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Supervisors: Professor Nigel Klein, Dr Louis Grandjean
Identifying Hotspots of Tuberculosis Transmission by Combining Bacterial Whole Genome Sequencing and Global Position System Tracking
Background:
Tuberculosis causes more deaths globally than any other infectious disease [1]. When successfully transmitted, children have the highest incidence of death of any age group [2]. Peru has a high incidence of tuberculosis disease that is hyperendemic in the shantytowns around Lima. It is estimated that over 80% of all tuberculosis transmission events occur outside the home [3]. In resource rich settings tuberculosis programs employ contact tracing questionnaires together with genetic data to link paired incident cases with closely related genotypes in time and space [4]. Low and middle income (LMIC) settings more frequently focus resources solely on treating active cases of disease thereby missing the opportunity of identifying transmission hotspots and preventing tuberculosis transmission in the community. In a successful pilot study our research group in Peru has combined Global Positioning System (GPS) tracking and Whole Genome Sequencing (WGS) to identify venues associated with pairs of patients who share identical tuberculosis genomes [5]. The same study has also developed a statistical framework that has demonstrated that patients who share identical bacterial genomes more frequently overlap in time and space than matched controls (Bui et al In Press). Extending this novel approach with prospectively collected data will allow us to identify many more sites of transmission with greater statistical precision and relevance to the majority of transmission events.
Aims/Objectives:
1) Identify clusters of patients who share identical bacterial genomes (2019 sequenced samples);
2) Consent and track paired patients with GPS monitoring relative to control pairs;
3) Use the technique to identify new community areas of tuberculosis transmission, write an R-shiny platform to implement the platform for the national tuberculosis health program.
Methods:
Ethical and institutional approval has already been approved by Universidad Peruana Cayetano Heredia and the Peruvian Ministry of Health. Additional ethical approval will also be obtained from UCL before the study commences. The study will take place both in metropolitan Lima and in the isolated Amazonian city of Iquitos. Standardized data capture sheets will be used to collect demographic information (e.g., age, sex, income, education, household size) from consenting participants during face-to-face interviews. We will use the Qstarz BT-Q1000XT GPS logger to gather data on case and control movements over one month of observation as soon as the patient considers themselves to have resumed normal activities following their diagnosis of tuberculosis. Home range analysis, a suite of geospatial analytical methods developed by spatial ecologists to study regular wildlife movement patterns and spatial overlap, will be used to identify areas of concentrated tuberculosis case activity overlap. We will use the "adehabitatHR" package in R to implement home range analysis functions and convert GPS data into a Utility Distribution (UD) - a smoothed contoured kernel density map of GPS movements. The same package will be used to quantify the UD Overlap Index (UDOI) for both cases and controls. Statistically significant UDOI's of paired cases versus controls will be recorded and presented to the national tuberculosis program as important tuberculosis transmission hotspots likely to benefit from intervention.
Timeline:
References:
1. Paulson T. Epidemiology: A mortal foe. Nature. 2013;502: S2-3. doi:10.1038/502S2a
2. Miftode EG, et al. Tuberculous Meningitis in Children and Adults: A 10-Year Retrospective Comparative Analysis. PloS One. 2015;10: e0133477. doi:10.1371/journal.pone.0133477
3. Verver S, et al. Proportion of tuberculosis transmission that takes place in households in a high-incidence area. The Lancet. 2004;363: 212–214. doi:10.1016/S0140-6736(03)15332-9
4. Cavany SM, et al. An evaluation of tuberculosis contact investigations against national standards. Thorax. 2017;72: 736–745. doi:10.1136/thoraxjnl-2016-209677
5. Bui D et al. A Case Control Study to Identify Community Venues Associated with Genetically Clustered Multidrug-Resistant Tuberculosis Disease in Lima, Peru. Clin Infect Dis Off Publ Infect Dis Soc Am. 2018; doi:10.1093/cid/ciy746
