Real-time monitoring and predictive modelling of the impact of human behaviour and vaccine characteristics on COVID-19 vaccination in Scotland

人类行为和疫苗特征对苏格兰 COVID-19 疫苗接种影响的实时监测和预测建模

基本信息

批准号：
ES/W001489/1
负责人：
Rowland Kao
金额：
$ 33.05万
依托单位：
University of Edinburgh
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ES%2FW001489%2F1
关键词：
Real time monitoring predictive modelling

项目摘要

While COVID-19 vaccination will likely be transformative, many uncertainties may influence how quickly and comprehensively vaccination will have an impact. Current evidence suggests high levels of protection from the available vaccines, with some evidence that it also reduces transmission. However the evaluation of the evidence is ongoing, with the potential for new variants of concern to change the overall picture. To evaluate this in real time, we shall address here two interlinked factors: the potential for vaccinated individuals to shed and transmit virus without displaying clinical symptoms, and the rate of vaccination uptake and how it may cluster in communities. We shall work with Public Health Scotland, to exploit real-time monitoring of vaccine uptake, COVID-19 testing and cases, to identify geographical localised impacts on infection rates. Wastewater surveillance data will help to identify possible shedding of vaccinated individuals by comparing detection rates before and after vaccination, with a signal either indicating potential for transmission or a signal that must be accounted for to reduce the likelihood of false alarms in future situations where wastewater surveillance is being utilised. Using an established agent-based model fitted to cases across Scotland, we shall use these data to make short term forecasts for COVID-19 case numbers to support PHS planning. Long-term projections will consider vaccine-induced and natural immunity, clustering of low vaccine uptake, logistics, and possible loss of immunity. An online survey will build on the ongoing OPTIMUM study by correlating vaccination attitudes and ease of access to Scottish demography, mapping these geographically via the Scottish index of multiple deprivation (SIMD). We shall use models of 'vaccination games' to consider possible future scenarios where combinations of hesitancy, refusal and difficulties of access could result in lower uptake rates in some communities and therefore continued higher levels of infection or risk of outbreaks. We shall embed these scenarios into our simulation models. From this project, we shall have a more refined understanding of COVID-19 epidemiology in Scotland under vaccination, and better predictions of epidemic trajectories to aid in planning, to inform possible stresses on hospitals and ICU, and to target vaccine deployment and information strategies. Our results will more generally inform relationships amongst vaccine attitudes, accessibility, and regions of low vaccine uptake and refine approaches to surveillance and control.

虽然 COVID-19 疫苗接种可能会带来变革，但许多不确定性可能会影响疫苗接种产生影响的速度和全面程度。目前的证据表明现有疫苗可以提供高水平的保护，一些证据表明它还可以减少传播。然而，对证据的评估仍在进行中，新的变体可能会改变整体情况。为了实时评估这一点，我们将在此讨论两个相互关联的因素：接种疫苗的个体在不表现出临床症状的情况下传播和传播病毒的可能性，以及疫苗接种率及其如何在社区中聚集。我们将与苏格兰公共卫生局合作，利用对疫苗接种、COVID-19 检测和病例的实时监测，以确定对感染率的地理局部影响。废水监测数据将通过比较疫苗接种前后的检出率，帮助识别接种疫苗的个体可能的脱落情况，信号要么表明潜在的传播可能性，要么必须考虑到信号，以减少未来废水监测情况下误报的可能性。正在被利用。使用适合苏格兰各地病例的已建立的基于代理的模型，我们将使用这些数据对 COVID-19 病例数进行短期预测，以支持 PHS 规划。长期预测将考虑疫苗诱导的免疫和自然免疫、疫苗接种率低的聚集、后勤以及可能的免疫力丧失。一项在线调查将建立在正在进行的 OPTIMUM 研究的基础上，将疫苗接种态度与苏格兰人口统计的便利性联系起来，并通过苏格兰多重剥夺指数 (SIMD) 在地理上绘制这些数据。我们将使用“疫苗接种游戏”模型来考虑未来可能出现的情况，在这种情况下，犹豫、拒绝和获取困难的组合可能会导致某些社区的接种率降低，从而导致感染水平或爆发风险持续较高。我们将把这些场景嵌入到我们的模拟模型中。通过该项目，我们将对苏格兰疫苗接种情况下的 COVID-19 流行病学有更深入的了解，并更好地预测流行轨迹，以帮助制定规划，告知医院和 ICU 可能面临的压力，并制定有针对性的疫苗部署和信息策略。我们的结果将更广泛地揭示疫苗态度、可及性和疫苗接种率低的地区之间的关系，并完善监测和控制方法。