RAMP has now reached the end of its initially intended duration of April-July inclusive. This means that those who have volunteered for its various initiatives should feel no continuing obligation to remain involved. However, we hope many volunteers will in fact remain involved, and indeed that the majority of RAMP's activities will continue in some form or another. Our continuity plans are described below in an update of our last progress report which is still available on our "Previous updates" page.
Now that the official volunteer phase of RAMP is formally at an end, we once again thank all RAMP volunteers for their support, including those whose offers of assistance were not ultimately called upon by RAMP.
1. UKRI funding has been secured to maintain the infrastructure of the RAMP forums until late 2021. The same funding will also support a central data, coordination and software activity, led by Graeme Ackland from Edinburgh. The RAMP Forums will therefore continue to operate largely as now, and we hope that many people who have developed an interest in COVID-19 modelling and science will actively participate by nominating and evaluating papers for community review. Papers of potential policy relevance will then continue to be screened by RAMP's Rapid Review Group (RRG) and passed by them through to Government advice channels. These channels are currently being restructured at national level, with the Joint Biosecurity Centre (JBC) taking increasing responsibility for day-to-day COVID-19 planning within England, with SAGE and SPI-M increasingly focussed on longer-term aspects of pandemic planning. Although COVID-19 planning decisions are increasingly political in character, the UK's national COVID-19 response continues to depend crucially on the latest science, which is evolving at pace. Therefore involvement of the wider RAMP community in screening and commenting on this science, via the RAMP Forums, will remain invaluable for the foreseeable future.
2. RAMP is now working on a funding bid to UKRI to secure a further 18 months funding to cover (a) a series of meetings, workshops and study groups, primarily organized by the Isaac Newton Institute, that will allow continued discussion among the many interdisciplinary teams that RAMP has established; (b) continuation of RRG's activities; and (c) continued engagement by staff of the Royal Society Science Policy Centre to ensure that scientific outputs identified as important by RAMP (including but not limited to those generated by its own teams) are communicated to the most relevant parts of Government as the latter evolve during the transition from emergency to "new normal" response modes. These all build on working patterns that have become well established in the past few months, with (a) building on the Newton Institute programme on Infectious Dynamics of Pandemics which runs through to the end of September, and (b) led from the outset by an Oxford team headed by Alain Goriely and Philip Maini.
3. Neither of the above UKRI bids will directly fund researcher time for continuation of the many projects RAMP has begun. These are so numerous and diverse that any kind of centralized funding structure for their continuation would be unmanageable, although the forums, RRG, and networking activities described above will all add value to the new ecosystem RAMP has created by maintaining strong collaboration channels between its various parts. Instead, individual RAMP Task Teams and Projects have been encouraged to apply to UKRI for their own continuation funding with official RAMP support. Six such bids have so far been made to UKRI, with an aggregate proposed budget of well over £4M. Among Task Teams already submitting bids are New Epidemic Modelling (2 bids); Urban Analytics; Environmental and Aerosol Transmission (2); and Structured Expert Judgement. The remaining Task Teams on Comorbidities; Human Dynamics in Small Spaces; and Within-Host Disease Dynamics are yet to finalize their continuity plans which may include further UKRI bids in due course.
4. Many individual volunteers were seconded by RAMP to existing teams within SPI-M. As with all RAMP volunteers, there is no obligation that these secondments continue after July, but of course they can do so by mutual agreement. RAMP volunteers have played active roles in many SPI-M projects leading to a number of reports (see item 5 below). Alongside this, several groups among the RAMP Task Teams have developed close collaborations with SPI-M member teams, such as one on Structured Expert Judgement for Primary School reopenings (using head-teacher expertise) led by vulcanologist Steve Sparks and epidemiologist Ellen Brooks-Pollock of SPI-M (both from Bristol). Several of these cross-disciplinary, group-to-group collaborations are likely to remain in place for the longer term, and may well nucleate further UKRI bids beyond those mentioned already above. In some other cases, RAMP's constituent groups, such as a large team led by Richard Reeve on behalf of Scottish Covid Response Consortium (SCRC) are already directly involved in Government advice channels and may at some point join SPI-M in their own right. The SCRC project, which lies within RAMP's New Epidemic Modelling Task, now involves over 100 scientists from 21 Universities plus partner organizations such as UKAEA, Man Group PLC, BioStatistics Scotland, and Invenia Labs.
5. As mentioned already, seconded RAMP volunteers have actively participated in many SPI-M teams covering all aspects of pandemic modelling. However, because of the urgency with which SPI-M modellers have had to deliver results to Government, the ability of these teams to write up work for mainstream scientific publication has been severely impaired in recent months. (This has led to a peculiar situation where the scientific productivity of these experts may temporarily have appeared to be less than new entrants to the field.) We are currently gathering data on reports where coauthorship by RAMP volunteers has occurred or is intended. Plans are afoot to have a special issue of Phil Trans Roy Soc B in which some of these papers will appear. The possibility of a second such issue, allowing coverage of papers from RAMP Task Teams as well as SPI-M, is also under discussion. Meanwhile the Royal Society's Open Science journal actively encourages COVID-19 submissions from RAMP participants among others.
6. The RAMP forums have discussed over 250 papers and reports with about 10% of these being passed through to the RRG for Rapid Review. Since April the RRG has scrutinized about 60 nominations for Rapid Review, not only from the forums and from SPI-M but in some cases direct from Government. The RRG has commissioned rapid reviews (usually two per paper) on over 40 of the reports nominated.
7. New Epidemic Modelling
A number of groups contributed to RAMP by setting up modelling efforts from scratch. We identified a small number of lead groups, and assigned volunteers to them. We saw value in this in terms of avoiding potential "groupthink", while trialling and testing new concepts, software engineering and model comparison. The guiding principle was to do "high-risk, high-reward" work that might lead to methodological breakthroughs, at a time when the well-established teams of SPI-M were generally too busy running their models for forecasting purposes to innovate much on the model-building or coding side. RAMP's code development work has been successful on many fronts, although so far its deployment to directly inform policy has been frustrated by the difficulties (often shared by SPI-M) in accessing UK disease data more detailed than openly available. This situation is gradually changing in part by the authorities getting to grips with the widely identified 'data problem' and in part by the RAMP teams joining forces with SPI-M groups.
Highlights have so far included the wide range of interlinked models with a shared 'data pipeline' structure developed by SCRC (see item 5 above), and novel compartment models 'PyRoss' and 'PyRossGeo' developed in Cambridge. These modelling platforms allow consideration of interactions from an individual level, building upwards to make predictions at a national level. Assumptions about what processes are important for disease dynamics of course involve a degree of expert judgement, but numerical parameter estimates can be learned reliably from data, once enough of this is available. Both these platforms are already fully functional and producing results based on the publicly available data from the UK and elsewhere.
These and other New Modelling projects can best be appreciated via the following github repositories:
A second thread in the New Epidemic Modelling Task Team has been to study existing codes, to do code comparisons, as well as sensitivity and validity testing. Significant work has been done on the Imperial COVIDSim code, including Red-Team testing at Edinburgh and parameter sentivity tests at UCL, and with Microsoft Research new work has been done on the development of an open framework for pandemic modelling which facilitates model comparison and unifies data formats.
8. Urban Analytics
The Urban Analytics Task Team comprises over 30 individuals. It has developed a fully functional model for the County of Devon and has developed tools to compare intervention scenarios for this demonstrator region. A national model is being developed in engagement with both the Met Office and computer games company Improbable as technology partners. The team is cooperating with the Connected Places Catapult in code verification and in access to high-resolution mobile phone data, and is also partnering with a number of small companies to allow integration of mobility and economic infrastructure data into models that combine human behaviour with disease dynamics. A continuation funding bid has been submitted to UKRI, led by the Alan Turing Institute, with University partners from Cambridge, Exeter, Leeds and UCL, various non-academic partners and support from the JBC. Overall, substantial progress has been made towards the hugely challenging goal of creating genuinely integrated urban pandemic models during RAMP's voluntary period, and there is every possibility of genuine breakthroughs if the project is continued, as we hope, with UKRI support.
9. Human Dynamics in Small Spaces
From the open call, 11 groups volunteered their services, most of whom had a professional interest in how the Pandemic was affecting movement in small spaces such as buildings, larger complexes such as stations and malls, and streets as well as within vehicles and spaces within buildings. Unlike most of the volunteers, in this area, non-academic organisations were dominant and the work they were already doing with respect to their own normal activities was the focus of their interest in linking to other features of modelling the Pandemic which they were not expert in. The groups in question are BAESystems, CASA-UCL/Tesco, Centre for Numerical Modelling & Fire Safety Group–University of Greenwich, City Modelling Lab – Arup, Connected Places Catapult, Martin Centre –Cambridge, Network Rail, Ordnance Survey–Rapid Prototyping Team, Pamela Lab–CEGE–UCL, PWC–Artificial Intelligence Team, Technology & Investment Group, and Sainsbury’s. Each group has produced substantial demonstrators and about half have produced papers for the academic/professional audiences. All groups were represented in presentations to the Newton Institute IDP Programme. Continuing work is being developed with these groups in touch with one another. There have been two outputs thus far: Ordnance Survey's "Predicting the Geospatial Spread of Disease using Spatial Interaction Modelling with Gridded Data" and Sainsbury’s "Simulating human interactions in supermarkets to measure the risk of COVID-19 contagion at scale".
10. Environmental and Aerosol Transmission
This Task Team has engaged approximately 190 researchers led by Paul Linden (Cambridge) and Christopher Pain (Imperial). The Team is split into six subgroups, as follows: (a) Exhalation and Ventilation: This group plans to produce a final document on wintertime ventilation. (b) Aerosols: Work is ongoing to agree baseline specification to characterise the initial conditions for the production of droplet clouds resulting from various types of respiration activity. A sub-group has formed which will look for funding to investigate brass, woodwind and singing. (c) People Movement: A paper on modelling wake mixing has been accepted in the Journal of Fluid Mechanics and another paper has been submitted showing dispersal of CO2 (as a proxy for virus-laden aerosol) over 15m from source in a hospital setting. The group has developed models of dispersal and work is ongoing on these. (d) Deposition: This group is developing a continuous time-based model for looking at risk from a variety of different routes in an office environment. (e) Inhalation: This group is developing a systems-based model for infectious bio-aerosols and will apply for funding to take this forward. (f) Case-studies: This group has considered the above topics through the lens of real-life situations working with industry. One proposal looking at transportation has already been funded, and one focused on schools has been submitted to UKRI. There will be an online conference for the Task findings on September 8th.
11. Within-Host Dynamics
The task team was divided into two sub-groups – one focussed on investigating a viral load model, and the other focussed on various aspects of a multiscale spatio-temporal model. Each group met weekly via Zoom. Developing a meaningful viral-load model at this time requires good data and efforts so far have therefore focussed on finding data and datasets to analyse. Currently the group is studying longitudinal data from ICU COVID patients at Columbia Medical Center (NYC), and also lymphocyte counts in blood from 20 patients from collaborators at St George's Hospital (London). A mathematical framework for evaluating the role of nucleoside analogue drugs against coronavirus using genome sequence data has been developed and the analysis of data examining the consequences of remdesivir upon the within-host evolution of the virus has started.
For the spatio-temporal modelling, progress has been made through an interntional collaboration developing the following SARS-CoV-2 tissue simulator:
A framework paper describing a new approach to modelling pandemics (including within-host dynamics) has been accepted for publication.
Work on an agent-based model of how the virus affects lung tissue is also being developed through a 6-month grant funded by the Chief Scientist Office (CSO) Call for Rapid Research in COVID-19 programme.
Both sub-groups will continue the work and efforts reported above.
This group undertook an investigation of the role of individual comorbidities in COVID-19 outcomes. Data on patients admitted to hospital with COVID-19 were available from the International Severe Acute Respiratory and emerging Infections Consortium WHO Clinical Characterisation Protocol UK (ISARIC WHO CCP-UK), which has collected clinical care data from 260 hospitals in England, Scotland, and Wales on patients admitted to hospital since January 2020. Comorbidities investigated included obesity, diabetes, chronic cardiac disease, smoking and 14 others, and the role of sex and age were also investigated. By incorporating Health Survey for England data on population prevalence of comorbidities we were able to estimate the risk of hospitalisation with COVID-19 by sex, age and comorbidities. Using the ISARIC data on over 40,000 patients admitted to hospital since Match 10th 2020 we estimated risk of death and ICU admission in patients hospitalised due to COVID-19 by sex, age and comorbidities, alongside estimates of rates of transitions between different levels of care and the expected length of stay in different states. We developed techniques for estimation of conditional length of stay, given the eventual patient pathway through levels of care in the hospital. Two reports based on the work of this group have been presented: “Relative risks of COVID-19 hospitalisation and mortality-in-hospital by long-term health conditions in the UK population” and “Risk of death and ICU admission, state transitions and length of stay in people hospitalised with COVID-19 in the UK: associations with sex, age, and comorbidities”. A further report and accompanying github repository are in preparation, alongside a paper describing the statistical methodological developments made.
13. Structured Expert Judgement
The Structured Expert Judgement Task Team conducted a structured expert elicitation of Primary School Headteachers to quantify contact patterns within schools in pre-COVID-19 times and how these patterns were expected to change upon re-opening. Additionally, we surveyed school Headteachers about risk mitigation strategies and their anticipated effectiveness. Our findings suggest that while DfE guidelines form the basis for risk mitigation generically, individual schools have adopted their own bespoke strategies, often going beyond the guidelines. A follow-up elicitation, prior to schools fully re-opening in September, could beneficially inform COVID-19 transmission modelling. The Task Team also constructed a COVID-19 infection hazard model for the return of pupils that takes into account uncertainties in model input parameters, derived from the elicitation. The model estimated likely number of primary schools with one or more infected persons under three different return-to-school scenarios: 1st June when schools re-opened with about one-third of pupils; mid-June full return of children in the same age cohorts; and return of all primary age children in September. The infection hazard model has been evaluated with different levels of community prevalence and inventories of children, teachers and support staff per school. The Team have prepared two full-length manuscripts reporting the work just outlined, which will soon appear on MedrXiv.
RAMP is now over half-way through its intended duration of April-July inclusive. Our present focus is not to start further new initiatives, but to build and deliver on the various tasks already underway. These were laid out in our 8 May update, still available.
TBelow is further information on RAMP's ongoing activities and plans. Once again, we thank all RAMP volunteers for their very generous offers of support for RAMP. This includes not only our deployed volunteers but all those whose offers of assistance have not, in the event, been taken up.
1. At least 200 RAMP volunteers have been deployed directly on research projects, either seconded to existing SPI-M modelling teams or within the new research teams that RAMP has helped to create. This volunteer workforce has had a material effect on the ability of SPI-M, SAGE and others to create scientifically sound policy advice.
2. The Isaac Newton Institute (INI) programme on Infectious Dynamics of Pandemics (IDP) is now in full swing, with about 80 long-term participants. Within the last two weeks there have been discussion sessions on contact tracing (with recommendations reported here), structured expert judgement, and whole-cost modelling. Many more topics are planned, including urban analytics, human dynamics in small spaces and environmental and aerosol transmission. Real-time participation at INI events is by invitation, but it is possible to register interest at the IDP homepage https://www.newton.ac.uk/event/idp. This page also has links to recordings and slides of the INI discussions.
3. The RAMP discussion forums (https://ramp-forums.epcc.ed.ac.uk) are fully operational with over 300 members. More would be welcome: any scientist willing to read and comment on research papers on COVID-19, even occasionally, can make an important contribution to the work of RAMP through the forums. They provide a mechanism to help scan the emerging COVID-19 modelling literature and identify items of potential policy value, which then get passed on for rapid expert review (see next item).
4. The RAMP Rapid Review Group offers high quality, rapid-turnaround reviews for work of potential policy relevance, including items identified via the forums. Review is available not only for scientific reports and papers but also for software and codebases. The hard work of the RRG, with its team of over 100 expert reviewers, has proven valuable not only to SPI-M and SAGE but also directly to other parts of Government.
5. The activities of RAMP's New Epidemic Modelling Task can best be gauged by visiting the following open repositories created by its project teams’ participants as indicated:
Other contributions will follow shortly from teams at Durham, Frazer Nash, Edinburgh and elsewhere. A RAMP team in Edinburgh instrumental in red-team testing the Imperial College CovidSim codes available at https://github.com/mrc-ide/covid-sim/ and this is now being tested for parameter sensitivity by a team at UCL.
6. Work is continuing apace from the RAMP Task Teams on Urban Analytics; Human Dynamics in Small Spaces; Environmental and Aerosol Transmission; and Within-Host Disease Dynamics. We expect the first results from each of these teams to emerge in the next few weeks.
7. The RAMP Task Team on Comorbidity Factors is fully operational and is now co-led by Ruth Keogh and Karla Diaz-Ordaz of the London School of Hygiene and Tropical Medicine alongside the Institute for Actuaries' Covid-19 Actuarial Response group. It has access to high-resolution patient data which will be combined with demographic data to help identify the subpopulations most in need of shielding or other protection against contracting the virus.
8. The RAMP Task Team on Structured Expert Judgement has performed its first exploratory study with participation from head teachers and others to help assess risk scenarios and strategies involved in reopening schools.
9. RAMP is planning how best to build on the above achievements following the wind-down of its volunteer programme at the end of July. Of course, RAMP volunteers are free to continue with the work they have started beyond that date, as per their arrangements with the respective task leadership.
10. A central support operation for coordination, reporting, and the RAMP forums has now been funded by a UKRI grant (PI Graeme Ackland, Edinburgh) for 18 months. The Rapid Review Group, led from Oxford, expects to remain in operation on a voluntary basis at least until the end of the summer.
11. RAMP will not apply in its own right for large-scale research funding after the voluntary period ends. Instead, individual Task Team and Project Leads within RAMP have been urged to make proposals to UKRI and/or other funding agencies to allow continuation of their activities on a sustainable, fully funded basis. The RAMP central team will assist where possible, e.g. by coordinating cross-membership of steering groups for the individual projects.
This update informs the RAMP community about what has happened in the three weeks since the previous (16 April) update – which remains available. We do not repeat what we said on 16 April, except to reiterate that he RAMP team remains immensely grateful for all the support offers received.
The previous update includes advice for those who have not yet been tasked with specific roles to proceed by yourselves if you prefer. If you tell us what you are planning, and if we think it worth adopting as an official RAMP project, you may then be able to request additional RAMP volunteers for your team.
1. We have continued to reinforce SPI-M teams with skilled volunteers. This effort has been appreciated and has facilitated research outcomes reported to SAGE.
2. In partnership with RAMP, an online programme of the Isaac Newton Institute on Infectious Dynamics of Pandemics has been established to run 5 May - 31 August. Prof Deirdre Hollingsworth chairs the Organizing Committee. For details see https://www.newton.ac.uk/event/idp. This programme will span invitation-only technical discussion sessions, via workshops open for qualified applicants, to the production of online pedagogical materials such as those already available (from a previous programme) at https://www.newton.ac.uk/event/idd/seminars
3. The RAMP discussion forums are now open at https://ramp-forums.epcc.ed.ac.uk. All RAMP volunteers and their team-members are invited to sign up for the forums. In the event of heavy traffic, it may take some time to approve your registration. Of special significance is the Research Outputs: Community Review forum. There, you and your colleagues are invited to nominate, review and discuss research outputs of potential significance to managing the pandemic. A team of moderators, led by staff of the Mathematics and Biology Departments of the University of York, will use these "community reviews" to identify work potentially worthy of the attention of SPI-M and/or SAGE. Such work will be fed via a small triage team into the RAMP Rapid Review Group (see next item) for consideration. Outputs for community review can include datasets and codebases as well as preprints or journal papers. They can come from any source, but must be open access.
4. The RAMP Rapid Review Group (RRG) is led by Alain Goriely and Phillip Maini from the University of Oxford. The group is tasked to commission rapid expert assessment of research outputs, reports and codebases nominated directly by SPI-M members, SAGE members, or RAMP Task Team Leaders, and decide whether these have the combination of scientific importance and policy relevance that merits their consideration by SPI-M /SAGE in their roles as advice channels to the Government.
5. Updates from the RAMP Task Teams:
5.1. The RAMP Task Team on new epidemic modelling is being led by Prof Graeme Ackland (Edinburgh). Developments include:
(a) a major consortium is being led by SCRC (Scottish COVID-19 Research Consortium), initiated by scientists from four core institutions in Scotland, but now has over 100 contributors from 12 organisations across the UK. This consortium is working on several separate codebases, mostly originating from prior work on animal epidemics.
(b) A team of RAMP volunteers from Cambridge has created a Python library for general compartment models, for example SEI(...)R, with unlimited disease stages, structured by age. The library simulates both stochastic and deterministic versions of the compartment dynamics. The library enables fully Bayesian forecasts, including data and parameter uncertainties convolved with intrinsic stochasticity. The library allows Bayesian estimates of NPI impacts, and can compute optimal strategies for these, given cost functions. A beta version of the library is freely available at https://github.com/rajeshrinet/pyross and has numerous Jupyter notebook examples. The team also has a geographical version of this code which will be released soon.
(c) A team from Nottingham has started work on developing simple, yet sufficiently realistic models, to be fitted to the limited data available to address questions such as how infection rates have varied over time (before and after the lockdown), quantify uncertainty about model parameters and predictions, and determine the ability of those models to capture features of the more complex, individual-based models.
(d) A team from Frazer-Nash Consultancy is developing a holistic cost model that relates intervention policy to health and economic impacts, accounting for the impact of public perception on policy effectiveness. The modelling approach, which has been used successfully in the energy and defence sectors, explicitly considers the uncertainty in future predictions and how this can be reduced through testing and data collection.
(e) A RED team of RAMP volunteers from Edinburgh has helped stress-test COVID-sim, the Microsoft implementation of the Ferguson/Imperial College model. The latter implementation is now open access at https://github.com/mrc-ide/covid-sim and has been ported to major UKRI computing platforms. With help from the RED team, an independent RAMP team from UCL has begun systematic parameter-sensitivity testing for COVID-sim.
5.2. The RAMP Task Team on urban analytics / social modelling has been set up in partnership with the Alan Turing Institute and is led by Prof Mark Birkin (Leeds & Turing), comprising about 25 RAMP volunteers. The team is working to address the following two goals: (a) to use modelling of human behaviour including use of transport systems, shopping and leisure facilities to better inform the parameters of epidemic models by understanding human encounter dynamics in various environments; (b) to integrate disease status, as an attribute, into models of human behaviour so as to create a new platform for epidemic modelling in which full social interactions are present from the outset rather than introduced to estimate parameters such as rates for infection.
5.3. A RAMP Task Team has been established to apply social modelling methodologies to human dynamics in small spaces (relative to those traditionally considered in urban analytics – for instance, within a supermarket). This team is led by Prof Mike Batty (UCL), and has about 20 FTE of staff effort from RAMP volunteers; alongside academics it includes staff from at least six partners across the commercial and industrial sectors.
5.4. A RAMP Task Team has been established to investigate and model environmental and aerosol transmission, including interaction of aerosols and droplets with air flow especially in buildings and other confined spaces, and its relation to the physical transport of virus particles between individuals either directly airborne or via intervening surfaces. This team is led by Prof Paul Linden (Cambridge) and Prof Chris Paine (ICL).
5.5. A RAMP Task team has been established to investigate and model the within-host disease dynamics of COVID-19 within individuals carrying the infection, to understand better the time evolution of the disease, its level of variability, possible groups at risk of severe outcomes or of being more infectious than others, and other factors. This team is led by Prof Mark Chaplain (St Andrews).
5.6. The Institute for Actuaries' Covid-19 Actuarial Response group, led by Mohammad Khan and Gavin McInally, has convened a RAMP Task Team to attempt to identify comorbidity factors, including but not limited to pre-existing medical conditions, and estimate the prevalences of these factors and combinations thereof across the population, with the goal of better informing decisions about shielding strategies during the unlock period and their consequences.
5.7. A RAMP Task Team, led by Steve Sparks and Willy Aspinall from Bristol, has been established to consider, on behalf of RAMP and DELVE (our sister-project on data analytics also run by the Royal Society), the potential role of Structured Expert Judgement (SEJ) for parameter estimation and decision support – such as the effect of opening schools on transmission rates – when data is of insufficient quality or quantity for fully evidence-based scientific advice. Two demonstration applications are being developed.
6. Access to patient data, even anonymised, presents a serious bottleneck to epidemic modellers, including SPI-M teams, but particularly for those entering the field for the first time. A number of agencies across Government are now working hard to solve this problem and provide streamlined access. RAMP is in direct discussions with several of these organizations. However RAMP modelling teams cannot expect to receive large amounts of clean UK data on the time-scale they might like and even then such data may require permissions protocols.
7. One significant data access channel is HDRUK. This has a newly developed portal at https://www.hdruk.ac.uk/covid-19/, and RAMP is trying to take a generic request for stratified population data through the process and will follow up with other requests. Such data will be shared across RAMP to whatever extent the data-release conditions permit this. We are engaging with HDRUK to streamline and document the process and requirements for access to data that can then be used for RAMP research. RAMP volunteers are of course free to use this and other data-access routes directly for themselves, but must not claim without authorization to be doing so on behalf of RAMP.
8. With patient-specific data rather than stratified population data, the situation is more complex still. Generally it may prove necessary to import the model into a safe-haven where the data resides and export it again with parameters fit to that data but without the data itself. Such safe-havens so far exist separately in different UK Nations and data is not easily accessed without detailed permissions and not easily transferred from one safe-haven to another.
9. A possible route to obtain access to patient data, both population-level and individual, is for researchers or teams to find collaborators who are running closely related projects that can be amended to include the planned COVID-19 research. This would require the amendment practice for the original research to be followed. See for instance https://www.hra.nhs.uk/covid-19-research/covid-19-guidance-sponsors-site..., but note that procedures may differ between safe-havens.
10. Before patient data arrives, anyone with a new epidemic model (model A) is advised to test it first on synthetic data from one or more other models of similar or greater complexity (say model B). This allows the predictive power of model A to be ascertained in the context of a 'alternative pandemic' where model B is in fact true. If this predictive power is lacking, then it is likely also lacking for the true pandemic – unless the real-world evidence base for the prior assumptions behind model A is vastly stronger than the corresponding evidence base for model B. It may also be possible to test your model against real-world data from other countries that are further along the unlock path than the UK and/or have a more relaxed attitude to patient data. Such tests might significantly improve the prioritization of any request you make (via RAMP or otherwise) for UK patient data. They are also separately valuable for studies of how the UK response differs from that of other countries.
11. Anyone coming across datasets for patient outcomes that are clearly in the public domain and do not carry confidentiality issues, is warmly encouraged to post links to these resources on the RAMP forums under 'Research Outputs: Community Review' tagging the output as 'data' (as well as by subject area). Others are then invited to comment on their utility.
12. 'Social data' refers to data about human behaviour – typically mobility, commuting, shopping habits and the like. Large amounts of such data are already available, and the RAMP Task Team working on urban analytics / social modelling has strong experience in accessing it. Unlike patient data, this is 'big data' for which appropriate skills are needed. Commercial confidentiality restrictions very often apply but there may be ways to share such data with other teams across RAMP if a good case can be made for it. Additionally, RAMP (alongside DELVE) is actively investigating data-sharing arrangements with potential partners particularly in the mobile phone and banking sectors.
13. Anyone coming across datasets for social data that are officially in the public domain and do not carry confidentiality issues, is warmly encouraged to post links to these resources on the RAMP forums under 'Research Outputs: Community Review' tagging the output as 'data' (as well as by subject area). Others are then invited to comment on their utility.
The RAMP team is immensely grateful for all the support offers received. We are conscious that the majority of responders have not yet been invited to participate in specific tasks. This update is intended to say what we have done so far and why, say what we are doing now, and give a rough idea of the offers of help we've received and how these have been prioritized. There is also some advice, in the final section, for those wanting to proceed independently rather than wait to be either tasked, or stood down, by the RAMP team.
1. We have identified about 70 responders whose technical skills match urgent requests from existing pandemic modelling teams. These are the teams represented by SPI-M, the Scientific Pandemic Influenza Group on Modelling, pronounced Spy-M, which advises SAGE, the Scientific Advisory Group on Emergencies, which in turn advises COBRA. (COBRA is the Civil Contingencies Committee that is convened by the Prime Minister to handle matters of national emergency.) SPI-M teams are currently recruiting secondees from this pool to fill immediate staffing needs, allowing a broader range of advice to Government on all topics relating to the pandemic. A second wave of secondments may follow.
Reason: Reinforcing existing SPI-M teams with the staff they need to operate effectively during the crisis has been, and remains, the first priority of RAMP.
2. We have identified and contacted, among RAMP responders, a small number of pre-existing collaborations, recently set up or of longer standing, that are positioned to independently create first-principles human epidemic models to allow greater diversity of model-based advice. Among these we particularly seek models at a level of social granularity and geographic comparable to the model of Prof. Neil Ferguson's group at Imperial College (which is part of SPI-M). These new teams are being reinforced with volunteer technical staff among other RAMP responders to allow rapid progress, on similar terms to the SPI-M teams.
Reason: The "Ferguson model" is one important strand of SPI-M's evidence base. While decisions are not based on this alone, corroboration is often via models addressing more specific aspects of disease dynamics, rather than system-level models of the unfolding pandemic across the UK. Having a broader base of equally detailed but independently formulated models will allow better assessment of the robustness of system-level predictions. Robustness becomes crucial as the predictions become more complex and detailed, as they will have to be, in order to compare the various exit scenarios from lockdown that are beginning to come under discussion.
3. We have identified among RAMP responders, and are in the process of contacting, a number of key teams in the area of urban analytics or social modelling. With the offer of other volunteer support, these will be tasked to rapidly address the following two goals: (a) to use modelling of human behaviour including use of transport systems, shopping/ eating areas and (largely for the first time) open spaces, to better inform the parameters of epidemic models by understanding human encounter dynamics in various environments; (b) to integrate disease status, as an attribute, into models of human behaviour so as to create a new platform for epidemic modelling in which full social interactions are present from the outset rather than introduced to estimate parameters such as rates for infection.
Reason: In recent years, much more has become known about the social dynamics of humans, particularly in urban environments, than has ever been incorporated into the modelling of disease dynamics. In the short term, this new knowledge has the potential to support existing types of pandemic model via by more informed parameter choices, for instance in studying the effect of individual lockdown measures and their reversals. In the medium term it might lead to a completely new level of predictive power for disease spread. This would be relevant to managing any long-term distancing measures required after peak COVID-19, and for future pandemics.
4. We have identified, and are now approaching, a panel of senior scientists led by and including RAMP responders, tasked to provide (a) a rapid-response peer-review and feedback service to SPI-M groups, helping SPI-M to prioritize outputs for its own discussions and for those of SAGE, and (b) a similar review function for papers appearing in the world literature that SPI-M have identified as potentially important but do not have time to read. In time, this function will expand to include (c) RAMP outputs and (d) those nominated through a crowd-sourced procedure involving RAMP volunteers (see item 10 in the 'what we are doing now' Section).
Reason: each SPI-M team is at full stretch producing their own results with little time to read that of other teams let alone the exploding world literature. Devolving this task to additional senior scientists not directly involved in the SPI-M modelling efforts will create a more sustainable advice system and help distinguish signal from noise in the world literature on COVID-19 (published and unpublished).
5. We are identifying among RAMP responders, and will soon be approaching, teams and individuals placed to contribute to a rapid response research effort on the fluid dynamics of aerosols, their interaction with air flow especially in buildings and other confined spaces, and related topics that will inform decision making about social distancing recommendations indoors and out, use of masks, hygiene and cleaning schedules, and other topics related to the physical transport of virus particles between individuals either directly airborne or via intervening surfaces.
6. We are identifying among RAMP responders, and will soon be approaching, teams and individuals placed to contribute to a rapid response research effort on the dynamic of COVID-19 within individuals carrying the infection, to understand better the time evolution of the disease, its level of variability, possible groups at risk of severe outcomes or of being more infectious than others, and other factors that can inform the parameters of epidemic modellers.
7. With advice from SPI-M and SAGE, we are formulating a further list of questions that will guide additional efforts to convene researchers that have volunteered for RAMP and ensure that any new groupings, alongside existing ones, focus strongly on questions that will actually inform policy-making rather than questions less likely to do so in the short or medium term relevant to the current crisis. Where appropriate we plan to broker additional collaborations among RAMP responders to address these questions.
8. RAMP is collaborating with the Isaac Newton Institute for Mathematical Sciences (INI) on a virtual programme to be held there. Potential organizers have been or are being approached, and the programme is set to begin within about 2 weeks. (This compares to a typical lead-in time of about 2 years for most INI programmes.) The programme will involve targetted discussion groups and webinars among invited participants, including suitably qualified RAMP responders. It will also involve live and recorded presentations, some of which will be pedagogical in character. There is already free access to many such presentations, and also written resources, resulting from the successful 2013 INI programme on Infectious Disease Dynamics. These resources are available at https://www.newton.ac.uk/event/idd (overview + links to reports) and at https://www.newton.ac.uk/event/idd/seminars (seminar listings with recordings and pdf slides) and are recommended to those with strong mathematical backgrounds wishing to learn more about pandemic modelling.
9. We are exploring possible platforms on which to build a dedicated RAMP discussion space. This will enable (a) rolling rather than static updates of RAMP activities; (b) questions to be asked of RAMP participants by SPI-M and SAGE; (c) linking by RAMP participants to their own reports and datasets, and also nomination of other reports in the wider literature; (d) open discussion of such reports, including a voting-type system to aid 'crowd-sourced' identification of contributions of high potential policy impact. We plan for a team of moderators to monitor these discussions, and draw out key reports for further triage by the rapid review team established in item 4 in the 'what have we done and why' section. This discussion space will be accessed by link from the RAMP home page, alongside other resources such as GitLab and/or GitHub repositories for ongoing RAMP collaborations.
10. We are liaising with a number of other organizations and efforts whose goals are related to RAMP, in some cases brokering introductions with RAMP responders whose skills may be better aligned to these efforts than RAMP itself. These include, but are not limited to, DELVE (Royal Society, focussing on data-driven and inferential, rather than mechanistic, modelling of disease spread and outcomes); DECOVID (Alan Turing Institute, focusing on near-patient logistics and supply chain issues); HECBiosym COVID-19 initiative (molecular modelling); N-CORN (National COVID-19 Operational Research Network); COVID-19 Actuarial Response Group. We are also in touch with the UKRI COVID-19 R&D Coordination Group.
We had over 1800 responses to our survey, about half from sole researchers and half representing teams. Many of the latter offered the full-time equivalent of several whole people (several FTEs) as effort.
First and foremost, we thank every team and every individual for their support. Every single offer of support has increased the choices available to RAMP, and allowed us to envisage more ambitious work than otherwise. Nonetheless, this is a larger potential labour force than RAMP can effectively deploy on the timescale of weeks or even months. We are trying to allocate people power to tasks in stages so that the most urgent tasks get addressed first. Please therefore be patient, or if you cannot be patient, please follow the advice under (d-h) below. Here is how we have done things, followed by some advice for the majority of responders who are yet to be contacted.
(a) For specific secondment roles to SPI-M teams (or new RAMP teams) we have identified people with the highly specific skills that these teams require – typically, programming in a less-common language or familiar with particular software platforms. Of these, we have prioritized those able to commit a relatively large percentage of their time.
(b) To lead in nucleating new research we have prioritized pre-existing teams with the right skills sets. Thus far, this has involved either existing experience in, or demonstrable rapid progress towards, the construction of epidemic models or social dynamics models with the scale or complexity referred to in items 2 and 3 of the 'what have we done so far and why' section. This is not because simpler models are of no value; it is because we don't need big teams to make them. It is likely that further team-level approaches will be made soon.
(c) As we move through the tasks listed in items 5-10 of the 'what are we doing now' section, RAMPs need for people will diversify. Some of those with appropriate skills but relatively small time availability per head are likely to be enlisted for literature-review or discussion moderation roles where the workload is easily subdivided. Certain large modelling teams, especially those with strong management structures of their own, may be asked to second en-bloc to one of the RAMP or SPI-M teams referred to above. This requires matching of skills sets and, although preliminary discussions are now beginning in some cases, first requires the teams leading the science to scope out the way such deployment might operate.
(d) Only a fraction of the people RAMP will need have so far been assigned to tasks. Please be patient while we complete this job. The large number of responders does mean that many people will not be found a specific role, but we are not asking people to stand down at this stage because the needs are evolving daily. Should you wish to stand down yourself or your team, please do not contact us but instead desubscribe from the RAMP mailing list. We will endeavour to check that people are still subscribed to the main list before approaching them.
(e) Any individual who is strongly committed to working on COVID-19 pandemic modelling, and who is not assigned to a task before the end of April, is invited to educate themselves via the Isaac Newton Institute resources such as those at https://www.newton.ac.uk/event/idd (overview + links to reports) and at https://www.newton.ac.uk/event/idd/seminars (seminar listings with recordings and pdf slides) and then join in the RAMP discussion forums when these are launched. In general we anticipate a greater need for scrutinizing and prioritizing the emerging literature on COVID-19 than for adding to it. Please see the following article by Julia Gog for further advice on this point:
(f) There may also be teams that are already strongly committed to working on COVID-19 pandemic modelling even if not called upon to do so by RAMP. Anyone who would do this at scale, by which we mean with at least 4 FTEs of effort, is invited to contact us with a brief report of what they plan to do. RAMP will attempt to coordinate these efforts and avoid duplication, either with others of the same type, or with the projects RAMP has prioritized or will prioritize for direct support. Such teams are invited to link reports of their work to the RAMP discussion platform once established. They are also invited to make use of the INI resources mentioned in item (e) above.
(g) Teams or individuals with specific requests for RAMP volunteer resources to help their own efforts should contact us. Priority will be given to those already contributing effectively to pandemic modelling research.
(h) RAMP has received a number of offers from large modelling teams, or even entire departments, with diverse skills but no direct plans to start their own epidemic modelling projects. Typically such teams have research interests remote from disease dynamics, but are offering to lend a hand, or indeed many hands, with coding and other technical tasks. We are enormously grateful for these generous offers. It is likely that only a proportion of them will be accepted in the next month or so because they currently outnumber the epidemic modelling teams capable of making good use of them. However, as new RAMP projects get established we may return to some of these offers in the hope that they remain open. We will quite understand, of course, if by then they do not. Anyone requiring certainty on this for their own planning purposes is invited to contact us with a firm deadline for when a decision is needed from RAMP either to deploy their team or step it down.
The survey is now closed.
The recent call for volunteers brought an enormous response and we are currently processing the offers of help. We are currently prioritising areas that can best support the national effort to tackle the pandemic. We will contact those who we feel can best contribute to these as we define them in the coming days and weeks.
Meanwhile, the RAMP Steering Committee thanks everyone who has responded to the RAMP survey for for their overwhelming offers of support.
We are extremely busy at the moment but please email us if you need further information.