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.