| EPSRC Reference: |
EP/T003618/1 |
| Title: |
Mathematical tools to inform sustainable interventions against schistosomiasis in Uganda |
| Principal Investigator: |
Prada, Dr JM |
| Other Investigators: |
|
| Researcher Co-Investigators: |
|
| Project Partners: |
|
| Department: |
Veterinary Medicine & Science |
| Organisation: |
University of Surrey |
| Scheme: |
GCRF (EPSRC) |
| Starts: |
01 October 2019 |
Ends: |
31 May 2023 |
Value (£): |
463,680
|
| EPSRC Research Topic Classifications: |
| Mathematical Aspects of OR |
Statistics & Appl. Probability |
|
| EPSRC Industrial Sector Classifications: |
|
| Related Grants: |
|
| Panel History: |
|
|
Summary on Grant Application Form |
Schistosomiasis is a neglected parasitic disease, second only to malaria in its socio-economic and public-health importance. It is estimated that 240 million people are infected worldwide, most of which belong to the poorest populations in many sub-Saharan African countries. Individuals with this disease, the majority children, acquire the infection when they contact infected fresh water through behaviours such as fishing and bathing, and transmit the disease in areas with inadequate sanitation. There is a drug available to treat cases, and the World Health Organization recommends mass treatment of school-age children or whole communities, depending on the disease burden. However, the drug does not prevent reinfection, which occurs rapidly.
Combining the drug treatment campaigns with improving sanitation infrastructure could hold the key to finally controlling and eventually eliminating this disease in these populations. The question then becomes what is the best intervention, or combination of interventions, that will most rapidly and efficiently reduce disease burden. To tackle this, we propose to develop a mathematical approach, that integrates the biological infection process (i.e. modelling transmission of infections) with individual behaviour and preference for the different interventions (i.e. using a process of modelling interaction between humans called game theory). Combining these two we can find the most suitable combination of interventions that would be successful in the community and decrease disease. We can then explore the costs associated with the combination of interventions, to leverage the most popular and effective, with the most affordable. Another important aspect is evaluating the interventions, once they are implemented, to measure progress. We need to add the use of different diagnostics to our model, as they might provide different information depending on the setting.
The combination of all of this work will then enable us to write recommendations for a field trial to test a control program, with an efficient and popular campaign, which we can effectively monitor. The project is highly multidisciplinary and will bring together expertise from mathematics and biology, as well as, statistics, epidemiology and health economics. We will build the models based on our previous work and additional published materials in the literature, using social and economic data locally collected with our partners in Uganda. The project outcomes, specific to local populations in Uganda, could then be expanded to other countries by engaging with other communities and key stakeholders.
|
|
Key Findings |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
|
Potential use in non-academic contexts |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
|
Impacts |
|
| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
|
| Date Materialised |
|
|
|
|
Sectors submitted by the Researcher |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
| Project URL: |
|
| Further Information: |
|
| Organisation Website: |
http://www.surrey.ac.uk |