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Infectious Dynamics of Pandemics: Mathematical and statistical challenges in understanding the dynamics of infectious disease pandemics

5th May 2020 to 18th December 2020
Deirdre Hollingsworth
Julia Gog
Hans Heesterbeek
Valerie Isham
Denis Mollison
Phil O'Neill
Sylvia Richardson
Nigel Shadbolt
Caroline Trotter
Alan Wilson

Due to current events, this is a virtualised programme 

Please note that at the start of October 2020 the decision was made to extend the IDP programme until the end of the year, at the very least. The organisers requested this extension in order to build on the work of the programme with outputs that may be helpful for future research. The stated goal is to produce a set of papers summarising the challenges that scientists need to address in order to help the world avoid, or deal better, with future pandemics.

Programme Description

Mathematical modelling has played an unprecedented role in informing public health policy on the control of the current COVID19 pandemic. Infectious disease modelling groups in the UK and globally have necessarily been working in ‘response’ mode to provide real-time modelling of the pandemic as it unfolds. However, this has left limited time for longer-term thinking about the challenges of understanding the dynamics of this particular pandemic. There is therefore an additional need for experts to discuss, explore and analyse surrounding issues including model assumptions, strategies for surveillance, contact tracing, use of diagnostics, and social distancing.  A key aim of this programme is to address this need for longer-term thinking.

This programme will support the activities of the Royal Society’s Rapid Assistance in Modelling the Pandemic (RAMP) programme through additional capacity to provide rapid assessment of strategies of immediate policy relevance.  Furthermore, programme participants will provide critical assessment of extant models, considering alternatives and identifying improvements. This is vital to avoid duplication of effort and the potential for analyses which misinterpret key aspects of the epidemiology or make incorrect assumptions regarding underlying data. Finally, this programme will provide the space for considered, collaborative thinking, providing new ideas and directions, forging novel interdisciplinary links as well as reflecting on lessons learned for future pandemics with regard to planning, prevention and control.

Through a range of virtual events this programme will bring together researchers from a broad range of disciplines, from applied epidemiology to fundamental mathematics. Events will include virtual study groups and webinars. It is hoped that this programme will provide a community of researchers to support the mathematical modelling work to address this current pandemic globally.

If you would like to note your interest in participating in the programme, please complete the 'expression of interest' form. Once the required details are submitted, we will contact you regarding the outcome of your application in 3-5 working days.


Workshops: Details of Workshops 1 and 2 are given below.  Details for subsequent workshops will be posted in due course.

Workshop 1: Models for an exit strategy, 11-15 May
Following the successful reduction in transmission in many countries, questions of how and when to lift interventions are being asked. In this workshop we will address the models and underlying assumptions which would be used to inform these discussion by evaluating assumptions underlying possible exit strategies. This will include measurement and modelling of contacts, immunity, surveillance, and transmission route, and will include participants from both infectious disease modelling and other fields. This workshop will branch out into a number of different work streams over the following weeks.
Workshop 2: Models old and new, 18-22 May

This workshop will examine, compare and discuss the approaches being currently used for modelling the pandemic with potential new approaches. Participants from outside the traditional epidemiological modelling field can bring experience of modelling, for example, behaviour, movement and social structure, as well as of computational optimisation and data visualisation.


University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons