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During 2020, Victoria was the Australian state hardest hit by COVID-19, but was successful in controlling its second wave through aggressive policy interventions. We calibrated a detailed compartmental model of Victoria’s second wave to multiple geographically-structured epidemic time-series indicators. We achieved a good fit overall and for individual health services through a combination of time-varying processes, including case detection, population mobility, school closures, physical distancing and face covering usage. Estimates of the risk of death in those aged ≥75 and of hospitalisation were higher than international estimates, reflecting concentration of cases in high-risk settings. We estimated significant effects for each of the calibrated time-varying processes, with estimates for the individual-level effect of physical distancing of 37.4% (95%CrI 7.2−56.4%) and of face coverings of 45.9% (95%CrI 32.9−55.6%). That the multi-faceted interventions led to the dramatic reversal in the epidemic trajectory is supported by our results, with face coverings likely particularly important., The state of Victoria, Australia experienced a substantial second wave of COVID-19 but brought it under control with strict non-pharmaceutical interventions. Here, the authors model the second wave in Victoria to estimate the impacts of the different interventions.

RationaleThe heterogeneity in efficacy observed in studies of BCG vaccination is not fully explained by currently accepted hypotheses, such as latitudinal gradient in non-tuberculous mycobacteria exposure.MethodsWe updated previous systematic reviews of the effectiveness of BCG vaccination to 31 December 2020. We employed an identical search strategy and inclusion/exclusion criteria to these earlier reviews, but reclassified several studies, developed an alternative classification system and considered study demography, diagnostic approach and tuberculosis (TB)-related epidemiological context.Main resultsOf 21 included trials, those recruiting neonates and children aged under 5 were consistent in demonstrating considerable protection against TB for several years. Trials in high-burden settings with shorter follow-up also showed considerable protection, as did most trials in settings of declining burden with longer follow-up. However, the few trials performed in high-burden settings with longer follow-up showed no protection, sometimes with higher case rates in the vaccinated than the controls in the later follow-up period.ConclusionsThe most plausible explanatory hypothesis for these results is that BCG protects against TB that results from exposure shortly after vaccination. However, we found no evidence of protection when exposure occurs later from vaccination, which would be of greater importance in trials in high-burden settings with longer follow-up. In settings of declining burden, most exposure occurs shortly following vaccination and the sustained protection observed for many years thereafter represents continued protection against this early exposure. By contrast, in settings of continued intense transmission, initial protection subsequently declines with repeated exposure toMycobacterium tuberculosisor other pathogens.

IntroductionAs of 3rdJune 2021, Malaysia is experiencing a resurgence of COVID-19 cases. In response, the federal government has implemented various non-pharmaceutical interventions (NPIs) under a series of Movement Control Orders and, more recently, a vaccination campaign to regain epidemic control. In this study, we assessed the potential for the vaccination campaign to control the epidemic in Malaysia and four high-burden regions of interest, under various public health response scenarios.MethodsA modified susceptible-exposed-infectious-recovered compartmental model was developed that included two sequential incubation and infectious periods, with stratification by clinical state. The model was further stratified by age and incorporated population mobility to capture NPIs and micro-distancing (behaviour changes not captured through population mobility). Emerging variants of concern (VoC) were included as an additional strain competing with the existing wild-type strain. Several scenarios that included different vaccination strategies (i.e. vaccines that reduce disease severity and/or prevent infection, vaccination coverage) and mobility restrictions were implemented.ResultsThe national model and the regional models all fit well to notification data but underestimated ICU occupancy and deaths in recent weeks, which may be attributable to increased severity of VoC or saturation of case detection. However, the true case detection proportion showed wide credible intervals, highlighting incomplete understanding of the true epidemic size. The scenario projections suggested that under current vaccination rates complete relaxation of all NPIs would trigger a major epidemic. The results emphasise the importance of micro-distancing, maintaining mobility restrictions during vaccination roll-out and accelerating the pace of vaccination for future control. Malaysia is particularly susceptible to a major COVID-19 resurgence resulting from its limited population immunity due to the country’s historical success in maintaining control throughout much of 2020.

Objectives To analyse the outcomes of COVID-19 vaccination by vaccine type, age group eligibility, vaccination strategy, and population coverage. Design Epidemiologic modelling to assess the final size of a COVID-19 epidemic in Australia, with vaccination program (Pfizer, AstraZeneca, mixed), vaccination strategy (vulnerable first, transmitters first, untargeted), age group eligibility threshold (5 or 15 years), population coverage, and pre-vaccination effective reproduction number ( R eff v ¯ ) for the SARS-CoV-2 Delta variant as factors. Main outcome measures Numbers of SARS-CoV-2 infections; cumulative hospitalisations, deaths, and years of life lost. Results Assuming R eff v ¯ = 5, the current mixed vaccination program (vaccinating people aged 60 or more with the AstraZeneca vaccine and people under 60 with the Pfizer vaccine) will not achieve herd protection unless population vaccination coverage reaches 85% by lowering the vaccination eligibility age to 5 years. At R eff v ¯ = 3, the mixed program could achieve herd protection at 60-70% population coverage and without vaccinating 5-15-year-old children. At R eff v ¯ = 7, herd protection is unlikely to be achieved with currently available vaccines, but they would still reduce the number of COVID-19-related deaths by 85%. Conclusion Vaccinating vulnerable people first is the optimal policy when population vaccination coverage is low, but vaccinating more socially active people becomes more important as the R eff v ¯ declines and vaccination coverage increases. Assuming the most plausible R eff v ¯ of 5, vaccinating more than 85% of the population, including children, would be needed to achieve herd protection. Even without herd protection, vaccines are highly effective in reducing the number of deaths.

The Australian National Cabinet four-step plan to transition to post-pandemic re-opening begins with vaccination to achieve herd protection and protection of the health system against a surge in COVID-19 cases. Assuming a pre-vaccination reproduction number for the Delta variant of 5, we show that for the current Mixed program of vaccinating over 60s with AstraZeneca and 16-60s with Pfizer we would not achieve herd immunity. We would need to cover 85% of the population (including many 5-16 year-olds to achieve herd immunity). At lower reproduction number of 3 and our current Mixed strategy, we can achieve herd immunity without vaccinating 5-15 year olds. This will be achieved at a 60% coverage pursuing a strategy targetting high transmitters or 70% coverage using a strategy targetting the vulnerable first. A reproduction number of 7 precludes achieving herd immunity, however vaccination is able to prevent 75% of deaths compared with no vaccination. We also examine the impact of vaccination on death in the event that herd immunity is not achieved. Direct effects of vaccination on reducing death are very good for both Pfizer and AstraZeneca vaccines. However we estimate that the Mixed or Pfizer program performs better than the AstraZeneca program. Furthermore, vaccination levels below the herd immunity threshold can lead to substantial (albeit incomplete) indirect protection for both vaccinated and unvaccinated populations. Given the potential for not reaching herd immunity, we need to consider what level of severe disease and death is acceptable, balanced against the consequences of ongoing aggressive control strategies. O_TEXTBOXThe known: SARS CoV-2 variants are known to be more transmissible than the original Wuhan strain, making herd immunity challenging. The new: We find that vaccinating the older-vulnerable age groups first leads to fewer deaths and is the optimal strategy vaccine coverage is under 70%. Herd immunity achieved solely through vaccinating adults is unlikely, but can still be expected to prevent substantial numbers of deaths. The implications: Australia is unlikely to achieve herd immunity unless vaccination is combined with substantial public health measures. Even without herd immunity, vaccination remains a highly effective means to mitigate the impact of COVID-19. C_TEXTBOX

Mathematical modelling has played a pivotal role in understanding the epidemiology of and guiding public health responses to the ongoing coronavirus disease of 2019 (COVID-19) pandemic. Here, we review the role of epidemiological models in understanding evolving epidemic characteristics, including the effects of vaccination and Variants of Concern (VoC). We highlight ways in which models continue to provide important insights, including (1) calculating the herd immunity threshold and evaluating its limitations; (2) verifying that nascent vaccines can prevent severe disease, infection, and transmission but may be less efficacious against VoC; (3) determining optimal vaccine allocation strategies under efficacy and supply constraints; and (4) determining that VoC are more transmissible and lethal than previously circulating strains, and that immune escape may jeopardize vaccine-induced herd immunity. Finally, we explore how models can help us anticipate and prepare for future stages of COVID-19 epidemiology (and that of other diseases) through forecasts and scenario projections, given current uncertainties and data limitations.

Background We aimed to estimate the disease burden of Tuberculosis (TB) and return on investment of TB care in selected high-burden countries of the Western Pacific Region (WPR) until 2030. Methods We projected the TB epidemic in Viet Nam and Lao People's Democratic Republic (PDR) 2020–2030 using a mathematical model under various scenarios: counterfactual (no TB care); baseline (TB care continues at current levels); and 12 different diagnosis and treatment interventions. We retrieved previous modeling results for China and the Philippines. We pooled the new and existing information on incidence and deaths in the four countries, covering >80% of the TB burden in WPR. We estimated the return on investment of TB care and interventions in Viet Nam and Lao PDR using a Solow model. Findings In the baseline scenario, TB incidence in the four countries decreased from 97•0/100,000/year (2019) to 90•1/100,000/year (2030), and TB deaths from 83,300/year (2019) to 71,100/year (2030). Active case finding (ACF) strategies (screening people not seeking care for respiratory symptoms) were the most effective single interventions. Return on investment (2020–2030) for TB care in Viet Nam and Lao PDR ranged US$4-US$49/dollar spent; additional interventions brought up to US$2•7/dollar spent. Interpretation In the modeled countries, TB incidence will only modestly decrease without additional interventions. Interventions that include ACF can reduce TB burden but achieving the End TB incidence and mortality targets will be difficult without new transformational tools (e.g. vaccine, new diagnostic tools, shorter treatment). However, TB care, even at its current level, can bring a multiple-fold return on investment. Funding World Health Organization Western Pacific Regional Office; Swiss National Science Foundation Grant 163878.

Background COVID-19 initially caused less severe outbreaks in many low- and middle-income countries (LMIC) compared with many high-income countries, possibly because of differing demographics, socioeconomics, surveillance, and policy responses. Here, we investigate the role of multiple factors on COVID-19 dynamics in the Philippines, a LMIC that has had a relatively severe COVID-19 outbreak. Methods We applied an age-structured compartmental model that incorporated time-varying mobility, testing, and personal protective behaviors (through a "Minimum Health Standards" policy, MHS) to represent the first wave of the Philippines COVID-19 epidemic nationally and for three highly affected regions (Calabarzon, Central Visayas, and the National Capital Region). We estimated effects of control measures, key epidemiological parameters, and interventions. Findings Population age structure, contact rates, mobility, testing, and MHS were sufficient to explain the Philippines epidemic based on the good fit between modelled and reported cases, hospitalisations, and deaths. The model indicated that MHS reduced the probability of transmission per contact by 13-27%. The February 2021 case detection rate was estimated at ~8%, population recovered at ~9%, and scenario projections indicated high sensitivity to MHS adherence. Interpretation COVID-19 dynamics in the Philippines are driven by age, contact structure, mobility, and MHS adherence. Continued compliance with low-cost MHS should help the Philippines control the epidemic until vaccines are widely distributed, but disease resurgence may be occurring due to a combination of low population immunity and detection rates and new variants of concern. Funding This work was supported by the World Health Organization Regional Office for the Western Pacific. Tagalog translation of the abstract (Appendix 2).

RationaleThe Marshall Islands implemented ambitious population-based screening programs for latent and active tuberculosis in 2017 and 2018. These interventions’ long-term effects remain to be estimated.ObjectivesTo predict the long-term impact of the previous interventions and identify strategies to drive tuberculosis towards elimination.MethodsWe built a transmission model of tuberculosis informed by local data to capture the epidemic’s historical dynamics. We used the model to project the future epidemic trajectory following the screening interventions, as well as considering a counterfactual scenario with no intervention. We also simulated future scenarios including periodic interventions similar to those previously implemented, to assess the feasibility of reaching the End TB Strategy targets and tuberculosis pre-elimination.Measurements and Main ResultsThe 2017-2018 screening activities were estimated to have reduced tuberculosis incidence and mortality by more than one third in 2020, and are predicted to achieve the End TB Strategy milestone of 50% incidence reduction by 2025 compared to 2015. Interventions had a considerably greater impact when individuals were also screened for latent infection than active case finding alone. Such combined programs implemented at the national level could achieve tuberculosis pre-elimination by 2035 if repeated every two years, and around 2045 if repeated every five years.ConclusionsWe predict that it is possible to achieve tuberculosis pre-elimination by 2035 in the Marshall Islands through periodic repetition of the same ambitious interventions as those previously implemented. Including latent infection testing in active screening activities will be a critical pillar for achieving these ambitious goals.

BackgroundAntimicrobial resistance develops following the accrual of mutations in the bacterial genome, and may variably impact organism fitness and hence, transmission risk. Classical representation of tuberculosis (TB) dynamics using a single or two strain (DS/MDR-TB) model typically does not capture elements of this important aspect of TB epidemiology. To understand and estimate the likelihood of resistance spreading in high drug-resistant TB incidence settings, we used molecular understanding to develop a compartmental epidemiological model of Mycobacterium tuberculosis (Mtb) transmission.MethodsA four-strain (drug-susceptible (DS), isoniazid mono-resistant (INH-R), rifampicin mono-resistant (RIF-R) and multidrug-resistant (MDR)) compartmental deterministic Mtb transmission model was developed to explore the progression from DS-to MDR-TB. The model incorporated strain-specific fitness costs and was calibrated using data from national tuberculosis prevalence surveys and drug resistance surveys from Philippines and Viet Nam. Using an adaptive Metropolis algorithm, we estimated drug resistance amplification and transmission rates.ResultsThe posterior estimates for the proportion of isoniazid mono-resistant amplification among treatment failure was 0.75 (0.64 – 0.85) for Philippines and 0.55 (0.39 – 0.63) for Viet Nam. The proportion of rifampicin mono-resistant amplification among treatment failure was 0.05 (0.04 – 0.06) for Philippines and 0.011 (0.010 – 0.012) for Viet Nam. In Philippines, the estimated proportion of primary resistance resulting from transmission was 56% (42 – 68) for INH-R, 48% (34 – 62) for RIF-R and 42% (34 – 50) for MDR-TB. For Viet Nam, the estimated proportion of drug resistance due to transmission was 79% (70 – 86) for INH-R, 68% (58 – 75) for RIF-R and 50% (45 – 53) for MDR-TB.DiscussionRIF-R strains were more likely to be transmitted than acquired through amplification, while both mechanisms of acquisition were important contributors in the case of INH-R. These findings highlight the complexity of drug resistance dynamics in high-incidence settings, and emphasize the importance of prioritizing testing algorithms which also allow for early detection of INH-R.

International audience; In many optimal design searches, the function to optimise is a simulator that is computationally expensive. While current High Performance Computing (HPC) methods are not able to solve such problems efficiently, parallelism can be coupled with approximate models (surrogates or meta-models) that imitate the simulator in timely fashion to achieve better results. This combined approach reduces the number of simulations thanks to surrogate use whereas the remaining evaluations are handled by supercomputers. While the surrogates' ability to limit computational times is very attractive, integrating them into the over-arching optimization process can be challenging. Indeed, it is critical to address the major trade-off between the quality (precision) and the efficiency (execution time) of the resolution. In this article, we investigate Evolution Controls (ECs) which are strategies that define the alternation between the simulator and the surrogate within the optimization process. We propose a new EC based on the prediction uncertainty obtained from Monte Carlo Dropout (MCDropout), a technique originally dedicated to quantifying uncertainty in deep learning. Investigations of such uncertainty-aware ECs remain uncommon in surrogate-assisted evolutionary optimization. In addition, we use parallel computing in a complementary way to address the high computational burden. Our new strategy is implemented in the context of a pioneering application to Tuberculosis Transmission Control. The reported results show that the MCDropout-based EC coupled with massively parallel computing outperforms strategies previously proposed in the field of surrogate-assisted optimization.

In anticipation of COVID-19 vaccine deployment, we use an age-structured mathematical model to investigate the benefits of optimizing age-specific dose allocation to suppress the transmission, morbidity and mortality of SARS-CoV-2 and the associated disease, COVID-19. To minimize transmission, we find that the highest priority individuals across 179 countries are typically those between 30 and 59 years of age because of their high contact rates and higher risk of infection and disease. Conversely, morbidity and mortality are initially most effectively reduced by targeting 60+ year olds who are more likely to experience severe disease. However, when population-level coverage is sufficient — such that herd immunity can be achieved through targeted dose allocation — prioritizing middle-aged individuals becomes the most effective strategy to minimize hospitalizations and deaths. For each metric considered, we show that optimizing the allocation of vaccine doses can more than double their effectiveness.

BackgroundIf SARS-CoV-2 elimination is not feasible, strategies are needed to minimise the impact of COVID-19 in the medium-to-long term, until safe and effective vaccines can be used at the population-level.MethodsUsing a mathematical model, we identified contact mitigation strategies that minimised COVID-19-related deaths or years of life lost (YLLs) over a time-horizon of 15 months, using an intervention lasting six or 12 months, in Belgium, France, Italy, Spain, Sweden and the UK. We used strategies that either altered age- or location-specific contact patterns. The optimisation was performed under the constraint that herd immunity should be achieved by the end of the intervention period if post-infection immunity was persistent. We then tested the effect of waning immunity on the strategies.FindingsStrategies of contact mitigation by age were much more effective than those based on mitigation by location. Extremely stringent contact reductions for individuals aged over 50 were required in most countries to minimise deaths or YLLs. The median final proportion of the population ever-infected with SARS-CoV-2 after herd immunity was reached ranged between 30% and 43%, depending on the country and intervention duration. Compared to an unmitigated scenario, optimised age-specific mitigation was predicted to avert over 1 million deaths across the six countries. The optimised scenarios assuming persistent immunity resulted in comparable hospital occupancies to that experienced during the March-April European wave. However, if immunity was shortlived, high burdens were expected without permanent contact mitigation.InterpretationOur analysis suggests that age-selective mitigation strategies can reduce the mortality impacts of COVID-19 dramatically even when significant transmission occurs. The stringency of the required restrictions in some groups raises concerns about the practicality of these strategies. If post-infection immunity was short-lived, solutions based on a mitigation period designed to increase population immunity should be accompanied with ongoing contact mitigation to prevent large epidemic resurgence.

Infectiousness heterogeneity among individuals with tuberculosis (TB) is substantial and is likely to have a significant impact on the long-term dynamics of TB and the effectiveness of interventions. However, there is a gap in capturing heterogeneous infectiousness and evaluating its impact on the effectiveness of interventions.Informed by observed distribution of secondary infections, we constructed a deterministic model of TB transmission using ordinary differential equations. The model incorporated assumption of heterogeneous infectiousness with three levels of infectivity, namely non-spreaders, low-spreaders and super-spreaders. We evaluated the effectiveness of dynamic transmission untargeted and targeted implementation of an intervention intended to represent active case finding with a point-of-care diagnostic tool. The simulated intervention detected 20% of all TB patients who would otherwise have been missed by the health system during their disease episode and was compared across four epidemiological scenarios.Our model suggested that targeting the active case finding intervention towards super-spreaders was more effective than untargeted intervention in all setting scenarios, with more effectiveness in settings with low case detection and high transmission intensity. For instance, a targeted intervention achieved a 42.2% reduction in TB incidence, while the untargeted intervention achieved only a 20.7% reduction over 20 years, given the same number of people treated. Although the most marked impact on equilibrium TB incidence came from the rate of late reactivation, the proportion of super-spreaders and their relative infectiousness had shown substantial impact.Targeting active case-finding interventions to highly infectious cases likely to be particularly beneficial in settings where case detection is poor. Heterogeneity-related parameters had an equivalent effect to several other parameters that have been established as being very important to TB transmission dynamics.

International audience; Surrogate-based optimization is widely used to deal with long-running black-box simulation-based objective functions. Actually, the use of a surrogate model such as Kriging or Artificial Neural Network allows to reduce the number of calls to the CPU time-intensive simulator. Bayesian optimization uses the ability of surrogates to provide useful information to help guiding effectively the optimization process. In this paper, the Efficient Global Optimization (EGO) reference framework is challenged by a Bayesian Neural Network-assisted Genetic Algorithm, namely BNN-GA. The Bayesian Neural Network (BNN) surrogate is chosen for its ability to provide an uncertainty measure of the prediction that allows to compute the Expected Improvement of a candidate solution in order to improve the exploration of the objective space. BNN is also more reliable than Kriging models for high-dimensional problems and faster to set up thanks to its incremental training. In addition, we propose a batch-based approach for the parallelization of BNN-GA that is challenged by a parallel version of EGO, called q-EGO. Parallel computing is a highly important complementary way (to surrogates) to deal with the computational burden of simulation-based optimization. The comparison of the two parallel approaches is experimentally performed through several benchmark functions and two real-world problems within the scope of Tuberculosis Transmission Control (TBTC). The study presented in this paper proves that parallel batched BNN-GA is a viable alternative to q-EGO approaches being more suitable for high-dimensional problems, parallelization impact, bigger databases and moderate search budgets. Moreover, a significant improvement of the solutions is obtained for the two TBTC problems tackled.

Background: COVID-19 appears to have caused less severe outbreaks in many low- and middle-income countries (LMIC) compared with high-income countries, possibly because of differing demographics, socio-economics, climate, surveillance, and policy responses. The Philippines is a LMIC that has had a relatively severe COVID-19 outbreak but has recently curtailed transmission while gradually easing interventions. Methods: We applied an age-structured compartmental model that incorporated time-varying mobility, testing, and personal protective behaviors (through a “Minimum Health Standards” policy, MHS) to represent the Philippines COVID-19 epidemic nationally and for three highly affected regions (Calabarzon, Central Visayas, and the National Capital Region). We estimated effects of control measures, key epidemiological parameters, and projected the impacts of easing interventions. Findings: Population age structure, contact rates, mobility, testing, and MHS were sufficient to explain the Philippines epidemic based on the good fit between modelled and reported cases, hospitalisations, and deaths. Several of the fitted epidemiological parameters were consistent with those reported in high-income settings. The model indicated that MHS reduced the probability of transmission per contact by 15-32%. The December 2020 case detection rate was estimated at ~14%, population recovered at ~12%, and scenario projections indicated high sensitivity to MHS adherence. Interpretation: COVID-19 dynamics in the Philippines are driven by age, contact structure, and mobility, and the epidemic can be understood within a similar framework as for high-income settings. Continued compliance with low-cost MHS measures should allow the Philippines to maintain epidemic control, but disease resurgence remains a threat due to low population immunity and detection rates. Funding Statement: This work was supported by the World Health Organization Regional Office for the Western Pacific to provide modelling advice to Member States. JMS is supported by a NASA Ecological Forecasting grant (NNX17AI21G). EDLT, TRT, MRJEE, and RFRS are supported by a project grant from the Philippine Council for Health Research and Development, Department of Science and Technology, Philippines. JMT is supported by an Early Career Fellowship from the National Health and Medical Research Council (APP1142638). Declaration of Interests: The authors declare no conflicts of interest.

Background: We aimed to estimate the disease burden of Tuberculosis (TB) and return on investment of TB care in the Western Pacific Region (WPR) until 2030. Methods: We projected the TB epidemic in Viet Nam and Lao People’s Democratic Republic (PDR) for the years 2020-30 with TIME Impact model under various scenarios: no TB care; baseline (TB care continues at current levels); and several interventions. We extracted literature results for China and the Philippines to estimate region-level incidence and deaths. We used a Solow model to estimate the return on investment of TB care and interventions. Findings: In the baseline scenario, TB incidence in the WPR decreased from 95·2/100,000/year (2019) to 91·5/100,000/year (2030), and TB deaths from 79,000/year (2019) to 68,100/year (2030). Active case finding (ACF) strategies (screening people not seeking care for respiratory symptoms) were the most effective single intervention. A combination intervention resulted in TB incidence of 43·9/100,000/year and 18,000 TB deaths in 2030. Estimated return on investment (2020-2030) for TB care in Viet Nam and Lao PDR ranged US$4 to US$49 per dollar spent, Additional interventions brought returns of up to US$2·7 per dollar spent. Interpretation: TB incidence will only modestly decrease in the WPR without additional interventions. Intervention strategies that include ACF can reduce TB burden, but achieving the End TB incidence and mortality targets will be difficult without new transformational tools. TB care can bring a multiple-fold return on investment. Funding Statement: This study was funded by the World Health Organization Western Pacific Regional Office and the Swiss National Science Foundation (grant 163878). Declaration of Interests: We declare no competing interests.

Background: Latent tuberculosis infection (LTBI) is a critical driver of the global burden of active TB, and therefore LTBI treatment is key for TB elimination. Treatment regimens for LTBI include self-administered daily isoniazid for 6 (6H) or 9 (9H) months, self-administered daily rifampicin plus isoniazid for 3 months (3RH), self-administered daily rifampicin for 4 months (4R) and weekly rifapentine plus isoniazid for 3 months self-administered (3HP-SAT) or administered by a healthcare worker as directly observed therapy (3HP-DOT). Data on the relative cost-effectiveness of these regimens are needed to assist policymakers and clinicians in selecting an LTBI regimen. Objectives: To evaluate the cost-effectiveness of all regimens for treating LTBI. Methods: We developed a Markov model to investigate the cost-effectiveness of 3HP-DOT, 3HP-SAT, 4R, 3RH, 9H and 6H for LTBI treatment in a cohort of 10000 adults with LTBI. Cost-effectiveness was evaluated from a health system perspective over a 20 year time horizon. Results: Compared with no preventive treatment, 3HP-DOT, 3HP-SAT, 4R, 3RH, 9H and 6H prevented 496, 470, 442, 418, 370 and 276 additional cases of active TB per 10000 patients, respectively. All regimens reduced costs and increased QALYs compared with no preventive treatment. 3HP was more cost-effective under DOT than under SAT at a cost of US$27948 per QALY gained. Conclusions: Three months of weekly rifapentine plus isoniazid is more cost-effective than other regimens. Greater recognition of the benefits of short-course regimens can contribute to the scale-up of prevention and achieving the ‘End TB’ targets.

Although different structures are used in modern tuberculosis (TB) models to simulate TB latency, it remains unclear whether they are all capable of reproducing the particular activation dynamics empirically observed. We aimed to determine which of these structures replicate the dynamics of progression accurately. We reviewed 88 TB-modelling articles and classified them according to the latency structure employed. We then fitted these different models to the activation dynamics observed from 1352 infected contacts diagnosed in Victoria (Australia) and Amsterdam (Netherlands) to obtain parameter estimates. Six different model structures were identified, of which only those incorporating two latency compartments were capable of reproducing the activation dynamics empirically observed. We found important differences in parameter estimates by age. We also observed marked differences between our estimates and the parameter values used in many previous models. In particular, when two successive latency phases are considered, the first period should have a duration that is much shorter than that used in previous studies. In conclusion, structures incorporating two latency compartments and age-stratification should be employed to accurately replicate the dynamics of TB latency. We provide a catalogue of parameter values and an approach to parameter estimation from empiric data for calibration of future TB-models.

Setting: Isoniazid preventive therapy (IPT) is effective for preventing active tuberculosis (TB), although its mechanism of action is poorly understood and the optimal disease burden for IPT use has not been defined. Objective: To describe the relationship between TB incidence and IPT effectiveness. Methods: We constructed a model of TB transmission dynamics to investigate IPT effectiveness under various epidemiological settings. The model structure was intended to be highly adaptable to uncertainty in both input parameters and the mechanism of action of IPT. To determine the optimal setting for IPT use, we identified the lowest number needed to treat (NNT) with IPT to prevent one case of active TB. Results: We found that the NNT as a function of TB incidence shows a ‘U-shape’, whereby IPT impact is greatest at an intermediate incidence and attenuated at both lower and higher incidence levels. This U-shape was observed over a broad range of parameter values; the optimal TB incidence was between 500 and 900 cases per 100 000 per year. Conclusions: TB burden is a critical factor to consider when making decisions about communitywide implementation of IPT. We believe that the total disease burden should not preclude programmatic application of IPT.

Background Tuberculosis (TB) control efforts are hampered by an imperfect understanding of TB epidemiology. The true age distribution of disease is unknown because a large proportion of individuals with active TB remain undetected. Understanding of transmission is limited by the asymptomatic nature of latent infection and the pathogen’s capacity for late reactivation. A better understanding of TB epidemiology is critically needed to ensure effective use of existing and future control tools. Methods We use an agent-based model to simulate TB epidemiology in the five highest TB burden countries—India, Indonesia, China, the Philippines and Pakistan—providing unique insights into patterns of transmission and disease. Our model replicates demographically realistic populations, explicitly capturing social contacts between individuals based on local estimates of age-specific contact in household, school and workplace settings. Time-varying programmatic parameters are incorporated to account for the local history of TB control. Results We estimate that the 15–19-year-old age group is involved in more than 20% of transmission events in India, Indonesia, the Philippines and Pakistan, despite representing only 5% of the local TB incidence. According to our model, childhood TB represents around one fifth of the incident TB cases in these four countries. In China, three quarters of incident TB were estimated to occur in the ≥ 45-year-old population. The calibrated per-contact transmission risk was found to be similar in each of the five countries despite their very different TB burdens. Conclusions Adolescents and young adults are a major driver of TB in high-incidence settings. Relying only on the observed distribution of disease to understand the age profile of transmission is potentially misleading.

As Bulgaria transitions away from Global Fund grant, robust estimates of the comparative impact of the various response strategies under consideration are needed to ensure sustained effectiveness of the tuberculosis (TB) programme. We tailored an established mathematical model for TB control to the epidemic in Bulgaria to project the likely outcomes of seven intervention scenarios. Under existing programmatic conditions projected forward, the country's targets for achieving TB elimination in the coming decades will not be achieved. No interventions under consideration were predicted to accelerate the baseline projected reduction in epidemiological indicators significantly. Discontinuation of the ‘Open Doors’ program and activities of non-governmental organisations would result in a marked exacerbation of the epidemic (increasing incidence in 2035 by 6–8% relative to baseline conditions projected forward). Changing to a short course regimen for multidrug-resistant TB (MDR-TB) would substantially decrease MDR-TB mortality (by 21.6% in 2035 relative to baseline conditions projected forward). Changing to ambulatory care for eligible patients would not affect TB burden but would be markedly cost-saving. In conclusion, Bulgaria faces important challenges in transitioning to a primarily domestically-financed TB programme. The country should consider maintaining currently effective programs and shifting towards ambulatory care to ensure program sustainability.

BackgroundTuberculosis (TB) natural history remains poorly characterised and new investigations are impossible as it would be unethical to follow up TB patients without treating them. Estimates of TB burden and mortality rely heavily on TB self-recovery and mortality rates, as around 40% of individuals with TB are never detected, making their prognosis entirely dependent on the disease natural history.MethodsWe considered the reports identified in a previous systematic review of studies from the prechemotherapy era, and extracted detailed data on mortality over time. We used a continuous-time Markov model in a Bayesian framework to estimate the rates of TB-induced mortality and self-cure. A hierarchical model was employed to allow estimates to vary by cohort. Inference was performed separately for smear-positive TB (SP-TB) and smear-negative TB (SN-TB).ResultsWe included 41 cohorts of SP-TB patients and 19 cohorts of pulmonary SN-TB patients in the analysis. No data were available on extrapulmonary TB. The posterior median estimates of the TB-specific mortality rates were 0.390 year−1 (0.329-0.452, 95% credible interval) and 0.025 year−1 (0.016-0.036) for SP-TB and SN-TB patients, respectively. The estimates for self-recovery rates were 0.233 year−1 (0.179-0.293) and 0.147 year−1 (0.087-0.248) for SP-TB and SN-TB patients, respectively. These rates correspond to average durations of untreated TB of 1.57 years (1.37-1.81) and 5.35 years (3.42-8.23) for SP-TB and SN-TB, respectively, when assuming a natural mortality rate of 0.014 year−1 (i.e. a 70-year life expectancy).ConclusionsTB-specific mortality rates are around 15 times higher for SP-TB than for SN-TB patients. This difference was underestimated dramatically in previous TB modelling studies that parameterised models based on the ratio of 3.3 between the 10-year case fatality of SP-TB and SN-TB. Our findings raise important concerns about the accuracy of past and current estimates of TB mortality and predicted impact of control interventions on TB mortality.

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. Keywords: Mathematical modelling, Tuberculosis, Heterogeneous infectiousness, Systematic review

The tuberculosis (TB) health burden in Fiji has been declining in recent years, although challenges remain in improving control of the diabetes co-epidemic and achieving adequate case detection across the widely dispersed archipelago. We applied a mathematical model of TB transmission to the TB epidemic in Fiji that captured the historical reality over several decades, including age stratification, diabetes, varying disease manifestations, and incorrect diagnoses. Next, we simulated six intervention scenarios that are under consideration by the Fiji National Tuberculosis Program. Our findings show that the interventions were able to achieve only modest improvements in disease burden, with awareness raising being the most effective intervention to reduce TB incidence, and treatment support yielding the highest impact on mortality. These improvements would fall far short of the ambitious targets that have been set by the country, and could easily be derailed by moderate increases in the diabetes burden. Furthermore, the effectiveness of the interventions was limited by the extensive pool of latent TB infection, because the programs were directed at only active cases, and thus were unlikely to achieve the desired reductions in burden. Therefore, it is essential to address the co-epidemic of diabetes and treat people with latent TB infection.

[Extract] Pandemic responses across the world have been highly reactive. However, there remain only three strategic options to managing coronavirus disease 2019 (COVID-19): mitigation, suppression and elimination. With the promise of efficacious new vaccines, mitigation is appropriately not considered as part of Australia's national strategy. However, our stated goal of achieving "no community transmission" remains poorly defined and risks missing important distinctions between elimination and suppression.

Background Tuberculosis (TB) transmission often occurs within a household or community, leading to heterogeneous spatial patterns. However, apparent spatial clustering of TB could reflect ongoing transmission or co-location of risk factors and can vary considerably depending on the type of data available, the analysis methods employed and the dynamics of the underlying population. Thus, we aimed to review methodological approaches used in the spatial analysis of TB burden. Methods We conducted a systematic literature search of spatial studies of TB published in English using Medline, Embase, PsycInfo, Scopus and Web of Science databases with no date restriction from inception to 15 February 2017. The protocol for this systematic review was prospectively registered with PROSPERO (CRD42016036655). Results We identified 168 eligible studies with spatial methods used to describe the spatial distribution (n = 154), spatial clusters (n = 73), predictors of spatial patterns (n = 64), the role of congregate settings (n = 3) and the household (n = 2) on TB transmission. Molecular techniques combined with geospatial methods were used by 25 studies to compare the role of transmission to reactivation as a driver of TB spatial distribution, finding that geospatial hotspots are not necessarily areas of recent transmission. Almost all studies used notification data for spatial analysis (161 of 168), although none accounted for undetected cases. The most common data visualisation technique was notification rate mapping, and the use of smoothing techniques was uncommon. Spatial clusters were identified using a range of methods, with the most commonly employed being Kulldorff’s spatial scan statistic followed by local Moran’s I and Getis and Ord’s local Gi(d) tests. In the 11 papers that compared two such methods using a single dataset, the clustering patterns identified were often inconsistent. Classical regression models that did not account for spatial dependence were commonly used to predict spatial TB risk. In all included studies, TB showed a heterogeneous spatial pattern at each geographic resolution level examined. Conclusions A range of spatial analysis methodologies has been employed in divergent contexts, with all studies demonstrating significant heterogeneity in spatial TB distribution. Future studies are needed to define the optimal method for each context and should account for unreported cases when using notification data where possible. Future studies combining genotypic and geospatial techniques with epidemiologically linked cases have the potential to provide further insights and improve TB control. Electronic supplementary material The online version of this article (10.1186/s12916-018-1178-4) contains supplementary material, which is available to authorized users.

Nicolas Menzies and colleagues1 presented a systematic review of the latency structures used in tuberculosis models, which was similar to our 2017 review.2 The main additional contribution of this latest work is the detailed literature search that provides a complete picture of the range of structures in use and highlights the variability in approaches. We strongly agree with the finding that modelled reactivation profiles differ profoundly between studies and that many commonly used structures and parameters fit empirical data poorly. The parameter range findings on goodness of fit validate the results of both studies. However, important differences between the reviews in the two empirical comparison datasets should be highlighted.

Multidrug-resistant and rifampicin-resistant tuberculosis (MDR/RR-TB) represent an important challenge for global tuberculosis (TB) control. The high rates of MDR/RR-TB observed among re-treatment cases can arise from diverse pathways: de novo amplification during initial treatment, inappropriate treatment of undiagnosed MDR/RR-TB, relapse despite appropriate treatment, or reinfection with MDR/RR-TB. Mathematical modelling allows quantification of the contribution made by these pathways in different settings. This information provides valuable insights for TB policy-makers, allowing better contextualised solutions. However, mathematical modelling outputs need to consider local data and be easily accessible to decision makers in order to improve their usefulness. We present a user-friendly web-based modelling interface, which can be used by people without technical knowledge. Users can input their own parameter values and produce estimates for their specific setting. This innovative tool provides easy access to mathematical modelling outputs that are highly relevant to national TB control programs. In future, the same approach could be applied to a variety of modelling applications, enhancing local decision making.

Background Globally 3.9% of new and 21% of re-treatment tuberculosis (TB) cases are multidrug-resistant or rifampicin-resistant (MDR/RR), which is often interpreted as evidence that drug resistance results mainly from poor treatment adherence. This study aims to assess the respective contributions of the different causal pathways leading to MDR/RR-TB at re-treatment. Methods We use a simple mathematical model to simulate progression between the different stages of disease and treatment for patients diagnosed with TB. The model is parameterised using region and country-specific TB disease burden data reported by the World Health Organization (WHO). The contributions of four separate causal pathways to MDR/RR-TB among re-treatment cases are estimated: I) initial drug-susceptible TB with resistance amplification during treatment; II) initial MDR/RR-TB inappropriately treated as drug-susceptible TB; III) MDR/RR-TB relapse despite appropriate treatment; and IV) re-infection with MDR/RR-TB. Results At the global level, Pathways I, II, III and IV contribute 38% (28–49, 95% Simulation Interval), 44% (36–52, 95% SI), 6% (5–7, 95% SI) and 12% (7–19, 95% SI) respectively to the burden of MDR/RR-TB among re–treatment cases. Pathway II is dominant in the Western Pacific (74%; 67–80 95% SI), Eastern Mediterranean (68%; 60–74 95% SI) and European (53%; 48–59 95% SI) regions, while Pathway I makes the greatest contribution in the American (53%; 40–66 95% SI), African (43%; 28–61 95% SI) and South-East Asian (50%; 40–59 95% SI) regions. Conclusions Globally, failure to diagnose MDR/RR-TB at first presentation is the leading cause of the high proportion of MDR/RR-TB among re-treatment cases. These findings highlight the need for contextualised solutions to limit the impact and spread of MDR/RR-TB. Electronic supplementary material The online version of this article (doi:10.1186/s12879-016-2171-1) contains supplementary material, which is available to authorized users.

Background. Hepatitis C virus (HCV) infection is often asymptomatic, and the date of infection is almost impossible to determine. Furthermore, spontaneous clearance (SC) may occur, but little is known about its time of occurrence. Methods. Data on human immunodeficiency virus (HIV)-HCV coinfected individuals were used to inform a stochastic simulation model of HCV viral load kinetics, alanine aminotransferase (ALT), and HCV antibodies during acute hepatitis C. The dates of diagnosis and potential SC were estimated through a Bayesian approach. Hepatitis C virus diagnosis was assumed to be based on an elevated ALT level detected during a control visit for HIV-infected individuals, which occurred every 3 months (scenario A) or every 6 months (scenario B). Results. We found that HCV diagnosis occurred after a median of 115 days and 170 days of infection in scenarios A and B, respectively. Among spontaneous clearers, SC occurred after a median time of 184 days after infection. Seven percent (scenario B) to 10% (scenario A) of SCs appeared more than 6 months after diagnosis, and 3% (both scenarios) of SCs appeared more than 1 year after diagnosis. Conclusions. Acute hepatitis C diagnosis occurs late in HIV-HCV coinfected individuals. Screening for HCV in HIV-infected individuals should be performed frequently to reduce delays. Our findings about late occurrence of SC support “wait and see” strategies for treatment initiation from an individual basis. However, early treatment initiation may reduce HCV transmission.

Vaccine effect, as measured in clinical trials, may not accurately reflect population-level impact. Furthermore, little is known about how sensitive apparent or real vaccine impacts are to factors such as the risk of re-infection or the mechanism of protection. We present a dynamic compartmental model to simulate vaccination for endemic infections. Several measures of effectiveness are calculated to compare the real and apparent impact of vaccination and assess the effect of a range of infection and vaccine characteristics on these measures. Although broadly correlated, measures of real and apparent vaccine effectiveness can differ widely. Vaccine impact is markedly underestimated when primary infection provides partial natural immunity, when coverage is high and when post-vaccination infectiousness is reduced. Despite equivalent efficacy, ‘all or nothing’ vaccines are more effective than ‘leaky’ vaccines, particularly in settings with high risk of re-infection and transmissibility. Latent periods result in greater real impacts when risk of re-infection is high, but this effect diminishes if partial natural immunity is assumed. Assessments of population-level vaccine effects against endemic infections from clinical trials may be significantly biased and vaccine and infection characteristics should be considered when modelling outcomes of vaccination programs, as their impact may be dramatic.