Stochastic agent-based modeling of tuberculosis in Canadian Indigenous communities

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

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.

Latent tuberculosis and computational biology: A less-talked affair

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.

Evaluation of TB elimination strategies in Canadian Inuit populations: Nunavut as a case study

Tuberculosis (TB) continues to disproportionately affect Inuit populations in Canada with some communities having over 300 times higher rate of active TB than Canadian-born, non-Indigenous people. Inuit Tuberculosis Elimination Framework has set the goal of reducing active TB incidence by at least 50% by 2025, aiming to eliminate it by 2030. Whether these goals are achievable with available resources and treatment regimens currently in practice has not been evaluated. We developed an agent-based model of TB transmission to evaluate timelines and milestones attainable in Nunavut, Canada by including case findings, contact-tracing and testing, treatment of latent TB infection (LTBI), and the government investment on housing infrastructure to reduce the average household size. The model was calibrated to ten years of TB incidence data, and simulated for 20 years to project program outcomes. We found that, under a range of plausible scenarios with tracing and testing of 25%–100% of frequent contacts of detected active cases, the goal of 50% reduction in annual incidence by 2025 is not achievable. If active TB cases are identified rapidly within one week of becoming symptomatic, then the annual incidence would reduce below 100 per 100,000 population, with 50% reduction being met between 2025 and 2030. Eliminating TB from Inuit populations would require high rates of contact-tracing and would extend beyond 2030. The findings indicate that time-to-identification of active TB is a critical factor determining program effectiveness, suggesting that investment in resources for rapid case detection is fundamental to controlling TB.

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TB elimination in Inuit populations would likely extend beyond timelines outlined in action plans.

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Rapid case findings combined with testing of frequent contacts are fundamental to TB control.

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Reducing average household size has minimal effect on rates of TB incidence.

Representing Tuberculosis Transmission with Complex Contagion: An Agent-Based Simulation Modeling Approach

OBJECTIVE

A recent study reported a tuberculosis (TB) outbreak in which, among newly infected individuals, exposure to additional active infections was associated with a higher probability of developing active disease. Referred to as complex contagion, multiple reexposures to TB within a short period after initial infection is hypothesized to confer a greater likelihood of developing active infection in 1 y. The purpose of this article is to develop and validate an agent-based simulation model (ABM) to study the effect of complex contagion on population-level TB transmission dynamics.

METHODS

We built an ABM of a TB epidemic using data from a series of outbreaks recorded in the 20th century in Saskatchewan, Canada. We fit 3 dynamical schemes: base, with no complex contagion; additive, in which each reexposure confers an independent risk of activated infection; and threshold, in which a small number of reexposures confers a low risk and a high number of reexposures confers a high risk of activation.

RESULTS

We find that the base model fits the mortality and incidence output targets best, followed by the threshold and then the additive models. The threshold model fits the incidence better than the base model does but overestimates mortality. All 3 models produce qualitatively realistic epidemic curves.

CONCLUSION

We find that complex contagion qualitatively changes the trajectory of a TB epidemic, although data from a high-incidence setting are reproduced better with the base model. Results from this model demonstrate the feasibility of using ABM to capture nuances in TB transmission.

Sociocultural, behavioural and political factors shaping the COVID-19 pandemic: the need for a biocultural approach to understanding pandemics and (re)emerging pathogens

Although there has been increasing focus in recent years on interdisciplinary approaches to health and disease, and in particular the dimension of social inequalities in epidemics, infectious diseases have been much less focused on. This is especially true in the area of cultural dynamics and their effects on pathogen behaviours, although there is evidence to suggest that this relationship is central to shaping our interactions with infectious disease agents on a variety of levels. This paper makes a case for a biocultural approach to pandemics such as COVID-19. It then uses this biocultural framework to examine the anthropogenic dynamics that influenced and continue to shape the COVID-19 pandemic, both during its initial phase and during critical intersections of the pandemic. Through this understanding of biocultural interactions between people, animals and pathogens, a broader societal and political dimension is drawn as a function of population level and international cultures, to reflect on the culturally mediated differential burden of the pandemic. Ultimately, it is argued that a biocultural perspective on infectious disease pandemics will allow for critical reflection on how culture shapes our behaviours at all levels, and how the effects of these behaviours are ultimately foundational to pathogen ecology and evolution.

Heterogeneous infectiousness in mathematical models of tuberculosis: A systematic review

TB mathematical models employ various assumptions and approaches in dealing with the heterogeneous infectiousness of persons with active TB. We reviewed existing approaches and considered the relationship between them and existing epidemiological evidence. We searched the following electronic bibliographic databases from inception to 9 October 2018: MEDLINE, EMBASE, Biosis, Global Health and Scopus. Two investigators extracted data using a standardised data extraction tool. We included in the review any transmission dynamic model of M. tuberculosis transmission explicitly simulating heterogeneous infectiousness of person with active TB. We extracted information including: study objective, model structure, number of active TB compartments, factors used to stratify the active TB compartment, relative infectiousness of each active TB compartment and any intervention evaluated in the model. Our search returned 1899 unique references, of which the full text of 454 records were assessed for eligibility, and 99 studies met the inclusion criteria. Of these, 89 used compartmental models implemented with ordinary differential equations, while the most common approach to stratification of the active TB compartment was to incorporate two levels of infectiousness. However, various clinical characteristics were used to stratify the active TB compartments, and models differed as to whether they permitted transition between these states. Thirty-four models stratified the infectious compartment according to sputum smear status or pulmonary involvement, while 18 models stratified based on health care-related factors. Variation in infectiousness associated with drug-resistant M. tuberculosis was the rationale for stratifying active TB in 33 models, with these models consistently assuming that drug-resistant active TB cases were less infectious. Given the evidence of extensive heterogeneity in infectiousness of individuals with active TB, an argument exists for incorporating heterogeneous infectiousness, although this should be considered in light of the objectives of the study and the research question. PROSPERO Registration: CRD42019111936.

Tuberculosis transmission in the Indigenous peoples of the Canadian prairies

SETTING

The prairie provinces of Canada.

OBJECTIVE

To characterize tuberculosis (TB) transmission among the Indigenous and non-Indigenous Canadian-born peoples of the prairie provinces of Canada.

DESIGN

A prospective epidemiologic study of consecutively diagnosed adult (age ≥ 14 years) Canadian-born culture-positive pulmonary TB cases on the prairies, hereafter termed "potential transmitters," and the transmission events generated by them. "Transmission events" included new positive tuberculin skin tests (TSTs), TST conversions, and secondary cases among contacts.

RESULTS

In the years 2007 and 2008, 222 potential transmitters were diagnosed on the prairies. Of these, the vast majority (198; 89.2%) were Indigenous peoples who resided in either an Indigenous community (135; 68.2%) or a major metropolitan area (44; 22.2%). Over the 4.5-year period between July 1st, 2006 and December 31st 2010, 1085 transmission events occurred in connection with these potential transmitters. Most of these transmission events were attributable to potential transmitters who identified as Indigenous (94.5%). With a few notable exceptions most transmitters and their infected contacts resided in the same community type. In multivariate models positive smear status and a higher number of close contacts were associated with increased transmission; adjusted odds ratios (ORs) and 95% confidence intervals (CIs), 4.30 [1.88, 9.84] and 2.88 [1.31, 6.34], respectively. Among infected contacts, being Indigenous was associated with disease progression; OR and 95% CI, 3.59 [1.27, 10.14] and 6.89 [2.04, 23.25] depending upon Indigenous group, while being an infected casual contact was less likely than being a close contact to be associated with disease progression, 0.66 [0.44, 1.00].

CONCLUSION

In the prairie provinces of Canada and among Canadian-born persons, Indigenous peoples account for the vast majority of cases with the potential to transmit as well as the vast majority of infected contacts. Active case finding and preventative therapy measures need to focus on high-incidence Indigenous communities.