Epidemic modelling and statistical support for policy: sub-populations, forecasting, and long-term planning
Lead Research Organisation:
University of Manchester
Department Name: Mathematics
Abstract
The ongoing COVID-19 epidemic requires careful monitoring as a variety of measures such as lockdown and social distancing are introduced and subsequently relaxed, leading to varying levels of demand for and capacity within the healthcare system. The disease has varying expected outcomes depending on the age, sex, and underlying comorbidities of cases. Epidemic dynamics, particularly in the presence of changing control policies, will shift the dominant modes of transmission and hence the distribution of disease. We will develop models to integrate the diverse but often noisy and incomplete datasets available, providing real-time policy support together with quantification of uncertainty. We will address three particular challenges. (1) Understanding spread in closely connected sub-populations in which there are close, repeated contacts capable of spreading disease such as households, hospitals, prisons, and care homes. Data from these contexts allow epidemiological parameters relating to infection risk conditional on contact to be identified in statistical work, and they are also important foci for policies. (2) Making short- and medium-term predictions of the epidemic trajectory and healthcare demand with appropriate uncertainty quantification. (3) Modelling long-term prospects for the epidemic, including the likelihood of eventual endemicity, the consequences of different virological assumptions about SARS-CoV-2, and how the different scenarios in this context will interact with long-term societal and health consequences of the pandemic. The project will use mathematical methodology, integrated with interdisciplinary expertise from social science, biology, clinical medicine, and epidemiology.
Publications
Hilton J
(2022)
A computational framework for modelling infectious disease policy based on age and household structure with applications to the COVID-19 pandemic
in PLOS Computational Biology
House T
(2022)
Inferring risks of coronavirus transmission from community household data.
in Statistical methods in medical research
Lythgoe K
(2023)
Lineage replacement and evolution captured by 3 years of the United Kingdom Coronavirus (COVID-19) Infection Survey
in Proceedings of the Royal Society B: Biological Sciences
Overton CE
(2022)
EpiBeds: Data informed modelling of the COVID-19 hospital burden in England.
in PLoS computational biology
Overton CE
(2020)
Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example.
in Infectious Disease Modelling
Pellis L
(2021)
Challenges in control of COVID-19: short doubling time and long delay to effect of interventions.
in Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Description | We have made important discoveries about: - Transmission of SARS-CoV2 in close contact populations like households and care homes, and how these interact with other parts of the population - Trade-offs in policy, particularly modelling of test and trace - Hospital capacity modelling - Consideration of longer-term prognosis for pandemic - Application of machine learning methods to statistical data |
Exploitation Route | The work *is* currently being used by: the Government, through the SPI-M sub-committee of SAGE as well as other sub-committees the PI and CoIs attend; by the Office for National Statistics as part of its running the Coronavirus Infection Survey; and the NHS in the North West and more widely which is using this in planning; and the UKHSA who have found the work important enough to fund a significant fraction of the PI's time. |
Sectors | Communities and Social Services/Policy Healthcare Government Democracy and Justice |
URL | https://personalpages.manchester.ac.uk/staff/thomas.house |
Description | The work is currently being used in at least the following contexts: (1) The Government through the SPI-MO sub-committee of SAGE as well as other sub-committees the PI and CoIs attended during the pandemic. The work also continues to feed in to SPI-M running as an advisory committee for DHSC (2) The Office for National Statistics as part of its running the Coronavirus Infection Survey. Household code is routinely run as part of the survey, and now this is being paused, will be expected to be further developed and documented for future use and retrospective analysis. (3) The NHS in the North West and more widely is using outputs from this project in planning. This is being developed as a more general tool for use in hospital planning. (4) UKHSA who have found the work important enough to fund a significant fraction of the PI's time as Joint Chief Data Scientist, a 30% secondment to their Data Science division. (5) The WHO is currently negotiating to fund the PI to roll out household analysis and code across its European region. |
First Year Of Impact | 2020 |
Sector | Communities and Social Services/Policy,Healthcare,Government, Democracy and Justice |
Impact Types | Societal Economic Policy & public services |