scholarly journals Promoting the adoption of agent-based modelling for synergistic interventions and decision-making during pandemic outbreaks

2021 ◽  
Vol 2 ◽  
pp. 1-5
Author(s):  
P. Kyriakidis ◽  
D. Kavroudakis ◽  
P. Fayad ◽  
S. Hadjipetrou ◽  
G. Leventis ◽  
...  
Keyword(s):  
Decision Making ◽  
Human Mobility ◽  
Coupled Model ◽  
Disease Spread ◽  
Minimal Set ◽  
Human Environment ◽  
Agent Based ◽  
Contact Points ◽  
Home Work ◽  
Support Decision Making

Abstract. Geography has long sought to explain spatial relationships between social and physical processes, including the spread of infectious diseases, within the context of modelling human-environment interactions. The spread of the recent COVID-19 pandemic, and its devastating effects on human activity and welfare, represent but examples of such complex human-environment interactions. In this paper, we discuss the value of agent-based models for simulating the spread of the COVID-19 virus to support decision-making with regards to non-pharmaceutical interventions, e.g., lock-down. We also develop a prototype agent-based model using a minimal set of rules regarding patterns of human mobility within a hypothetical town, and couple that with an epidemiological model of infectious disease spread. The coupled model is used to: (a) create synthetic trajectories corresponding to daily and weekly activities postulated between a set of predefined points of interest (e.g., home, work), and (b) simulate new infections at contact points and their subsequent effects on the spread of the disease. We finally use the model simulations as a means of evaluating decisions regarding the number and type of activities to be limited during a planned lockdown in a COVID-19 pandemic context.

scholarly journals Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models

2019 ◽  
Vol 11 (4) ◽  
pp. 92 ◽  
Author(s):  
Jürgen Hackl ◽  
Thibaut Dubernet
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Human Mobility ◽  
Urban Setting ◽  
Disease Spread ◽  
Model Parameters ◽  
Epidemic Spreading ◽  
Agent Based ◽  
Spread Model ◽  
Spread Dynamics

Human mobility is a key element in the understanding of epidemic spreading. Thus, correctly modeling and quantifying human mobility is critical for studying large-scale spatial transmission of infectious diseases and improving epidemic control. In this study, a large-scale agent-based transport simulation (MATSim) is linked with a generic epidemic spread model to simulate the spread of communicable diseases in an urban environment. The use of an agent-based model allows reproduction of the real-world behavior of individuals’ daily path in an urban setting and allows the capture of interactions among them, in the form of a spatial-temporal social network. This model is used to study seasonal influenza outbreaks in the metropolitan area of Zurich, Switzerland. The observations of the agent-based models are compared with results from classical SIR models. The model presented is a prototype that can be used to analyze multiple scenarios in the case of a disease spread at an urban scale, considering variations of different model parameters settings. The results of this simulation can help to improve comprehension of the disease spread dynamics and to take better steps towards the prevention and control of an epidemic.

scholarly journals A Statistical Examination of Distinct Characteristics Influencing the Performance of Vector-Borne Epidemiological Agent-Based Simulation Models

Modelling ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 166-196
Author(s):  
Anna Paula Galvão Scheidegger ◽  
Henrique dos Santos Maxir ◽  
Amarnath Banerjee
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Human Behavior ◽  
Simulation Models ◽  
Disease Spread ◽  
Epidemiological Models ◽  
Agent Based Simulation ◽  
Single Strain ◽  
Agent Based ◽  
Vector Borne

The spread of infectious diseases is a complex system in which pathogens, humans, the environment, and sometimes vectors interact. Mathematical and simulation modelling is a suitable approach to investigate the dynamics of such complex systems. The 2019 novel coronavirus (COVID-19) pandemic reinforced the importance of agent-based simulation models to quickly and accurately provide information about the disease spread that would be otherwise hard or risky to obtain, and how this information can be used to support infectious disease control decisions. Due to the trade-offs between complexity, time, and accuracy, many assumptions are frequently made in epidemiological models. With respect to vector-borne diseases, these assumptions lead to epidemiological models that are usually bounded to single-strain and single-vector scenarios, where human behavior is modeled in a simplistic manner or ignored, and where data quality is usually not evaluated. In order to leverage these models from theoretical tools to decision-making support tools, it is important to understand how information quality, human behavior, multi-vector, and multi-strain affect the results. For this, an agent-based simulation model with different parameter values and different scenarios was considered. Its results were compared with the results of a traditional compartmental model with respect to three outputs: total number of infected individuals, duration of the epidemic, and number of epidemic waves. Paired t-test showed that, in most cases, data quality, human behavior, multi-vector, and multi-strain were characteristics that lead to statistically different results, while the computational costs to consider them were not high. Therefore, these characteristics should be investigated in more detail and be accounted for in epidemiological models in order to obtain more reliable results that can assist the decision-making process during epidemics.

scholarly journals AN OVERVIEW OF AGENT-BASED MODELLING APPROACHES FOR INTEGRATED FLOOD MANAGEMENT

2021 ◽  
Vol 6 (24) ◽  
pp. 290-300
Author(s):  
Ling Sie Chiew ◽  
Shahabuddin Amerudin ◽  
Zainab Mohamed Yusof
Keyword(s):  
Decision Making ◽  
Flood Risk ◽  
Flood Management ◽  
Computational Method ◽  
Dynamic Responses ◽  
Human Environment ◽  
Agent Based ◽  
Autonomous Decision ◽  
Spatial Environment ◽  
Risk Assessment And Management

Previously, Integrated Flood Management (IFM) system has been implemented by several hydrological researchers in order to minimize the global flood risk by providing a convincing flood risk assessment and management, as well as sustainable adaptation and disaster alleviation policy. Flood risk is dynamic interaction between natural disasters and human vulnerability. Basically, methods for quantifying flood risk are fully-fledged but tend to treat artificial and economic vulnerabilities as static or subject to changes in external trends. However, interpretive research is rarely conducted to investigate people’s decision-making and acknowledge to flood warnings during flood event. The integration of Agent-Based Model (ABM) in simulating the interactions and dynamic responses of individual or organizations in a spatial environment during the flood events or prior to the events were reviewed. The ABM model is defined as a computational method used to simulate the behaviour and the interaction of autonomous decision-making entities in a network or system it is used to evaluate their impact on the entire system. Therefore, the ABM approach has been chosen to emulate the complexity of the IFM process due to its capability and flexibility to simulate the dynamic of human-environment scenarios in the spatial environment.

Mobility modelling for simulation of spatial spread of infectious diseases

2021 ◽  
Author(s):  
Krzysztof Knop ◽  
Kamil Smolak ◽  
Barbara Kasieczka ◽  
Witold Rohm ◽  
Tomasz Smolarczyk ◽  
...  
Keyword(s):  
Public Health ◽  
Temporal Resolution ◽  
Human Mobility ◽  
Virus Transmission ◽  
Mobility Model ◽  
Health Policies ◽  
Disease Spread ◽  
Time Step ◽  
Agent Based ◽  
Public Health Policies

<p>The COVID-19 pandemic has highlighted the importance of public health policies and crisis management. The spread of diseases is a complex phenomenon with many time-dependent variables, which hampers an accurate prediction of epidemic evolution. Models of epidemic spread play an important role in guiding in designing public health policies, enabling hypothetical scenarios simulation and rapid analyses of ongoing epidemics.</p><p>Over the last century disease spread models evolved from deterministic compartmental models into complex metapopulation and agent-based simulations. Today’s solutions consider many factors, not limiting to the disease itself but also simulating socio-demographic structure and population flows. In the era of globalisation, human mobility became the major factor of rapid disease spread. Although current models consider international and regional travels, used mobility models are simplistic. This limits the accuracy and spatio-temporal resolution of these simulations, providing daily cases updates aggregated to large regions.</p><p>We propose an agent-based mobility model, offering a simulation of hourly temporal resolution depicting mobility with less than a few hundreds of meters spatial precision. Agent-based models allow each simulation agent to assign different characteristics, e.g. susceptibility to infection, mobility behaviour.</p><p>We integrate our mobility model with disease spread simulation, using an agent’s interaction to detect virus transmission. In every time step of the model, the interaction between the agents, their current state and localisation of interaction are used to determine the probability of infection. Social interactions in the context of the spread of the disease are a fundamental element influencing the temporal and spatial extent of the disease. An essential aspect of our model is the integration of the simulation environment with the points-of-interests (POIs), which represent the destination of the majority of non-home-work related activities. We validate the accuracy of mobility replication and present hypothetical scenarios of disease spread in one of the large European cities, presenting capabilities of our solution.</p>

Linking MODFLOW with an Agent-Based Land-Use Model to Support Decision Making

Ground Water ◽  
2010 ◽  
Vol 48 (5) ◽  
pp. 649-660 ◽  
Author(s):  
Howard W. Reeves ◽  
Moira L. Zellner
Keyword(s):  
Decision Making ◽  
Land Use ◽  
Agent Based ◽  
Land Use Model ◽  
Support Decision Making

Agent-based modeling of emergency evacuation in a railway station square under sarin terrorist attack

2016 ◽  
Vol 08 (02) ◽  
pp. 1750022 ◽  
Author(s):  
Liang Ma ◽  
Bin Chen ◽  
Sihang Qiu ◽  
Zhen Li ◽  
Xiaogang Qiu
Keyword(s):  
Decision Making ◽  
Terrorist Attack ◽  
Emergency Evacuation ◽  
Railway Station ◽  
Evacuation Modeling ◽  
Agent Based ◽  
Proposed Model ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Support Decision Making

Evacuation modeling is a promising measure to support decision making in scenarios such as flooding, explosion, terrorist attack and other emergency incidents. Given the special attention to the terrorist attack, we build up an agent-based evacuation model in a railway station square under sarin terrorist attack to analyze such incident. Sarin dispersion process is described by Gaussian puff model. Due to sarin’s special properties of being colorless and odorless, we focus more on the modeling of agents’ perceiving and reasoning process and use a Belief, Desire, Intention (BDI) architecture to solve the problem. Another contribution of our work is that we put forward a path planning algorithm which not only take distance but also comfort and threat factors into consideration. A series of simulation experiments demonstrate the ability of the proposed model and examine some crucial factors in sarin terrorist attack evacuation. Though far from perfect, the proposed model could serve to support decision making.

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