scholarly journals An agent-based model that simulates the spatio-temporal dynamics of sources and transfer mechanisms contributing faecal indicator organisms to streams. Part 1: Background and model description

2020 ◽  
Vol 270 ◽  
pp. 110903 ◽  
Author(s):  
Aaron J. Neill ◽  
Doerthe Tetzlaff ◽  
Norval J.C. Strachan ◽  
Rupert L. Hough ◽  
Lisa M. Avery ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Model Description ◽  
Indicator Organisms ◽  
Agent Based Model ◽  
Agent Based ◽  
Spatio Temporal ◽  
Transfer Mechanisms

scholarly journals Temporal dynamics of viral load and false negative rate influence the levels of testing necessary to combat COVID-19 spread

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Katherine F. Jarvis ◽  
Joshua B. Kelley
Keyword(s):  
Viral Load ◽  
Temporal Dynamics ◽  
Reproduction Number ◽  
False Negative ◽  
False Negative Rate ◽  
Agent Based Model ◽  
Agent Based ◽  
Viral Spread ◽  
Negative Rate

AbstractColleges and other organizations are considering testing plans to return to operation as the COVID-19 pandemic continues. Pre-symptomatic spread and high false negative rates for testing may make it difficult to stop viral spread. Here, we develop a stochastic agent-based model of COVID-19 in a university sized population, considering the dynamics of both viral load and false negative rate of tests on the ability of testing to combat viral spread. Reported dynamics of SARS-CoV-2 can lead to an apparent false negative rate from ~ 17 to ~ 48%. Nonuniform distributions of viral load and false negative rate lead to higher requirements for frequency and fraction of population tested in order to bring the apparent Reproduction number (Rt) below 1. Thus, it is important to consider non-uniform dynamics of viral spread and false negative rate in order to model effective testing plans.

scholarly journals Simulating Spatio-Temporal Patterns of Bicycle Flows with an Agent-Based Model

2021 ◽  
Vol 10 (2) ◽  
pp. 88
Author(s):  
Dana Kaziyeva ◽  
Martin Loidl ◽  
Gudrun Wallentin
Keyword(s):  
Regional Scale ◽  
Evidence Base ◽  
Wide Field ◽  
Traffic Patterns ◽  
Agent Based Model ◽  
Validation Data ◽  
Agent Based ◽  
Mobility Behavior ◽  
Spatio Temporal ◽  
Models Validation

Transport planning strategies regard cycling promotion as a suitable means for tackling problems connected with motorized traffic such as limited space, congestion, and pollution. However, the evidence base for optimizing cycling promotion is weak in most cases, and information on bicycle patterns at a sufficient resolution is largely lacking. In this paper, we propose agent-based modeling to simulate bicycle traffic flows at a regional scale level for an entire day. The feasibility of the model is demonstrated in a use case in the Salzburg region, Austria. The simulation results in distinct spatio-temporal bicycle traffic patterns at high spatial (road segments) and temporal (minute) resolution. Scenario analysis positively assesses the model’s level of complexity, where the demographically parametrized behavior of cyclists outperforms stochastic null models. Validation with reference data from three sources shows a high correlation between simulated and observed bicycle traffic, where the predictive power is primarily related to the quality of the input and validation data. In conclusion, the implemented agent-based model successfully simulates bicycle patterns of 186,000 inhabitants within a reasonable time. This spatially explicit approach of modeling individual mobility behavior opens new opportunities for evidence-based planning and decision making in the wide field of cycling promotion

scholarly journals Constructing the Singular Roesser State-Space Model Description of 3D Spatio-Temporal Dynamics From the Polynomial System Matrix

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 45632-45641
Author(s):  
Mohamed S. Boudellioua ◽  
Bartlomiej Sulikowski ◽  
Krzysztof Galkowski ◽  
Eric Rogers
Keyword(s):  
State Space ◽  
Temporal Dynamics ◽  
Polynomial System ◽  
State Space Model ◽  
Model Description ◽  
System Matrix ◽  
Space Model ◽  
Spatio Temporal

scholarly journals A Conceptual Design of Spatio-Temporal Agent-Based Model for Volcanic Evacuation

Systems ◽  
2017 ◽  
Vol 5 (4) ◽  
pp. 53 ◽  
Author(s):  
Jumadi ◽  
Steve Carver ◽  
Duncan Quincey
Keyword(s):  
Conceptual Design ◽  
Agent Based Model ◽  
Agent Based ◽  
Spatio Temporal

scholarly journals Coalescent models for developmental biology and the spatio-temporal dynamics of growing tissues

2016 ◽  
Vol 13 (117) ◽  
pp. 20160112 ◽  
Author(s):  
Patrick Smadbeck ◽  
Michael P. H. Stumpf
Keyword(s):  
Developmental Biology ◽  
Large Scale ◽  
Cell Tracking ◽  
Temporal Dynamics ◽  
Growth Models ◽  
Lineage Tracing ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Spatio Temporal ◽  
Large Scale Simulations

Development is a process that needs to be tightly coordinated in both space and time. Cell tracking and lineage tracing have become important experimental techniques in developmental biology and allow us to map the fate of cells and their progeny. A generic feature of developing and homeostatic tissues that these analyses have revealed is that relatively few cells give rise to the bulk of the cells in a tissue; the lineages of most cells come to an end quickly. Computational and theoretical biologists/physicists have, in response, developed a range of modelling approaches, most notably agent-based modelling. These models seem to capture features observed in experiments, but can also become computationally expensive. Here, we develop complementary genealogical models of tissue development that trace the ancestry of cells in a tissue back to their most recent common ancestors. We show that with both bounded and unbounded growth simple, but universal scaling relationships allow us to connect coalescent theory with the fractal growth models extensively used in developmental biology. Using our genealogical perspective, it is possible to study bulk statistical properties of the processes that give rise to tissues of cells, without the need for large-scale simulations.

scholarly journals Integrating human behavior and snake ecology with agent-based models to predict snakebite in high risk landscapes

2021 ◽  
Vol 15 (1) ◽  
pp. e0009047
Author(s):  
Eyal Goldstein ◽  
Joseph J. Erinjery ◽  
Gerardo Martin ◽  
Anuradhani Kasturiratne ◽  
Dileepa Senajith Ediriweera ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Ecological Factors ◽  
Epidemiological Studies ◽  
Decision Makers ◽  
Agent Based ◽  
Snake Species ◽  
Relative Contribution ◽  
As Species ◽  
Spatio Temporal ◽  
The Tropics

Snakebite causes more than 1.8 million envenoming cases annually and is a major cause of death in the tropics especially for poor farmers. While both social and ecological factors influence the chance encounter between snakes and people, the spatio-temporal processes underlying snakebites remain poorly explored. Previous research has focused on statistical correlates between snakebites and ecological, sociological, or environmental factors, but the human and snake behavioral patterns that drive the spatio-temporal process have not yet been integrated into a single model. Here we use a bottom-up simulation approach using agent-based modelling (ABM) parameterized with datasets from Sri Lanka, a snakebite hotspot, to characterise the mechanisms of snakebite and identify risk factors. Spatio-temporal dynamics of snakebite risks are examined through the model incorporating six snake species and three farmer types (rice, tea, and rubber). We find that snakebites are mainly climatically driven, but the risks also depend on farmer types due to working schedules as well as species present in landscapes. Snake species are differentiated by both distribution and by habitat preference, and farmers are differentiated by working patterns that are climatically driven, and the combination of these factors leads to unique encounter rates for different landcover types as well as locations. Validation using epidemiological studies demonstrated that our model can explain observed patterns, including temporal patterns of snakebite incidence, and relative contribution of bites by each snake species. Our predictions can be used to generate hypotheses and inform future studies and decision makers. Additionally, our model is transferable to other locations with high snakebite burden as well.

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