. Author manuscript; available in PMC: 2020 Mar 1.

Published in final edited form as: Lancet Infect Dis. 2018 Dec 13;19(3):e89–e95. doi: 10.1016/S1473-3099(18)30443-2

Table 2:

Examples of use of spatial and pathogen genetic data to understand local transmission

Study population and setting	Spatial data	Pathogen genetic data	Conclusion
All people with culture positive tuberculosis in a mid-sized city in Brazil⁴²	Household location	IS6110 RFLP	Most transmission observed outside the household but within locations less than 2000 meters away
All people diagnosed with tuberculosis in 12 of the 43 districts of metropolitan Lima, Peru²³	Household location	24-loci MIRU-VNTR	Location was an equally strong risk factor for MDR-TB as history of prior tuberculosis treatment
All people with culture positive tuberculosis in two urban communities in South Africa⁴³	Household location	IS6110 RFLP	Within a very high tuberculosis incidence community there was extensive transmission outside the household but within the community
All people diagnosed with tuberculosis in a rural town in eastern Uganda⁴⁴	Household, healthcare, work and social locations	Spoligotyping	Transmission likely at healthcare and social venues
Most people diagnosed with tuberculosis in 14 Inuit communities in Canada⁴⁵	Community location	Whole Genome Sequencing	Transmission more common within each community than between communities with limited contact
All people with culture positive tuberculosis in a suburb of Shanghai⁴⁶	Household location	12-loci MIRU-VNTR and Whole Genome Sequencing for strains within MIRU clusters	Spatial proximity positively associated with genomic similarity among strains within a MIRU cluster, consistent with local transmission

RFLP = Restriction Fragment Length Polymorphism

MIRU-VNTR = Mycobacterial Interspersed Repetitive Units - Variable Numbers of Tandem Repeats

Spoligotyping = Spacer oligonucleotide typing