Quarantines as a Targeted Immunization Strategy
- URL: http://arxiv.org/abs/2008.08262v3
- Date: Sun, 21 Feb 2021 01:43:28 GMT
- Title: Quarantines as a Targeted Immunization Strategy
- Authors: Jessica Hoffmann, Matt Jordan, Constantine Caramanis
- Abstract summary: We show that immunizing high-degree nodes can efficiently guarantee herd immunity.
We propose an opening and closing strategy aiming at immunizing the graph while infecting the minimum number of individuals.
- Score: 26.562338722051866
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the context of the recent COVID-19 outbreak, quarantine has been used to
"flatten the curve" and slow the spread of the disease. In this paper, we show
that this is not the only benefit of quarantine for the mitigation of an SIR
epidemic spreading on a graph. Indeed, human contact networks exhibit a
powerlaw structure, which means immunizing nodes at random is extremely
ineffective at slowing the epidemic, while immunizing high-degree nodes can
efficiently guarantee herd immunity. We theoretically prove that if quarantines
are declared at the right moment, high-degree nodes are disproportionately in
the Removed state, which is a form of targeted immunization. Even if
quarantines are declared too early, subsequent waves of infection spread slower
than the first waves. This leads us to propose an opening and closing strategy
aiming at immunizing the graph while infecting the minimum number of
individuals, guaranteeing the population is now robust to future infections. To
the best of our knowledge, this is the only strategy that guarantees herd
immunity without requiring vaccines. We extensively verify our results on
simulated and real-life networks.
Related papers
- Opponent Shaping for Antibody Development [49.26728828005039]
Anti-viral therapies are typically designed to target only the current strains of a virus.
therapy-induced selective pressures act on viruses to drive the emergence of mutated strains, against which initial therapies have reduced efficacy.
We build on a computational model of binding between antibodies and viral antigens to implement a genetic simulation of viral evolutionary escape.
arXiv Detail & Related papers (2024-09-16T14:56:27Z) - DNA: Differentially private Neural Augmentation for contact tracing [62.740950398187664]
Contact tracing is an effective way to reduce infection rates by detecting potential virus carriers early.
We substantially improve the privacy guarantees of the current state of the art in decentralized contact tracing.
This work marks an important first step in integrating deep learning into contact tracing while maintaining essential privacy guarantees.
arXiv Detail & Related papers (2024-04-20T13:43:28Z) - Graph Adversarial Immunization for Certifiable Robustness [63.58739705845775]
Graph neural networks (GNNs) are vulnerable to adversarial attacks.
Existing defenses focus on developing adversarial training or model modification.
We propose and formulate graph adversarial immunization, i.e., vaccinating part of graph structure.
arXiv Detail & Related papers (2023-02-16T03:18:43Z) - Dense Feature Memory Augmented Transformers for COVID-19 Vaccination
Search Classification [60.49594822215981]
This paper presents a classification model for detecting COVID-19 vaccination related search queries.
We propose a novel approach of considering dense features as memory tokens that the model can attend to.
We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task.
arXiv Detail & Related papers (2022-12-16T13:57:41Z) - Evaluating vaccine allocation strategies using simulation-assisted
causal modelling [7.9656669215132005]
Early on during a pandemic, vaccine availability is limited, requiring prioritisation of different population groups.
We develop a model to retrospectively evaluate age-dependent counterfactual vaccine allocation strategies against the COVID-19 pandemic.
We compare Israel's implemented vaccine allocation strategy in 2021 to counterfactual strategies such as no prioritisation, prioritisation of younger age groups or a strict risk-ranked approach.
arXiv Detail & Related papers (2022-12-14T14:24:17Z) - VacciNet: Towards a Smart Framework for Learning the Distribution Chain
Optimization of Vaccines for a Pandemic [0.0]
We put forward a novel framework leveraging Supervised Learning and Reinforcement Learning (RL) which we call VacciNet.
RL is capable of learning to predict the demand of vaccination in a state of a country as well as suggest optimal vaccine allocation in the state for minimum cost of procurement and supply.
arXiv Detail & Related papers (2022-08-01T19:37:33Z) - Reducing COVID-19 Cases and Deaths by Applying Blockchain in Vaccination
Rollout Management [2.0154553201329715]
We model a trustable and reliable management system based on blockchain for vaccine distribution.
We show that the proposed system can reduce up to 2.5 million cases and half a million deaths in the most demanding scenarios.
arXiv Detail & Related papers (2022-01-27T18:31:41Z) - Understanding Human Innate Immune System Dependencies using Graph Neural
Networks [0.0]
We propose a graph neural network-based model that exploits the interactions between pattern recognition receptors (PRRs) to predict activation requirements of each PRR.
Results show an average IFNs activation prediction accuracy of 90%, compared to 85% using feed-forward neural networks.
arXiv Detail & Related papers (2021-08-05T22:30:47Z) - Adversarial Immunization for Certifiable Robustness on Graphs [47.957807368630995]
Graph neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning models.
We propose and formulate the graph adversarial immunization problem, i.e., vaccinating an affordable fraction of node pairs, connected or unconnected, to improve robustness of graph against any admissible adversarial attack.
arXiv Detail & Related papers (2020-07-19T10:41:10Z) - A Deep Q-learning/genetic Algorithms Based Novel Methodology For
Optimizing Covid-19 Pandemic Government Actions [63.669642197519934]
We use the SEIR epidemiological model to represent the evolution of the virus COVID-19 over time in the population.
The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system.
We prove that our methodology is a valid tool to discover actions governments can take to reduce the negative effects of a pandemic in both senses.
arXiv Detail & Related papers (2020-05-15T17:17:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.