WHO target product profiles for TB preventive treatment.

BACKGROUND: The WHO has developed target product profiles (TPPs) describing the most appropriate qualities for future TPT regimens to assist developers in aligning the characteristics of new treatments with programmatic requirements.METHODS: A technical consultation group was convened by the WHO to determine regimen attributes with greatest potential impact for patients (i.e., improved risk/benefit profile) and populations (i.e., reduction in transmission and TB prevalence). The group categorised regimen attributes as 'priority´ or 'desirable´; and defined for each attribute the minimum requirements and optimal targets.RESULTS: Nine priority attributes were defined, including efficacy, treatment duration, safety, drug-drug interactions, barrier to emergence of drug resistance, target population, formulation, dosage, frequency and route of administration, stability and shelf life. Regimens meeting optimal targets were characterised, for example, as having superior efficacy, treatment duration of ≤2 weeks, and improved tolerability and safety profile compared with current regimens. The four desirable attributes included regimen cost, safety in special populations, treatment adherence and need for drug susceptibility testing in the index patient.DISCUSSION: It may be difficult for a single regimen to satisfy all characteristics so regimen developers may have to consider trade-offs. Additional operational aspects may be relevant to the feasibility and public health impact of new TPT regimens.

Modelling the potential impact of adherence technologies on tuberculosis in India.

BACKGROUND: For patients taking standard first-line tuberculosis treatment, missing 10% or more of their doses increases the risk of relapse six-fold. Digital technologies offer new approaches to adherence support for TB patients. We estimated the potential impact of new adherence technologies in India.METHOD: We developed a mathematical model of TB transmission dynamics in India, capturing the independent effects of missed doses and treatment default on post-treatment recurrence. We simulated the impact of interventions to address both missed doses and treatment default in the public and private healthcare sector.RESULTS: Adherence interventions, if deployed optimally in the public sector alone, would reduce cumulative TB incidence by 7.3% (95% credible intervals [CrI] 4.7-11) between 2020 and 2030, and by 16% (95% CrI 11-23) if also deployed in the private sector. This impact is roughly proportional to the effectiveness of the interventions. Reducing missed doses reduced incidence by 12% (95% CrI 7.0-18), while reducing treatment default reduced incidence by 7.9% (95% CrI 3.2-13).CONCLUSION: Minimising missed doses is at least as important as promoting treatment completion. Our results suggest that emerging technologies to improve treatment adherence could have a substantial impact on TB incidence and mortality in India.

Population implications of the deployment of novel universal vaccines against epidemic and pandemic influenza.

There is increasing interest in the development of new, 'universal' influenza vaccines (UIVs) that--unlike current vaccines--are effective against a broad range of seasonal influenza strains, as well as against novel pandemic viruses. While the existing literature discusses the potential epidemiological benefits of UIVs, it is also important to anticipate their potential unintended population consequences. Using mathematical modelling, we illustrate two such types of adverse consequences. First, by reducing the amount of infection-induced immunity in a population without fully replacing it, a seasonal UIV programme may permit larger pandemics than in the absence of vaccination. Second, the more successful a future UIV programme is in reducing transmission of seasonal influenza, the more vulnerable the population could become to the emergence of a vaccine escape variant. These risks could be mitigated by optimal deployment of any future UIV vaccine: namely, the use of a combined vaccine formulation (incorporating conventional as well as multiple universal antigenic targets) and achieving sufficient population coverage to compensate for any reductions in infection-induced immunity. In the absence of large-scale trials of UIVs, disease-dynamic models can provide helpful, early insights into their potential impact. In future, data from continuing vaccine development will be invaluable in developing robustly predictive modelling approaches.

Estimating tuberculosis incidence from primary survey data: a mathematical modeling approach.

BACKGROUND: There is an urgent need for improved estimations of the burden of tuberculosis (TB). OBJECTIVE: To develop a new quantitative method based on mathematical modelling, and to demonstrate its application to TB in India. DESIGN: We developed a simple model of TB transmission dynamics to estimate the annual incidence of TB disease from the annual risk of tuberculous infection and prevalence of smear-positive TB. We first compared model estimates for annual infections per smear-positive TB case using previous empirical estimates from China, Korea and the Philippines. We then applied the model to estimate TB incidence in India, stratified by urban and rural settings. RESULTS: Study model estimates show agreement with previous empirical estimates. Applied to India, the model suggests an annual incidence of smear-positive TB of 89.8 per 100 000 population (95%CI 56.8-156.3). Results show differences in urban and rural TB: while an urban TB case infects more individuals per year, a rural TB case remains infectious for appreciably longer, suggesting the need for interventions tailored to these different settings. CONCLUSIONS: Simple models of TB transmission, in conjunction with necessary data, can offer approaches to burden estimation that complement those currently being used.

Transmission and control in an institutional pandemic influenza A(H1N1) 2009 outbreak.

A pandemic influenza A(H1N1) 2009 outbreak in a summer school affected 117/276 (42%) students. Residential social contact was associated with risk of infection, and there was no evidence for transmission associated with the classroom setting. Although the summer school had new admissions each week, which provided susceptible students the outbreak was controlled using routine infection control measures (isolation of cases, basic hygiene measures and avoidance of particularly high-risk social events) and prompt treatment of cases. This was in the absence of chemoprophylaxis or vaccination and without altering the basic educational activities of the school. Modelling of the outbreak allowed estimation of the impact of interventions on transmission. These models and follow-up surveillance supported the effectiveness of routine infection control measures to stop the spread of influenza even in this high-risk setting for transmission.

Evolution and emergence of novel human infections.

Some zoonotic pathogens cause sporadic infection in humans but rarely propagate further, while others have succeeded in overcoming the species barrier and becoming established in the human population. Adaptation, driven by selection pressure in human hosts, can play a significant role in allowing pathogens to cross this species barrier. Here we use a simple mathematical model to study potential epidemiological markers of adaptation. We ask: under what circumstances could ongoing adaptation be signalled by large clusters of human infection? If a pathogen has caused hundreds of cases but with little transmission, does this indicate that the species barrier cannot be crossed? Finally, how can case reports be monitored to detect an imminent emergence event? We distinguish evolutionary scenarios under which adaptation is likely to be signalled by large clusters of infection and under which emergence is likely to occur without any prior warning. Moreover, we show that a lack of transmission never rules out adaptability, regardless of how many zoonoses have occurred. Indeed, after the first 100 zoonotic cases, continuing sporadic zoonotic infections without onward, human-to-human transmission offer little extra information on pathogen adaptability. Finally, we present a simple method for monitoring outbreaks for signs of emergence and discuss public health implications.

Logistics of control for an influenza pandemic.

Antiviral drugs will initially be the mainstay of pharmaceutical intervention in an influenza pandemic. Used primarily for therapeutic treatment, they can also lower transmission in the community. We use mathematical modelling to study the impact of different antiviral coverage strategies, with a limited stockpile. Aggressive coverage offers several advantages, even if this should exhaust the stockpile prematurely. However, where a sub-group of the population has a high mortality risk, a strategy to minimise deaths must be carefully balanced, and is not always in agreement with prioritising treatment for those at risk. We discuss implications for public health planning.

Antiviral treatment for the control of pandemic influenza: some logistical constraints.

Disease control programmes for an influenza pandemic will rely initially on the deployment of antiviral drugs such as Tamiflu, until a vaccine becomes available. However, such control programmes may be severely hampered by logistical constraints such as a finite stockpile of drugs and a limit on the distribution rate. We study the effects of such constraints using a compartmental modelling approach. We find that the most aggressive possible antiviral programme minimizes the final epidemic size, even if this should lead to premature stockpile run-out. Moreover, if the basic reproductive number R(0) is not too high, such a policy can avoid run-out altogether. However, where run-out would occur, such benefits must be weighed against the possibility of a higher epidemic peak than if a more conservative policy were followed. Where there is a maximum number of treatment courses that can be dispensed per day, reflecting a manpower limit on antiviral distribution, our results suggest that such a constraint is unlikely to have a significant impact (i.e. increasing the final epidemic size by more than 10%), as long as drug courses sufficient to treat at least 6% of the population can be dispensed per day.