blue circle and arrow).