Beyond COVID-19: network science and sustainable exit strategies

Bell, James; Bianconi, Ginestra; Butler, David K.; Crowcroft, Jon; Davies, Paul; Hicks, C; Kim, H.; Kiss, István Z.; Lauro, Francesco Di; Maple, Carsten; Paul, Ayan; Prokopenko, Mikhail; Tee, Philip; Walker, Sara Imari

doi:10.1088/2632-072x/abcbea

Cited by 20 publications

(16 citation statements)

References 89 publications

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“…This captures interlinkages that may form (or disappear) at levels higher than the interaction of individual species. This may occur through decoupling of habitats (e.g., damming or dividing habitats by road/structure construction) or via coupling the previously unconnected (e.g., linking underwater oil reserves to surface waters and beaches in the Deepwater Horizon oil spill (Beyer et al 2016), air travel linkages that connect pathogen hotspots globally (Tatem et al 2006;Bell et al 2021), or maritime transport that facilitates long-range dispersals (Wilson et al 2009;Blakeslee et al 2010;Lymperopoulou and Dobbs 2017)). 9.…”

Section: Change In Community Compositionmentioning

confidence: 99%

Symbiosis and the Anthropocene

Hom

Penn

2021

Symbiosis

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Recent human activity has profoundly transformed Earth biomes on a scale and at rates that are unprecedented. Given the central role of symbioses in ecosystem processes, functions, and services throughout the Earth biosphere, the impacts of human-driven change on symbioses are critical to understand. Symbioses are not merely collections of organisms, but co-evolved partners that arise from the synergistic combination and action of different genetic programs. They function with varying degrees of permanence and selection as emergent units with substantial potential for combinatorial and evolutionary innovation in both structure and function. Following an articulation of operational definitions of symbiosis and related concepts and characteristics of the Anthropocene, we outline a basic typology of anthropogenic change (AC) and a conceptual framework for how AC might mechanistically impact symbioses with select case examples to highlight our perspective. We discuss surprising connections between symbiosis and the Anthropocene, suggesting ways in which new symbioses could arise due to AC, how symbioses could be agents of ecosystem change, and how symbioses, broadly defined, of humans and “farmed” organisms may have launched the Anthropocene. We conclude with reflections on the robustness of symbioses to AC and our perspective on the importance of symbioses as ecosystem keystones and the need to tackle anthropogenic challenges as wise and humble stewards embedded within the system.

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Section: Change In Community Compositionmentioning

confidence: 99%

Symbiosis and the Anthropocene

Hom

Penn

2021

Symbiosis

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“…As it would be unpractical or even impossible to deal with the full hierarchy of evolution equations, it is common to close the system by expressing the higher-order joint probabilities in terms of lower-order ones. In the context of epidemic models, various closure methods have been adopted to develop individual and pair based models of continuoustime stochastic systems, both in the homogeneous and heterogeneous mean field approximation [11,24,25].…”

Section: A Individual-based Seair Modelmentioning

confidence: 99%

“…In the context of mathematical epidemiology, similar bottom-up approaches have been adopted to develop individual and pair-based continuous-time models on networks, and applied, for instance, to the SIR process in the homogeneous [24] and heterogeneous [25] mean-field approximation. Individual and pair-based approximations for the SIR process have also been analyzed in the discrete-time case where, unlike the standard continuoustime BBGKY-type hierarchy, the master equations describing the dynamics of the system are expressed in terms of joint probabilities, whose order is governed by the network structure itself via the degrees of individual nodes [26], thus resulting in a richer although more complicated mathematical structure.…”

Section: Introductionmentioning

confidence: 99%

Individual- and pair-based models of epidemic spreading: master equations and analysis of their forecasting capabilities

Federico¹,

Gallo²,

Frasca³

et al. 2021

Preprint

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Epidemic models are crucial to understand how an infectious disease spreads in a population, and to devise the best containment strategies. Compartmental models can provide a fine-grained description of the evolution of an epidemic when microscopic information on the network of contacts among individuals is available. However, coarser-grained descriptions prove also to be useful in many aspects. They allow to derive closed expressions for key parameters, such as the basic reproduction number, to understand the relationship between the model parameters, and also to derive fast and reliable predictions of macroscopic observables for a disease outbreak. The so-called population models can be developed at different levels of coarse-graining, so that it is crucial to determine: i) to which extent and how the existing correlations in the contact network have to be included in these models, and ii) what is their impact on the model ability to reproduce and predict the time evolution of the populations at the various stage of the disease. In this work, we address these questions through a systematic analysis of two discrete-time SEAIR (Susceptible-Exposed-Asymptomatic-Infected-Recovered) population models: the first one developed assuming statistical independence at the level of individuals, and the other one assuming independence at the level of pairs. We provide a detailed derivation and analysis of both models, focusing on their capability to reproduce an epidemic process on different synthetic networks, and then comparing their predictions under scenarios of increasing complexity. We find that, although both models can fit the time evolution of the compartment populations obtained through microscopic simulations, the epidemic parameters adopted by the individual-based model for this purpose may significantly differ from those of the microscopic simulations. However, pair-based model provides not only more reliable predictions of the dynamical evolution of the variables, but also a good estimation of the epidemic parameters. The difference between the two models is even more evident in the particularly challenging scenario when one or more variables are not measurable, and therefore are not available for model tuning. This occurs for instance with asymptomatic infectious individuals in the case of COVID-19, an issue that has become extremely relevant during the recent pandemic. Under these conditions, the pairwise model again proves to perform much better than the individual-based representation, provided that it is fed with adequate information which, for instance, to be collected, may require a more detailed contact tracing. Overall, our results thus hallmark the importance of acquiring the proper empirical data to fully unfold the potentialities of models incorporating more sophisticated assumptions on the correlations among nodes in the contact network.

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“…Many works have discussed the challenges of epidemic spreading modelling [5,6], a number of works have addressed outstanding theoretical problems that the current pandemic has highlighted [7,8,9,10,11,12] and a vast attention has been devoted to extract information from epidemic data [4,13,14,15]. Additionally scientific research has informed policy makers [16,17] establishing the role that containment measures such as social distancing, or contact tracing [18,19,20,21,22,23] have in mitigating the epidemic spread.…”

Section: Introductionmentioning

confidence: 99%

Critical time-dependent branching process modelling epidemic spreading with containment measures

Sun,

Kryven,

Bianconi

2022

Preprint

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During the COVID pandemic, periods of exponential growth of the disease have been mitigated by containment measures that in different occasions have resulted in a power-law growth of the number of cases. The first observation of such behaviour has been obtained from 2020 late spring data coming from China by Ziff and Ziff in Ref. [1]. After this important observation the power-law scaling (albeit with different exponents) has also been observed in other countries during periods of containment of the spread. Early interpretations of these results suggest that this phenomenon might be due to spatial effects of the spread. Here we show that temporal modulations of infectivity of individuals due to containment measures can also cause power-law growth of the number of cases over time. To this end we propose a stochastic wellmixed Susceptible-Infected-Removed (SIR) model of epidemic spreading in presence of containment measures resulting in a time dependent infectivity and we explore the statistical properties of the resulting branching process at criticality. We show that at criticality it is possible to observe power-law growth of the number of cases with exponents ranging between one and two. Our asymptotic analytical results are confirmed by extensive Monte Carlo simulations. Although these results do not exclude that spatial effects might be important in modulating the power-law growth of the number of cases at criticality, this work shows that even well-mixed populations may already feature non trivial power-law exponents at criticality.

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Beyond COVID-19: network science and sustainable exit strategies

Cited by 20 publications

References 89 publications

Symbiosis and the Anthropocene

Symbiosis and the Anthropocene

Individual- and pair-based models of epidemic spreading: master equations and analysis of their forecasting capabilities

Critical time-dependent branching process modelling epidemic spreading with containment measures

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