Compartmental structures used in modeling COVID-19: a scoping review

Kong, Lingcai; Duan, Mengwei; Shi, Jin; Hong, Jie; Chang, Zhaorui; Zhang, Zhijie

doi:10.1186/s40249-022-01001-y

Cited by 37 publications

(29 citation statements)

References 59 publications

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“…Combining the transmission characteristics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we established the COVID-19 transmission dynamics model based on the classic SEIR model [ 12 ]. The structure of the dynamic model compartment is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Optimization of COVID-19 prevention and control measures during the Beijing 2022 Winter Olympics: a model-based study

et al. 2022

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Background The continuous mutation of severe acute respiratory syndrome coronavirus 2 has made the coronavirus disease 2019 (COVID-19) pandemic complicated to predict and posed a severe challenge to the Beijing 2022 Winter Olympics and Winter Paralympics held in February and March 2022. Methods During the preparations for the Beijing 2022 Winter Olympics, we established a dynamic model with pulse detection and isolation effect to evaluate the effect of epidemic prevention and control measures such as entry policies, contact reduction, nucleic acid testing, tracking, isolation, and health monitoring in a closed-loop management environment, by simulating the transmission dynamics in assumed scenarios. We also compared the importance of each parameter in the combination of intervention measures through sensitivity analysis. Results At the assumed baseline levels, the peak of the epidemic reached on the 57th day. During the simulation period (100 days), 13,382 people infected COVID-19. The mean and peak values of hospitalized cases were 2650 and 6746, respectively. The simulation and sensitivity analysis showed that: (1) the most important measures to stop COVID-19 transmission during the event were daily nucleic acid testing, reducing contact among people, and daily health monitoring, with cumulative infections at 0.04%, 0.14%, and 14.92% of baseline levels, respectively (2) strictly implementing the entry policy and reducing the number of cases entering the closed-loop system could delay the peak of the epidemic by 9 days and provide time for medical resources to be mobilized; (3) the risk of environmental transmission was low. Conclusions Comprehensive measures under certain scenarios such as reducing contact, nucleic acid testing, health monitoring, and timely tracking and isolation could effectively prevent virus transmission. Our research results provided an important reference for formulating prevention and control measures during the Winter Olympics, and no epidemic spread in the closed-loop during the games indirectly proved the rationality of our research results. Graphical Abstract

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Section: Methodsmentioning

confidence: 99%

Optimization of COVID-19 prevention and control measures during the Beijing 2022 Winter Olympics: a model-based study

et al. 2022

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“…However, despite these innovations, compartmental models, where the population is split into a number of subgroups and disease transmission is modelled by a system of differential equations (Abou-Ismail 2020 ), remain a popular choice for epidemiologists and have been widely used for modelling the COVID-19 pandemic (Kong et al. 2022 ). As discussed in (Kong et al.…”

Section: Introductionmentioning

confidence: 99%

“…As discussed in (Kong et al. 2022 ), a number of different compartment structures have been used, while many authors have also sought to model the effect of government interventions and quarantining procedures (Vardavas et al. 2021 ; de Camino-Beck 2020 ; Adhikari et al.…”

Section: Introductionmentioning

confidence: 99%

Asymptotic Analysis of Optimal Vaccination Policies

Penn

Donnelly

2023

Bull Math Biol

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Targeted vaccination policies can have a significant impact on the number of infections and deaths in an epidemic. However, optimising such policies is complicated, and the resultant solution may be difficult to explain to policy-makers and to the public. The key novelty of this paper is a derivation of the leading-order optimal vaccination policy under multi-group susceptible–infected–recovered dynamics in two different cases. Firstly, it considers the case of a small vulnerable subgroup in a population and shows that (in the asymptotic limit) it is optimal to vaccinate this group first, regardless of the properties of the other groups. Then, it considers the case of a small vaccine supply and transforms the optimal vaccination problem into a simple knapsack problem by linearising the final size equations. Both of these cases are then explored further through numerical examples, which show that these solutions are also directly useful for realistic parameter values. Moreover, the findings of this paper give some general principles for optimal vaccination policies which will help policy-makers and the public to understand the reasoning behind optimal vaccination programs in more generic cases.

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“…In another review paper, Rahimi et al [ 14 ] reported that SEIR models are in the top three most common COVID-19 outbreak modeling methods. SEIR-type models have been commonly applied in modeling COVID-19 epidemics and in capturing its complexities, incorporating external factors such as non-pharmaceutical interventions (NPIs) and vaccination [ 15 ]. The SEIR-type compartment model is relatively easy to understand, formulate, analyze, and extend to accommodate a wide range of disease-related processes.…”

Section: Introductionmentioning

confidence: 99%

Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model

Hatami

Chen

Paul

et al. 2022

IJERPH

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The global COVID-19 pandemic has taken a heavy toll on health, social, and economic costs since the end of 2019. Predicting the spread of a pandemic is essential to developing effective intervention policies. Since the beginning of this pandemic, many models have been developed to predict its pathways. However, the majority of these models assume homogeneous dynamics over the geographic space, while the pandemic exhibits substantial spatial heterogeneity. In addition, spatial interaction among territorial entities and variations in their magnitude impact the pandemic dynamics. In this study, we used a spatial extension of the SEIR-type epidemiological model to simulate and predict the 4-week number of COVID-19 cases in the Charlotte–Concord–Gastonia Metropolitan Statistical Area (MSA), USA. We incorporated a variety of covariates, including mobility, pharmaceutical, and non-pharmaceutical interventions, demographics, and weather data to improve the model’s predictive performance. We predicted the number of COVID-19 cases for up to four weeks in the 10 counties of the studied MSA simultaneously over the time period 29 March 2020 to 13 March 2021, and compared the results with the reported number of cases using the root-mean-squared error (RMSE) metric. Our results highlight the importance of spatial heterogeneity and spatial interactions among locations in COVID-19 pandemic modeling.

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Compartmental structures used in modeling COVID-19: a scoping review

Cited by 37 publications

References 59 publications

Optimization of COVID-19 prevention and control measures during the Beijing 2022 Winter Olympics: a model-based study

Optimization of COVID-19 prevention and control measures during the Beijing 2022 Winter Olympics: a model-based study

Asymptotic Analysis of Optimal Vaccination Policies

Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model

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