A hybrid gravity and route choice model to assess vector traffic in large-scale road networks

Fischer, Samuel M.; Beck, Martina; Herborg, Leif-Matthias; Lewis, Mark A.

doi:10.1098/rsos.191858

Cited by 8 publications

(10 citation statements)

References 37 publications

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“…The trip counts were highest between subbasins close to major cities, as anglers show a strong preference for fishing destinations near their homes. This agrees well with earlier studies (Drake & Mandrak, 2010;Papenfuss et al, 2015;Fischer et al, 2020). At the same time, we found that the subbasins' attractiveness to anglers varied strongly, emphasizing the need for considering covariates capturing this heterogeneity when estimating angler traffic.…”

Section: Discussionsupporting

confidence: 92%

“…Without this distinction (e.g. Drake & Mandrak, 2010;, residuals would be solely attributed to the stochasticity in the individuals' decisions, and the dispersion parameter would be overestimated (Fischer et al, 2020). Then, low-traffic angler flows gain an inordinate weight when the model is fitted to observations, resulting in decreased model accuracy (see SI F).…”

Section: Discussionmentioning

confidence: 99%

“…geographical and socio-economic data, models can provide detailed estimates of vector traffic on broad scales and may even allow insights into the mechanisms behind traffic patterns. In the past, vector traffic models have been fitted to data collected via mail-out surveys (Potapov et al, 2010;Chivers & Leung, 2012;Drake & Mandrak, 2014), roadside traffic surveys (Fischer et al, 2020), on-site surveys at origins and destinations (Leung et al, 2004;Bossenbroek et al, 2007), or registration records from origins and destinations (Bossenbroek et al, 2001;Prasad et al, 2010). However, since gathering data via these methods is often costly and the data may represent specific locations and time frames only, app data are a promising alternative resource for fitting vector traffic models.…”

Section: Introductionmentioning

confidence: 99%

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Boosting propagule transport models with individual-specific data from mobile apps

Fischer¹,

Ramazi²,

Simmons³

et al. 2021

Preprint

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1. Management of invasive species and pathogens requires information about the traffic of potential vectors. Such information is often taken from vector traffic models fitted to survey data. Here, user-specific data collected via mobile apps offer new opportunities to obtain more accurate estimates and to analyze how vectors' individual preferences affect propagule flows. However, data voluntarily reported via apps may lack some trip records, adding a significant layer of uncertainty.We show how the benefits of app-based data can be exploited despite this drawback.2. Based on data collected via an angler app, we built a stochastic model for angler traffic in the Canadian province Alberta. There, anglers facilitate the spread of whirling disease, a parasiteinduced fish disease. The model is temporally and spatially explicit and accounts for individual preferences and repeating behaviour of anglers, helping to address the problem of missing trip records.3. We obtained estimates of angler traffic between all subbasins in Alberta. The model's accuracy exceeds that of direct empirical estimates even when fewer data were used to fit the model. The results indicate that anglers' local preferences and their tendency to revisit previous destinations reduce the number of long inter-waterbody trips potentially dispersing whirling disease. According to our model, anglers revisit their previous destination in 64% of their trips, making these trips 1

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Boosting propagule transport models with individual-specific data from mobile apps

Fischer¹,

Ramazi²,

Simmons³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, libraries for automatic differentiation are widely available and easy to use (e.g. Albertsen et al 2015), making RVM applicable to complex models (see Fischer et al 2020, for such an application of RVM). 3.…”

Section: Discussionmentioning

confidence: 99%

A robust and efficient algorithm to find profile likelihood confidence intervals

Fischer

Lewis

2021

Stat Comput

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Profile likelihood confidence intervals are a robust alternative to Wald’s method if the asymptotic properties of the maximum likelihood estimator are not met. However, the constrained optimization problem defining profile likelihood confidence intervals can be difficult to solve in these situations, because the likelihood function may exhibit unfavorable properties. As a result, existing methods may be inefficient and yield misleading results. In this paper, we address this problem by computing profile likelihood confidence intervals via a trust-region approach, where steps computed based on local approximations are constrained to regions where these approximations are sufficiently precise. As our algorithm also accounts for numerical issues arising if the likelihood function is strongly non-linear or parameters are not estimable, the method is applicable in many scenarios where earlier approaches are shown to be unreliable. To demonstrate its potential in applications, we apply our algorithm to benchmark problems and compare it with 6 existing approaches to compute profile likelihood confidence intervals. Our algorithm consistently achieved higher success rates than any competitor while also being among the quickest methods. As our algorithm can be applied to compute both confidence intervals of parameters and model predictions, it is useful in a wide range of scenarios.

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“…To define an appropriate function to compute i and a j based on the covariates, we categorized the covariates into groups  (Table 1), each accounting for a different component that is necessary for high angler traffic between an origin and a destination (cf. Fischer et al, 2020). For each origin or destination k, we assigned the score…”

Section: Submodels For Day Suitability Angler Activeness and Destinat...mentioning

confidence: 99%

Boosting propagule transport models with individual‐specific data from mobile apps

Fischer

Ramazi

Simmons³

et al. 2023

Journal of Applied Ecology

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Management of invasive species and pathogens requires information about the traffic of potential vectors. Such information is often taken from vector traffic models fitted to survey data. Here, user‐specific data collected via mobile apps offer new opportunities to obtain more accurate estimates and to analyse how vectors' individual preferences affect propagule flows. However, data voluntarily reported via apps may lack some trip records, adding a significant layer of uncertainty. We show how the benefits of app‐based data can be exploited despite this drawback. Based on data collected via an angler app, we built a stochastic model for angler traffic in the Canadian province Alberta. There, anglers facilitate the spread of whirling disease, a parasite‐induced fish disease. The model is temporally and spatially explicit and accounts for individual preferences and repeating behaviour of anglers, helping to address the problem of missing trip records. We obtained estimates of angler traffic between all subbasins in Alberta. The model's accuracy exceeds that of direct empirical estimates even when fewer data were used to fit the model. The results indicate that anglers' local preferences and their tendency to revisit previous destinations reduce the number of long interwaterbody trips potentially dispersing whirling disease. According to our model, anglers revisit their previous destination in 64% of their trips, making these trips irrelevant for the spread of whirling disease. Furthermore, 54% of fishing trips end in individual‐specific spatially contained areas with mean radius of 54.7 km. Finally, although the fraction of trips that anglers report was unknown, we were able to estimate the total yearly number of fishing trips in Alberta, matching an independent empirical estimate. Policy implications. We make two major contributions: (1) we provide a model that uses mobile app data to boost the mechanistic accuracy of classic propagule transport models, and (2) we demonstrate the importance of individual‐specific behaviour of vectors for propagule transport. Ignoring vectors' local preferences and their tendency to revisit previous destinations can lead to significant overestimates of vector traffic and biased estimates of propagule flows. This has clear implications for the management of invasive species and animal diseases.

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A hybrid gravity and route choice model to assess vector traffic in large-scale road networks

Cited by 8 publications

References 37 publications

Boosting propagule transport models with individual-specific data from mobile apps

Boosting propagule transport models with individual-specific data from mobile apps

A robust and efficient algorithm to find profile likelihood confidence intervals

Boosting propagule transport models with individual‐specific data from mobile apps

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