Increasing human travel is a major driver of its persistence in areas targeted for elimination of malaria and of faster the spread of epidemics of a variety of diseases. Lack of information on travel patterns and their impact on the spread of disease is a major constraint to planning interventions to control and eliminate malaria. These patterns, and the demographics of people with malaria who travel vary widely within and between countries. The patterns also change over time and it is important for National Malaria Control Programmes to have up to date information on which people with malaria are travelling and to where.
Large financial investments are currently being made in malaria control and elimination worldwide and there is a need to target interventions to use these resources most efficiently. Predictive models are being used to guide these efforts and estimate the costs involved but there are large gaps in available data used to build these models, in particular travel.
This DPhil project will use demographic and travel survey data collected from thousands of people with malaria across multiple countries in Africa and Asia as part of a set of large multicenter clinical research projects. Methods will be developed to present these data and distill the results of analysis into policy implications that will be shared prior to publication with National Malaria Control Programmes. Data will be analysed and mapped and results compared between countries and continents. Methods will be developed to summarise these travel patterns using statistical models. These will then be used to parameterize mathematical models of population movement to predict the impact and cost of intervention strategies to control and eliminate malaria.
Students will join a dynamic team of epidemiologists, modellers and clinicians at the MORU offices in Bangkok. There will be opportunities to visit field sites in malaria-endemic areas and to interact with government staff. Training in spatial epidemiology, GIS mapping, data management, statistical analysis and mechanistic modelling will be provided. Students will also have access to epidemiology training resources at MORU, including an on-site library, will join weekly epidemiology team meetings and epidemiology and modelling journal clubs, as well training sessions on coding and weekly scientific seminars, plus generic skills training for postgraduate students.
Project reference number: 1003
|Professor Richard J Maude||Tropical Medicine||Oxford University, Bangkok||THAfirstname.lastname@example.org|
|Dr Mehul Dhorda||Tropical Medicine||Oxford University, Bangkok||THA||Mehul@tropmedres.ac|
As Africa-wide malaria prevalence declines, an understanding of human movement patterns is essential to inform how best to target interventions. We fitted movement models to trip data from surveys conducted at 3-5 sites throughout each of Mali, Burkina Faso, Zambia and Tanzania. Two models were compared in terms of their ability to predict the observed movement patterns - a gravity model, in which movement rates between pairs of locations increase with population size and decrease with distance, and a radiation model, in which travelers are cumulatively "absorbed" as they move outwards from their origin of travel. The gravity model provided a better fit to the data overall and for travel to large populations, while the radiation model provided a better fit for nearby populations. One strength of the data set was that trips could be categorized according to traveler group - namely, women traveling with children in all survey countries and youth workers in Mali. For gravity models fitted to data specific to these groups, youth workers were found to have a higher travel frequency to large population centers, and women traveling with children a lower frequency. These models may help predict the spatial transmission of malaria parasites and inform strategies to control their spread. Hide abstract