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Stonedahl, Forrest; Weintrop, David; Blikstein, Paulo; Shannon, Christine

doi:10.1145/2445196.2445518

“…Infected nodes will be recovered and change their colour to grey with probability γ. When a node becomes recovered, the links between it and its neighbours change their colour to grey and are no longer possible vectors for contagion spreading [18]. The results we obtain here are in agreement with those of Section 3 (compare Figures 2 and 3).…”

Section: The Risk Of Contagion Spreading and Its Optimal Control In Tsupporting

confidence: 91%

The Risk of Contagion Spreading and its Optimal Control in the Economy

Kostylenko

¹

,

Rodrigues

²

,

Torres

³

2019

Stat., optim. inf. comput.

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The global crisis of 2008 provoked a heightened interest among scientists to study the phenomenon, its propagation and negative consequences. The process of modelling the spread of a virus is commonly used in epidemiology. Conceptually, the spread of a disease among a population is similar to the contagion process in economy. This similarity allows considering the contagion in the world financial system using the same mathematical model of infection spread that is often used in epidemiology. Our research focuses on the dynamic behaviour of contagion spreading in the global financial network. The effect of infection by a systemic spread of risks in the network of national banking systems of countries is tested. An optimal control problem is then formulated to simulate a control that may avoid significant financial losses. The results show that the proposed approach describes well the reality of the world economy, and emphasizes the importance of international relations between countries on the financial stability.

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“…It suggests that the COVID-19 infection network is a sparse small network (iHHIN) compared to the overall population interaction network (HHIN). Thus, models based on homogenous mixing or averaging statistical models are likely to be of limited use 19,20 since they fail to capture nonlinearity and complexity emerging from stochastic and sparse events in uenced by individual human behavior as has been pointed out previously [21][22][23][24] . Remarkably, the heterogeneous model offers insight into the bimodal behavior of SARS-CoV-2 infection dynamics and demonstrates that effective interventions require strict execution (stringency) and careful temporal control (timing).…”

Section: Discussionmentioning

confidence: 99%

Optimality in COVID-19 vaccination strategies determined by heterogeneity in human-human interaction networks

Goldenbogen

¹

,

Adler

²

,

Bodeit

³

et al. 2020

Preprint

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Interactions between humans cause transmission of SARS-CoV-2. We demonstrate that heterogeneity in human-human interactions give rise to non-linear infection networks that gain complexity with time. Consequently, targeted vaccination strategies are challenged as such effects are not accurately captured by epidemiological models assuming homogeneous mixing. With vaccines being prepared for global deployment determining optimality for swiftly reaching population level immunity in heterogeneous local communities world-wide is critical. We introduce a model that predicts the effect of vaccination into an ongoing COVID-19 outbreak using precision simulation of human-human interaction and infection networks. We show that simulations incorporating non-linear network complexity and local heterogeneity can enable governance with performance-quantified vaccination strategies. Vaccinating highly interactive people diminishes the risk for an infection wave, while vaccinating the elderly reduces fatalities at low population level immunity. Interestingly, a combined strategy is not better due to non-linear effects. While risk groups should be vaccinated first to minimize fatalities, significant optimality branching is observed with increasing population level immunity. Importantly, we demonstrate that regardless of immunization strategy non-pharmaceutical interventions are required to prevent ICU overload and breakdown of healthcare systems. The approach, adaptable in real-time and applicable to other viruses, provides a highly valuable platform for the current and future pandemics.

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“…The copyright holder for this this version posted December 18, 2020. ; https://doi.org/10.1101/2020.12.16.20248301 doi: medRxiv preprint has been pointed out previously [21][22][23][24] . Remarkably, the heterogeneous model offers insight into the bimodal behavior of SARS-CoV-2 infection dynamics and demonstrates that effective interventions require strict execution (stringency) and careful temporal control (timing).…”

Section: Discussionmentioning

confidence: 99%

Adaptive combination of interventions required to reach population immunity due to stochastic community dynamics and limited vaccination

Goldenbogen

¹

,

Adler

²

,

Bodeit

³

et al. 2020

Preprint

View full text Add to dashboard Cite

Interactions between humans cause transmission of SARS-CoV-2. We demonstrate that heterogeneity in human-human interactions give rise to non-linear infection networks that gain complexity with time. Consequently, targeted vaccination strategies are challenged as such effects are not accurately captured by epidemiological models assuming homogeneous mixing. With vaccines being prepared for global deployment determining optimality for swiftly reaching population level immunity in heterogeneous local communities world-wide is critical. We introduce a model that predicts the effect of vaccination into an ongoing COVID-19 outbreak using precision simulation of human-human interaction and infection networks. We show that simulations incorporating non-linear network complexity and local heterogeneity can enable governance with performance-quantified vaccination strategies. Vaccinating highly interactive people diminishes the risk for an infection wave, while vaccinating the elderly reduces fatalities at low population level immunity. Interestingly, a combined strategy is not better due to non-linear effects. While risk groups should be vaccinated first to minimize fatalities, significant optimality branching is observed with increasing population level immunity. Importantly, we demonstrate that regardless of immunization strategy non-pharmaceutical interventions are required to prevent ICU overload and breakdown of healthcare systems. The approach, adaptable in real-time and applicable to other viruses, provides a highly valuable platform for the current and future pandemics.

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Cited by 8 publications

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The Risk of Contagion Spreading and its Optimal Control in the Economy

The Risk of Contagion Spreading and its Optimal Control in the Economy

Optimality in COVID-19 vaccination strategies determined by heterogeneity in human-human interaction networks

Adaptive combination of interventions required to reach population immunity due to stochastic community dynamics and limited vaccination

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