| EPSRC Reference: |
EP/T003650/1 |
| Title: |
Mathematical Modelling for Infectious Disease Dynamics and Control in East Africa (MMIDD-EA) |
| Principal Investigator: |
Trotter, Dr CL |
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| Department: |
Veterinary Medicine |
| Organisation: |
University of Cambridge |
| Scheme: |
GCRF (EPSRC) |
| Starts: |
01 April 2020 |
Ends: |
30 September 2021 |
Value (£): |
204,822
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| EPSRC Research Topic Classifications: |
| Mathematical Analysis |
Statistics & Appl. Probability |
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Summary on Grant Application Form |
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Computer-based mathematical modelling of infectious diseases is an essential tool to understanding how diseases spread. They can also be extremely useful in designing effective control strategies and policies. The East Africa region is a hotspot for emerging and endemic diseases caused by bacteria and viruses, including those spread from animals to humans (known as zoonoses). Despite the potential for mathematical modelling to address public health challenges in the region and the availability of relatively cheap computing power and free programming platforms, these skills are rarely applied by academics and researchers in low and middle income countries. Instead modelling work is done by researchers from high income countries and often for diseases threatening global health such as epidemics caused by the Ebola virus. We aim to develop a network of East-African based mathematical modellers to build skills and capacity so that this work can be performed for priority infectious diseases in the region and by local researchers, who have a superior understanding of the social, economic, geographic and political context of infectious disease spread and control. To do this we will organise three activities. First, we will develop and run an intensive short-course in mathematical modelling of infectious disease dynamics for 20 people in Kenya. Applications will be sought from researchers based in East Africa with skills in mathematics and an interest in quantitative approaches to infectious disease dynamics and control in humans and animals. A competitive selection procedure will prioritise candidates with both institutional support and defined modelling projects relevant to the region to carry forward. The course will be based upon the well-established and highly regarded Mathematical Modelling of Infectious Disease Dynamics residential course supported by the Wellcome Trust, led Dr Andrew Conlan (University of Cambridge) and modified to be appropriate to the needs identified in East Africa. The course will have a strong emphasis on building practical skills using the free software R and Rstudio, and focussed on infectious diseases that are important in the region. Second, five fellowships will be awarded to course attendees to enable them to further develop their skills. Each fellow will be matched to a mentor from the University of Cambridge or the broader course faculty (drawn from across the UK and Africa) who will work with them to develop their skills and collaborate on a selected modelling projects. The fellows will have the opportunity to spend up to 3 months in Cambridge (or other UK Institute, as befits their project and development needs) as well as the opportunity to spend time at the University of Nairobi and interact with their cohort. Third, we will support the development of an East African infectious disease modelling network, by linking with other complementary initiatives in the region. This will increase the self-reliance of the cohort and help to sustain further capacity building.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.cam.ac.uk |