A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar

Abstract

Background: Quality surveillance data used to build tuberculosis (TB) transmission models are frequently unavailable and may overlook community intrinsic dynamics that impact TB transmission. Social network analysis (SNA) generates data on hyperlocal social-demographic structures that contribute to disease transmission.

Methods: We collected social contact data in five villages and built SNA-informed village-specific stochastic TB transmission models in remote Madagascar. A name-generator approach was used to elicit individual contact networks. Recruitment included confirmed TB patients, followed by snowball sampling of named contacts. Egocentric network data were aggregated into village-level networks. Network- and individual-level characteristics determining contact formation and structure were identified by fitting an exponential random graph model (ERGM), which formed the basis of the contact structure and model dynamics. Models were calibrated and used to evaluate WHO-recommended interventions and community resiliency to foreign TB introduction.

Results: Inter- and intra-village SNA showed variable degrees of interconnectivity, with transitivity (individual clustering) values of 0.16, 0.29, and 0.43. Active case finding and treatment yielded 67%-79% reduction in active TB disease prevalence and a 75% reduction in TB mortality in all village networks. Following hypothetical TB elimination and without specific interventions, networks A and B showed resilience to both active and latent TB reintroduction, while Network C, the village network with the highest transitivity, lacked resiliency to reintroduction and generated a TB prevalence of 2% and a TB mortality rate of 7.3% after introduction of one new contagious infection post hypothetical elimination.

Conclusion: In remote Madagascar, SNA-informed models suggest that WHO-recommended interventions reduce TB disease (active TB) prevalence and mortality while TB infection (latent TB) burden remains high. Communities' resiliency to TB introduction decreases as their interconnectivity increases. "Top down" population level TB models would most likely miss this difference between small communities. SNA bridges large-scale population-based and hyper focused community-level TB modeling.

Keywords: Modeling; Public Health; Social network analysis; Tuberculosis.

Fig. 1

Study area and social network analysis: The Androrangavola commune and social networks created from each village (V1 to V5). Green nodes represent participants with TB disease or those in the “infected” model category, red nodes represent participants with TB infection or those in the “exposed” model category, and black either had negative tests or were untested and are hence confirmed or presumptively in the “susceptible” model category. Villages V1 and V2, represented in Network A, were combined due to geographical proximity and the small size of Village 2. Villages V3 and V4 are represented in Network B and make up a single network because questionnaires revealed they were socially interconnected. Village V5 is represented by Network C

Fig. 2

Structure of tuberculosis SEIR transmission model: Primary data informing the model is shown in blue. This includes act rate, or the number of interactions between egos and alters per week, and prevalence of TB infection (latent TB) and TB disease (active TB). Metrics on the natural evolution of disease extracted from the literature are in green. Infection rate calibrated from community-specific social networks metrics is presented in red. Slow or rapid progression from infection to disease is based on time since initial exposure (see Table 1)

Fig. 3

Tuberculosis evolution modeling at community level Impact of model conditions on social networks