• Corpus ID: 246276390

Optimal Lockdown for Pandemic Control

@inproceedings{Ma2020OptimalLF,
  title={Optimal Lockdown for Pandemic Control},
  author={Qianqian Ma and Yangyang Liu and Alexander Olshevsky},
  year={2020}
}
As a common strategy of contagious disease containment, lockdown will inevitably weaken the economy. The ongoing COVID-19 pandemic underscores the trade-off arising from public health and economic cost. An optimal lockdown policy to resolve this trade-off is highly desired. Here we propose a mathematical framework of pandemic control through an optimal non-uniform lockdown, where our goal is to reduce the economic activity as little as possible while decreasing the number of infected… 

Intermittent non-pharmaceutical strategies to mitigate the COVID-19 epidemic in a network model of Italy via constrained optimization

By studying a variational equation for the dynamics of the infected in a network model of the epidemic spread, a condition is derived that can be used to guarantee that, in epidemiological terms, the effective reproduction number is less than unity.

Mitigating virus spread through dynamic control of community-based social interactions for infection rate and cost

Two methods are proposed to determine dynamically the extent of contact restriction during a virus outbreak, and the effectiveness of the methods in decreasing both infection rate and social distancing cost compared to naive methods is demonstrated.

Minimum Effort Decentralized Control Design for Contracting Network Systems

We consider the problem of making a networked system contracting by designing “minimal effort” local controllers. Our method combines a hierarchical contraction characterization and a

Adaptive Trajectory Prediction via Transferable GNN

A novel Transferable Graph Neural Network frame-work is proposed, which jointly conducts trajectory prediction as well as domain alignment in a unified framework and an attention-based adaptive knowledge learning module is further proposed to explore fine-grained individual-level feature representations for knowledge transfer.

Generative Multi-Label Correlation Learning

A general and compact Multi-Label Correlation Learning (MUCO) framework that explicitly and effectively learns the latent label correlations by updating a label correlation tensor, which provides high accurate and interpretable prediction results.

Generic Multi-label Annotation via Adaptive Graph and Marginalized Augmentation

A generic multi-label learning framework based on Adaptive Graph and Marginalized Augmentation (AGMA) in a semi-supervised scenario and makes use of a small amount of labeled data associated with a lot of unlabeled data to boost the learning performance.

Confident Anchor-Induced Multi-Source Free Domain Adaptation

A novel Confident-Anchor-induced multisource-free Domain Adaptation (CAiDA) model is developed, which is a pioneer exploration of knowledge adaptation from multiple source domains to the unlabeled target domain without any source data, but with only pre-trained source models.

Semi-Supervised Domain Adaptive Structure Learning

An adaptive structure learning method to regularize the cooperation of SSL and DA, inspired by the multi-views learning, that applies the maximum mean discrepancy (MMD) distance minimization and self-training (ST) to project the contradictory structures into a shared view to make the reliable final decision.

Task Oriented Video Coding: A Survey

Recent progress on computer vision task oriented video coding and emerging video coding standard, Video Coding for Machines, is explored and summarized.

References

SHOWING 1-10 OF 75 REFERENCES

A Simple Planning Problem for COVID-19 Lockdown

We study the optimal lockdown policy for a planner who wants to control the fatalities of a pandemic while minimizing the output costs of the lockdown. We use the SIR epidemiology model and a linear

A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic

This work confirms the effectiveness at the regional level of the national lockdown strategy and proposes coordinated regional interventions to prevent future national lockdowns, while avoiding saturation of the regional health systems and mitigating impact on costs.

Optimal Resource Allocation for Control of Networked Epidemic Models

This paper proposes and analyzes a generalized epidemic model over arbitrary directed graphs with heterogeneous nodes. The proposed model, called the generalized–susceptible exposed infected

Optimal Targeted Lockdowns in a Multi-Group Sir Model

It is found that optimal policies differentially targeting risk/age groups significantly outperform optimal uniform policies and most of the gains can be realized by having stricter lockdown policies on the oldest group.

Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures

Data on population movements can be helpful in designing targeted policy responses to curb epidemic spread. However, it is not clear how to exactly leverage such data and how valuable they might be

Optimal control of epidemics in metapopulations

It is shown, for a system with two interconnected regions and an epidemic in which infected individuals recover and can be reinfected, that equalizing infection in the two regions is the worst possible strategy in minimizing the total level of infection.

The Optimal Control of Infectious Diseases Via Prevention and Treatment

This paper fully characterizes the optimal control of a recurrent infectious disease through the use of (non-vaccine) prevention and treatment. The dynamic system may admit multiple steady states and
...