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. 2024 Mar 1;19(3):e0298932.
doi: 10.1371/journal.pone.0298932. eCollection 2024.

Multi-feature SEIR model for epidemic analysis and vaccine prioritization

Yingze Hou  1 , Hoda Bidkhori  1   2
Affiliations

Multi-feature SEIR model for epidemic analysis and vaccine prioritization

Yingze Hou et al. PLoS One. .

Abstract

The SEIR (susceptible-exposed-infected-recovered) model has become a valuable tool for studying infectious disease dynamics and predicting the spread of diseases, particularly concerning the COVID pandemic. However, existing models often oversimplify population characteristics and fail to account for differences in disease sensitivity and social contact rates that can vary significantly among individuals. To address these limitations, we have developed a new multi-feature SEIR model that considers the heterogeneity of health conditions (disease sensitivity) and social activity levels (contact rates) among populations affected by infectious diseases. Our model has been validated using the data of the confirmed COVID cases in Allegheny County (Pennsylvania, USA) and Hamilton County (Ohio, USA). The results demonstrate that our model outperforms traditional SEIR models regarding predictive accuracy. In addition, we have used our multi-feature SEIR model to propose and evaluate different vaccine prioritization strategies tailored to the characteristics of heterogeneous populations. We have formulated optimization problems to determine effective vaccine distribution strategies. We have designed extensive numerical simulations to compare vaccine distribution strategies in different scenarios. Overall, our multi-feature SEIR model enhances the existing models and provides a more accurate picture of disease dynamics. It can help to inform public health interventions during pandemics/epidemics.

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Conflict of interest statement

Since the authors are affiliated with the University of Pittsburgh and George Mason University, they have competing interests with those institutes. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. The classic SEIR model.
Each state are noted in hollow letters. Their corresponding rate of change to the next state are marked on the arrow.
Fig 2
Fig 2. Multi-feature SEIR model.
Population is classified into difference (i, j) divisions, with sensitivity si and contact rate cj.
Fig 3
Fig 3. Population changes for St population.
All four kinds of people are susceptible at time t. White people have no changes and remain susceptible for time t + 1. Green people get vaccinated during this time and are not exposed to the virus. Red people get virus-transmitted from other virus carriers and do not receive vaccination. Green-red people get vaccinated and get virus-transmitted. But they are treated as vaccinated people with immunity, and will not be counted as exposed for time t + 1.
Fig 4
Fig 4. Weekly COVID confirmed and estimated cases of new infection in Allegheny County.
The vertical axis is the infected population. We compare the estimation of the infected population using historical data among the classic SEIR model, our proposed multi-feature SEIR, and actual confirmed infection cases. The observation period concludes on June 30th, 2021.
Fig 5
Fig 5. Weekly COVID confirmed and estimated cases of new infection in Hamilton County.
The vertical axis is the infected population. We compare the estimates of the infected population using the classic SEIR model, our proposed multi-feature SEIR model, and the actual confirmed infection cases. The observation period concludes on July 5th, 2021.
Fig 6
Fig 6. Population change of exposed, infected, and dead state in situation 3 using intuition-based strategies and approximated optimal strategies.
The population changes under given parameters and different vaccination strategies. All strategies perform similarly. Situation 1 and 2 present observably indifferent results, so only Situation 3 is presented.
See this image and copyright information in PMC

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