scholarly journals Geospatially Referenced Demographic Agent-Based Modeling of SARS-CoV-2-Infection (COVID-19) Dynamics and Mitigation Effects in a Real-world Community

2020 ◽  
Author(s):  
SO Adler ◽  
O Bodeit ◽  
L Bonn ◽  
B Goldenbogen ◽  
X Escalera-Fanjul ◽  
...  
Keyword(s):  
Real World ◽  
Policy Development ◽  
Household Composition ◽  
Mitigation Strategies ◽  
List Type ◽  
Human Beings ◽  
School Closures ◽  
Agent Based ◽  
Infection Dynamics ◽  
Spatio Temporal

AbstractRe-opening societies and economies across the globe following the initial wave of the severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) pandemic requires scientifically-guided decision processes and policy development. Public health authorities now consider it highly likely that transmission of SARS-CoV-2 and COVID-19 will follow a pattern of seasonal circulation globally. To guide mitigation strategies and tactics in a location-specific manner, accurate simulation of prolonged or intermittent patterns of social/physical distancing is required in order to prevent healthcare systems and communities from collapsing. It is equally important to capture the stochastic appearance of individual transmission events. Traditional epidemiological/statistical models cannot make predictions in a geospatial temporal manner based on human individuals in a community. Thus, the challenge is to conduct spatio-temporal simulations of transmission chains with real-world geospatial and georeferenced information of the dynamics of the disease and the effect of different mitigation strategies such as isolation of infected individuals or location closures. Here, we present a stochastic, geospatially referenced and demography-specific agent-based model with agents representing human beings and include information on age, household composition, daily occupation and schedule, risk factors, and other relevant properties. Physical encounters between humans are modeled in a time-dependent georeferenced network of the population. The model (GERDA-1) can predict infection dynamics under normal conditions and test the effect of different mitigation scenarios such as school closures, reduced social contacts as well as closure or reopening of public/work spaces. Specifically, it also includes the fate and influence of health care workers and their access to protective gear. Key predictions so far entail:the effect of specific groups on the spreading, specifically that children in school contribute substantially to distribution.the result of reopening society depends crucially on how strict the measures have been during lock-down.the outcome of reopening is a stochastic process - in the majority of cases, we must expect a second wave, in some cases not. To the best of our best knowledge, the GERDA-1 model is the first model able to predict a bimodal behavior of SARS-Cov-2 infection dynamics.Given the criticality of the global situation, informing the scientific community, decision makers and the general public seems prudent. Therefore, we here provide a pre-print of the GERDA-1 model together with a first set of predictions and analyses as work in progress.

scholarly journals A Spatio-Temporal Investigation of the Growth Rate of COVID-19 Incidents in Ohio, USA, Early in the Pandemic

2021 ◽  
Vol 121 (2) ◽  
pp. 33-47
Author(s):  
Alessandro M. Selvitella ◽  
Liam Carolan ◽  
Justin Smethers ◽  
Christopher Hernandez ◽  
Kathleen L. Foster
Keyword(s):  
Growth Rate ◽  
Transmission Rate ◽  
Time Lag ◽  
Early Period ◽  
Mitigation Strategies ◽  
Initial Growth ◽  
School Closures ◽  
Policy Makers ◽  
Susceptible Population ◽  
Spatio Temporal

Understanding the initial growth rate of an epidemic is important for epidemiologists and policy makers as it can impact their mitigation strategies such as school closures, quarantines, or social distancing. Because the transmission rate depends on the contact rate of the susceptible population with infected individuals, similar growth rates might be experienced in nearby geographical areas. This research determined the growth rate of cases and deaths associated with COVID-19 in the early period of the 2020 pandemic in Ohio, United States. The evolution of cases and deaths was modeled through a Besag-York-Molliè model with linear- and power-type deterministic time dependence. The analysis showed that the growth rate of the time component of the model was subexponential in both cases and deaths once the time-lag across counties of the appearance of the first COVID-19 case was considered. Moreover, deaths in the northeast counties in Ohio were strongly related to the deaths in nearby counties.

scholarly journals A hybrid PDE–ABM model for viral dynamics with application to SARS–CoV–2 and influenza

2021 ◽  
Author(s):  
Sadegh Marzban ◽  
Renji Han ◽  
Nóra Juhász ◽  
Gergely Röst
Keyword(s):  
Continuous Variable ◽  
Virus Concentration ◽  
Viral Dynamics ◽  
Agent Based ◽  
Ode System ◽  
Infection Dynamics ◽  
A Cell ◽  
Spatio Temporal ◽  
Insight Into

We propose a hybrid partial differential equation - agent-based (PDE-ABM) model to describe the spatio-temporal viral dynamics in a cell population. The virus concentration is considered as a continuous variable and virus movement is modeled by diffusion, while changes in the states of cells (i.e. healthy, infected, dead) are represented by a stochastic agent-based model. The two subsystems are intertwined: the probability of an agent getting infected in the ABM depends on the local viral concentration, and the source term of viral production in the PDE is determined by the cells that are infected. We develop a computational tool that allows us to study the hybrid system and the generated spatial patterns in detail. We systematically compare the outputs with a classical ODE system of viral dynamics and find that the ODE model is a good approximation only if the diffusion coefficient is large. We demonstrate that the model is able to predict SARS-CoV-2 infection dynamics and replicate the output of in vitro experiments. Applying the model to influenza as well, we can gain insight into why the outcomes of these two infections are different.

Understanding Anasazi Culture Change Through Agent-Based Modeling

2000 ◽  
Author(s):  
Jeffrey S. Dean ◽  
George J. Gumerman
Keyword(s):  
Real World ◽  
Initial Conditions ◽  
Explanatory Power ◽  
Objective Evaluation ◽  
Agent Based Modeling ◽  
Human Beings ◽  
Agent Based ◽  
Archaeological Data ◽  
Internal And External Factors ◽  
Social Units

Traditional narrative explanations of prehistory have become increasingly difficult to operationalize as models and to test against archaeological data. As such models become more sophisticated and complex, they also become less amenable to objective evaluation with anthropological data. Nor is it possible to experiment with living or prehistoric human beings or societies. Agentbased modeling offers intriguing possibilities for overcoming the experimental limitations of archaeology by representing the behavior of culturally relevant agents on landscapes. Manipulating the behavior of artificial agents on such landscapes allows us to, as it were, "rewind the tape" of sociocultural history and to experimentally examine the relative contributions of internal and external factors to sociocultural evolution (Gumerman and Kohler in press). Agent-based modeling allows the creation of variable resource (or other) landscapes that can be wholly imaginary or that can capture important aspects of real-world situations. These landscapes are populated with heterogeneous agents. Each agent is endowed with various attributes (e.g., life span, vision, movement capabilities, nutritional requirements, consumption and storage capacities) in order to replicate important features of individuals or relevant social units such as households, lineages, clans, and villages. A set of anthropologically plausible rules defines the ways in which agents interact with the environment and with one another. Altering the agents' attributes, their interaction rules, and features of the landscape allow experimental examination of behavioral responses to different initial conditions, relationships, and spatial and temporal parameters. The agents' repeated interactions with their social and physical landscapes reveal ways in which they respond to changing environmental and social conditions. As we will see, even relatively simple models may illuminate complex sociocultural realities. While potentially powerful, agent-based models in archaeology remain unverified until they are evaluated against actual cases. The degree of fit between a model and real-world situations allows the model's validity to be assessed. A close fit between all or part of a model and the test data indicates that the model, albeit highly simplified, has explanatory power. Lack of fit implies that the model is in some way inadequate.

scholarly journals A hybrid PDE–ABM model for viral dynamics with application to SARS-CoV-2 and influenza

2021 ◽  
Vol 8 (11) ◽  
Author(s):  
Sadegh Marzban ◽  
Renji Han ◽  
Nóra Juhász ◽  
Gergely Röst
Keyword(s):  
Continuous Variable ◽  
Virus Concentration ◽  
Viral Dynamics ◽  
Agent Based ◽  
Ode System ◽  
Infection Dynamics ◽  
A Cell ◽  
Spatio Temporal ◽  
Insight Into

We propose a hybrid partial differential equation–agent-based (PDE–ABM) model to describe the spatio-temporal viral dynamics in a cell population. The virus concentration is considered as a continuous variable and virus movement is modelled by diffusion, while changes in the states of cells (i.e. healthy, infected, dead) are represented by a stochastic ABM. The two subsystems are intertwined: the probability of an agent getting infected in the ABM depends on the local viral concentration, and the source term of viral production in the PDE is determined by the cells that are infected. We develop a computational tool that allows us to study the hybrid system and the generated spatial patterns in detail. We systematically compare the outputs with a classical ODE system of viral dynamics, and find that the ODE model is a good approximation only if the diffusion coefficient is large. We demonstrate that the model is able to predict SARS-CoV-2 infection dynamics, and replicate the output of in vitro experiments. Applying the model to influenza as well, we can gain insight into why the outcomes of these two infections are different.

scholarly journals Modeling Epidemic Spread among a Commuting Population Using Transport Schemes

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1861
Author(s):  
Daniela Calvetti ◽  
Alexander P. Hoover ◽  
Johnie Rose ◽  
Erkki Somersalo
Keyword(s):  
Network Model ◽  
Transport Theory ◽  
Optimal Transport ◽  
State Level ◽  
Compartment Model ◽  
Census Bureau ◽  
Mitigation Strategies ◽  
Mitigation Measures ◽  
Metapopulation Model ◽  
Infection Dynamics

Understanding the dynamics of the spread of COVID-19 between connected communities is fundamental in planning appropriate mitigation measures. To that end, we propose and analyze a novel metapopulation network model, particularly suitable for modeling commuter traffic patterns, that takes into account the connectivity between a heterogeneous set of communities, each with its own infection dynamics. In the novel metapopulation model that we propose here, transport schemes developed in optimal transport theory provide an efficient and easily implementable way of describing the temporary population redistribution due to traffic, such as the daily commuter traffic between work and residence. Locally, infection dynamics in individual communities are described in terms of a susceptible-exposed-infected-recovered (SEIR) compartment model, modified to account for the specific features of COVID-19, most notably its spread by asymptomatic and presymptomatic infected individuals. The mathematical foundation of our metapopulation network model is akin to a transport scheme between two population distributions, namely the residential distribution and the workplace distribution, whose interface can be inferred from commuter mobility data made available by the US Census Bureau. We use the proposed metapopulation model to test the dynamics of the spread of COVID-19 on two networks, a smaller one comprising 7 counties in the Greater Cleveland area in Ohio, and a larger one consisting of 74 counties in the Pittsburgh–Cleveland–Detroit corridor following the Lake Erie’s American coastline. The model simulations indicate that densely populated regions effectively act as amplifiers of the infection for the surrounding, less densely populated areas, in agreement with the pattern of infections observed in the course of the COVID-19 pandemic. Computed examples show that the model can be used also to test different mitigation strategies, including one based on state-level travel restrictions, another on county level triggered social distancing, as well as a combination of the two.

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 Comparison of spatio-temporal transmission characteristics of COVID-19 and its mitigation strategies in China and the US

2020 ◽  
Vol 30 (12) ◽  
pp. 1963-1984
Author(s):  
Zhiming Feng ◽  
Chiwei Xiao ◽  
Peng Li ◽  
Zhen You ◽  
Xu Yin ◽  
...  
Keyword(s):  
Mitigation Strategies ◽  
Transmission Characteristics ◽  
The Us ◽  
Spatio Temporal

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

Spatio-Temporal Modeling on Village Land Feature Change

2012 ◽  
Vol 204-208 ◽  
pp. 2721-2725
Author(s):  
Hua Ji Zhu ◽  
Hua Rui Wu
Keyword(s):  
Real World ◽  
Temporal Change ◽  
End Users ◽  
Temporal Modeling ◽  
The Real ◽  
Change Type ◽  
Feature Change ◽  
Spatio Temporal ◽  
The Village ◽  
Land Changes

Village land continually changes in the real world. In order to keep the data up-to-date, data producers need update the data frequently. When the village land data are updated, the update information must be dispensed to the end-users to keep their client-databases current. In the real world, village land changes in many forms. Identifying the change type of village land (i.e. captures the semantics of change) and representing them in the data world can help end-users understand the change commonly and be convenient for end-users to integrate these change information into their databases. This work focuses on the model of the spatio-temporal change. A three-tuple model CAR for representing the spatio-temporal change is proposed based on the village land feature set before change and the village land feature set after change, change type and rules. In this model, the C denotes the change type. A denotes the attribute set; R denotes the judging rules of change type. The rule is described by the IF－THEN expressions. By the operations between R and A, the C is distinguished. This model overcomes the limitations of current methods. And more, the rules in this model can be easy realized in computer program.

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