Areas of Expertise (5)
Infectious Disease Modelling
Social Contact Networks
Dr Ellen Brooks Pollock is an expert mathematical modelling and epidemiology. She is based at the Bristol Veterinary School where her focus is on using infectious disease modelling, disease dynamic theory and epidemiological data to answer applied questions about the transmission and control of infectious diseases. During the COVID-19 pandemic she collaborated with other academics to investigate mapping and mitigation strategies within schools, as well as exploring quantitative predictions in response to the evolving nature of the pandemic. She has also studied the national prevalence of Hepatitis B and Zoonotic Tuberculosis.
In tandem with her teaching and research commitments, Dr Brooks-Pollock is focused on developing tools for communicating about the nature of infectious diseases to non-modellers, particularly with a view to answering policy-relevant questions. She has featured on BBC News and BBC Radio 4 Today, as well as Newsnight, Countryfile and Farming Today. Dr Brooks-Pollock is also a member of the government’s SPI-M modelling group, as well as the SAGE-subgroup on children and schools, a member of the JUNIPER (Joint UNIversities Pandemic and Epidemiological Research) consortium, and a member of the UK government’s Animal and Plant Health Agency’s National Expert Group (NEG) for outbreaks.
University College London: MSci, Mathematics 2003
University of Warwick: PhD, Maths/Biology 2008
- SPI-M modelling group : Member
- JUNIPER (Joint UNIversities Pandemic and Epidemiological Research) consortium : Member
- Animal and Plant Health Agency’s National Expert Group : Member
Media Appearances (5)
Scientific evidence that informed UK Government’s response to COVID-19 including first lockdown is published
Scientific evidence that was used to inform the UK government’s key policies impacting millions of people during the first wave of COVID-19 including the rule of six and the first national stay-at-home order is published today [31 May] in the journal of the Royal Society. The Special Theme issue is compiled and guest edited by SPI-M scientists including infectious disease modellers Drs Ellen Brooks Pollock and Leon Danon at the University of Bristol.
Kent Covid variant has a much higher mortality rate and poses ‘a threat that should be taken seriously’
Fortunately, the mutation present in the B.1.1.7 ‘happened in part of the genome covered by routine testing’, explains Dr Ellen Brooks Pollock - Senior Lecturer in Infectious Disease Mathematical Modelling at the University – however, ‘future mutations could arise and spread unchecked.’
Covid Christmas risk 'hard to control'
BBC News online
According to Dr Ellen Brooks-Pollock, a member of the team, if people understand the scale of the risks, then it will help them work out how best to respond.
Covid: concerns raised over plan for mass testing of students
The Guardian online
Caution was also advised by experts about the potential reach of LFTs. The self-administered tests, which can deliver a result in as little as 30 minutes, are rapid but do not necessarily detect as many infected people as the PCR testing used at NHS sites, warned Dr Ellen Brooks Pollock, a lecturer in infectious disease modelling at the University of Bristol, who has led research into Covid-19 among students.
University lockdown for two weeks before Christmas ‘too late’ to halt spread of coronavirus, scientist warns
Dr Ellen Brooks-Pollock said: ‘If there’s already disseminated infections, many of which are unobserved, two weeks wouldn’t be long enough at the end of term — it’s too late essentially’
Sheep scab transmission: a spatially explicit dynamic metapopulation modelVeterinary Research
Psoroptic mange (sheep scab), caused by the parasitic mite, Psoroptes ovis, is an important disease of sheep worldwide. It causes chronic animal welfare issues and economic losses. Eradication of scab has proved impossible in many sheep-rearing areas and recent reports of resistance to macrocyclic lactones, a key class of parasiticide, highlight the importance of improving approaches to scab management. To allow this, the current study aimed to develop a stochastic spatial metapopulation model for sheep scab transmission which can be adapted for use in any geographical region, exhibited here using data for Great Britain.
A spatial model of COVID-19 transmission in England and Wales: early spread, peak timing and the impact of seasonalityPhilosophical Transactions of the Royal Society B
An outbreak of a novel coronavirus was first reported in China on 31 December 2019. As of 9 February 2020, cases have been reported in 25 countries, including probable human-to-human transmission in England. We adapted an existing national-scale metapopulation model to capture the spread of COVID-19 in England and Wales.
Modelling that shaped the early COVID-19 pandemic response in the UKPhilosophical Transactions of the Royal Society B
nfectious disease modelling has played an integral part of the scientific evidence used to guide the response to the COVID-19 pandemic. In the UK, modelling evidence used for policy is reported to the Scientific Advisory Group for Emergencies (SAGE) modelling subgroup, SPI-M-O (Scientific Pandemic Influenza Group on Modelling-Operational).
Mapping social distancing measures to the reproduction number for COVID-19Philosophical Transactions of the Royal Society B
In the absence of a vaccine, severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) transmission has been controlled by preventing person-to-person interactions via social distancing measures. In order to re-open parts of society, policy-makers need to consider how combinations of measures will affect transmission and understand the trade-offs between them. We use age-specific social contact data, together with epidemiological data, to quantify the components of the COVID-19 reproduction number.
Household bubbles and COVID-19 transmission: insights from percolation theoryPhilosophical Transactions of the Royal Society B
In the era of social distancing to curb the spread of COVID-19, bubbling is the combining of two or more households to create an exclusive larger group. The impact of bubbling on COVID-19 transmission is challenging to quantify because of the complex social structures involved. We developed a network description of households in the UK, using the configuration model to link households. We explored the impact of bubbling scenarios by joining together households of various sizes.