Situating agent-based modelling in population health research

Silverman, Eric; Gostoli, Umberto; Picascia, Stefano; Almagor, Jonatan; McCann, Mark; Shaw, RJ; Angione, Claudio

doi:10.1186/s12982-021-00102-7

Cited by 45 publications

(40 citation statements)

References 58 publications

(60 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For the time in between, we assume E 0 = 0 and that prevalence grows exponentially, and we set I 0 's mean and variance to guarantee growth into the associated I t estimates on March 1. 3 Now, with the transmission model fully specified, we can assess its quality by sampling a bunch of trajectories and using them to compute things. We visualize 6 illustrative examples in Fig.…”

Section: A Mechanistic Transmission Modelmentioning

confidence: 99%

“…While it's sometimes conceptually appealing to build from the ground up, first characterizing the biology within individuals and then carefully working towards larger collections, it becomes clear quickly that "more is different" [2]. Population dynamics both poorly constrain and are poorly constrained by tractable interaction models [3], and so it's often neither feasible nor desirable to try to approach the daily practice of public health in this way [4]. In analogy to statistical mechanics, where the major philosophical lesson is that understanding the emergent macroscopic physics (i.e.…”

mentioning

confidence: 99%

See 1 more Smart Citation

COVID-19 epidemiology as emergent behavior on a dynamic transmission forest

Thakkar¹,

Famulare²

2022

Preprint

View full text Add to dashboard Cite

In this paper we create a compartmental, stochastic process model of SARS-CoV-2 transmission, where the process's mean and variance have distinct dynamics. The model is fit to time series data from Washington from January 2020 to March 2021 using a deterministic, biologically-motivated signal processing approach, and we show that the model's hidden states, like population prevalence, agree with survey and other estimates. Then, in the paper's second half, we demonstrate that the same model can be reframed as a branching process with a dynamic degree distribution. This perspective allows us to generate approximate transmission trees and estimate some higher order statistics, like the clustering of cases as outbreaks, which we find to be consistent with related observations from contact tracing and phylogenetics.

show abstract

Section: A Mechanistic Transmission Modelmentioning

confidence: 99%

mentioning

confidence: 99%

COVID-19 epidemiology as emergent behavior on a dynamic transmission forest

Thakkar¹,

Famulare²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Complexity (see also Batty, 2007;Bedau, 2003;Holland, 1996) and emergence (see also Bedau, 1997Bedau, , 2003Silverman et al, 2021) are very important aspects of agent-based models. Emergent behaviors can be best captured with agent-based models.…”

Section: Emergence and Complexity In Agent-based Modelingmentioning

confidence: 99%

Etmen-tabanlı modellemede belirme ve karmaşıklık: Güncel araştırmaların incelenmesi

Cenani

2021

Journal of Computational Design

View full text Add to dashboard Cite

Agent-based systems are an important application area of artificial intelligence and are used in decision support systems. Rather than being a problem-solving tool, agent-based system is a tool for developing and testing alternative solutions according to various scenarios. In this context, agent-based modeling is a very effective method to support decision makers in emergency situations to evaluate different risk scenarios and then make decisions quickly and effectively. Moreover, agent-based modeling is a very useful method to support decision makers in situations of high complexity and uncertainty. This paper introduces current studies performed with several agent-based modeling tools and software environments such as NetLogo, AnyLogic, MATSim and Repast. Apart from these, various agent-based modeling tools exist, but these four tools have been chosen because they are still receiving software updates and being widely used in the most current studies. The aims of this study are to review these four agent-based modeling tools, present state-of-the-art research conducted with these tools and provide a reference of agent-based modeling tools for researchers who are developing decision support systems in architectural, urban and transportation design research fields. In this paper, after giving a brief definition of an agent-based system and explaining the importance of concepts such as emergence and complexity in the field of agent-based modeling, it is explained who uses the agentbased models for what purpose, when, where, why and how to use agent-based modeling through selected examples from state-of-the-art studies carried out in different research fields. Furthermore, what current studies and agent-based modeling tools teach us and how future studies can benefit from agent-based models are briefly discussed.

show abstract

“…Hence, the spatial dynamics are used to define the evolution of a temporal contact network. A particularly relevant extension to model spreading on contact networks is the inclusion of adaptive behaviour [54], which defines the class of adaptive networks. In this class of network models, the dynamical rules that shape the network structure change in response to the dynamics of the spreading process [27,28].…”

Section: Introductionmentioning

confidence: 99%

S̲tochastic S̲imulation A̲lgorithm For Effective Spreading Dynamics On T̲ime-Evolving A̲daptive N̲etworX̲ (SSATAN-X)

Malysheva¹,

Wang²,

Kleist³

2022

Math. Model. Nat. Phenom.

View full text Add to dashboard Cite

Modelling and simulating of pathogen spreading has been proven crucial to inform containment strategies, as well as cost-effectiveness calculations. Pathogen spreading is often modelled as a stochastic process that is driven by pathogen exposure on time-evolving contact networks. In adaptive networks, the spreading process depends not only on the dynamics of a contact network, but vice versa, infection dynamics may alter risk behaviour and thus feed back onto contact dynamics, leading to emergent complex dynamics. However, stochastic simulation of such processes is currently computationally prohibitive. To overcome this computational bottleneck, we propose SSATAN-X. The key idea of this algorithm is to only capture contact dynamics at time-points relevant to the spreading process. We demonstrate that the statistics of the contact- and spreading process are accurate, while achieving ∼ 100 fold speed-up over exact stochastic simulation. SSATAN-X’s performance increases further when contact dynamics are fast in relation to the spreading process, as applicable to most infectious diseases. We envision that SSATAN-X may extend the scope of analysis of pathogen spreading on adaptive networks. Moreover, it may serve to create benchmark data sets to validate novel numerical approaches for simulation, or for the data-driven analysis of the spreading dynamics on adaptive networks.

show abstract

Situating agent-based modelling in population health research

Cited by 45 publications

References 58 publications

COVID-19 epidemiology as emergent behavior on a dynamic transmission forest

COVID-19 epidemiology as emergent behavior on a dynamic transmission forest

Etmen-tabanlı modellemede belirme ve karmaşıklık: Güncel araştırmaların incelenmesi

S̲tochastic S̲imulation A̲lgorithm For Effective Spreading Dynamics On T̲ime-Evolving A̲daptive N̲etworX̲ (SSATAN-X)

Contact Info

Product

Resources

About