Seasonal influenza: Modelling approaches to capture immunity propagation

Hill, Edward M.; Petrou, Stavros; Lusignan, Simon de; Yonova, Ivelina; Keeling, Matthew James

doi:10.1101/637074

Cited by 15 publications

(13 citation statements)

References 59 publications

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“…For a variety of other diseases, there is a precedent for combining modelling approaches with health economic evaluations to inform vaccine policy decisions based on a willingness to pay for each Quality Adjusted Life Year (QALY) saved [14-17]. Utilising this framework, we also consider how vaccination may be optimised to minimise the loss in QALYs, rather than simply the number of deaths.…”

Section: Introductionmentioning

confidence: 99%

Modelling optimal vaccination strategy for SARS-CoV-2 in the UK

Moore

Hill

Dyson

et al. 2020

Preprint

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The COVID-19 outbreak has highlighted our vulnerability to novel infections. Faced with this threat and no effective treatment, in line with many other countries, the UK adopted enforced social distancing (lockdown) to reduce transmission- successfully reducing the reproductive number, R, below one. However, given the large pool of susceptible individuals that remain, complete relaxation of controls is likely to generate a substantial second wave. Vaccination remains the only foreseeable means of both containing the infection and returning to normal interactions and behaviour. Here, we consider the optimal targeting of vaccination within the UK, with the aim of minimising future deaths or quality adjusted life year (QALY) losses. We show that, for a range of assumptions on the action and efficacy of the vaccine, targeting older age groups first is optimal and can avoid a second wave if the vaccine prevents transmission as well as disease.

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Section: Introductionmentioning

confidence: 99%

Modelling optimal vaccination strategy for SARS-CoV-2 in the UK

Moore

Hill

Dyson

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Assessment of the population-level impact of vaccine candidates through mathematical modeling is a critical component of the process of vaccine development, value proposition, licensure, decision-making, and pathways and costs of vaccine administration, and has been utilized for a wide range of infectious diseases [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. In early stages of development, modeling is used to define the vaccine's key preferred product characteristics, by estimating levels of efficacy necessary to observe significant population-level impact, determining necessary duration of protection/immunity incurred by the vaccine, and identifying priority populations for optimal effectiveness [21,29,30].…”

Section: Introductionmentioning

confidence: 99%

“…(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. effectiveness [21,28,29,31,32]. Once key attributes are established, modeling plays an integral role in building the case for investment in vaccine development, and in ensuring rapid roll-out post-licensing, through assessment of risks, costs, and predicted returns associated with different immunization strategies [29,33].…”

Section: Introductionmentioning

confidence: 99%

Epidemiological impact of SARS-CoV-2 vaccination: mathematical modeling analyses

Makhoul

Ayoub

Chemaitelly

et al. 2020

Preprint

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Lebanon * These authors contributed equally to the work. Word count: Abstract: 257 words, Main Text: 3,166 words. Number of figures: 7. Number of tables: 1. Running head: SARS-CoV-2 vaccination impact. Abstract Background: Several SARS-CoV-2 vaccine candidates are currently in the pipeline. This study aims to inform SARS-CoV-2 vaccine development, licensure, decision-making, and implementation by determining key preferred vaccine product characteristics and associated population-level impact. Methods: Vaccination impact was assessed at various efficacies using an age-structured mathematical model describing SARS-CoV-2 transmission and disease progression, with application for China. Results: A prophylactic vaccine with efficacy against acquisition ( S VE ) of ≥70% is needed to eliminate this infection. A vaccine with S VE <70% will still have a major impact, and may control the infection if it reduces infectiousness or infection duration among those vaccinated who acquire the infection, or alternatively if supplemented with a moderate social-distancing intervention (<20% reduction in contact rate), or complemented with herd immunity. Vaccination is cost-effective. For a vaccine with S VE of 50%, number of vaccinations needed to avert one infection is only 2.4, one severe disease case is 25.5, one critical disease case is 33.2, and one death is 65.1. Gains in effectiveness are achieved by initially prioritizing those ≥60 years. Probability of a major outbreak is virtually zero with a vaccine with S VE ≥70%, regardless of number of virus introductions. Yet, an increase in social contact rate among those vaccinated (behavior compensation) can undermine vaccine impact. Conclusions: Even a partially-efficacious vaccine can offer a fundamental solution to control SARS-CoV-2 infection and at high cost-effectiveness. In addition to the primary endpoint on infection acquisition, developers should assess natural history and disease progression outcomes All rights reserved. No reuse allowed without permission.

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“…Besides, there exists a natural process of decreasing trend of antibody titers after vaccination [5], suggesting people need to be vaccinated periodically, leading to a huge burden to low-and-middle income countries. Therefore, to compensate for the huge cost by prevention strategies, it is quite necessary to make informed prevention decisions to guarantee that limited resources are allocated to the places where they are most needed [6]. One of the fundamental steps for decision making in influenza prevention is to characterize its spatio-temporal trend, especially on the key problem about how influenza transmits among adjacent places and how much impact the influenza of one place could have on its neighbors.…”

Section: Introductionmentioning

confidence: 99%

Spatial transmission network construction of influenza-like illness using dynamic Bayesian network and vector-autoregressive moving average model

Qiu

Wang

et al. 2021

BMC Infect Dis

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Background Although vaccination is one of the main countermeasures against influenza epidemic, it is highly essential to make informed prevention decisions to guarantee that limited vaccination resources are allocated to the places where they are most needed. Hence, one of the fundamental steps for decision making in influenza prevention is to characterize its spatio-temporal trend, especially on the key problem about how influenza transmits among adjacent places and how much impact the influenza of one place could have on its neighbors. To solve this problem while avoiding too much additional time-consuming work on data collection, this study proposed a new concept of spatio-temporal route as well as its estimation methods to construct the influenza transmission network. Methods The influenza-like illness (ILI) data of Sichuan province in 21 cities was collected from 2010 to 2016. A joint pattern based on the dynamic Bayesian network (DBN) model and the vector autoregressive moving average (VARMA) model was utilized to estimate the spatio-temporal routes, which were applied to the two stages of learning process respectively, namely structure learning and parameter learning. In structure learning, the first-order conditional dependencies approximation algorithm was used to generate the DBN, which could visualize the spatio-temporal routes of influenza among adjacent cities and infer which cities have impacts on others in influenza transmission. In parameter learning, the VARMA model was adopted to estimate the strength of these impacts. Finally, all the estimated spatio-temporal routes were put together to form the final influenza transmission network. Results The results showed that the period of influenza transmission cycle was longer in Western Sichuan and Chengdu Plain than that in Northeastern Sichuan, and there would be potential spatio-temporal routes of influenza from bordering provinces or municipalities into Sichuan province. Furthermore, this study also pointed out several estimated spatio-temporal routes with relatively high strength of associations, which could serve as clues of hot spot areas detection for influenza surveillance. Conclusions This study proposed a new framework for exploring the potentially stable spatio-temporal routes between different places and measuring specific the sizes of transmission effects. It could help making timely and reliable prediction of the spatio-temporal trend of infectious diseases, and further determining the possible key areas of the next epidemic by considering their neighbors’ incidence and the transmission relationships.

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Seasonal influenza: Modelling approaches to capture immunity propagation

Cited by 15 publications

References 59 publications

Modelling optimal vaccination strategy for SARS-CoV-2 in the UK

Modelling optimal vaccination strategy for SARS-CoV-2 in the UK

Epidemiological impact of SARS-CoV-2 vaccination: mathematical modeling analyses

Spatial transmission network construction of influenza-like illness using dynamic Bayesian network and vector-autoregressive moving average model

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