scholarly journals Modelling aerosol-based exposure to SARS-CoV-2 by an agent based Monte Carlo method: Risk estimates in a shop and bar

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260237
Author(s):  
Henri Salmenjoki ◽  
Marko Korhonen ◽  
Antti Puisto ◽  
Ville Vuorinen ◽  
Mikko J. Alava
Keyword(s):  
High Risk ◽  
Quantitative Difference ◽  
Equilibrium Concentration ◽  
Infection Risk ◽  
Space Time ◽  
Aerosol Concentration ◽  
Model Parameters ◽  
Single Individual ◽  
Agent Based ◽  
Aerosol Inhalation

Present day risk assessment on the spreading of airborne viruses is often based on the classical Wells-Riley model assuming immediate mixing of the aerosol into the studied environment. Here, we improve on this approach and the underlying assumptions by modeling the space-time dependency of the aerosol concentration via a transport equation with a dynamic source term introduced by the infected individual(s). In the present agent-based methodology, we study the viral aerosol inhalation exposure risk in two scenarios including a low/high risk scenario of a “supermarket”/“bar”. The model takes into account typical behavioral patterns for determining the rules of motion for the agents. We solve a diffusion model for aerosol concentration in the prescribed environments in order to account for local exposure to aerosol inhalation. We assess the infection risk using the Wells-Riley model formula using a space-time dependent aerosol concentration. The results are compared against the classical Wells-Riley model. The results indicate features that explain individual cases of high risk with repeated sampling of a heterogeneous environment occupied by non-equilibrium concentration clouds. An example is the relative frequency of cases that might be called superspreading events depending on the model parameters. A simple interpretation is that averages of infection risk are often misleading. They also point out and explain the qualitative and quantitative difference between the two cases—shopping is typically safer for a single individual person.

Adapting Gravity Model for Predicting the Risk of COVID -19 in Nigeria

Coronaviruses ◽  
2020 ◽  
Vol 01 ◽  
Author(s):  
Olanrewaju Samson Olaitan ◽  
Olowoporoku Oluwaseun
Keyword(s):  
High Risk ◽  
Gross Domestic Product ◽  
Gravity Model ◽  
Infection Risk ◽  
Risk Level ◽  
Ogun State ◽  
Factors Influencing ◽  
Government Data ◽  
Housing Standards ◽  
Very High

Background: It is against the background of the emerging incidence of coronavirus pandemic in Nigeria, and the need for its management that this study adapts gravity model for predicting the risk of the disease across states of the country. Methods: The paper relied on published government data on population, and gross domestic product, while the distance of town to the nearest international airport was also obtained. These data were log transformed and further used in the calculation of gravity scores for each state of the federation. Results: The study discovered that with the gravity score ranging from 2.942 to 4.437, all the states of the federation have the risk of being infected with the pandemic. Meanwhile Ogun State (4.837) has a very high risk of being infected with the disease. Other states with high risks are Oyo (4.312), Jigawa (4.235), Niger (4.148) and Katsina (4.083). However, Taraba State has the least infection risk of the pandemic in Nigeria. Factors influencing the risk level of the pandemic are proximity, porous boundary between states, and elitism. Conclusion: The paper advocates border settlement planning, review of housing standards, and advocacy for sanitation in different states. It therefore concludes that adequate urban planning in unison with economic and epidemiology techniques will provide a strong strategy for the management of the disease.

scholarly journals Analysis of Cooperative Perception in Ant Traffic and Its Effects on Transportation System by Using a Congestion-Free Ant-Trail Model

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2393
Author(s):  
Prafull Kasture ◽  
Hidekazu Nishimura
Keyword(s):  
Average Velocity ◽  
Transportation System ◽  
Self Organization ◽  
Model Parameters ◽  
Agent Based ◽  
Model Simulations ◽  
Mathematical Representations ◽  
Traffic Conditions ◽  
Traffic Jams ◽  
Ant Trail

We investigated agent-based model simulations that mimic an ant transportation system to analyze the cooperative perception and communication in the system. On a trail, ants use cooperative perception through chemotaxis to maintain a constant average velocity irrespective of their density, thereby avoiding traffic jams. Using model simulations and approximate mathematical representations, we analyzed various aspects of the communication system and their effects on cooperative perception in ant traffic. Based on the analysis, insights about the cooperative perception of ants which facilitate decentralized self-organization is presented. We also present values of communication-parameters in ant traffic, where the system conveys traffic conditions to individual ants, which ants use to self-organize and avoid traffic-jams. The mathematical analysis also verifies our findings and provides a better understanding of various model parameters leading to model improvements.

scholarly journals Is the Green Wave Really Green? The Risks of Rebound Effects When Implementing “Green” Policies

2021 ◽  
Vol 13 (10) ◽  
pp. 5411
Author(s):  
Elisabeth Bloder ◽  
Georg Jäger
Keyword(s):  
Climate Change ◽  
High Risk ◽  
Urban Areas ◽  
The Other ◽  
Agent Based ◽  
Traffic Lights ◽  
Traffic System ◽  
Rebound Effects ◽  
Car Use ◽  
Reduce Emissions

Traffic and transportation are main contributors to the global CO2 emissions and resulting climate change. Especially in urban areas, traffic flow is not optimal and thus offers possibilities to reduce emissions. The concept of a Green Wave, i.e., the coordinated switching of traffic lights in order to favor a single direction and reduce congestion, is often discussed as a simple mechanism to avoid breaking and accelerating, thereby reducing fuel consumption. On the other hand, making car use more attractive might also increase emissions. In this study, we use an agent-based model to investigate the benefit of a Green Wave in order to find out whether it can outweigh the effects of increased car use. We find that although the Green Wave has the potential to reduce emissions, there is also a high risk of heaving a net increase in emissions, depending on the specifics of the traffic system.

scholarly journals Evolution of social learning when high expected payoffs are associated with high risk of failure

2011 ◽  
Vol 8 (64) ◽  
pp. 1604-1615 ◽  
Author(s):  
Michal Arbilly ◽  
Uzi Motro ◽  
Marcus W. Feldman ◽  
Arnon Lotem
Keyword(s):  
High Risk ◽  
Social Learning ◽  
Risk Aversion ◽  
Individual Risk ◽  
Individual Experience ◽  
Agent Based ◽  
Risk Of Failure ◽  
Expected Payoffs ◽  
Maladaptive Behaviours ◽  
Optimal Behaviour

In an environment where the availability of resources sought by a forager varies greatly, individual foraging is likely to be associated with a high risk of failure. Foragers that learn where the best sources of food are located are likely to develop risk aversion, causing them to avoid the patches that are in fact the best; the result is sub-optimal behaviour. Yet, foragers living in a group may not only learn by themselves, but also by observing others. Using evolutionary agent-based computer simulations of a social foraging game, we show that in an environment where the most productive resources occur with the lowest probability, socially acquired information is strongly favoured over individual experience. While social learning is usually regarded as beneficial because it filters out maladaptive behaviours, the advantage of social learning in a risky environment stems from the fact that it allows risk aversion to be circumvented and the best food source to be revisited despite repeated failures. Our results demonstrate that the consequences of individual risk aversion may be better understood within a social context and suggest one possible explanation for the strong preference for social information over individual experience often observed in both humans and animals.

scholarly journals DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

2020 ◽  
Author(s):  
Mahdi Rezaei ◽  
Mohsen Azarmi
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
World Health Organisation ◽  
Infection Risk ◽  
Superior Performance ◽  
World Health ◽  
Mapping Technique ◽  
People Detection ◽  
Social Distancing ◽  
Risk Zones

Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-meter physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a Deep Neural Network-based Model for automated people detection, tracking, and inter-people distances estimation in the crowd, using common CCTV security cameras. The proposed DNN model along with an inverse perspective mapping technique leads to a very accurate people detection and social distancing monitoring in challenging conditions, including people occlusion, partial visibility, and lighting variations. We also provide an online infection risk assessment scheme by statistical analysis of the Spatio-temporal data from the moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infection. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The efficiency of the proposed methodology is evaluated on the Oxford Town Centre dataset, with superior performance in terms of accuracy and speed compared to three state-of-the-art methods.

Simulation of HIV Infection Propagation Networks

2013 ◽  
Vol 5 (1) ◽  
pp. 53-63 ◽  
Author(s):  
Alfredo Tirado-Ramos ◽  
Chris Kelley
Keyword(s):  
High Risk ◽  
Drug Users ◽  
Hiv Transmission ◽  
State Of The Art ◽  
Complex Nature ◽  
Model Framework ◽  
Agent Based ◽  
Current State ◽  
Future Work ◽  
Risk Infection

Simulating the transmission of HIV requires a model framework that can account for the complex nature of HIV transmission. In this paper the authors present the current state of the art for simulating HIV with agent-based models and highlight some of the significant contributions of current research. The authors then propose opportunities for future work including their plan that involves identifying and monitoring high-risk drug users that can potentially initiate high-risk infection propagation networks.

A MODEL FOR BRAND COMPETITION WITHIN A SOCIAL NETWORK

2010 ◽  
Vol 21 (12) ◽  
pp. 1457-1467
Author(s):  
R. HUERTA-QUINTANILLA ◽  
E. CANTO-LUGO ◽  
M. RODRÍGUEZ-ACHACH
Keyword(s):  
Social Network ◽  
Network Topology ◽  
Decision Rules ◽  
Nearest Neighbors ◽  
Product Market ◽  
Economic Benefits ◽  
Model Parameters ◽  
Agent Based ◽  
Absorbing State ◽  
Brand Competition

An agent-based model was built representing an economic environment in which m brands are competing for a product market. These agents represent companies that interact within a social network in which a certain agent persuades others to update or shift their brands; the brands of the products they are using. Decision rules were established that caused each agent to react according to the economic benefits it would receive; they updated/shifted only if it was beneficial. Each agent can have only one of the m possible brands, and she can interact with its two nearest neighbors and another set of agents which are chosen according to a particular set of rules in the network topology. An absorbing state was always reached in which a single brand monopolized the network (known as condensation). The condensation time varied as a function of model parameters is studied including an analysis of brand competition using different networks.

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