Agent-based modelling of reactive vaccination of workplaces and schools against COVID-19

Faucher, Benjamin; Assab, Rania; Roux, Jonathan; Lévy-Brühl, Daniel; Kiem, Cécile Tran; Cauchemez, Simon; Zanetti, Laura; Colizza, Vittoria; Boëlle, Pierre‐Yves; Poletto, Chiara

doi:10.1038/s41467-022-29015-y

Cited by 24 publications

(12 citation statements)

References 55 publications

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“…Second, we used a rather simple coupling with the community, through regular introduction of cases, as the data we considered do not include contacts occurring outside of the studied context. This implies that we do not evaluate the impact of the interventions on the community: different approaches would be needed for this purpose [ 22 , 53 ], which, however, would lose resolution within each setting. Without going to such large-scale agent-based models, a possible improvement would be to inform the model with empirical data on the contacts that individuals have with members of the community, or with one another outside of school.…”

Section: Discussionmentioning

confidence: 99%

Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases

Contreras

Colosi

Bassignana

et al. 2022

J. R. Soc. Interface.

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Computational models offer a unique setting to test strategies to mitigate the spread of infectious diseases, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high-resolution data describing contacts between individuals are increasingly available, data gathering remains challenging, especially during a health emergency. Many models thus use synthetic data or coarse information to evaluate intervention protocols. Here, we evaluate how the representation of contact data might affect the impact of various strategies in models, in the realm of COVID-19 transmission in educational and work contexts. Starting from high-resolution contact data, we use detailed to coarse data representations to inform a model of SARS-CoV-2 transmission and simulate different mitigation strategies. We find that coarse data representations estimate a lower risk of superspreading events. However, the rankings of protocols according to their efficiency or cost remain coherent across representations, ensuring the consistency of model findings to inform public health advice. Caution should be taken, however, on the quantitative estimations of those benefits and costs triggering the adoption of protocols, as these may depend on data representation.

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Section: Discussionmentioning

confidence: 99%

Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases

Contreras

Colosi

Bassignana

et al. 2022

J. R. Soc. Interface.

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“…We set 𝜔 = 0.3 (Ref. 25 ), 𝜙 = = 1 (i.e. all household contacts obey homophily) and 𝜙 > = 0.25 and investigated alternative choices in the appendix.…”

Section: Proportion Of the Reproduction Ratio Attributable To Vaccina...mentioning

confidence: 99%

The importance of increasing primary vaccinations against COVID-19 in Europe

Boëlle

Valdano

2023

Preprint

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In the European Union, mass vaccination against COVID-19 staved off the strict restrictions that had characterized early epidemic response. Now, vaccination campaigns are focusing on booster doses, and primary vaccinations have all but halted. Still, 52 million European adults are unvaccinated. We investigated if reaching the still unvaccinated population in future vaccination campaigns would substantially decrease the current burden of COVID-19, which is substantial. We focused on vaccination homophily, whereby those who are unvaccinated are mostly in contact with other unvaccinated, making COVID-19 circulation easier. We quantified vaccination homophily and estimated its impact on COVID-19 circulation. We used an online survey of 1,055,286 people from 22 European countries during early 2022. We computed vaccination homophily as the association between reported vaccination status and perceived vaccination uptake among one's own social contacts, using a case-referent design and a hierarchical logistic model. We used this information in an analysis of the COVID-19 reproduction ratio to determine the impact of vaccine homophily in transmission. Vaccination homophily was present and strong everywhere: the average odds ratio of being vaccinated for a 10-percentage-point increase in coverage among contacts was 1.66 (95% CI=(1.60, 1.72)). Homophily was positively associated with the strictness of COVID-19-related restrictions in 2020 (Pearson=0.49, p-value=0.03). In the countries studied, 12%-to-18% of the reproduction ratio would be attributable to vaccine homophily. Reducing vaccination homophily may curb the reproduction ratio substantially even to the point of preventing recurrent epidemic waves. In addition to boosting those already vaccinated, increasing primary vaccination should remain a high priority in future vaccination campaigns, to reduce vaccination homophily: this combined strategy may decrease COVID-19 burden.

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“…Consequently, these efforts are hindered by the limitations of compartmental models and hence they are not able to incorporate subject‐specific risk and contact information. Given this, another stream of work, which has recently gained momentum, focuses on constructing more accurate agent‐based simulations that overcome these drawbacks (e.g., Faucher et al, 2022; Gomez Vazquez et al, 2022; Lee, Zabinsky, et al, 2022; Trad & El Falou, 2022). Although this line of work can provide valuable insights on the impact of certain policies on the disease dynamics, they unfortunately do not investigate the structure and composition of optimal vaccine distribution strategies.…”

Section: Introduction Motivation and Backgroundmentioning

confidence: 99%

Quantifying the benefits of customized vaccination strategies: A network‐based optimization approach

Butenko

2023

Naval Research Logistics

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We study the problem of designing vaccine distribution strategies that maximally mitigate the negative impact of an infectious disease outbreak. This is achieved through a multiperiod optimization‐based framework that embeds important subject‐specific risk and contact information into the decision‐making process. By analyzing the structure of the resulting optimization problem, we identify key structural properties which we use to construct a globally convergent solution scheme (suitable for smaller problem instances) and two, more scalable, heuristic schemes. We demonstrate the benefits of the considered framework through a case study on COVID‐19 in Texas. Our results highlight the importance of considering risk and contact information as doing so substantially reduces the total expected number of fatalities over conventional compartmental‐based approaches. These findings indicate that customization can have a significant benefit, particularly for community‐scale planning.

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Agent-based modelling of reactive vaccination of workplaces and schools against COVID-19

Cited by 24 publications

References 55 publications

Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases

Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases

The importance of increasing primary vaccinations against COVID-19 in Europe

Quantifying the benefits of customized vaccination strategies: A network‐based optimization approach

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