Title: HPRU in Modelling and Health Economics at Imperial College London
Funder: Department of Health and Social Care

Abstract: Protecting the public s health from both acute and long-term threats requires timely and reliable evidence to inform decisions. Public Health England (PHE) has met these needs through epidemiological analysis of the outputs of its renowned surveillance systems, and increasingly modelling and health economics have played key roles in PHE's decisions. However, new technology is now delivering a data revolution &ndash; enabling digital capture and linkage of ever larger volumes of health, environmental and socio-economic data together with increasingly routine use of genetic sequencing. Therefore a key priority is developing novel analytical and computational tools to allow these data streams to be fully exploited to protect and improve public health. Addressing this challenge is the core mission of the proposed HPRU in Modelling and Health Economics. This HPRU will combine synergistically the complementary strengths of the three largest and most diverse teams of public health modellers and economists in the UK, at PHE, Imperial College and LSHTM, to deliver a step change in research capacity available for health protection modelling/economics. Through extensive discussions with staff across PHE (Chilton, Colindale, Field Service, and Porton Down), we have developed multi-disciplinary teams, and refined plans to address PHE s priorities across many areas, including outbreaks, health-system pressures, health economics, environment and climate change, and complex interactions between environment/climate, chronic and infectious disease. Common to the resulting applied research themes are several cross-cutting methodological challenges: integration and analysis of diverse surveillance data, statistical methods for estimating unknown model parameters, development of improved tools for forecasting outbreaks and health demand, and the operationalisation of many of these methods into robust open-source software tools. Capacity-building and enhanced dissemination are important objectives. Our HPRU will strengthen collaborations across geographic sites within PHE, deepen long-standing PHE links with Imperial and LSHTM, and address needs of local-level frontline responders, and national-level decision-makers. Our leads for Knowledge Mobilisation, Training, and Patient and Public Involvement and Engagement will work closely with the research themes to design and implement a comprehensive package of activities. This will include: (a) applying our research to improve PHE s capacity to collect, analyse, model and interpret diverse datasets by developing robust easy-to-use computer software for use by public health professionals; (b) designing, with PHE and the community of HRPUs, a comprehensive training programme tailored to the needs of different staff, ranging from modelling specialists to front-line responders investigating and controlling outbreaks to national-level decision-makers designing cost-effective policies.

Lay abstract: Protecting the public s health from both acute and long-term threats requires timely and reliable evidence to inform decisions. Public Health England (PHE) has met these needs through epidemiological analysis of the outputs of its renowned surveillance systems, and increasingly modelling and health economics have played key roles in PHE's decisions. However, new technology is now delivering a data revolution &ndash; enabling digital capture and linkage of ever larger volumes of health, environmental and socio-economic data together with increasingly routine use of genetic sequencing. Therefore a key priority is developing novel analytical and computational tools to allow these data streams to be fully exploited to protect and improve public health. Addressing this challenge is the core mission of the proposed HPRU in Modelling and Health Economics. This HPRU will combine synergistically the complementary strengths of the three largest and most diverse teams of public health modellers and economists in the UK, at PHE, Imperial College and LSHTM, to deliver a step change in research capacity available for health protection modelling/economics. Through extensive discussions with staff across PHE (Chilton, Colindale, Field Service, and Porton Down), we have developed multi-disciplinary teams, and refined plans to address PHE s priorities across many areas, including outbreaks, health-system pressures, health economics, environment and climate change, and complex interactions between environment/climate, chronic and infectious disease. Common to the resulting applied research themes are several cross-cutting methodological challenges: integration and analysis of diverse surveillance data, statistical methods for estimating unknown model parameters, development of improved tools for forecasting outbreaks and health demand, and the operationalisation of many of these methods into robust open-source software tools. Capacity-building and enhanced dissemination are important objectives. Our HPRU will strengthen collaborations across geographic sites within PHE, deepen long-standing PHE links with Imperial and LSHTM, and address needs of local-level frontline responders, and national-level decision-makers. Our leads for Knowledge Mobilisation, Training, and Patient and Public Involvement and Engagement will work closely with the research themes to design and implement a comprehensive package of activities. This will include: (a) applying our research to improve PHE s capacity to collect, analyse, model and interpret diverse datasets by developing robust easy-to-use computer software for use by public health professionals; (b) designing, with PHE and the community of HRPUs, a comprehensive training programme tailored to the needs of different staff, ranging from modelling specialists to front-line responders investigating and controlling outbreaks to national-level decision-makers designing cost-effective policies.

Research activity codes: 2.6 RESOURCES AND INFRASTRUCTURE (AETIOLOGY)
Health categories: INFECTION