Project title
Impact of maternal vaccines and monoclonal antibodies for the prevention of respiratory syncytial virus (RSV) disease in young children: a modelling analysis to assess the importance of real-world variation in intervention coverage and disease risk.
Supervisory team
LSHTM
- Lead: Dr. Andrew Clark (Andrew.Clark@lshtm.ac.uk, Faculty of Public Health and Policy)
- Prof. Mark Jit (Mark.Jit@lshtm.ac.uk)
Nagasaki University
Project
Background: Respiratory syncytial virus (RSV) is an important cause of acute lower respiratory infections in young infants. Most RSV deaths occur in low- and middle-income countries (LMICs) (Li et al, Lancet 2022). Maternal vaccines and monoclonal antibodies (mAb) have been approved for clinical use. The impact of RSV interventions will depend on the real-world coverage that can be achieved among those at greatest risk (Mulholland et al, WHO Bulletin 2008). However, most mathematical models used to assess the impact of vaccination programmes do not account for real-world variation in intervention coverage and disease risk. This could lead to over-estimation of the potential health impact.
Proposed project: This project will quantify the extent to which models that do not account for real-world variation in intervention coverage and disease risk may be overstating the potential impact of maternal vaccination and infant mAb administration in LMICs.
This will involve:
a) developing a mathematical model to explore the impact of RSV interventions for a single country, or range of LMIC population contexts with varying levels of heterogeneity in coverage and disease risk; and,
b) gathering and synthesising model input parameters from the scientific literature and household survey datasets e.g. the Demographic and Health Surveys (DHS – www.dhsprogram.com) and Multiple Indicator Cluster Surveys (MICS – www.mics.unicef.org).
Impact: This project aims to provide more realistic estimates of the potential impact of RSV interventions in LMICs, and may help to inform the design of strategies that could be used to increase the impact of these strategies.
References:
Li Y., Wang X., Blau D.M., Caballero M.T., Feikin D.R., Gill C.J., et al. Global, regional, and national disease burden estimates of acute lower respiratory infections due to respiratory syncytial virus in children younger than 5 years in 2019: a systematic analysis. Lancet. 2022
Mulholland K, Smith L, Carneiro I, Becher H, Lehmann D. Equity and child-survival strategies. Bull World Health Organ. 2008
The role of LSHTM and NU in this collaborative project
Dr. Andrew Clark and Prof. Mark Jit are both based at LSHTM and have expertise in the development of analytical models to inform decisions about new vaccines in LMICs.
Dr. Bhim Gopal Dhoubhadel is based at Nagasaki University’s School of Tropical Medicine and Global Health, and has expertise in clinical tropical medicine and paediatric respiratory diseases.
The team members bring together complementary expertise matching the proposed project.
Particular prior educational requirements for a student undertaking this project
The student should have an master's degree, preferably with some prior experience in developing analytical models. The student will ideally have experience of programming mathematical models e.g. in Excel, R, Python. The student should have basic knowledge of infectious disease epidemiology and vaccine immunology.
Skills we expect a student to develop/acquire whilst pursuing this project
The student can expect to develop skills in:
- using mathematical modelling to estimate the effectiveness of a public health intervention;
- identifying and synthesising appropriate evidence from the scientific literature and large databases e.g. household surveys;
- communicating effectively both orally and in scientific writing; and,
- project management.