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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. [ABSTRACT FROM AUTHOR]

Myxobolus cerebralis is the parasite causing whirling disease, which has dramatic ecological impacts due to its potential to cause high mortality in salmonids. The large-scale efforts, necessary to underpin an effective surveillance program, have practical and economic constraints. There is, hence, a clear need for models that can predict the parasite spread. Model development, however, often heavily depends on knowing influential variables and governing mechanisms. We have developed a graphical model for the establishment and spread of M. cerebralis by synthesizing experts' opinion and empirical studies. First, we conducted a series of workshops with experts to identify variables believed to impact the establishment and spread of the parasite M. cerebralis and visualized their interactions via a directed acyclic graph. Then we refined the graph by incorporating empirical findings from the literature. The final graph's nodes correspond to variables whose considerable impact on M. cerebralis establishment and spread is either supported by empirical data or confirmed by experts, and the graph's directed edges represent direct causality or strong correlation. This graphical model facilitates communication and education of whirling disease and provides an empirically driven framework for constructing future models, especially Bayesian networks. [ABSTRACT FROM AUTHOR]

Planning forest management relies on predicting insect outbreaks such as mountain pine beetle, particularly in the intermediate‐term future, e.g., 5‐year. Machine‐learning algorithms are potential solutions to this challenging problem due to their many successes across a variety of prediction tasks. However, there are many subtle challenges in applying them: identifying the best learning models and the best subset of available covariates (including time lags) and properly evaluating the models to avoid misleading performance‐measures. We systematically address these issues in predicting the chance of a mountain pine beetle outbreak in the Cypress Hills area and seek models with the best performance at predicting future 1‐, 3‐, 5‐ and 7‐year infestations. We train nine machine‐learning models, including two generalized boosted regression trees (GBM) that predict future 1‐ and 3‐year infestations with 92% and 88% AUC, and two novel mixed models that predict future 5‐ and 7‐year infestations with 86% and 84% AUC, respectively. We also consider forming the train and test datasets by splitting the original dataset randomly rather than using the appropriate year‐based approach and show that this may obtain models that score high on the test dataset but low in practice, resulting in inaccurate performance evaluations. For example, a k‐nearest neighbor model with the actual performance of 68% AUC, scores the misleadingly high 78% on a test dataset obtained from a random split, but the more accurate 66% on a year‐based split. We then investigate how the prediction accuracy varies with respect to the provided history length of the covariates and find that neural network and naive Bayes, predict more accurately as history‐length increases, particularly for future 1‐ and 3‐year predictions, and roughly the same holds with GBM. Our approach is applicable to other invasive species. The resulting predictors can be used in planning forest and pest management and planning sampling locations in field studies. [ABSTRACT FROM AUTHOR]

The need for improved models that can accurately predict COVID-19 dynamics is vital to managing the pandemic and its consequences. We use machine learning techniques to design an adaptive learner that, based on epidemiological data available at any given time, produces a model that accurately forecasts the number of reported COVID-19 deaths and cases in the United States, up to 10 weeks into the future with a mean absolute percentage error of 9%. In addition to being the most accurate long-range COVID predictor so far developed, it captures the observed periodicity in daily reported numbers. Its effectiveness is based on three design features: (1) producing different model parameters to predict the number of COVID deaths (and cases) from each time and for a given number of weeks into the future, (2) systematically searching over the available covariates and their historical values to find an effective combination, and (3) training the model using "last-fold partitioning", where each proposed model is validated on only the last instance of the training dataset, rather than being cross-validated. Assessments against many other published COVID predictors show that this predictor is 19–48% more accurate. [ABSTRACT FROM AUTHOR]

Perpetual fluctuations between decisions are reported as the dominant asymptotic outcome of imitative behaviours, yet little attempt has been made to characterize them, particularly in heterogeneous populations. We study a finite well-mixed heterogeneous population of individuals choosing between the two strategies, cooperation and defection , and earning based on their payoff matrices that can be unique to each individual. At each time step, an arbitrary individual becomes active to update her decision by imitating the highest earner in the population. We show that almost surely the dynamics reach either an equilibrium state or a non-singleton minimal positively invariant set, a fluctuation set , in the long run. In addition to finding all equilibria, we characterize the fluctuation sets, provide necessary conditions and sufficient conditions for their existence, and approximate their basins of attraction. Our results theoretically explain earlier reported simulation results and shed new light on the boundedly rational nature of imitation behaviours. [ABSTRACT FROM AUTHOR]

Motivated by practical applications in satellite formations and directional antenna arrays, the problem of targeting a planar array of point sources at one common object of interest is proposed and then solved using novel distributed coordination strategies. These point sources are arbitrarily located on the x – y plane and the boundary point sources, whose either x or y coordinate is extremal, already orient to the non-coplanar target point. The only global information shared among the remaining point sources is the positive directions of the global coordinate axes x and y , and hence, they have to rely on sensing the changes of the orientation angles of their nearest neighbors to adjust their own orientation. We prove that under our control law, the orientation lines will asymptotically intersect at the same point of concurrency as the boundary point sources. The main idea behind the designed control law is the intuitive argument from Euclidean geometry that reducing the differences between the distances to the x – y plane of the pairwise intersection points of the orientation lines leads to realizing concurrent targeting. We further show the boundedness and exponential convergence speed of the orientation angles. [ABSTRACT FROM AUTHOR]

Oscillations often take place in populations of decision makers that are either a coordinator , who takes action only if enough others do so, or an anticoordinator , who takes action only if few others do so. Populations consisting of exclusively one of these types are known to reach an equilibrium, where every individual is satisfied with her decision. Yet it remains unknown whether oscillations take place in a population consisting of both types, and if they do, what features they share. We study a well-mixed population of individuals, which are either a coordinator or anticoordinator, each associated with a possibly unique threshold and initialized with the strategy A or B. At each time, an agent becomes active to update her strategy based on her threshold: an active coordinator (resp. anticoordinator) updates her strategy to A (resp. B) if the portion of other agents who have chosen A exceeds (falls short of) her threshold, and updates to B (resp. A) otherwise. We define the state of the population dynamics as the distribution over the thresholds of those who have chosen A. We show that the population can admit several minimally positively invariant sets, where the solution trajectory oscillates. We explicitly characterize a class of positively invariant sets, prove their invariance, and provide a necessary and sufficient condition for their stability. Our results highlight the possibility of non-trivial, complex oscillations in the absence of noise and population structure and shed light on the reported oscillations in nature and human societies. [ABSTRACT FROM AUTHOR]

Imitation is widely observed in nature and often used to model populations of decision-making agents, but it is not yet known under what conditions a network of imitators will reach a state where they are satisfied with their decisions. We show that every network in which agents imitate the best performing strategy in their neighborhood will reach an equilibrium in finite time, provided that all agents are opponent coordinating , i.e., earn a higher payoff if their opponent plays the same strategy as they do. It follows that any non-convergence observed in imitative networks is not necessarily a result of population heterogeneity nor special network topology, but rather must be caused by other factors such as the presence of non-opponent-coordinating agents. To strengthen this result, we show that large classes of imitative networks containing non-opponent-coordinating agents never equilibrate even when the population is homogeneous. Comparing to best-response dynamics where equilibration is guaranteed for every network of homogeneous agents playing 2 × 2 matrix games, our results imply that networks of imitators have a lower equilibration tendency. [ABSTRACT FROM AUTHOR]

Understanding the joint impact of vaccination and non-pharmaceutical interventions on COVID-19 development is important for making public health decisions that control the pandemic. Recently, we created a method in forecasting the daily number of confirmed cases of infectious diseases by combining a mechanistic ordinary differential equation (ODE) model for infectious classes and a generalized boosting machine learning model (GBM) for predicting how public health policies and mobility data affect the transmission rate in the ODE model (Wang et al. in Bull Math Biol 84:57, 2022). In this paper, we extend the method to the post-vaccination period, accordingly obtain a retrospective forecast of COVID-19 daily confirmed cases in the US, and identify the relative influence of the policies used as the predictor variables. In particular, our ODE model contains both partially and fully vaccinated compartments and accounts for the breakthrough cases, that is, vaccinated individuals can still get infected. Our results indicate that the inclusion of data on non-pharmaceutical interventions can significantly improve the accuracy of the predictions. With the use of policy data, the model predicts the number of daily infected cases up to 35 days in the future, with an average mean absolute percentage error of 20.15 % , which is further improved to 14.88 % if combined with human mobility data. Moreover, the most influential predictor variables are the policies of restrictions on gatherings, testing and school closing. The modeling approach used in this work can help policymakers design control measures as variant strains threaten public health in the future. [ABSTRACT FROM AUTHOR]

Accurate prediction of the number of daily or weekly confirmed cases of COVID-19 is critical to the control of the pandemic. Existing mechanistic models nicely capture the disease dynamics. However, to forecast the future, they require the transmission rate to be known, limiting their prediction power. Typically, a hypothesis is made on the form of the transmission rate with respect to time. Yet the real form is too complex to be mechanistically modeled due to the unknown dynamics of many influential factors. We tackle this problem by using a hypothesis-free machine-learning algorithm to estimate the transmission rate from data on non-pharmaceutical policies, and in turn forecast the confirmed cases using a mechanistic disease model. More specifically, we build a hybrid model consisting of a mechanistic ordinary differential equation (ODE) model and a gradient boosting model (GBM). To calibrate the parameters, we develop an “inverse method” that obtains the transmission rate inversely from the other variables in the ODE model and then feed it into the GBM to connect with the policy data. The resulting model forecasted the number of daily confirmed cases up to 35 days in the future in the USA with an averaged mean absolute percentage error of 27%. It can identify the most informative predictive variables, which can be helpful in designing improved forecasters as well as informing policymakers. [ABSTRACT FROM AUTHOR]

In wastewater treatment plants (WWTPs), the stochastic nature of influent wastewater and operational and weather conditions cause fluctuations in effluent quality. Data-driven models can forecast effluent quality a few hours ahead as a response to the influent characteristics, providing enough time to adjust system operations and avoid undesired consequences. However, existing data for training models are often incomplete and contain missing values. On the other hand, collecting additional data by installing new sensors is costly. The trade-off between using existing incomplete data and collecting costly new data results in three data challenges faced when developing data-driven WWTP effluent forecasters. These challenges are to determine important variables to be measured, the minimum number of required data instances, and the maximum percentage of tolerable missing values that do not impede the development of an accurate model. As these issues are not discussed in previous studies, in this research, for the first time, a comprehensive analysis is done to provide answers to these challenges. Another issue that arises in all data-driven modeling is how to select an appropriate forecasting model. This paper addresses these issues by first testing nine machine learning models on data collected from three wastewater treatment plants located in Iran, Australia, and Spain. The most accurate forecaster, Bayesian network, was then used to address the articulated challenges. Key variables in forecasting effluent characteristics were flow rate, total suspended solids, electrical conductivity, phosphorus compounds, wastewater temperature, and air temperature. A minimum of 250 samples was needed during the model training to achieve a great reduction in the forecasting error. Moreover, a steep increase in the error was observed should the portion of missing values exceed 10%. The results assist plant managers in estimating the necessary data collection effort to obtain an accurate forecaster, contributing to the quality of the effluent. [Display omitted] • Bayesian network is benchmarked against eight AI models to predict wastewater quality. • Bayesian networks can handle missing value in input data due to probabilistic nature. • Key variables in WWTP quality and quantity were specified. • Input data quality has a huge role in successful development of data-driven models. • The prediction error sharply increases when the portion of missing data exceeds 10%. [ABSTRACT FROM AUTHOR]