Epidemic Progression and Vaccination in a Heterogeneous Population. Application to the Covid-19 epidemic

Volpert, Vitaly; Banerjee, Malay; Sharma, Swarnali

doi:10.1101/2020.12.06.20244731

Cited by 3 publications

(5 citation statements)

References 26 publications

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“…Such a model was also applied to Luxembourg in [23], to study the interplay between the epidemiological and economic aspects of the COVID-19 pandemic. Multiple authors have used equation-based models to study optimal strategies for lifting restrictions [24] and vaccination [25, 26]. A approach utilising Bayesian techniques, and a game theoretical modelling of adherence to restrictions, has been applied in [27], while the use of game theory and social network models for decision making on vaccination programmes has been further emphasised in [28].…”

Section: State Of the Artmentioning

confidence: 99%

“…With several vaccines having being developed against COVID-19, a large number of articles have been written investigating their potential impact. Consider, for example [64][65][66][67][68][69][70][71][72][73][74][75]. In [66] and [73], the impact of vaccination on cases, hospitalisations and deaths was studied using agent-based models, these two articles focussing on areas in the United States and Canada, respectively.…”

Section: State Of the Artmentioning

confidence: 99%

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Estimating the impact of interventions against COVID-19: from lockdown to vaccination

Thompson

Wattam²

2021

Preprint

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Coronavirus disease 2019 (COVID-19) is an infectious disease of humans caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the first case was identified in China in December 2019 the disease has spread worldwide, leading to an ongoing pandemic. In this article, we present a detailed agent-based model of COVID-19 in Luxembourg, and use it to estimate the impact, on cases and deaths, of interventions including testing, contact tracing, lockdown, curfew and vaccination. Our model is based on collation, with agents performing activities and moving between locations accordingly. The model is highly heterogeneous, featuring spatial clustering, over 2000 behavioural types and a 10 minute time resolution. The model is validated against COVID-19 clinical monitoring data collected in Luxembourg in 2020. Our model predicts far fewer cases and deaths than the equivalent equation-based SEIR model. In particular, with $R_0 = 2.45$, the SEIR model infects 87% of the resident population while our agent-based model results, on average, in only around 23% of the resident population infected. Our simulations suggest that testing and contract tracing reduce cases substantially, but are much less effective at reducing deaths. Lockdowns appear very effective although costly, while the impact of an 11pm-6am curfew is relatively small. When vaccinating against a future outbreak, our results suggest that herd immunity can be achieved at relatively low levels, with substantial levels of protection achieved with only 30% of the population immune. When vaccinating in midst of an outbreak, the challenge is more difficult. In this context, we investigate the impact of vaccine efficacy, capacity, hesitancy and strategy. We conclude that, short of a permanent lockdown, vaccination is by far the most effective way to suppress and ultimately control the spread of COVID-19.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Estimating the impact of interventions against COVID-19: from lockdown to vaccination

Thompson

Wattam²

2021

Preprint

View full text Add to dashboard Cite

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“…Specifically, [20] captured two different epidemic patterns in different regions of Italy using probKMA algorithm [22], and [21] revealed different patterns of the epidemic across countries with functional principle component analysis. In addition, [23, 24, 25, 26, 27] adapted SEIR / Susceptible-Exposed-Infected (SEI) / Susceptible-Infected (SI) compartmental models similar to this paper. Among these works, [23, 24, 25] considered heterogeneity in the aspects of age groups, social links, and vaccination status separately.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, [23, 24, 25, 26, 27] adapted SEIR / Susceptible-Exposed-Infected (SEI) / Susceptible-Infected (SI) compartmental models similar to this paper. Among these works, [23, 24, 25] considered heterogeneity in the aspects of age groups, social links, and vaccination status separately. [26, 27], including spatial heterogeneity in one county and among states respectively, bore more similarity with this paper since they also allowed transmission parameters to be spatially heterogeneous and involved transportation between different regions.…”

Section: Introductionmentioning

confidence: 99%

Statistical Inference Using GLEaM Model with Spatial Heterogeneity and Correlation between Regions

Tan

Zhang

Cheng

et al. 2022

Preprint

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A better understanding of the various patterns in the coronavirus disease 2019 (COVID-19) spread in different parts of the world is crucial to its prevention and control. Motivated by the celebrated GLEaM model (Balcan et al., 2010 [1]), this paper proposes a stochastic dynamic model to depict the evolution of COVID-19. The model allows spatial and temporal heterogeneity of transmission parameters and involves transportation between regions. Based on the proposed model, this paper also designs a two-step procedure for parameter inference, which utilizes the correlation between regions through a prior distribution that imposes graph Laplacian regularization on transmission parameters. Experiments on simulated data and real-world data in China and Europe indicate that the proposed model achieves higher accuracy in predicting the newly confirmed cases than baseline models.

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“…Mathematical modelling of within-host dynamics of SARS-CoV-2 (Bouchnita and Jebrane, 2020;Bouchnita et al, 2021b) and its interaction with the immune system (Fadai et al, 2021;Jenner et al, 2021) also provided many insights into potential treatments and therapeutics for COVID-19. When vaccines became available, epidemiological models including vaccination effects were employed to understand the effects of vaccination on slowing the pandemic (Volpert et al, 2021), inform vaccination programs (Anit ¸a et al, 2021;Hogan et al, 2021;Li et al, 2021;Nguyen et al, 2021), and investigate the effects of easing restrictions under a partially vaccination population (Olivares and Staffetti, 2021;Le et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Bayesian uncertainty quantification to identify population level vaccine hesitancy behaviours

Warne

Varghese

Browning

et al. 2022

Preprint

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When effective vaccines are available, vaccination programs are typically one of the best defences against the spread of an infectious disease. Unfortunately, vaccination rates may be suboptimal for a prolonged duration as a result of slow uptake of vaccines by the public. Key factors driving slow vaccination uptake can be a complex interaction of vaccine roll-out policies and logistics, and vaccine hesitancy behaviours potentially caused by an inflated sense of risk in adverse reactions in some populations or community complacency in communities that have not yet experienced a large outbreak. In the recent COVID-19 pandemic, public health responses around the world began to include vaccination programs from late 2020 to early 2021 with an aim of relaxing non-pharmaceutical interventions such as lockdowns and travel restrictions. For many jurisdictions there have been challenges in getting vaccination rates high enough to enable the relaxation of restrictions based on non-pharmaceutical interventions. A key concern during this time was vaccine hestitancy behaviours potentially caused by vaccine safety concerns fuelled by misinformation and community complacency in jurisdictions that had seen very low COVID-19 case numbers throughout 2020, such as Australia and New Zealand. We develop a novel stochastic epidemiological model of COVID-19 transmission that incorporates changes in population behaviour relating to responses based on non-pharmaceutical interventions and community vaccine uptake as functions of the reported COVID-19 cases, deaths, and vaccination rates. Through a simulation study, we develop a Bayesian analysis approach to demonstrate that different factors inhibiting the uptake of vaccines by the population can be isolated despite key model parameters being subject to substantial uncertainty. In particular, we are able to identify the presence of vaccine hesitancy in a population using reported case, death and vaccination count data alone. Furthermore, our approach provides insight as to whether the dominant concerns driving hesitancy are related to vaccine safety or complacency. While our simulation study is inspired by the COVID-19 pandemic, our tools and techniques are general and could be enable vaccination programs of various infectious diseases to be adapted rapidly in response to community behaviours moving forward into the future.

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Epidemic Progression and Vaccination in a Heterogeneous Population. Application to the Covid-19 epidemic

Cited by 3 publications

References 26 publications

Estimating the impact of interventions against COVID-19: from lockdown to vaccination

Estimating the impact of interventions against COVID-19: from lockdown to vaccination

Statistical Inference Using GLEaM Model with Spatial Heterogeneity and Correlation between Regions

Bayesian uncertainty quantification to identify population level vaccine hesitancy behaviours

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