scholarly journals Prioritizing allocation of COVID-19 vaccines based on social contacts increases vaccination effectiveness

2021 ◽  
Author(s):  
Jiangzhuo Chen ◽  
Stefan Hoops ◽  
Achla Marathe ◽  
Henning Mortveit ◽  
Bryan Lewis ◽  
...  
Keyword(s):  
Structural Properties ◽  
Social Contact ◽  
Contact Network ◽  
Social Contacts ◽  
Agent Based ◽  
Poor Countries ◽  
Time Estimates ◽  
Realistic Representation ◽  
Spatio Temporal ◽  
Structural Measure

AbstractWe study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Even optimistic estimates suggest that most countries will likely take 6 to 24 months to vaccinate their citizens. These time estimates and the emergence of new viral strains urge us to find quick and effective ways to allocate the vaccines and contain the pandemic. While current approaches use combinations of age-based and occupation-based prioritizations, our strategy marks a departure from such largely aggregate vaccine allocation strategies. We propose a novel agent-based modeling approach motivated by recent advances in (i) science of real-world networks that point to efficacy of certain vaccination strategies and (ii) digital technologies that improve our ability to estimate some of these structural properties. Using a realistic representation of a social contact network for the Commonwealth of Virginia, combined with accurate surveillance data on spatio-temporal cases and currently accepted models of within- and between-host disease dynamics, we study how a limited number of vaccine doses can be strategically distributed to individuals to reduce the overall burden of the pandemic. We show that allocation of vaccines based on individuals’ degree (number of social contacts) and total social proximity time is significantly more effective than the currently used age-based allocation strategy in terms of number of infections, hospitalizations and deaths. Our results suggest that in just two months, by March 31, 2021, compared to age-based allocation, the proposed degree-based strategy can result in reducing an additional 56–110k infections, 3.2–5.4k hospitalizations, and 700–900 deaths just in the Commonwealth of Virginia. Extrapolating these results for the entire US, this strategy can lead to 3–6 million fewer infections, 181–306k fewer hospitalizations, and 51–62k fewer deaths compared to age-based allocation. The overall strategy is robust even: (i) if the social contacts are not estimated correctly; (ii) if the vaccine efficacy is lower than expected or only a single dose is given; (iii) if there is a delay in vaccine production and deployment; and (iv) whether or not non-pharmaceutical interventions continue as vaccines are deployed. For reasons of implementability, we have used degree, which is a simple structural measure and can be easily estimated using several methods, including the digital technology available today. These results are significant, especially for resource-poor countries, where vaccines are less available, have lower efficacy, and are more slowly distributed.

scholarly journals Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility

2021 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Lennart Adenaw ◽  
Markus Lienkamp
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
User Behavior ◽  
Transport Sector ◽  
Simulation Framework ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Charging Station ◽  
Spatio Temporal

In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.

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 Assessing the scaling of a blockchain-based social contact tracking system

2021 ◽  
Vol 16 (3) ◽  
pp. 0
Author(s):  
Ilya Shmelev
Keyword(s):  
Conceptual Model ◽  
Tracking System ◽  
Social Contact ◽  
Hybrid Architecture ◽  
Unresolved Issue ◽  
Source Information ◽  
Social Contacts ◽  
Digital Infrastructure ◽  
Contact Tracking ◽  
The Common

During a pandemic, one of the most important tasks is to track social contacts with those who are sick. This article categorizes projects that track these contacts. Projects are classified by architecture and the common components of such systems are highlighted. It is concluded that the hybrid architecture of such a solution based on an exclusive blockchain will have several advantages, and a conceptual model of such a system is described. However, an analysis of existing blockchain projects showed that their main problem is the unresolved issue of scaling such kinds of systems, which is becoming a key issue in the context of creating a global digital infrastructure of society. Further, the scaling of the system's conceptual model is assessed based on open-source information about the Moscow metro, and the main conclusions about the selected architectural solutions are confirmed.

scholarly journals Geospatial Analysis and Mapping Strategies for Fine Grained and Detailed COVID-19 Data With Geographic Information Systems

2021 ◽  
Author(s):  
ÁNGEL MIRAMONTES CARBALLADA ◽  
JOSE BALSA-BARREIRO
Keyword(s):  
Geographic Location ◽  
Social Contact ◽  
Temporal Behaviour ◽  
Fine Grained ◽  
Health Authorities ◽  
Public Health Authorities ◽  
Spatio Temporal ◽  
Media Reports ◽  
Predictive Risk ◽  
The Impact

Abstract The CoVID-19 pandemic is showing a dramatic impact across the world. To the tragedy of the loss of human lives, we must add the great uncertainty that the new coronavirus is causing to our lives. Governments and public health authorities must be able to respond this emergency by taking the appropriate decisions for minimizing the impact of the virus. In the absence of an immediate solution, governments have concentrated their efforts on adopting non-pharmaceutical interventions for restricting the mobility of people and reducing the social contact. Health authorities are publishing most of data for supporting their interventions and policies. The geographic location of the cases is a vital information with exceptional value for analysing the spatio-temporal behaviour of the virus, doing feasible to anticipate potential outbreaks and to elaborate predictive risk mapping. In fact, a great number of media reports, research papers, and web-browsers have presented the COVID-19 disease spreading by using maps. However, processing and visualization of this sort of data presents some aspects that must be carefully reviewed. Based on our experience with fine-grained and detailed data related to COVID-19 in a Spanish region, we present a bunch of mapping strategies and good practices using geospatial tools. The ultimate goal is create appropriate maps at any spatial scale while avoiding conflicts with data such as those related to patients’ privacy.

Modeling Environmental Impacts on Viticultural Ecosystems

2017 ◽  
Vol 8 (3) ◽  
pp. 1-20
Author(s):  
Cyril Tissot ◽  
Etienne Neethling ◽  
Mathias Rouan ◽  
Gérard Barbeau ◽  
Hervé Quénol ◽  
...  
Keyword(s):  
Environmental Impacts ◽  
Environmental Conditions ◽  
Management Strategies ◽  
Temporal Data ◽  
Agent Based ◽  
Knowledge Database ◽  
Behavioral Dynamics ◽  
Regulatory Data ◽  
Temporal Data Models ◽  
Spatio Temporal

This paper focuses on simulating environmental impacts on grapevine behavioral dynamics and vineyard management strategies. The methodology presented uses technology from geomatics object oriented databases and spatio-temporal data models. Our approach has two principle objectives, first, to simulate grapevine phenology and grape ripening under spatial and temporal environmental conditions and constraints and secondly, to simulate viticultural practices and adaptation strategies under various constraints (environmental, economical, socio-technical). The approach is based on a responsive agent-based structure where environmental conditions and constraints are considered as a set of forcing data (biophysical, socio-economic and regulatory data) that influences the modelled activities. The experiment was conducted in the regulated wine producing appellation Grand Cru “Quarts de Chaume”, situated in the middle Loire Valley, France. All of the methodology, from the implementation of the knowledge database to the analysis of the first simulation, is presented in this paper.

scholarly journals The Use of “the Big Five” in Assessing Levels of Adaptation in Dental Students at Various Stages of Their Education

2019 ◽  
Vol 70 ◽  
pp. 08025
Author(s):  
Larisa Litvinova ◽  
Lyubov’ Gubareva ◽  
Atsamaz Kaloyev ◽  
Yelena Grishilova
Keyword(s):  
Big Five ◽  
Practical Knowledge ◽  
Social Contact ◽  
Dental Students ◽  
Theoretical Knowledge ◽  
Human Adaptation ◽  
Social Contacts ◽  
Actual Behaviour ◽  
Individual Character ◽  
Academic Workload

Current approaches in psychology look at human adaptation reserves as part of individual character. “the Big Five” evaluates subjects’ actual behaviour and levels of reserves of adaptation on each of the five scales. The results show reliably identifiable changes between the first and third years of study. Students acquire traits associated with introversion and reduce their social contacts (р≤0.01). A reduction to average is also visible in “Attachment – Detachment” (р≤0.05) and “Playfulness – Practicality” (р≤0.01). It can be seen that there is a reduction in the adaptation abilities of dental students between the first and third years in terms of social contact and the application of practical knowledge. Mastering the disciplines under study becomes harder, while the amount of theoretical knowledge and practical ability required increases, necessitating higher levels of concentration. Taking into account the increase in academic workload during this period, some of the above can be put down to stress.

scholarly journals COEXISTENCE OF SOCIAL NORMS BASED ON IN- AND OUT-GROUP INTERACTIONS

2007 ◽  
Vol 10 (02) ◽  
pp. 271-286 ◽  
Author(s):  
THOMAS FENT ◽  
PATRICK GROEBER ◽  
FRANK SCHWEITZER
Keyword(s):  
Social Behavior ◽  
Social Norms ◽  
Temporal Distribution ◽  
Group Interactions ◽  
Agent Based ◽  
Continuous State Space ◽  
Continuous State ◽  
Microscopic Interactions ◽  
Spatio Temporal ◽  
Repulsive Forces

The question how social norms can emerge from microscopic interactions between individuals is a key problem in social sciences to explain collective behavior. In this paper, we propose an agent-based model to show that randomly distributed social behavior by way of local interaction converges to a state with a multimodal distribution of behavior. This can be interpreted as a coexistence of different social norms, a result that goes beyond previous investigations. The model is discrete in time and space, behavior is characterized in a continuous state space. The adaptation of social behavior by each agent is based on attractive and repulsive forces caused by friendly and adversary relations among agents. The model is analyzed both analytically and by means of spatio-temporal computer simulations. It provides conditions under which we find convergence towards a single norm, coexistence of two opposing norms, and coexistence of a multitude of norms. For the latter case, we also show the evolution of the spatio-temporal distribution of behavior.

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