Using geostatistical methods to analyse and design neglected tropical disease surveys

Neglected Tropical Diseases (NTDs) are a group of 20 diverse diseases that primarily affect people in tropical and subtropical regions of the world. These diseases tend to afflict individuals in low-income and resource-constrained areas, and they receive less attention and funding for research and interventions than is indicated by their contribution to overall human suffering.

Model-based geostatistics (MBG), a branch of spatial statistics, has been increasingly used to map NTDs to support the decision making of national control programme. In particular, MBG has been used to inform policy for NTDs that are approaching elimination and to develop more efficient prevalence surveys.
As part of this project, the student will join the WHO Collaborating Centre on Geostatistical methods for Neglected Tropical Diseases research, led by the Dr Emanuele Giorgi, at Lancaster University. Depending on the background and interests of the students, the research carried out under this project can be either methodological or more applied and will focus mainly on two NTDs, soil transmitted helminths and trachoma. The research questions that students will investigate around these two NTDs are the following.

The research questions of the project will revolve around the following key aspects of NTD research.

1. Identification of Environmental Risk Factors: Explore the use environmental variables that can best assist the prediction of disease prevalence, contributing to a more nuanced understanding of NTD dynamics.
2. Mapping Inaccessible Areas: Investigate the application of geostatistical models to map disease prevalence in regions that are difficult to access, providing critical insights for targeted interventions.
3. Designing Surveys for Low Prevalence Diseases: Utilize geostatistical models to develop efficient survey designs tailored for diseases with low prevalence, optimizing resource allocation and data collection strategies.

These investigations will be conducted in collaboration with international partners from Sub-Saharan Africa and South America, adding a global perspective to the research. The student undertaking this project is expected to acquire proficiency in R programming and undergo training in advanced statistical methods, thereby contributing to the advancement of geostatistical approaches in combatting NTDs.

Where does this project lie in the translational pathway?

T1 – Basic Research
T3 Evidence into Practice

This project holds significant translational value by bridging advanced statistical methodologies with practical applications in the field of Neglected Tropical Diseases (NTDs). The focus on model-based geostatistics (MBG) serves as a crucial link between theoretical research and on-the-ground implementation, contributing directly to the improvement of public health outcomes. The translational aspects of this project can be highlighted in the following ways:

1. Policy Informatics and Decision Support: The use of MBG to inform policies for NTDs nearing elimination directly translates theoretical insights into actionable strategies. By providing decision-makers with accurate disease prevalence maps and efficient survey designs, the project aids in the development of targeted and impactful interventions. This aligns with the Medical Research Council's (MRC) skills priorities, emphasizing the application of research findings to influence health policies.
2. Addressing Resource Constraints: The project's emphasis on NTDs prevalent in low-income and resource-constrained areas directly addresses a priority in global health. By developing strategies that consider the limitations of these settings, the research aligns with MRC's focus on ensuring that scientific advancements are applicable and accessible, especially in contexts with limited resources.
3. International Collaboration: Collaboration with international partners from Sub-Saharan Africa and South America enhances the project's global impact. This aligns with MRC's recognition of the importance of international collaboration in addressing global health challenges, fostering the exchange of knowledge and expertise.
4. Training in Practical Skills: The project's requirement for the student to develop skills in R programming and undergo training in advanced statistical methods aligns with MRC's emphasis on building a skilled workforce capable of translating research findings into tangible outcomes. The emphasis on practical skills ensures that the researcher is well-equipped to apply their knowledge in real-world scenarios.
5. Focus on Elimination Strategies: By addressing questions related to diseases nearing elimination, the project directly contributes to the MRC's priority of understanding and implementing strategies for disease eradication. This aligns with the MRC's commitment to supporting research that leads to significant health improvements and interventions.

What are the methodological aspects of the PhD project? 

The PhD project involves a comprehensive set of methodological approaches, integrating advanced statistical techniques with the specific context of Neglected Tropical Diseases (NTDs). Here are the key methodological aspects of the project:
1. Model-Based Geostatistics (MBG): The core methodology revolves around MBG, a specialized branch of spatial statistics. MBG involves the use of statistical models to analyze and map the spatial distribution of diseases. This technique is instrumental in understanding the geographical variation of NTDs and provides a framework for making predictions about disease prevalence in different locations.
2. Environmental Risk Factor Analysis: The project involves investigating environmental risk factors associated with NTD prevalence. This includes the identification, collection, and analysis of environmental variables that contribute to the occurrence and spread of diseases. The goal is to enhance the predictive accuracy of disease prevalence models by incorporating relevant environmental factors.
3. Mapping of Inaccessible Areas: A key aspect of the project is the application of geostatistical models to map disease prevalence in areas that are traditionally difficult to access. This involves developing methodologies to account for incomplete or sparse data in remote regions, ensuring a more comprehensive understanding of the distribution of NTDs.
4. Survey Design for Low Prevalence Diseases: The project aims to develop geostatistical models specifically tailored for diseases with low prevalence. This includes designing surveys that are efficient in detecting and monitoring low-prevalence diseases, optimizing the allocation of resources for data collection and analysis.
5. Collaboration with International Partners: A methodological aspect involves establishing and maintaining collaborations with international partners from Sub-Saharan Africa and South America. This collaborative approach enhances the project's robustness by incorporating diverse datasets and perspectives, contributing to the generalizability of the research findings.
6. Skill Development in R Programming: The project requires the student to develop skills in R programming. R is a statistical programming language widely used in data analysis and visualization. Proficiency in R programming is essential for implementing and interpreting the results of the geostatistical models.
7. Training in Advanced Statistical Methods: The student is expected to undergo training in advanced statistical methods. This could include learning and applying sophisticated statistical techniques relevant to the analysis of spatial data and disease prevalence.

Overall, the methodological framework of the PhD project is multi-faceted, encompassing geostatistical modeling, environmental risk analysis, survey design, international collaboration, and the acquisition of practical skills in statistical programming and advanced statistical methods. This holistic approach is designed to address the specific challenges posed by NTDs and contribute to the development of effective strategies for their control and elimination.

What are the expected outputs of the PhD project? 

The expected outputs of the PhD project are likely to encompass a range of academic and practical contributions, with the potential for multiple research papers. Here are three hypothetical papers that could result from the project:

1.Title: "Spatial Analysis of Environmental Risk Factors for Soil-Transmitted Helminths: Implications for Control Strategies"

•Objective: Investigate and identify key environmental risk factors associated with the prevalence of soil-transmitted helminths (STH).

•Methodology: Utilize model-based geostatistics (MBG) to analyze spatial patterns of STH and assess the impact of environmental variables on disease distribution.

•Findings: Provide insights into the geographical variation of STH, highlighting areas with heightened risk. Propose targeted control strategies based on identified environmental factors.

•Significance: Contributes to the understanding of STH epidemiology, informing evidence-based interventions for control programs.

2.Title: "Geostatistical Mapping of Trachoma in Inaccessible Regions: A Case Study in Sub-Saharan Africa"

•Objective: Develop and apply geostatistical models to map trachoma prevalence in areas traditionally difficult to access.

•Methodology: Employ MBG techniques to account for spatial variability and sparse data. Collaborate with international partners to validate and refine the models.

•Findings: Present detailed prevalence maps of trachoma in inaccessible regions, offering a valuable resource for targeted interventions and resource allocation.

•Significance: Addresses the challenge of mapping diseases in remote areas, enhancing the feasibility and effectiveness of trachoma control efforts.

3.Title: "Optimizing Survey Design for Low-Prevalence Neglected Tropical Diseases: A Geostatistical Approach"

•Objective: Develop geostatistical models for designing prevalence surveys tailored to low-prevalence NTDs.

•Methodology: Utilize MBG to design surveys that efficiently detect and monitor diseases with low prevalence. Evaluate the cost-effectiveness of proposed survey designs.

•Findings: Provide guidelines for designing surveys that maximize information yield in low-prevalence settings, optimizing resource utilization.

•Significance: Addresses the unique challenges of studying and monitoring low-prevalence NTDs, contributing to more effective and targeted data collection.

These hypothetical papers demonstrate the diverse contributions the PhD project could make, ranging from understanding environmental risk factors to mapping diseases in inaccessible areas and optimizing survey designs. Each paper contributes valuable insights to the field of Neglected Tropical Diseases, with practical implications for public health interventions and policy development.

What external industry links or placements will be available

This project will be conducted in close collaboration with the Tropical Data team and the World Health Organization. Tropical Data (TD) comprises a consortium of partners from academia and charitable organizations, including LSHTM, RTI, and SightSavers.

Dr. Emma Harding-Esch, serving as a co-supervisor on this project, holds the position of Chief Scientist at TD. TD actively engages with health ministries, assisting them in conducting trachoma prevalence surveys. Over the past three years, TD and the CHICAS team in Lancaster have forged a strong collaborative relationship, working together to support health ministries in applying model-based geostatistical (MBG) methods for the elimination of trachoma.
Dr. Denise Mupdasoni, the third supervisor for this project, holds the role of Chief Scientist for soil-transmitted helminths at the World Health Organization. She will play a crucial role in establishing important connections with country managers and supporting the translation aspects of the research.

What training opportunities will be available 

The student will be expected to gain specialized statistical training as part of the MSc in Health Data Science delivered at Lancaster University. In addition, the student is entitled to participate in the post-graduate courses from the MSc in Statistics at Lancaster University.

What skills/experience/aptitudes should the student already possess?

The ideal candidate for this PhD project should possess a strong interest in quantitative science and willingness to learn advanced statistical skills. The project can be tailored to the background and interests of the student and be more applied or more statistically methodological. The student is expected to acquire proficiency in R programming and experience.  Some background in research methodology, data management, and an understanding of Neglected Tropical Diseases (NTDs) and global health issues would be desirable. Effective communication skills, both written and verbal, along with the ability to work collaboratively in interdisciplinary and international settings, are crucial. The candidate should demonstrate adaptability, problem-solving abilities, and cultural sensitivity. While prior experience in all areas is not mandatory, a combination of these skills, experiences, and aptitudes would contribute to the successful execution of the research project.

Publications that relate to this proposed project 

Sasanami, M., Amoah, B., Amza, A., Souley, A. S. Y, Diori, A. N., Bakhtiari, A., Kadri, B., Szwarcwald, C. L., Gomez, D. V. F., Almou, I., Lopes, M. F. C., Masika, M. P., Beidou, N., Boyd, S., Harding-Esch, E. M., Solomon, A. W, Giorgi, E., (2023) Using model-based geostatistics for assessing the elimination of trachoma. PLoS Neglected Tropical Diseases, 17(7): e0011476. https://doi.org/10.1371/journal.pntd.0011476

Johnson, O. O., Fronterre, C., Amoah, A., Montresor, A.,  Giorgi, E., , Midzic, N., Mutsaka-Makuvaza, M. J., Kargbo-Labord, I., Hodgese, M. H, Zhange, Y., Okoyog, C., Mwandawirog, C., Minnery, M., Digglea, P. J., Model-based Geostatistical Methods Enable Efficient Design and Analysis of Prevalence Surveys for Soil-Transmitted Helminth Infection and Other Neglected Tropical Disease. Clinical Infectious Diseases. 2021;72(S3):S172–9, DOI: 10.1093/cid/ciab192

Flueckiger, R., Giorgi, E., Cano, J., Abdala, M., Amiel, O. N., Baayenda, G., Bakhtiari, A., Batcho, W., Bennawi, K. H., Dejene, M., Elshafie, B. E., Elvis, A. A.,  Francois, M., Goepogui, A., Kalua, K., Kebede, B., Kiflu, G., Masika, M. P., Massangaie, M., Mpyet, C., Ndjemba, J., Ngondi, J. M., Olobio, N., Turyaguma, P., Willis, R., Yeo, S., Solomon, A., Pullan, R. (2019). Understanding the spatial distribution of trichiasis and its association with trachomatous inflammation—follicular.  BMC Infectious Diseases, 19:364, doi:10.1186/s12879-019-3935-1

Giorgi, E., Fronterre, C., Macharia, P., Alegana, V., Snow, R., Diggle, P. (2021) Model build-ing and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict. Journal of the Royal Society Interface. 18:20210104. http://doi.org/10.1098/rsif.2021.0104

Grau-Pujol, B., Mart`ı-Soler, H., Escola, V., Demontis, V., Jamine, J. C., Gandasegui, J.,Muchisse, O., Cambra-Pellej ́a, M., Cossa, A., Martinez-Valladares, M., Sacoor, C., Van Lieshout,L., Cano, J., Giorgi, E., Munoz, J. (2021). Towards soil-transmitted helminths transmission in-terruption: the impact of diagnostic tools on infection prediction in a low intensity setting in Southern Mozambique. PLoS Neglected Tropical Diseases. https://doi.org/10.1371/journal.pntd.0009803

Who will be the supervisors of the student?

Given the cross-institutional nature of the DTP there is an expectation that there will be supervisory input from individuals from LSTM and Lancaster. Proposed supervisory splits without representation from both institutions may not be considered. If you require assistance identifying a potential supervisor please contact Prof.Giancarlo Biagini or Dr. Chris Jewell as academic programme leads for each institution.