Potential limitations of community-wide strategy to treat Mycobacterium tuberculosis infection
Batabyal, S.; Urdahl, K.; Ganusov, V. V.
Show abstract
A quarter of the world's population has immunologic evidence of past or present Mycobacterium tuberculosis (Mtb) infection (MTBI) detected as TST or IGRA positivity. Community-based preventive treatment of individuals with MTBI has resulted in transient decreases in TB cases, but its long-term effectiveness has been controversial. Due to the likelihood that many of those with immune responses to Mtb antigens may no longer harbor Mtb, widespread treatment of all such individuals may result in unnecessary exposure to antibiotics. We raise an additional concern that preventive treatment of individuals with MTBI, who are not at the risk of disease progression, may result in loss of protective immunity, provided by the persistent infection, and enhanced risk of TB upon re-exposure to Mtb. There is evidence from human cohorts and animal studies that prior exposure to Mtb confers protection against TB development upon re-exposure, and that treatment of Mtb-infected animals often results in loss of this protection. We build a novel epidemiological model of Mtb dynamics and progression to TB in a community allowing for protection afforded by MTBI against exogenous reinfection-driven disease progression. We show that implementation of treatment of MTBI in the whole community will result in reduction of TB cases but stopping the program may result in an increase in new TB cases that may offset (or even exceed) benefits of the preventive treatment program. Our results suggest that better understanding protective effects provided by MTBI against progression to TB upon Mtb re-exposure and identification of Mtb-infected individuals who most benefit from preventive treatment must be a priority before preventive treatment of asymptomatic MTBI is widely implemented.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A model of tuberculosis clustering in low incidence countries reveals more on-going transmission in the United Kingdom than the Netherlands between 2010 and 2015. 95%
- Mitigating outbreaks in congregate settings by decreasing the size of the susceptible population 95%
- Epidemiological and health economic implications of symptom propagation in respiratory pathogens: A mathematical modelling investigation 94%
Similar papers in this journal
- Selection for infectivity profiles in slow and fast epidemics, and the rise of SARS-CoV-2 variants 94%
- Estimating the transmissibility of SARS-CoV-2 during periods of high, low and zero case incidence 94%
- Not all MDAs should be created equal-determinants of MDA impact and designing MDAs towards malaria elimination 94%
Similar papers in this journal
- Controlling COVID-19 via test-trace-quarantine 95%
- Assessing the impact of SARS-CoV-2 prevention measures in Austrian schools by means of agent-based simulations calibrated to cluster tracing data 95%
- Hyper-diverse antigenic variation and resilience to transmission-reducing intervention in falciparum malaria 94%
Similar papers in this journal
- Infection control strategies in essential industries: using COVID-19 in the food industry to model economic and public health trade-offs 95%
- Examining face-mask usage as an effective strategy to control COVID-19 spread 95%
- Identifying likely transmission pairs with pathogen sequence data using Kolmogorov Forward Equations; an application to M.bovis in cattle and badgers 95%
Similar papers in this journal
- Estimating local outbreak risks and the effects of non-pharmaceutical interventions in age-structured populations: SARS-CoV-2 as a case study 95%
- A virtual host model of Mycobacterium tuberculosis infection identifies early immune events as predictive of infection outcomes 94%
- The Role of Biofilms, Bacterial Phenotypes, and Innate Immune Response in Mycobacterium avium Colonization to Infection 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.