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Due to the COVID-19 pandemic various cross-border policies such as mandatory testing, quarantining upon arrival, travel restrictions have been implemented by the countries to minimize the risk of disease import. The strictness of these measures has varied over time. The aim of this study is twofold. First, we develop a spatially explicit SIR-type compartmental model to evaluate the impact of cross-border mobility between two countries on the disease dynamics under different epidemic conditions. We see a significant change in the time to reach the peak of infection if the cross-border mobility is permitted during disease outbreak. Moreover, our study shows that allowing cross-border mobility shortens the difference in peak timings in two countries in comparison to the scenario with no cross-border flux. Next, we present a method using stochastic simulation for estimating cross-border mobility flux between two regions from the difference in peak-timing in infection under some reasonable assumptions.

Retirement Home facilities have been widely affected by the COVID-19 pandemic. The residents in these homes are usually elderly people with a high risk of mortality from being infected. Since they are in contact with each other, once an infection arrives at the facility, it propagates quickly. To prevent the outbreaks, it has been demonstrated that regular testing of the residents is the most practical approach. However, testing may result in extra time for the staff that performs the test as well as residents' discontent, which presents a trade-off between the time invested in testing, daily caring activities, and viral spread containment. We introduce a novel optimization approach for testing schedule strategies in retirement homes. We develop a mixed-integer linear programming model for balancing the staff’s workload while minimizing the expected detection time of a probable infection inside the facility. We present a probabilistic approach in conjunction with the optimization models to compute the risk of infection, including contact rates, incidence status, and the probability of infection of the residents. To tackle the combinatorial nature of the problem, we proved an efficient property, called symmetry property of optimal testing strategy and utilized it in proposing an enhanced local search algorithm. We perform several experiments with real-size instances and show that the proposed approach can derive optimal testing strategies.

Countries all over the world have implemented various cross-border policies such as mandatory testing, quarantining upon arrival, and travel restrictions to minimize the risk of infection. The strength of these measures has varied over time. The aim of this study is twofold. First, we develop a spatially explicit SIR-type mechanistic model to assess the epidemiological consequences of allowing cross-border mobility between two countries under different epidemic conditions. We show that the time to achieve the peak of infection is significantly changed if cross-border mobility is allowed during disease outbreak. Moreover, if we compare between the scenarios with and without cross-border flux, the difference in peak timings in two countries is reduced in the latter case. Next, based on stochastic simulation, we present a method for estimating cross-border mobility flux between two regions from the difference in peak-timing in infection under some reasonable assumptions. As a case study, we apply the method to data from the Germany-Poland border region and quantify heterogeneity in cross-border fluxes along the border during the COVID-19 pandemic.

Additional file 2: Details on the R scripts used to generate the results presented in the main text.

Codes for "Optimal workplace occupancy strategies during the COVID-19 pandemic"

River basins across the world are shaped by local land topography and generally have a dendritic structure formed by convergence of river streams originating in a watershed until they end up in the main river. These river basins are also home to a plethora of aquatic lifeforms. Movement patterns of riverine biodiversity, especially fishes, are shaped by dendritic structure of river networks (see Figure 1) and habitat capacity of river basins. The ongoing river networks project at CASUS is specifically aimed at developing models to study the effects of dendritic network topology on fish biodiversity and thereby be able to predict biodiversity patterns across various river basins. Starting with an initial distribution of fish species on the river network, we explore how the biodiversity patterns, such as local species richness (LSR), in dendritic river networks evolve with time, under the assumption of species being equivalent on a per capita basis. Such neutral biodiversity models have been able to successfully explain a suite of biodiversity indices of plant and animal species across various ecological systems. In summary, the river project aims to bring together the neutral biodiversity theory and the framework of dispersal over networks to make predictions on biodiversity in riverine systems across the world. This would enable understanding the factors shaping present biodiversity and allow us to explore how climate change might affect future riverine biodiversity.

Comparing the non-pharmaceutical intervention (NPI) strategies different nations employed to combat COVID-19 is a key step in preparing for future pandemics. Conventional approaches to this problem focus on identifying and ranking individual NPI effects. These efforts are complicated by vastly different political, economic, and social conditions among nations, which we refer to collectively as national framework conditions (NFCs). Furthermore, NPIs are typically applied as packages of interventions, which makes identifying their independent effects challenging. In addition, conventional approaches to studying NPI effects frequently neglect the economic and social consequences of these measures. Here, we introduce a novel, scenario-based approach to understanding NPI effects across nations. Our method couples simple epidemiological, behavioral, and economic models, and allows us to transfer NPI strategies from a reference nation to a focal nation while preserving the packaged nature of NPIs and controlling for NFCs. We conclude by considering future extensions to our framework and discussing its potential to facilitate NPI inter-comparisons worldwide.

Algorithm for computing the optimal testing strategy and reproducing the figures.

The study of habitat selection is a foundational component of basic and applied animal ecology. Today, habitat selection in mammals is primarily studied using resource selection functions, a class of models that uses logistic regression to compare “used” to “available” habitat. However, these models have several statistical problems, including rampant pseudoreplication from failing to account for autocorrelation in modern animal movement data, no clear guidelines for sampling available habitat, and large amounts of numerical error from sampling too few available points. These problems are widely acknowledged but have no generally accepted solutions, so we propose three new methods for addressing them: likelihood weighting, Gaussian availability sampling, and numerical convergence checks. We demonstrate the practical advantages of these methods over conventional approaches using simulations and empirical data on a water mongoose (Atilax paludinosus), a caracal (Caracal caracal), and a serval (Leptailurus serval), and briefly demonstrate how to apply our methods to animal tracking data using the ‘ctmm’ R package. Broad uptake of these methods could substantially improve our estimates of habitat selection in mammals.

Data and code that can be used to reproduce the analyses underlying 'Mitigating pseudoreplication and bias in resource selection functions with autocorrelation-informed weighting' by Alston, Fleming, et al. (Preprint: https://doi.org/10.1101/2022.04.21.489059) For more detailed information, please visit the README file.

A flow map is a common visualization technique displaying the significant trends and changes in data over a given spatial and temporal domain. While most flow maps are constructed using trajectory datasets as input, only a few research initiatives focus on producing flow maps from other data forms. This paper uses the Earth Mover's Distance to extract flows from geospatial data in two consecutive temporal snapshots. We further perform a generalization of the flows to avoid visual cluttering. To demonstrate the proposed approach, we provide an interactive web application where the user can see the pre-calculated flows extracted from the 2021 COVID incidence data in the regions of Germany, Poland, and Czechia.

Codes for reproducing the results in the research article "Estimating cross-border mobility from the difference in peak-timing: A case study in Poland-Germany border regions"

Estimating animal home ranges is a primary purpose of collecting tracking data. Many widely used home range estimators, including conventional kernel density estimators, assume independently sampled data. In stark contrast, modern GPS animal tracking datasets are almost always strongly autocorrelated. The incongruence between estimator assumptions and empirical reality often leads to systematically underestimated home ranges. Autocorrelated kernel density estimation (AKDE) directly models the observed autocorrelation structure of tracking data during home range estimation, and has been shown to perform accurately across a broad range of tracking datasets. However, compared to conventional estimators, AKDE requires additional modeling steps and has heretofore only been accessible via the command-line ctmm R package. Here, we introduce ctmmweb, which provides a point-and-click graphical interface to ctmm and streamlines AKDE, its prerequisite autocorrelation modeling steps, and a number of additional movement analyses. We demonstrate ctmmweb’s capabilities, including AKDE home range estimation and subsequent home range overlap analysis, on a dataset of four jaguars from the Brazilian Pantanal. We intend ctmmweb to open AKDE and related autocorrelation-explicit analyses to a wider audience of wildlife and conservation professionals.

Data from: Estimating encounter location distributions from animal tracking data

1. Modern tracking devices allow for the collection of high-volume animal tracking data at improved sampling rates over VHF radiotelemetry. Home range estimation is a key output from these tracking datasets, but the inherent properties of animal movement can lead traditional statistical methods to under- or overestimate home range areas. 2. The Autocorrelated Kernel Density Estimation (AKDE) family of estimators were designed to be statistically efficient while explicitly dealing with the complexities of modern movement data: autocorrelation, small sample sizes, and missing or irregularly sampled data. Although each of these estimators has been described in separate technical papers, here we review how these estimators work and provide a user-friendly guide on how they may be combined to reduce multiple biases simultaneously. 3. We describe the magnitude of the improvements offered by these estimators and their impact on home range area estimates, using both empirical case studies and simulations, contrasting their computational costs. 4. Finally, we provide guidelines for researchers to choose among alternative estimators and an R script to facilitate the application and interpretation of AKDE home range estimates.

Autonomous vehicles (AV) are expected to play a key role in the future of transportation, introducing a disruptive yet potentially beneficial change for vehicle-wildlife interactions. However, this assumption has not been critically examined. Here, we introduce a new conceptual framework covering the intersection between AV technological innovation and wildlife conservation to reduce wildlife-vehicle collisions. We suggest future research within this framework to focus on developing robust warning systems and animal detection methods for AV systems, and to incorporate wildlife-vehicle interactions into decision-making algorithms. With large-scale deployment a looming reality, it is vital to incorporate conservation and sustainability into the societal, ethical, and legal implications of AV technology. We appeal for further debate and interdisciplinary collaborations between scientists, developers, and policymakers.

COVID-19 serves as an opportunity to apply scientific methodologies from various fields to better understand the nature of the spread. In this paper, we are specifically looking into the effect of the presence/absence of national borders on the spread of the disease. We work with the dataset aggregated from the seven-day incidence in the municipalities in Saxony, and Czechia. We further apply the regression model, where a metric to estimate the potential causality of incidence growth in each of two municipalities is dependent on the distance between them, their population ratio, and the existence of the national border. Our preliminary results showed that the effect of the borders is very strong in most of the study areas.

In this presentation, we provided an introduction to the animal movement research we conduct at CASUS.