A unified framework for analysis of individual-based models in ecology and beyond

Cornell, Stephen J.; Suprunenko, Yevhen F.; Finkelshtein, Dmitri; Somervuo, Panu; Ovaskainen, Otso

doi:10.1038/s41467-019-12172-y

Cited by 27 publications

(92 citation statements)

References 31 publications

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“…; Cornell et al . ): The dynamics of the model can be described by listing all events that can take place and the rates at which these events occur. These rates can depend on the current spatial configuration of all individuals (e.g.…”

Section: Model and Methodsmentioning

confidence: 99%

“…Formally, our model is a spatiotemporal point process, or in the mathematical terminology, a Markov evolution in the space of locally finite configurations (see e.g. Ovaskainen et al 2014;Cornell et al 2019): The dynamics of the model can be described by listing all events that can take place and the rates at which these events occur. These rates can depend on the current spatial configuration of all individuals (e.g.…”

Section: Overview Of the Individual-based Spatial Modelmentioning

confidence: 99%

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Habitat fragmentation and species diversity in competitive communities

2019

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Habitat loss is one of the key drivers of the ongoing decline of biodiversity. However, ecologists still argue about how fragmentation of habitat (independent of habitat loss) affects species richness. The recently proposed habitat amount hypothesis posits that species richness only depends on the total amount of habitat in a local landscape. In contrast, empirical studies report contrasting patterns: some find positive and others negative effects of fragmentation per se on species richness. To explain this apparent disparity, we devise a stochastic, spatially explicit model of competitive species communities in heterogeneous habitats. The model shows that habitat loss and fragmentation have complex effects on species diversity in competitive communities. When the total amount of habitat is large, fragmentation per se tends to increase species diversity, but if the total amount of habitat is small, the situation is reversed: fragmentation per se decreases species diversity.

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Section: Model and Methodsmentioning

confidence: 99%

Section: Overview Of the Individual-based Spatial Modelmentioning

confidence: 99%

Habitat fragmentation and species diversity in competitive communities

2019

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“…Many spatial population models are formulated in deterministic frameworks, such as reaction‐diffusion models for continuous populations (Maciel & Lutscher, 2013) or systems of differential equations for metapopulations (Hanski & Ovaskainen, 2000). Spatial population models have also been formulated in stochastic frameworks, such as cellular automata models on spatial grids (Bascompte & Sole, 1996), stochastic patch occupancy models (Moilanen, 2004) or models where individuals interact in continuous space (Bolker et al., 2000; Cornell et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

“…While spatiotemporal point processes can be used to model a rich array of ecological and evolutionary behaviours (e.g. Barraquand & Murrell, 2013; Bolker & Pacala, 1997; Cornell et al., 2019; Murrell & Law, 2003), and while they can arguably be considered to be more natural descriptions of many ecological systems that lattice‐based modes, to our knowledge there are no rigorous results concerning their critical behaviour. The analysis of spatiotemporal point processes has been mostly based on moment closure techniques (e.g.…”

Section: Introductionmentioning

confidence: 99%

Mathematical and simulation methods for deriving extinction thresholds in spatial and stochastic models of interacting agents

2020

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1. In ecology, one of the most fundamental questions relates to the persistence of populations, or conversely to the probability of their extinction. Deriving extinctionThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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“…Case Counts, mF = 0.90 and mf = 1.04 trials 13. 16 19 22 25 28 31 34 37 40 43 46 Case Counts, MingleFactor = 1.04…”

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confidence: 99%

CovidSIMVL --Transmission Trees, Superspreaders and Contact Tracing in Agent Based Models of Covid-19

Chang

Moselle

Richardson

2020

Preprint

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The agent-based model CovidSIMVL (github.com/ecsendmail/MultiverseContagion) is employed in this paper to delineate different network structures of transmission chains in simulated COVID-19 epidemics, where initial parameters are set to approximate spread from a single transmission source, and R0ranges between 1.5 and 2.5.The resulting Transmission Trees are characterized by breadth, depth and generations needed to reach a target of 50% infected from a starting population of 100, or self-extinction prior to reaching that target. Metrics reflecting efficiency of an epidemic relate closely to topology of the trees.It can be shown that the notion of superspreading individuals may be a statistical artefact of Transmission Tree growth, while superspreader events can be readily simulated with appropriate parameter settings. The potential use of contact tracing data to identify chain length and shared paths is explored as a measure of epidemic progression. This characterization of epidemics in terms of topological characteristics of Transmission Trees may complement equation-based models that work from rates of infection. By constructing measures of efficiency of spread based on Transmission Tree topology and distribution, rather than rates of infection over time, the agent-based approach may provide a method to characterize and project risks associated with collections of transmission events, most notably at relatively early epidemic stages, when rates are low and equation-based approaches are challenged in their capacity to describe or predict.MOTIVATION – MODELS KEYED TO CONTEMPLATED DECISIONSOutcomes are altered by changing the processes that determine them. If we wish to alter contagion-based spread of infection as reflected in curves that characterize changes in transmission rates over time, we must intervene at the level of the processes that are directly involved in preventing viral spread. If we are going to employ models to evaluate different candidate arrays of localized preventive policies, those models must be posed at the same level of granularity as the entities (people enacting processes) to which preventive measures will be applied. As well, the models must be able to represent the transmission-relevant dynamics of the systems to which policies could be applied. Further, the parameters that govern dynamics within the models must embody the actions that are prescribed/proscribed by the preventive measures that are contemplated. If all of those conditions are met, then at a formal or structural level, the models are conformant with the provisions of the Law of Requisite Variety1 or the restated version of that law – the good regulator theorem.2On a more logistical or practical level, the models must yield summary measures that are responsive to changes in key parameters, highlight the dynamics, quantify outcomes associated with the dynamics, and communicate that information in a form that can be understood correctly by parties who are adjudicating on policy options.If the models meet formal/structural requirements regarding requisite variety, and the parameters have a plausible interpretation in relationship to real-world situations, and the metrics do not overly-distort the data contents that they summarize, then the models provide information that is directly relevant to decision-making processes. Models that meet these requirements will minimize the gap that separates models from decisions, a gap that will otherwise be filled by considerations other than the data used to create the models (for equation-based models) or the data generated by the simulations.In this work, we present an agent-based model that targets information requirements of decision-makers who are setting policy at a local level, or translate population level directives to local entities and operations. We employ an agent-based modeling approach, which enables us to generate simulations that respond directly to the requirements of the good regulator theorem. Transmission events take place within a spatio-temporal frame of reference in this model, and rates are not conditioned by a reproduction rate (R0) that is specified a priori. Events are a function of movement and proximity. To summarize dynamics and associated outcomes of simulated epidemics, we employ metrics reflecting topological structure of transmission chains, and distributions of those structures. These measures point directly to dynamic features of simulated outbreaks, they operationalize the “efficiency” construct, and they are responsive to changes in parameters that govern dynamics of the simulations.

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A unified framework for analysis of individual-based models in ecology and beyond

Cited by 27 publications

References 31 publications

Habitat fragmentation and species diversity in competitive communities

Habitat fragmentation and species diversity in competitive communities

Mathematical and simulation methods for deriving extinction thresholds in spatial and stochastic models of interacting agents

CovidSIMVL --Transmission Trees, Superspreaders and Contact Tracing in Agent Based Models of Covid-19

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