# A dynamic model of COVID-19 infection quantifies the impact of preventive interventions on the infection of severely immunocompromised subjects in the United Kingdom

**Authors:** Carmen Pin, Sylvia Taylor, Catia Ferreira, Sofie Arnetorp, Holly Kimko

PMC · DOI: 10.1371/journal.pone.0341331 · PLOS One · 2026-02-23

## TL;DR

A mathematical model of COVID-19 in the UK shows that immunocompromised individuals remain at high risk despite general vaccination success, and targeted interventions like passive immunization could help.

## Contribution

A novel dynamic model quantifies the impact of preventive strategies on severely immunocompromised individuals during the COVID-19 pandemic.

## Key findings

- The UK vaccination program reduced general population hospitalizations and deaths but left severely immunocompromised individuals at high risk.
- Simulated protective strategies like passive immunization during SARS-CoV-2 peaks could significantly reduce infections in vulnerable groups.
- Mathematical models can effectively assess complex interactions and intervention impacts in both immunocompetent and immunosuppressed populations.

## Abstract

The disproportional risk of microbial infections affecting immunocompromised individuals underlines the critical need to develop effective infection preventive strategies. Using the COVID-19 pandemic as an example, we developed a mathematical model to evaluate interventions to protect severely immunocompromised (SIC) subjects against COVID-19. Predictions were well-aligned with UK available data for 2021 and 2022, and the model was used to retrospectively quantify the impact of preventive interventions in alternative scenarios during that period. Model simulations indicated that while the UK vaccination program reduced hospitalizations and deaths in the general population, SIC subjects remained at high risk of severe COVID-19. Simulated protective strategies, such as passive immunization, during seasonal SARS-CoV-2 peaks, showed potential to significantly reduce infection rates in this vulnerable group. We demonstrated the application of mathematical models to describe complex interactions among multiple dynamic processes and assess interventions to prevent disease transmission in both immunocompetent and immunosuppressed individuals.

## Linked entities

- **Diseases:** COVID-19 (MONDO:0100096)

## Full-text entities

- **Diseases:** microbial infections (MESH:D015163), pneumonia (MESH:D011014), primary immunodeficiency (MESH:D000081207), haematological malignancies (MESH:D009369), multiple myeloma (MESH:D009101), lymphomas (MESH:D008223), HIV (MESH:D015658), SIC (MESH:D045169), leukemias (MESH:D007938), viral or bacterial infections (MESH:D014777), coronavirus disease (MESH:D018352), death (MESH:D003643), COVID 19 infection (MESH:D000086382), Infection (MESH:D007239)
- **Species:** Severe acute respiratory syndrome coronavirus 2 (no rank) [taxon 2697049], Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

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## References

54 references — full list in the complete paper: https://tomesphere.com/paper/PMC12928435/full.md

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Source: https://tomesphere.com/paper/PMC12928435