Harnessing publicly available geospatial data to guide vector-borne disease interventions in sub-Saharan Africa

Climate and environmental factors are key drivers to the spatial and temporal distributions of vector-borne diseases affecting sub-Saharan Africa. For example, malaria hotspots are frequently located in rural areas which contain habitat favoured by the Anopheles mosquito vector. It is therefore possible to leverage the relationship between climate, environment and disease transmission risk to identify areas with the greatest need for control interventions and subsequently target resources more effectively.

We now live in a world where vast amount of information on climate and environment is being continuously collected by remote sensing methods e.g. satellites at ever higher spatial and temporal resolutions and increasingly being made publicly available.  National control programmes are also improving their data collection and reporting systems. For example, many countries now routinely collect health information e.g. number of confirmed malaria cases using digital health management information systems. While large-scale static maps of continent-level risk are increasingly commonplace, using methods such as geostatistical modelling or species distribution modelling to estimate disease burden and highlight areas of high transmission risk, the vast amount of public health information contained in these data sources has yet to be fully harnessed with respect to guiding national control programme activities. The aim of this project is to develop methods by which spatially and temporally tailored outputs of disease risk can be generated and utilised to optimise vector-borne disease control activities. The project will focus on malaria and NTD transmission in sub-Saharan Africa, and will make use of freely available resources such as Google Earth Engine and R Shiny to access data, analyse and present resulting outputs.

Where does the project lie on the Translational Pathway?

T3 (Evidence into Practice) + T4 (Practice into Policy/Population)

Expected Outputs

Technical outputs:

  • User-friendly products developed using R and Google Earth Engine identifying spatio-temporal patterns in disease risk


Institutional outputs:

  • Multiple publications relating to spatial influences of sub-national level disease transmission risk
  • Geospatial products that can be used to identify disease transmission hotspots, guide intervention study design and inform disease control programmes on intervention targeting strategies


Student career enhancement:

  • Individual will become highly skilled in spatial analysis and the use of geospatial technologies in public health
  • Mentorship in the production of high impact publications relating to PhD research, and opportunities to present in tropical medicine-focussed meetings and conferences
  • Opportunities to interact with disease control programmes e.g. National Malaria Control Programme in Malawi



  • Outputs will contribute towards large-scale future funding applications relating to disease surveillance and targeting interventions
  • Individual will be encouraged to apply for early career funding schemes to continue their research career if considered appropriate


Training Opportunities

The student will have the opportunity to undertaken formal and informal training in geospatial analysis, including statistical modelling and relevant machine learning techniques. This will take the form of in person and online training courses, as well as via peer-support at LSTM. The student will also gain exposure to disease control planning and policy making by attending meetings and workshops alongside national control programme members in sub-Saharan Africa.

Skills Required

  • Statistical analysis skills, including geostatistics
  • An interest in geospatial technologies, geographical information systems
  • Experience, or a desire to gain programming skills including R and Python


Key Publications associated with this project

M.C. Stanton, J. Estherhuizen, I. Tirados, H. Betts and S. J. Torr (2018) The development of high resolution tsetse presence and abundance maps for guiding human African trypanosomiasis elimination efforts in northern Uganda Parasites and Vectors, 11(340)

M.C. Stanton (2017) The role of spatial statistics in the control and elimination of neglected tropical diseases in sub-Saharan Africa: A focus on human African trypanosomiasis, schistosomiasis and lymphatic filariasis,Advances in Parasitology, 97:187-241

M.C Stanton, P. Kalonde, K. Zembere, R. Hoek Spaans, C.M. Jones (pre-print) The application of drones for mosquito larval habitat identification in rural environments: a practical approach for malaria control? https://doi.org/10.1101/2020.08.05.237933

J. Longbottom, A. Krause, S.J. Torr and M.C. Stanton (2020) Quantifying geographic accessibility to improve cost-effectiveness of entomological monitoring. PLoS NTDs https://doi.org/10.1371/journal.pntd.0008096

M.C. Stanton, D.H. Molyneux, D. Kyelem, R.W. Bougma, B.G. Koudou and L.A. Kelly-Hope(2013) Baseline drivers of lymphatic filariasis in Burkina Faso Geospatial Health 8(1):159-173

Deadline: Thursday 11th February 2021; 12:00 noon GMT

Further details on the MRC/DTP and CASE programmes and application guidance and process can be found here