[Paper Review] Optimal vaccination strategies for COVID-19 based on dynamical social networks with real-time updating

Sibo Cheng, Rossella Arcucci+2 more|arXiv (Cornell University)|Feb 19, 2021

COVID-19 epidemiological studies4 citations

TL;DR

This study proposes a data assimilation-based sequential network updating method to optimize COVID-19 vaccination strategies in dynamic, time-varying contact networks with limited and noisy data. By integrating real-time temporal information to refine network structures, the approach identifies high-degree or high-centrality individuals for prioritized vaccination, significantly improving effectiveness over standard or random strategies in SIR model simulations using real and synthetic networks.

ABSTRACT

Vaccination strategy is crucial in fighting against the COVID-19 pandemic. Since the supply is limited, contact network-based interventions can be most powerful to set an optimal strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamical systems where the contact networks are highly time-variant. Furthermore, numerous mutations of SARS-CoV-2 impact considerably the current infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from the assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamical networks collected in a high school, following by sequential multi-layer networks, generated relying on the Barabasi-Albert model emulating the department of Computing at Imperial College London in the UK as an example.

Motivation & Objective

• To address the challenge of limited and noisy contact network data in dynamic, time-varying social networks during the COVID-19 pandemic.
• To develop a real-time network updating mechanism that integrates temporal data for improved vaccination strategy design.
• To evaluate the effectiveness of network-based vaccination prioritization using dynamic, real-world and synthetic networks.
• To compare the proposed assimilation-based strategy with standard partial-network and random vaccination approaches in terms of epidemic control.

Proposed method

• The study employs data assimilation techniques to sequentially update dynamic contact networks using real-time temporal observations.
• It combines multiple sources of partial and noisy network data to estimate a more accurate, time-evolving network structure.
• High-degree and high-centrality individuals are identified from the assimilated networks as priority targets for vaccination.
• The vaccination strategies are evaluated using a stochastic SIR model on both real-world face-to-face interaction networks and synthetic multi-layer networks generated via the Barabasi-Albert model.
• The assimilation process enables continuous refinement of network topology, improving the accuracy of risk assessment over time.
• The approach is validated on empirical data from a high school and synthetic networks modeling a UK university department.

Experimental results

Research questions

• RQ1 How does real-time network updating via data assimilation improve the accuracy of contact network estimation in dynamic, partially observed systems?
• RQ2 What is the relative effectiveness of the assimilation-based vaccination strategy compared to standard network-based and random vaccination approaches?
• RQ3 How do network centrality and degree influence the success of targeted vaccination in controlling epidemic spread?
• RQ4 To what extent does the dynamic nature of contact networks affect the performance of static or delayed network-based vaccination strategies?
• RQ5 Can the proposed method maintain high vaccination effectiveness under limited and noisy network data?

Key findings

• The assimilation-based vaccination strategy significantly outperforms both the standard method using partially observed networks and random vaccination in reducing epidemic size.
• Prioritization based on assimilated network structures—particularly high-degree and high-centrality individuals—leads to more effective containment of disease spread.
• The method maintains high performance even with limited and noisy network observations by continuously updating network estimates.
• In simulations using real-world high school contact networks, the assimilation approach achieved superior epidemic control compared to baseline strategies.
• On synthetic multi-layer networks modeling a university department, the method demonstrated robustness and scalability across varying network topologies.
• The results confirm that real-time network updating enhances the precision of risk identification, directly translating into improved vaccination outcomes.