Coronavirus (Covid-19): Latest updates and information
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COVID 19

Members of the Zeeman Institute are contributing to the COVID-19 modelling response, both in the UK and abroad. This page details the research undertaken by the Zeeman Institute.

Our work includes:

Forecasts

Contact tracing

Exit strategies

School reopening

Vaccination

Universities

Workplaces

Social Bubbles

Forecasts for Kenya

Global predictions

Short breaks

Phylogenetics


UK short-term predictions

Our group supports the UK response to COVID-19 through membership of the Scientific Pandemic Influenza Group on Modelling (SPI-M), an expert group advising the Scientific Advisory Group for Emergencies (SAGE). Scientific evidence supporting the UK government response to COVID-19 can be found on the SAGE website, including modelling inputs and reports from SPI-M to SAGE.

Figure right shows how the short-term predictions from the Warwick model have changed over time for deaths in the Midlands. While it is clear that early forecasts were extremely pessimistic (as the impact of the lockdown was unknown), later forecasts are in far better agreement with the data and give us considerable certainty on the likely course of the epidemic over the next 2-3 weeks. Comparison of model predictions against data

Keeling, M.J., Dyson, L., Guyver-Fletcher, G., Holmes, A., Semple, M.G., ISASIC4C Investigators, Tildesley, M.J. & Hill, E.M. (2020) Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number medRxiv https://doi.org/10.1101/2020.08.04.20163782

Early Contact Tracing

Contact tracing together with testing is viewed as a necessary tool in the relaxation of lockdown methods. Work by SBIDER, in collaboration with Oxford and Lancaster Universities, considered the use of contact tracing in the early stages of the outbreak as a means of controlling or containing the pandemic in the UK.

Comparison of model predictions against data

Comparison of model predictions against data

Contact tracing before the implementation of social distancing and lockdown measures.

The top figure shows a cartoon of contact tracing around a central infected figure (blue); some individuals are known to the index case and match the tracing criteria (green), others are known but contact time is considered too short (grey), but some contacts are unknown and hence untraceable (orange).

Before lockdown, each person had many contacts that were made over a 7-day period (mean contacts 216, mean contacts to trace 36), so contact tracing logistics could soon be overwhelmed with just a few infectious cases.

If we are able to trace all the contacts meeting the close contact definition, then our results show that an outbreak could be contained, although the "untraceable" contacts could still give rise to a low number of secondary cases.

Keeling, M.J., Hollingsworth, T.D. & Read, J.M. (2020) "The Efficacy of Contact Tracing for the Containment of the 2019 Novel Coronavirus (COVID-19)" medRxiv. https://doi.org/10.1101/2020.02.14.20023036

(to appear in J. Epidemiology & Community Health) http://dx.doi.org/10.1136/jech-2020-214051


Potential exit strategies

The Warwick model for COVID-19 is a deterministic, age-structured, compartmental model in which the population is stratified into susceptibles (uninfected), exposed (infected, but not yet infectious), infectious and recovered. Infectious individuals may be either symptomatic (detectable) or asymptomatic (undetectable), and asymptomatic infections are assumed to transmit at a reduced rate. Further details regarding the model may be found in Keeling et al. (2020, medRxiv).

Diagram to describe the Warwick COVID modelFigure above. The flow through the multiple sections of the compartmental model, from susceptible to exposed, to either symptomatic or asymptomatic, to ultimately recovered.

Figure right. Examples of epidemic trajectories following different relaxations of lockdown for 2020 and lockdown in 2021. Very rapid relaxation leads to a pronounced early peak, whereas tight controls leads to a later peak in when controls are lifted in 2021.

Diagram to describe the Warwick COVID model

In this paper we also focus on potential exit strategies, such as different changes to the early lock-down restrictions, age-based easing of the lockdown and basing the lockdown on local ICU capacity. We conclude that any route out of lockdown that does not involve pharmaceutical interventions (such as vaccination) has be be very slow and measured.

Keeling, M. J., Hill, E., Gorsich, E., Penman, B., Guyver-Fletcher, G., Holmes, A., McKimm, H., Tamborrino, M, Dyson, L., and Tildesley, M. (2020). "Predictions of COVID-19 dynamics in the UK: short-term forecasting and analysis of potential exit strategies" PLoS Comput Biol 17(1): e1008619. https://doi.org/10.1371/journal.pcbi.1008619


Reopening Schools

We have used the Warwick COVID model for the UK to investigate potential scenarios for reopening schools in England. We consider different combinations of years returning to school, including the potential for teaching students in smaller classes which reduces infection risk. We find that, on its own, returning children to school is unlikely to lead to a second wave of infection, however there remains uncertainty if other measures are relaxed simultaneously.

A figure showing the total changes in new cases as a result of different school return scenarios. The main results of the figure are described below.

Even if R remains below one, any return of children to school will inevitably lead to some increase in transmission and therefore to an increase in cases, ICU admissions and, regrettably, deaths. We find that secondary school students returning leads to higher increases than if only primary schools reopen, though in all scenarios the magnitude of changes depends upon the wider context when the reopening of schools occurs. This can be seen in the figure above: if transmission in the general community increases, this alone increases cases (faded colours) and exacerbates the increases seen in by school reopening (solid colours). However the size of the increase due to schools returning is much smaller than the increase due directly to the increase in community transmission. More detail may be found in our preprint below. Please note that this work has not yet been peer reviewed.

Keeling, M. J., Tildesley, M. J., Atkins, B. D., Penman, B., Southall, E., Guyver-Fletcher, G., Holmes, A., McKimm, H., Gorsich, E., Hill, E. M., and Dyson, L. (2020). "The impact of school reopening on the spread of COVID-19 in England" medRxiv

Follow-up work has analysed data on pupil and staff absences due to confirmed COVID-19 infection during September-December 2020. There has also been the development of an individual-based model formed of exclusive year group bubbles to simulate infections over the course of a seven-week half-term, which was used to compare impact of differing isolation and rapid test strategies on transmission, absences, and testing volume.

Trystan Leng, Edward M Hill, Robin N Thompson, Michael J Tildesley, Matt J Keeling, Louise Dyson. (2021) "Assessing the impact of secondary school reopening strategies on within-school COVID-19 transmission and absences: a modelling study" medRxiv https://doi.org/10.1101/2021.02.11.21251587


Emma R Southall, Alex Holmes, Edward M Hill, Benjamin D Atkins, Trystan Leng, Robin NThompson, Louise Dyson, Matt J Keeling, Michael J Tildesley. (2021) "An analysis of school absences in England during the Covid-19 pandemic" medRxiv. https://doi.org/10.1101/2021.02.10.21251484


Vaccination strategies for SARS-CoV-2

In an extension to the Warwick COVID model, we introduce a vaccinated class and a population with health conditions that are believed to have a significant impact on COVID-19 outcomes.

A number of different vaccine candidates are in development, resulting in a large degree of uncertainty regarding the performance of these products. We therefore tested a range of efficacies (including reduced efficacy in the elderly) for three different types of vaccine each delivering a different level of protection:

  • Type 1: generates reduced susceptibility, preventing infection and onward transmission;
  • Type 2: generates reduced probability of becoming symptomatic, and hence partially reduces onward transmission;
  • Type 3: generates a reduced probability in experiencing severe symptoms, but has no impact on epidemiological spread.
Vaccination Figure Left: impact of vaccination on targeted at different age-groups.
Orange: targeting by age, the optimal is to vaccinate the oldest age groups first.
Purple: including co-morbidities, these are optimally ordered between 60 and 80 year olds.
Blue: random vaccination.

Moore, S., Hill, E.M., Dyson, L., Tildesley, M., Keeling, M.J. (2020) "Modelling optimal vaccination strategy for SARS-CoV-2" medRxiv 

Follow-up work has considered the interaction between the UK two-dose vaccination programme and future relaxation (or removal) of NPIs. Our predictions highlight the population-level risks of early relaxation leading to a pronounced wave of infections, and the individual-level risk relative to vaccine status.

Focusing on data from England, we also investigated prioritisation of a one dose or two dose SARS-CoV-2 vaccination schedule given a fixed number of vaccine doses and with respect to a measure of maximising averted deaths.

Hill, E.M., Keeling, M.J. (2021) "Comparison between one and two dose SARS-CoV-2 vaccine prioritisation for a fixed number of vaccine doses" medRxiv 


Transmission of SARS-CoV-2 in a university setting

The higher education system in the United Kingdom comprises a large student population. Therefore, in the setting of the COVID-19 pandemic bringing together these student communities presents questions regarding the strength of interventions required to control transmission. We constructed a network-based model to capture the interactions of a student population in different settings within a university environment (housing, social and study) and ran an SEIR type epidemic process.

uni_model_fig_a

Infection and isolation epidemiological measures over the autumn term under differing levels of adherence to nonpharmaceutical interventions.

Top figure: Proportion infected

Bottom figure: Proportion of time adhering students spend in isolation.

uni_model_figb

Distributions relative to students resident on-campus only (green violin plots), students resident off-campus only (orange violin plots) and to the overall student population (purple violin plots).

The white markers denote medians and solid black lines span the 25th to 75th percentiles.

Our work shows high adherence to isolation guidance and effective contact tracing both curbed transmission and reduced the expected time an adhering student would spend in isolation. Irrespective of the adherence to isolation measures, on average a higher proportion of students resident on-campus became infected compared with students resident off-campus. Room isolation as an additional intervention generated minimal benefits. Finally, a one-off mass-testing instance would not drastically reduce the term-long case load or end-of-term prevalence, but regular weekly or fortnightly testing could reduce both measures by more than 50% (compared to having no mass testing).

Hill, E.M., Atkins, B.D., Keeling, M.J., Tildesley, M., Dyson, L. (2020) "Modelling SARS-CoV-2 transmission in a UK university setting" medRxiv 


Workplace-targeted intervention strategies

As part of a concerted pandemic response to protect public health, businesses can enact non-pharmaceutical controls to minimise exposure to pathogens in workplaces and premises open to the public. Amendments to working practices can lead to the amount, duration and/or proximity of interactions being changed, ultimately altering the dynamics of disease spread. We used an individual-based network model to analyse transmission of SARS-CoV-2 amongst a working population that was stratified into work sectors.

Image described below

Infectious case prevalence, isolation and effective reproduction number temporal profiles under alternative worker practices and scheduling. Lighter intensities correspond to: a higher fraction of workers working from home (ranging from 0 to 1; left column); a greater number of days per week being spent working from home rather than spent at the workplace (ranging from 0 days to 5 days; central and right column).

Our study found the progress of an outbreak to be significantly hindered by instructing a significant proportion of the workforce to work from home. Furthermore, asynchronous work patterns may help to reduce infections when compared with scenarios where all workers work on the same days, particularly for longer working weeks. Finally, smaller work teams and a greater reduction in transmission risk led to a flatter temporal profile for both infections and the number of people isolating, and reduced the probability of large, long outbreaks.

Hill, E.M., Atkins, B.D., Keeling, M.J., Dyson, L., Tildesley, M. (2020) "A network modelling approach to assess non-pharmaceutical disease controls in a worker population: An application to SARS-CoV-2" medRxiv 


Social Bubbles

Social bubbles have been proposed as a means of allowing extended contacts beyond the household while minimising the associated transmission risks. The concept is that members of one household are allowed to meet exclusively with members of one other household - while this increases the risk of transmission the premise is that the bubble remains relatively isolated.

Diagram to describe the Warwick COVID model

Diagram to describe the Warwick COVID model

Diagram to describe the Warwick COVID model

Left: examples of how households and social bubbles can be captured by a next generation matrix.

Above: relative increase in fatalities and the reproductive number, R, due to different types of social bubble (Scenarios 1-6) and other forms of increased social interaction (C2 & C3).

Our modelling work shows that social bubbles reduced cases and fatalities by 17% compared to an unclustered increase of contacts. Social bubbles may be extremely effective if targeted towards those small isolated households with the greatest need for additional social interactions and support.

Leng, T., White, C., Hilton, J., Kucharski, A., Pellis, L., Stage, H., Davies, N., CMMID-Covid0-19 WG, Keeling, M.J., Flasche, S. (2020) The effectiveness of social bubbles as part of a Covid-19 lockdown exit strategy, a modelling study


Forecasting COVID-19 in Kenya

To investigate COVID-19 in Kenya, we developed KenyaCoV, a spatial- and age-structure stochastic model of SARS-CoV-2 transmission in Kenya. Epidemiological characteristics of SARS-CoV-2 were drawn from the literature, while the rate of contact between individuals currently in the same region was drawn from an estimated age-mixing contact matrix from Prem et al., 2017. Other modelling information may be found in Brand et al., 2020, and code and open source data may be found at https://github.com/SamuelBrand1/KenyaCoV.

Figure displaying various predicted epidemic curves following intervention efforts

We find that, if within-Kenya transmission becomes established, it is crucial to identify the role of asymptomatically infected individuals. If asymptomatically infected individuals transmit significantly we predict that the subsequent epidemic cannot be contained by case isolation alone, with the potential for a large proportion of the population becoming infected. Conversely, in a scenario in which undetected infections are not infectious, self-isolation may be sufficient to contain the epidemic. Unfortunately in all simulations including some asymptomatic transmission, case isolation was found to be insufficient to prevent a major outbreak or to substantially delay the peak or reduce total cases.

Brand, S. P., Aziza, R., Kombe, I. K., Agoti, C. N., Hilton, J., Rock, K. S., Parisi, A., Nokes, D.J., Keeling, M., and Barasa, E. (2020). "Forecasting the scale of the COVID-19 epidemic in Kenya". medRxiv.

Revealing the extent of the COVID-19 pandemic in Kenya based on serological and PCR-test data." medRxiv.


Global Predictions

The value of the basic reproductive ratio, R0, changes both according to the epidemiological characteristics of the virus, but also the context in which it spreads. Since susceptibility and transmission of COVID-19 changes with the age of the individual, the age-structure of the population can have a big effect on the value of R0. This work examines the risk posed by COVID-19 in different areas of the world. For some parameter choices (see below), the large proportion of children in Africa effectively buffer transmission leading to a much lower R0; notably under these parameters Italy is an obvious hot-spot while Germany has the lowest transmission in Europe.

R0 map of the World

Hilton, J., and Keeling, M. J. (2020). " Estimation of country-level basic reproductive ratios for novel Coronavirus (COVID-19) using synthetic contact matrices"


Precautionary Breaks

When cases of COVID-19 are rising exponentially, we consider the impact of a short 2-week period of intense control. Using two different modelling approaches we show that a short, sharp 2-week break leads to a decline in cases, with similar declines in hospitalisation and mortality over a short period - this could potentially reduce the acute load on the NHS enabling it to continue non-COVID care into the winter months.

Image described below

A two-week precautionary break during half time reduces infection (left) and introduces a temporal reset (right). We consider a range of control strengths during the break (lines from red to blue), and a range of growth rates outside of the precautionary break (x-axis).

A precautionary break is not a lasting control measure, but effectively buys more time to put other controls in place; it takes us ‘back to a time when cases were lower’. To save lives over longer time scales requires driving R below one.

The reduction in cases also allows measures which are resource limited (such as test-trace-and-isolate) to potentially have a greater impact.

Keeling, M.J., Guyver-Fletcher, G, Holmes, A., Dyson, L., Tildesley, M.J., Hill, E.M. and Medley, G.F. (2020) medRxiv. Precautionary breaks: planned, limited duration circuit breaks to control the prevalence of COVID-19.


Phylogenetics

Image described below Here, we analyse 208 publicly available SARS-CoV-2 genome sequences collected during the early outbreak phase. We combine phylogenetic analysis with Bayesian inference under an epidemiological model to trace person-to-person transmission. The dispersion parameter of the offspring distribution in the inferred transmission chain was estimated to be 0.23 (95% CI: 0.13–0.38), indicating there are individuals who directly infected a disproportionately large number of people. Our results showed that super-spreading events played an important role in the early stage of the COVID-19 outbreak.

Wang, L., Didelot, X., Yang, J., Wong, G., Shi, Y., Liu, W., Gao, G.F. and Bi, Y. (2020) "Inference of person-to-person transmission of COVID-19 reveals hidden super-spreading events during the early outbreak phase" Nature Communications 11 5006.

Fountain-Jones, N.M., Appaw, R.C., Carver, S., Didelot, X., Volz, E. and Charleston M. (2020) Emerging phylogenetic structure of the SARS-CoV-2 pandemic. Virus Evolution 6 veaa082 https://doi.org/10.1093/ve/veaa082

People

Warwick Researchers

Matt Keeling

Matt Keeling (Professor, Maths & SLS)

Louise Dyson

Louise Dyson (Associate Professor, Maths & SLS)

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Ed Hill (PDRA, Maths & SLS)

Photo of Mike Tildesley

Mike Tildesley (Reader, Maths & SLS)

Erin Gorsich

Erin Gorsich (Assistant Professor, Life Sciences)

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Bridget Penman (Assistant Professor, Life Sciences)

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Sam Brand (PDRA, Life Sciences)

Rabia Aziza (PDRA, Life Sciences)

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Joe Hilton (PDRA, Maths & SLS)

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James Nokes (Professor, Life Sciences)

Glen

Glen Guyver-Fletcher (PhD student, MIBTP)

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Alex Holmes (MathSys PhD student)

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Trystan Leng (MathSys PhD student)

Massimiliano Tamborrino

Massimiliano Tamborrino (Assistant Professor, Statistics)

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Emma Southall (MathSys PhD student)

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Ben Atkins (PDRA, Maths)

Sam Moore (PDRA, Maths)

Hector McKimm (OxWaSP PhD student)

Andrea Parisi (PDRA, Life Sciences)