Mathematical modelling informing policy on human infectious diseases, particularly pandemic influenza
Submitting Institution
University of WarwickUnit of Assessment
Mathematical SciencesSummary Impact Type
HealthResearch Subject Area(s)
Medical and Health Sciences: Clinical Sciences, Public Health and Health Services
Summary of the impact
Researchers in the Epidemiology Group at the University of Warwick have
an international reputation for high-quality mathematical modelling of
human infectious diseases, with particular emphasis on population
heterogeneity and variability. Such formulations and insights are an
important component of predictive modelling performed by Public Health
England (PHE), and are helping to shape national policy for a range of
vaccine-preventable infections.
The Warwick group was instrumental in providing a range of real-time
analyses and advice to UK authorities during the 2009 H1N1 (swine-flu)
pandemic, acknowledged by the Department of Health (DoH) to be
"fundamental to the construction of the UK's pandemic response" and making
an important contribution to the overall programme which "led to the
saving of many hundreds of millions of pounds of taxpayers money, while
greatly increasing the health of the Nation". Modelling and analysis
carried out at Warwick continue to provide insight into the control and
containment of future pandemics and are considered "essential in
determining UK pandemic policy".
Underpinning research
Statistical analysis of data and mathematical modelling of controls are
key tools in the modern public-health arsenal. Many decisions on health
policies must be shown to be cost-effective through mathematical and
economic modelling. Work within the Mathematics Institute at the
University of Warwick (performed by Keeling, House and Danon) has led to
both fundamental and applied insights in public health epidemiology. Here,
we highlight research on pandemic influenza, which addressed many pressing
issues during the 2009 pandemic and continues to influence policy planning
for future outbreaks.
One important component of the group's work has been methodological, and
the developments of mathematical techniques underlying modelling are used
extensively. Keeling's textbook [1] has become a standard text on
modelling infectious diseases (cited over 700 times), and as such is
developing the next generation of public-health researchers and career
academics.
During the 2009 influenza pandemic, there was considerable international
collaboration to understand the early infection dynamics. Danon worked
closely with researchers in the USA who were interested in the
public-health impacts in Mexico and the United States and published
several papers during that period. In particular, the number of influenza
incidents among foreign travellers was used to estimate the true burden of
disease in Mexico [2]. This work was one of the first to indicate that
there was a huge undiagnosed level of infection and that most infections
were mild.
A key issue in infectious disease policy is optimal use of a limited
vaccine supply. Work by Keeling, arising directly from a collaboration
with the health protection agency (HPA) during the pandemic, addressed the
trade-offs in vaccination between targeting of an epidemiologically
important group (such as primary school children) and a group in which the
outcomes of infection are severe [3]. Mathematical modelling verified the
decisions taken during the pandemic to first target vaccination at those
with severe health-risks and then at young children.
The hospital-based healthcare system was under severe stress in
particular localities during 2009, and it was suggested that localized
school closures might reduce transmission and, hence, relieve the local
burden. Our modelling and spatial statistics showed in 2011 that only
widespread extensive closure of schools (which is economically extremely
costly) would have had a substantial impact on cases of H1N1 [4].
As shown in [3], early case ascertainment (determining how many people
are infected when not all are severely ill) is key to understanding the
progression and control of an infection. In 2012, the group developed
techniques to quantify case ascertainment using the theory of
non-Markovian epidemic final outcomes and Bayesian MCMC (Markov Chain
Monte Carlo) together with household information on early cases in
Birmingham [5]. This work suggested that over 50% of cases were undetected
during the early stages of the 2009 pandemic, and provided a novel
framework for analysis of household data that our colleagues in the PHE
are considering for future outbreaks.
Clinical trials indicated that antivirals were effective against H1N1,
but their impact at the population level was not realized during the 2009
pandemic. In 2013, the group used Bayesian methods in conjunction with the
theory of path integrals for continuous time Markov chains to model
antiviral effects in a household-structured epidemic model [6]. This
showed that delays in delivery might account for the discrepancy between
clinical trials and population-level efficacy.
Key researchers at University of Warwick:
Prof. Matt Keeling (Lecturer 2002-05, Reader 2005-08, Professor, 2008-),
Dr. Leon Danon (Post- doctoral researcher & Leverhulme fellow,
2006-2013), Dr Thomas House (Post-doctoral researcher & EPSRC CAF
fellow, 2006-).
Key collaborators:
Dr Andrew Black & Dr Josh Ross (University of Adelaide), Prof. Pej
Rohani (University of Michigan), Prof. Marc Lipsitch (Harvard University).
References to the research
Key publications
1. 1. Keeling, M.J., & Rohani, P., Modeling Infectious
Diseases in Humans and Animals. Princeton University Press. 408 pp. (2007)
ISBN: 9780691116174
2. Lipsitch M, Lajous M, O'Hagan JJ, Cohen T, Miller JC, Goldstein E, Danon
L, Wallinga J, Riley S, Dowell SF, Reed C & McCarron, M., Use
of cumulative incidence of novel influenza A/H1N1 in foreign travellers
to estimate lower bounds on cumulative incidence in Mexico PLoS ONE,
4 (9) e6895. (2009) DOI: 10.1371/journal.pone.0006895
4. House T, Baguelin M, van Hoek AJ, White PJ, Sadique Z, Eames
K, Read JM, Hens N, Melegaro A, Edmunds WJ & Keeling, M.J., Modelling
the impact of local reactive school closures on critical care provision
during an influenza pandemic. Proc. R. Soc. Lond. B 278(1719)
2753- 2760 (2011) DOI: 10.1098/rspb.2010.2688
Key peer-reviewed grants and awards
7. Leach (HPA, PI), Grenfell (Cambridge), Keeling (Warwick)
"Application of HE computing to public health" EPSRC GR/S43214/01 Oct 2003
- Sept 2005 £90,776.
8. Keeling (Warwick, PI), Read (Liverpool) "Social contact survey
and modelling the spread of influenza". MRC G0701256 Oct 2008 - Sept 2011
£669,233.
9. Keeling (Warwick, PI) "State of the art models for infectious
disease spread". Wellcome Trust Jan 2010 - Sept 2013.
10. Keeling (Warwick, PI) "Implications of clustering
(motif-structure) for network-based processes". EPSRC EP/H016139/1 Jan
2010 - Jun 2013 £290,372.
11. Leverhulme Trust "Mobile phone data and infectious diseases". Dec
2010 - Nov 2013. Danon (Warwick, Early Career Fellowship)
12. House (Warwick, Career Acceleration Fellowship) "Disease
Transmission and Control in Complex, Structured Populations" EPSRC
EP/J002437/1 Oct 2011 - Sep 2016 £632,534
13. House (Warwick, PI) "Robust Mathematical Modelling of
Household-Stratified Epidemic Time- Series". EPSRC EP/K026550/1 Oct 2013 -
Sep 2016 £216,448
Details of the impact
When the outbreak of H1N1 influenza was first identified in Mexico there
was worldwide concern that this might signify the start of a pandemic with
levels of mortality comparable to those of the 1918 flu outbreak. Keeling,
House and Danon have a reputation for both cutting-edge mathematical
modelling and applied quantitative epidemiology; this has led to their
research being used to advise and inform policy and decision-making for
pandemic influenza (in 2009 and for future outbreaks), as well as for
other infections such as the recent measles outbreak in Swansea.
Informing UK policy through membership of DoH Advisory Committees.
Keeling became an active member of the government's Joint Committee on
Vaccination and Immunisation (JCVI- influenza) [16] and during 2009 was
the designated independent chair of the Scientific Pandemic Influenza
Subgroup on Modelling (SPI-M) [15, 17]. He remains a member of both. In
both these roles Keeling has provided, and continues to provide, detailed
mathematical/modelling advice to the DoH, based on his research described
above [1,2,14-16, 18, 19]. He has provided SPI-M with real-time modelling
updates on control and containment of pandemic influenza "which played an
important and often critical role in formulating the Group's [SPI-M's]
consensus forecasts of what was happening and what would happen later in
the pandemic" [15]. Both during and after the pandemic,
Keeling has provided analysis that "has been essential in determining UK
pandemic policy" [15]. As acknowledged by a Senior Principal Analyst at
DoH and HPA, Keeling "played a very significant role in generating the
SPI-M consensus view which has been fundamental to the construction of the
UK's pandemic response and the management of the 2009 pandemic" [15].
Since 2009, SPI-M has continued to meet and provide updated advice to the
Civil Contingencies Committee (commonly known as COBRA) in light of recent
scientific evidence.
A key issue during the early stages of any pandemic is determining the
true scale of the outbreak (whether all cases are severe and the outbreak
is currently small, or the outbreak is larger but fewer cases have severe
symptoms). This knowledge is vital in terms of effective public-health
planning. Danon and a team of researchers from around the world (USA,
Mexico, Hong Kong and the Netherlands) together with the CDC (Centres for
Disease Control, USA) provided early evidence that H1N1 was a mild
infection [1] in distinct contrast to all earlier reports. This
precipitated a change worldwide in the way the infection was considered.
The retrospective analysis undertaken with members of the HPA shows how
data from infected households during the early stage of any pandemic could
be used to infer the answers to many of these fundamental questions about
case severity [5]. As such, this strategy is being considered as part of
future planning both in the UK and overseas [14].
In addition to providing a quantitative estimate of the current
epidemiology, mathematical models were used to provide information about
the potential effect of control [3, 6]. During the pandemic there was
considerable debate around the optimal use of vaccination, in part driven
by poorly- posed findings in the early literature. Once vaccines were
available in the later stages of the pandemic, preliminary results (later
published as [3]) supported the general medical advice that vaccination
should be targeted first to those with severe health risks and then to
young children. Antivirals form the other pharmaceutical arm of control,
but they did not perform as well in practice during 2009 as indicated by
controlled tests; our results [3] showed that prompt antiviral treatment
was key to reducing population-level transmission and have informed debate
about future antiviral drug usage. A Senior Principal Analyst at DoH and
HPA, referring to the 2009 pandemic, acknowledged that Keeling's work
"formed an important contribution to the overall analytical programme
which led to the savings of many hundreds of millions of pounds while
greatly increasing the health of the Nation" [15].
Outreach to Public, Practitioners and Pupils. Our work in
this area has led to substantial impact in term of public interest and
engagement (impacts on society), impacts on practitioners and the
enhancement of science knowledge in school children both locally and
internationally. In all of these policy engagement events we highlight the
importance and significance of the impact of our mathematical models in
underpinning policy, and how we have substantially informed public debate
and stimulated public interest and engagement. Examples of public
activities include:
- Interviews with newspapers and media related to both H1N1 influenza
and subsequent research, including [20]:
Keeling's and Danon's Guardian interview (October 2011) (average daily
circulation of 265,000 in 2011) that linked their research work with the
film Contagion [20a];
Promoting the importance of childhood vaccination against influenza
through The Times of India [20b], the Mail Online (averages more than 8
million daily browsers) [20c], House interviewed by Reuters (January
2013) [20d], Danon on BBC Coventry & Warwickshire radio (November
2011) and House (January 2013) on WAMC Northeast Public Radio (public
radio station in New York) [20e].
- Presentations and interviews for NHS practitioners and coordinators
and associated services: For example, House presented at a public policy
exchange symposium (for public health officials, from NHS practitioners
and pandemic coordinators to local government officers in community
services and the Metropolitan Police) in April 2013 [21]. The company
Wellards, which provides training on all aspects of the sales
environment for professionals in the pharmaceutical and biotech
industries with over 16,000 users from 300 companies, has used the
research as one of its case studies on `Vaccines in the NHS' [22].
- Presentations to Maths `A'-level students: Keeling presented how we
predict epidemics and control to "two capacity audiences, each of 900
students engaging them with the significance of mathematics in his
research and the significance of his research to the general public" in
a Mathematics in Action programme (2011-12) organised by The Training
Partnership [23] (who provide events complementary to the A-level
curriculum).
Sources to corroborate the impact
14. Letter by Head of Bioterrorism and Emerging Disease Analysis, Public
Health England
15. Letter by Senior Principal Analyst, Department of Health, Health
Protection Analytical Team
Membership in DoH Advisory Committees
16. Member of Joint Committee on Vaccination and Immunisation:
https://www.gov.uk/government/policy-advisory-groups/joint-committee-on-vaccination-and-immunisation
17. Member of Scientific Pandemic Influenza Subgroup on Modelling:
https://www.gov.uk/government/policy-advisory-groups/scientific-pandemic-influenza-subgroup-on-modelling
Informing UK Government
18. Foresight Review "Detection and Identification of Infectious
Diseases" by Keeling commissioned by the Department for Business,
Innovations & Skills of the UK Government, section S9:
http://www.bis.gov.uk/foresight/our-work/projects/published-projects/infectious-diseases/reports-and-publications
Underpinning UK policy
19. House and Keeling cited as evidence in UK Influenza Pandemic
Preparedness Strategy (p 39): https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/134747/dh_131040.
pdf.pdf
Outreach to Public, Practitioners and Pupils
20. a
http://www.guardian.co.uk/education/2011/oct/17/kate-winslet-contagion-fight-disease
b.
http://articles.timesofindia.indiatimes.com/2009-06-18/health/28191041_1_vaccinate-kids-new-vaccine-potential-pandemic
c.
http://www.dailymail.co.uk/health/article-1193839/All-children-offered-swine-flu-vaccine-autumn-contain-spread-pandemic-say-researchers.html
d. http://uk.reuters.com/video/2013/01/22/data-model-could-improve-future-
pandemic?videoId=240644097&videoChannel=4000
e.
http://www.wamc.org/post/dr-thomas-house-university-warwick-mathematics-and-mapping-epidemics
21. http://publicpolicyexchange.co.uk/events/DD23-PPE.php
22. http://www.wellards.co.uk/courses/vaccines_in_the_nhs/screen28_case.html
23. Letter received from Managing Director of The Training Partnership