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
Wisniowski A
(2023)
The economic impact of the COVID-19 pandemic on ethnic minorities in Manchester: lessons from the early stage of the pandemic.
in Frontiers in sociology
Silva M
(2023)
Tracking the structure and sentiment of vaccination discussions on Mumsnet
in Social Network Analysis and Mining
Whitfield CA
(2023)
Modelling the impact of non-pharmaceutical interventions on workplace transmission of SARS-CoV-2 in the home-delivery sector.
in PloS one
Wing K
(2022)
Association between household composition and severe COVID-19 outcomes in older people by ethnicity: an observational cohort study using the OpenSAFELY platform.
in International journal of epidemiology
Waites W
(2022)
Transmission dynamics of SARS-CoV-2 in a strictly-Orthodox Jewish community in the UK.
in Scientific reports
Overton CE
(2022)
EpiBeds: Data informed modelling of the COVID-19 hospital burden in England.
in PLoS computational biology
Vihta K
(2022)
Symptoms and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Positivity in the General Population in the United Kingdom
in Clinical Infectious Diseases
House T
(2022)
Inferring risks of coronavirus transmission from community household data.
in Statistical methods in medical research
Vihta KD
(2022)
Omicron-associated changes in SARS-CoV-2 symptoms in the United Kingdom.
in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
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
Pellis L
(2022)
Authors' Reply to the Discussion of 'Estimation of Reproduction Numbers in Real Time: Conceptual and Statistical Challenges' by Pellis et al. in Session 3 of The Royal Statistical Society's Special Topic Meeting on COVID-19 Transmission: 11 June 2021
in Journal of the Royal Statistical Society Series A: Statistics in Society
Pellis L
(2022)
Estimation of Reproduction Numbers in Real Time: Conceptual and Statistical Challenges
in Journal of the Royal Statistical Society Series A: Statistics in Society
Pouwels KB
(2021)
Effect of Delta variant on viral burden and vaccine effectiveness against new SARS-CoV-2 infections in the UK.
in Nature medicine
Vekaria B
(2021)
Hospital length of stay for COVID-19 patients: Data-driven methods for forward planning.
in BMC infectious diseases
Dyson L
(2021)
Possible future waves of SARS-CoV-2 infection generated by variants of concern with a range of characteristics.
in Nature communications
Fyles M
(2021)
Using a household-structured branching process to analyse contact tracing in the SARS-CoV-2 pandemic.
in Philosophical transactions of the Royal Society of London. Series B, Biological sciences
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
Brooks-Pollock E
(2021)
The population attributable fraction of cases due to gatherings and groups with relevance to COVID-19 mitigation strategies.
in Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Shryane N
(2021)
Length of Stay in ICU of Covid-19 patients in England, March - May 2020.
in International journal of population data science
Fearon E
(2021)
SARS-CoV-2 antigen testing: weighing the false positives against the costs of failing to control transmission.
in The Lancet. Respiratory medicine
Walker AS
(2021)
Tracking the Emergence of SARS-CoV-2 Alpha Variant in the United Kingdom.
in The New England journal of medicine
Pouwels KB
(2021)
Community prevalence of SARS-CoV-2 in England from April to November, 2020: results from the ONS Coronavirus Infection Survey.
in The Lancet. Public health
Hall I
(2021)
Outbreaks in care homes may lead to substantial disease burden if not mitigated.
in Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Thompson RN
(2020)
Key questions for modelling COVID-19 exit strategies.
in Proceedings. Biological sciences
Overton CE
(2020)
Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example.
in Infectious Disease Modelling
| 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 |