Search

The degree to which individual heterogeneity in the production of secondary cases (“superspreading”) affects tuberculosis (TB) transmission has not been systematically studied. We searched for population-based or surveillance studies in which whole genome sequencing was used to estimate TB transmission and in which the size distributions of putative TB transmission clusters were enumerated. We fitted cluster-size–distribution data to a negative binomial branching process model to jointly infer the transmission parameters [Formula: see text] (the reproduction number) and the dispersion parameter, [Formula: see text] , which quantifies the propensity of superspreading in a population (generally, lower values of [Formula: see text] ([Formula: see text]) suggest increased heterogeneity). Of 4,796 citations identified in our initial search, 9 studies from 8 global settings met the inclusion criteria (n = 5 studies of all TB; n = 4 studies of drug-resistant TB). Estimated [Formula: see text] values (range, 0.10–0.73) were below 1.0, consistent with declining epidemics in the included settings; estimated [Formula: see text] values were well below 1.0 (range, 0.02–0.48), indicating the presence of substantial individual-level heterogeneity in transmission across all settings. We estimated that a minority of cases (range, 2%–31%) drive the majority (80%) of ongoing TB transmission at the population level. Identifying sources of heterogeneity and accounting for them in TB control may have a considerable impact on mitigating TB transmission.

Background Global progress towards reducing tuberculosis (TB) incidence and mortality has consistently lagged behind the World Health Organization targets leading to a perception that large reductions in TB burden cannot be achieved. However, several recent and historical trials suggest that intervention efforts that are comprehensive and intensive can have a substantial epidemiological impact. We aimed to quantify the potential epidemiological impact of an intensive but realistic, community-wide campaign utilizing existing tools and designed to achieve a “step change” in the TB burden. Methods We developed a compartmental model that resembled TB transmission and epidemiology of a mid-sized city in India, the country with the greatest absolute TB burden worldwide. We modeled the impact of a one-time, community-wide screening campaign, with treatment for TB disease and preventive therapy for latent TB infection (LTBI). This one-time intervention was followed by the strengthening of the tuberculosis-related health system, potentially facilitated by leveraging the one-time campaign. We estimated the tuberculosis cases and deaths that could be averted over 10 years using this comprehensive approach and assessed the contributions of individual components of the intervention. Results A campaign that successfully screened 70% of the adult population for active and latent tuberculosis and subsequently reduced diagnostic and treatment delays and unsuccessful treatment outcomes by 50% was projected to avert 7800 (95% range 5450–10,200) cases and 1710 (1290–2180) tuberculosis-related deaths per 1 million population over 10 years. Of the total averted deaths, 33.5% (28.2–38.3) were attributable to the inclusion of preventive therapy and 52.9% (48.4–56.9) to health system strengthening. Conclusions A one-time, community-wide mass campaign, comprehensively designed to detect, treat, and prevent tuberculosis with currently existing tools can have a meaningful and long-lasting epidemiological impact. Successful treatment of LTBI is critical to achieving this result. Health system strengthening is essential to any effort to transform the TB response.

Purpose Tuberculosis (TB) is geographically heterogeneous, and geographic targeting can improve the impact of TB interventions. However, standard TB notification data may not sufficiently capture this heterogeneity. Better understanding of patient reporting patterns (discrepancies between residence and place of presentation) may improve our ability to use notifications to appropriately target interventions. Methods Using demographic data and TB reports from Dhaka North City Corporation and Dhaka South City Corporation, we identified wards of high TB incidence and developed a TB transmission model. We calibrated the model to patient-level data from selected wards under four different reporting pattern assumptions and estimated the relative impact of targeted versus untargeted active case finding. Results The impact of geographically targeted interventions varied substantially depending on reporting pattern assumptions. The relative reduction in TB incidence, comparing targeted with untargeted active case finding in Dhaka North City Corporation, was 1.20, assuming weak correlation between reporting and residence, versus 2.45, assuming perfect correlation. Similar patterns were observed in Dhaka South City Corporation (1.03 vs. 2.08). Conclusions Movement of individuals seeking TB diagnoses may substantially affect ward-level TB transmission. Better understanding of patient reporting patterns can improve estimates of the impact of targeted interventions in reducing TB incidence. Incorporating high-quality patient-level data is critical to optimizing TB interventions.

Background Targeted testing and treatment (TTT) for latent tuberculosis (TB) infection (LTBI) is a recommended strategy to accelerate TB reductions and further TB elimination in the United States. Evidence on cost-effectiveness of TTT for key populations can help advance this goal. Methods We used a model of TB transmission to estimate the numbers of individuals who could be tested by interferon-γ release assay and treated for LTBI with 3 months of self-administered rifapentine and isoniazid (3HP) under various TTT scenarios. Specifically, we considered rapidly scaling up TTT among people who are non–US-born, diabetic, living with human immunodeficiency virus (HIV), homeless or incarcerated in California, Florida, New York, and Texas—states where more than half of US TB cases occur. We projected costs (from the healthcare system perspective, in 2018 dollars), 30-year reductions in TB incidence, and incremental cost-effectiveness (cost per quality-adjusted life-year [QALY] gained) for TTT in each modeled population. Results The projected cost-effectiveness of TTT differed substantially by state and population, while the health impact (number of TB cases averted) was consistently greatest among non–US-born individuals. TTT was most cost-effective among persons with HIV (from $2828/QALY gained in Florida to $11 265/QALY gained in New York) and least cost-effective among people with diabetes (from $223 041/QALY gained in California to $817 753/QALY in New York). Conclusions The modeled cost-effectiveness of TTT for LTBI varies across states but was consistently greatest among people with HIV; moderate among people who are non–US-born, incarcerated, or homeless; and least cost-effective among people with diabetes.

We developed a novel method to align two data sources (TB notifications and the Demographic Health Survey, DHS) captured at different geographic scales. We used this method to identify sociodemographic indicators – specifically population density – that were ecologically correlated with elevated TB notification rates across wards (~100 000 people) in Dhaka, Bangladesh. We found population density was the variable most closely correlated with ward-level TB notification rates (Spearman's rank correlation 0.45). Our approach can be useful, as publicly available data (e.g. DHS data) could help identify factors that are ecologically associated with disease burden when more granular data (e.g. ward-level TB notifications) are not available. Use of this approach might help in designing spatially targeted interventions for TB and other diseases in settings of weak existing data on disease burden at the subdistrict level.

The COVID-19 pandemic has disrupted systems of care for infectious diseases-including tuberculosis-and has exposed pervasive inequities that have long marred efforts to combat these diseases. The resulting health disparities often intersect at the individual and community levels in ways that heighten vulnerability to tuberculosis. Effective responses to tuberculosis (and other infectious diseases) must respond to these realities. Unfortunately, current tuberculosis programmes are generally not designed from the perspectives of affected individuals and fail to address structural determinants of health disparities. We describe a person-centred, equity-oriented response that would identify and focus on communities affected by disparities, tailor interventions to the mechanisms by which disparities worsen tuberculosis, and address upstream determinants of those disparities. We detail four key elements of the approach (data collection, programme design, implementation, and sustainability). We then illustrate how organisations at multiple levels might partner and adapt current practices to incorporate these elements. Such an approach could generate more substantial, sustainable, and equitable reductions in tuberculosis burden at the community level, highlighting the urgency of restructuring post-COVID-19 health systems in a more person-centred, equity-oriented way.

Since March 2020, when coronavirus disease 2019 (COVID-19) was declared a pandemic, many countries have applied unprecedented restrictive measures to contain the spread of the virus. This study aimed to explore the optimal social distancing policy for COVID-19 control in South Korea to safely reopen the society.We developed an age-specific, deterministic compartment epidemic model to examine the COVID-19 control decision-making process, including the epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between 1 July 2021 and 30 December 2022. The model consists of the natural history of COVID-19, testing performance, vaccinations, and social distancing enforcement measures to detect and control SARS-CoV-2. We modelled potential intervention scenarios with three distinct components: 1) social distancing duration and level; 2) testing intensity; and 3) vaccination uptake rate. The primary and secondary outcomes were COVID-19 incidence and prevalence of severe patients requiring intensive care unit (ICU) care.Four (or more) months of social distancing (that can reduce 40-60% transmission) may mitigate epidemic resurgence and ICU demand in the future and keep the cases below the capacity limit if the testing intensity and vaccination rate remain constant or increase by 20% (with respect to the current level). In contrast, two months of strict social distancing enforcement may also successfully mitigate future epidemic surge and ICU demand as long as testing intensity and vaccination rates are increased by 20%.In South Korea, given the relatively high vaccination coverage and low incidence, four or more months of social distancing enforcement can effectively mitigate epidemic resurgence after lifting the social distancing measures. In addition, increasing the testing intensity and vaccination rate may help reduce necessary social distancing levels and duration to prevent a future epidemic resurgence and mitigate social and economic damage.

Background Effective targeting of latent tuberculosis infection (LTBI) treatment requires identifying those most likely to progress to tuberculosis (TB). We estimated the potential health and economic benefits of diagnostics with improved discrimination for LTBI that will progress to TB. Methods A base-case scenario represented current LTBI testing and treatment services in the United States in 2020, with diagnosis via interferon-gamma release assay (IGRA). Alternative scenarios represented tests with higher positive predictive value (PPV) for future TB but similar price to IGRA, and scenarios that additionally assumed higher treatment initiation and completion. We predicted outcomes using multiple transmission-dynamic models calibrated to different geographic areas, and estimated costs from a societal perspective. Results In 2020, 2.1% (range across model results: 1.1%-3.4%) of individuals with LTBI were predicted to develop TB in their remaining lifetime. For IGRA, we estimated the PPV for future TB as 1.3% (0.6%-1.8%). Relative to IGRA, we estimated a test with 10% PPV would reduce treatment volume by 87% (82%-94%), reduce incremental costs by 30% (15%-52%), and increase quality-adjusted life years by 3% (2%-6%). Cost reductions and health improvements were substantially larger for scenarios in which higher PPV for future TB was associated with greater initiation and completion of treatment. Conclusions We estimated that tests with better predictive performance would substantially reduce the number of individuals treated to prevent TB, but would have a modest impact on incremental costs and health impact of TB prevention services, unless accompanied by greater treatment acceptance and completion.

Background Reductions in tuberculosis (TB) transmission have been instrumental in lowering TB incidence in the United States. Sustaining and augmenting these reductions are key public health priorities. Methods We fit mechanistic transmission models to distributions of genotype clusters of TB cases reported to the Centers for Disease Control and Prevention during 2012–2016 in the United States and separately in California, Florida, New York, and Texas. We estimated the mean number of secondary cases generated per infectious case (R0) and individual-level heterogeneity in R0 at state and national levels and assessed how different definitions of clustering affected these estimates. Results In clusters of genotypically linked TB cases that occurred within a state over a 5-year period (reference scenario), the estimated R0 was 0.29 (95% confidence interval [CI], .28–.31) in the United States. Transmission was highly heterogeneous; 0.24% of simulated cases with individual R0 >10 generated 19% of all recent secondary transmissions. R0 estimate was 0.16 (95% CI, .15–.17) when a cluster was defined as cases occurring within the same county over a 3-year period. Transmission varied across states: estimated R0s were 0.34 (95% CI, .3–.4) in California, 0.28 (95% CI, .24–.36) in Florida, 0.19 (95% CI, .15–.27) in New York, and 0.38 (95% CI, .33–.46) in Texas. Conclusions TB transmission in the United States is characterized by pronounced heterogeneity at the individual and state levels. Improving detection of transmission clusters through incorporation of whole-genome sequencing and identifying the drivers of this heterogeneity will be essential to reducing TB transmission.

BackgroundIn rapidly growing, high burden urban centers, identifying tuberculosis (TB) transmission hotspots and understanding potential impact of interventions can inform future control and prevention strategies.MethodsUsing data on local demography, TB reports, and patient reporting patterns in Dhaka South City Corporation (DSCC) and Dhaka North City Corporation (DNCC), Bangladesh, between 2010 and 2017, we developed maps of TB reporting rates across wards in DSCC and DNCC and identified wards with high rates of reported TB (i.e. “hotspots”) in DSCC and DNCC. We developed ward-level transmission models and estimated the potential epidemiological impact of three TB interventions: active case finding (ACF), mass preventive therapy (PT), and a combination of ACF and PT, implemented either citywide or targeted to high-incidence hotspots.ResultsThere was substantial geographic heterogeneity in the estimated TB incidence in both DSCC and DNCC: incidence in the highest-incidence wards was over ten times higher than in the lowest-incidence wards in each city corporation. ACF, PT and combined ACF plus PT delivered to 10% of the population reduced TB incidence by a projected 7-9%, 13-15%, and 19-23% over five years, respectively. Targeting TB hotspots increased the projected reduction in TB incidence achieved by each intervention 1.4- to 1.8-fold.ConclusionsThe geographical pattern of TB notifications suggests high levels of ongoing TB transmission in DSCC and DNCC, with substantial heterogeneity at the ward level. Interventions that reduce transmission are likely to be highly effective, and incorporating notification data at the local level can further improve intervention efficiency.

Subclinical tuberculosis includes disease forms that are detectable by radiographic or microbiological assays but do not cause recognizable symptoms. Population-based prevalence surveys demonstrate that the majority of individuals with culture-positive tuberculosis and corresponding radiographic abnormalities lack recognizable symptoms. Subclinical tuberculosis is often conceptualized as an early stage that generally progresses to recognizable active tuberculosis disease within months. However, many individuals with subclinical tuberculosis likely never develop recognizable symptoms, and consequently are never identified as having tuberculosis. A growing body of evidence indicates that, in high-burden settings, people with tuberculosis spend more time in a subclinical state than they spend with recognizable symptoms, and that people with subclinical tuberculosis may be infectious. As such, the subclinical period may represent a long window of potential Mycobacterium tuberculosis transmission. To appropriately prioritize interventions, there is a need to quantify the amount of transmission that occurs during the subclinical period, including the contribution of those who never develop clinically symptomatic disease. If individuals with subclinical tuberculosis are responsible for a large fraction of M. tuberculosis transmission, then ambitious reductions in tuberculosis incidence cannot be achieved without greater focus on early detection and treatment of subclinical tuberculosis. Novel diagnostic assays, population-based studies, and bioinformatic techniques could be used to better estimate the contribution of individuals with subclinical tuberculosis to overall transmission. This knowledge can help us understand whether finding and treating people with subclinical tuberculosis is essential or extraneous as part of a comprehensive strategy to end the epidemic.

Rationale: Mathematical modeling is used to understand disease dynamics, forecast trends, and inform public health prioritization. We conducted a comparative analysis of tuberculosis (TB) epidemiology and potential intervention effects in California, using three previously developed epidemiologic models of TB.Objectives: To compare the influence of various modeling methods and assumptions on epidemiologic projections of domestic latent TB infection (LTBI) control interventions in California.Methods: We compared model results between 2005 and 2050 under a base-case scenario representing current TB services and alternative scenarios including: 1) sustained interruption of Mycobacterium tuberculosis (Mtb) transmission, 2) sustained resolution of LTBI and TB prior to entry of new residents, and 3) one-time targeted testing and treatment of LTBI among 25% of non-U.S.-born individuals residing in California.Measurements and Main Results: Model estimates of TB cases and deaths in California were in close agreement over the historical period but diverged for LTBI prevalence and new Mtb infections-outcomes for which definitive data are unavailable. Between 2018 and 2050, models projected average annual declines of 0.58-1.42% in TB cases, without additional interventions. A one-time LTBI testing and treatment intervention among non-U.S.-born residents was projected to produce sustained reductions in TB incidence. Models found prevalent Mtb infection and migration to be more significant drivers of future TB incidence than local transmission.Conclusions: All models projected a stagnation in the decline of TB incidence, highlighting the need for additional interventions including greater access to LTBI diagnosis and treatment for non-U.S.-born individuals. Differences in model results reflect gaps in historical data and uncertainty in the trends of key parameters, demonstrating the need for high-quality, up-to-date data on TB determinants and outcomes.

The incidence of tuberculosis (TB) in the United States has stabilized, and additional interventions are needed to make progress toward TB elimination. However, the impact of such interventions depends on local demography and the heterogeneity of populations at risk. Using state-level individual-based TB transmission models calibrated to California, Florida, New York, and Texas, we modeled 2 TB interventions: 1) increased targeted testing and treatment (TTT) of high-risk populations, including people who are non–US-born, diabetic, human immunodeficiency virus (HIV)-positive, homeless, or incarcerated; and 2) enhanced contact investigation (ECI) for contacts of TB patients, including higher completion of preventive therapy. For each intervention, we projected reductions in active TB incidence over 10 years (2016–2026) and numbers needed to screen and treat in order to avert 1 case. We estimated that TTT delivered to half of the non–US-born adult population could lower TB incidence by 19.8%–26.7% over a 10-year period. TTT delivered to smaller populations with higher TB risk (e.g., HIV-positive persons, homeless persons) and ECI were generally more efficient but had less overall impact on incidence. TTT targeted to smaller, highest-risk populations and ECI can be highly efficient; however, major reductions in incidence will only be achieved by also targeting larger, moderate-risk populations. Ultimately, to eliminate TB in the United States, a combination of these approaches will be necessary.

Disease case definitions are important instruments for clinical care, interventional research, and surveillance. Therefore, it is concerning that the current case definitions for tuberculosis remain underscored by the classic paradigm of binary states of latent infection and active disease, with a stepwise, linear transition under which symptoms, bacteriological positivity, and disease pathology are assumed to emerge broadly together (figure, A).1 This assumption has resulted in a reliance on symptom screening to distinguish these two states. However, in recent prevalence surveys, 40–79% of bacteriologically positive tuberculosis occurs in the absence of patient-recognised tuberculosis symptoms.2 Rather than explicitly addressing this discordance, tuberculosis case definitions are often ambiguous regarding tuberculosis symptoms, or internally inconsistent.

Objectives. To illustrate the magnitude of between-state heterogeneities in tuberculosis (TB) incidence among US populations at high risk for TB that may help guide state-specific strategies for TB elimination. Methods. We used data from the National Tuberculosis Surveillance System and other public sources from 2011 to 2015 to calculate TB incidence in every US state among people who were non–US-born, had diabetes, or were HIV-positive, homeless, or incarcerated. We then estimated the proportion of TB cases that reflected the difference between each state’s reported risk factor–specific TB incidence and the lowest incidence achieved among 4 states (California, Florida, New York, Texas). We reported these differences for the 4 states and also calculated and aggregated across all 50 states to quantify the total percentage of TB cases nationally that reflected between-state differences in risk factor–specific TB incidence. Results. On average, 24% of recent TB incidence among high-risk US populations reflected heterogeneity at the state level. The populations that accounted for the greatest percentage of heterogeneity-reflective cases were non–US-born individuals (51%) and patients with diabetes (24%). Conclusions. State-level differences in TB incidence among key populations provide clues for targeting state-level interventions.

Although less well-recognized than for other infectious diseases, heterogeneity is a defining feature of tuberculosis (TB) epidemiology. To advance toward TB elimination, this heterogeneity must be better understood and addressed. Drivers of heterogeneity in TB epidemiology act at the level of the infectious host, organism, susceptible host, environment, and distal determinants. These effects may be amplified by social mixing patterns, while the variable latent period between infection and disease may mask heterogeneity in transmission. Reliance on notified cases may lead to misidentification of the most affected groups, as case detection is often poorest where prevalence is highest. Assuming that average rates apply across diverse groups and ignoring the effects of cohort selection may result in misunderstanding of the epidemic and the anticipated effects of control measures. Given this substantial heterogeneity, interventions targeting high-risk groups based on location, social determinants, or comorbidities could improve efficiency, but raise ethical and equity considerations., Heterogeneity in tuberculosis burden is driven by the organism, host, environment, and distal determinants. More reliable data are needed given inconsistent case ascertainment. Targeting high-risk groups is an important consideration in designing interventions, but raises equity and efficiency issues.

Summary Background After steady decline since the 1990s, tuberculosis incidence in New York City and throughout the USA has plateaued. We aimed to explore the major drivers of the flattening of tuberculosis incidence in New York City and to project the future trajectory of the tuberculosis epidemic in the absence of any additional intervention. Methods We developed a compartmental transmission model of tuberculosis in New York City. The model was parameterised with detailed epidemiological data and stratified by age and nativity (US-born vs foreign-born). We ran the model under five alternative scenarios representing different explanations for recent declines in tuberculosis incidence. We evaluated the relative likelihood of each scenario by comparing its output with available data. We used the most likely scenarios to explore potential mechanisms underlying the recent declines in tuberculosis in New York City and to describe the reasonable range of future epidemic trajectories. Our primary outcome was the projected rate of decline in tuberculosis incidence from 2015 to 2025. Model calibration yielded estimates of future disease incidence and reductions in incidence with 95% credible intervals (CrIs). Findings Demographic changes and declining tuberculosis transmission alone were insufficient to explain recent trends in tuberculosis incidence in New York City. Only scenarios that assumed contemporary changes in tuberculosis dynamics among foreign-born individuals—a declining rate of reactivation or a decrease in imported subclinical tuberculosis—could accurately describe the trajectory of disease incidence since 2007. In those scenarios, the projected decline in incidence from 2015 to 2025 varied from minimal (2·0% per year [95% CrI 0·4–3·5]) to similar to 2005 to 2009 trends (4·4% per year [2·5–6·4]). The primary factor differentiating optimistic from pessimistic projections was the degree to which improvements in tuberculosis dynamics among the foreign-born population continued into the coming decade. Interpretation Further progress towards elimination of tuberculosis in New York City requires additional focus on the foreign-born population. Without additional intervention in this group, tuberculosis incidence might not decline further. Funding None.

There is substantial state-to-state heterogeneity in tuberculosis (TB) in the United States; better understanding this heterogeneity can inform effective response to TB at the state level, the level at which most TB control efforts are coordinated.To characterize drivers of state-level heterogeneity in TB epidemiology in the four U.S. states that bear half the country's TB burden: California, Florida, New York, and Texas.We constructed an individual-based model of TB in the four U.S. states and calibrated the model to state-specific demographic and age- and nativity-stratified TB incidence data. We used the model to infer differences in natural history of TB and in future projections of TB.We found that differences in both demographic makeup (particularly the size and composition of the foreign-born population) and TB transmission dynamics contribute to state-level differences in TB epidemiology. The projected median annual rate of decline in TB incidence in the next decade was substantially higher in Texas (3.3%; 95% range, -5.6 to 10.9) than in California (1.7%; 95% range, -3.8 to 7.1), Florida (1.5%; 95% range, -7.4 to 14), and New York (1.9%; 95% range, -6.4 to 9.8). All scenarios projected a flattening of the decline in TB incidence by 2025 without additional resources or interventions.There is substantial state-level heterogeneity in TB epidemiology in the four states, which reflect both demographic factors and potential differences in the natural history of TB. These differences may inform resource allocation decisions in these states.

Optimizing the use of new tools, such as vaccines, may play a crucial role in reaching global targets for tuberculosis (TB) control. Some of the most promising candidate vaccines target adults, although high-coverage mass vaccinations may be logistically more challenging among this population than among children. Vaccine-delivery strategies that target high-risk groups or settings might yield proportionally greater impact than do those that target the general population. We developed an individual-based TB transmission model representing a hypothetical population consisting of people who worked in South African gold mines or lived in associated labor-sending communities. We simulated the implementation of a postinfection adult vaccine with 60% efficacy and a mean effect duration of 10 years. We then compared the impact of a mine-targeted vaccination strategy, in which miners were vaccinated while in the mines, with that of a community-targeted strategy, in which random individuals within the labor-sending communities were vaccinated. Mine-targeted vaccination averted an estimated 0.37 TB cases per vaccine dose compared with 0.25 for community-targeted vaccination, for a relative efficacy of 1.46 (95% range, 1.13-1.91). The added benefit of mine-targeted vaccination primarily reflected the disproportionate demographic burden of TB among the population of adult males as a whole. As novel vaccines for TB are developed, venue-based vaccine delivery that targets high-risk demographic groups may improve both vaccine feasibility and the impact on transmission.

Background Novel drug regimens are needed for tuberculosis (TB) treatment. New regimens aim to improve on characteristics such as duration, efficacy, and safety profile, but no single regimen is likely to be ideal in all respects. By linking these regimen characteristics to a novel regimen’s ability to reduce TB incidence and mortality, we sought to prioritize regimen characteristics from a population-level perspective. Methods and Findings We developed a dynamic transmission model of multi-strain TB epidemics in hypothetical populations reflective of the epidemiological situations in India (primary analysis), South Africa, the Philippines, and Brazil. We modeled the introduction of various novel rifampicin-susceptible (RS) or rifampicin-resistant (RR) TB regimens that differed on six characteristics, identified in consultation with a team of global experts: (1) efficacy, (2) duration, (3) ease of adherence, (4) medical contraindications, (5) barrier to resistance, and (6) baseline prevalence of resistance to the novel regimen. We compared scale-up of these regimens to a baseline reflective of continued standard of care. For our primary analysis situated in India, our model generated baseline TB incidence and mortality of 157 (95% uncertainty range [UR]: 113–187) and 16 (95% UR: 9–23) per 100,000 per year at the time of novel regimen introduction and RR TB incidence and mortality of 6 (95% UR: 4–10) and 0.6 (95% UR: 0.3–1.1) per 100,000 per year. An optimal RS TB regimen was projected to reduce 10-y TB incidence and mortality in the India-like scenario by 12% (95% UR: 6%–20%) and 11% (95% UR: 6%–20%), respectively, compared to current-care projections. An optimal RR TB regimen reduced RR TB incidence by an estimated 32% (95% UR: 18%–46%) and RR TB mortality by 30% (95% UR: 18%–44%). Efficacy was the greatest determinant of impact; compared to a novel regimen meeting all minimal targets only, increasing RS TB treatment efficacy from 94% to 99% reduced TB mortality by 6% (95% UR: 1%–13%, half the impact of a fully optimized regimen), and increasing the efficacy against RR TB from 76% to 94% lowered RR TB mortality by 13% (95% UR: 6%–23%). Reducing treatment duration or improving ease of adherence had smaller but still substantial impact: shortening RS TB treatment duration from 6 to 2 mo lowered TB mortality by 3% (95% UR: 1%–6%), and shortening RR TB treatment from 20 to 6 mo reduced RR TB mortality by 8% (95% UR: 4%–13%), while reducing nonadherence to the corresponding regimens by 50% reduced TB and RR TB mortality by 2% (95% UR: 1%–4%) and 6% (95% UR: 3%–10%), respectively. Limitations include sparse data on key model parameters and necessary simplifications to model structure and outcomes. Conclusions In designing clinical trials of novel TB regimens, investigators should consider that even small changes in treatment efficacy may have considerable impact on TB-related incidence and mortality. Other regimen improvements may still have important benefits for resource allocation and outcomes such as patient quality of life., Emily Kendall and colleagues simulate treatment regimens with differing characteristics and estimate the impacts on tuberculosis mortality and incidence., Author Summary Why Was This Study Done? Improvements in tuberculosis (TB) treatment are expected to play an important role in reaching the WHO goal of reducing TB deaths by 95%—that is, by more than 1.3 million deaths per year—between 2015 and 2035. Multiple aspects of existing treatment regimens have room for improvement, but it is unclear which types of improvement are most important. This study sought to prioritize different features of TB treatment regimens based on their potential to prevent TB deaths and new TB cases. What Did the Researchers Do and Find? The researchers developed a mathematical model to simulate the introduction of a novel regimen for the treatment of either drug-susceptible or multidrug-resistant (MDR) TB. They then compared regimens with different characteristics in terms of their ability to reduce TB deaths and new TB cases. Of the six characteristics modeled, regimen efficacy was the characteristic with the greatest potential to reduce TB cases and deaths, but other characteristics also had important effects (for example, shorter regimens could save health care resources). What Do These Findings Mean? It is important for developers of novel regimens to at least maintain the efficacy of existing regimens, and improvements in efficacy could prevent a large number of deaths. Other improvements, such as shorter duration and increased ease of adherence, may still have important effects by enabling more people with TB to receive appropriate and timely treatment.

Interactions arising from sequential viral and bacterial infections play important roles in the epidemiological outcome of many respiratory pathogens. Influenza virus has been implicated in the pathogenesis of several respiratory bacterial pathogens commonly associated with pneumonia. Though clinical evidence supporting this interaction is unambiguous, its population-level effects—magnitude, epidemiological impact and variation during pandemic and seasonal outbreaks—remain unclear. To address these unknowns, we used longitudinal influenza and pneumonia incidence data, at different spatial resolutions and across different epidemiological periods, to infer the nature, timing and the intensity of influenza-pneumonia interaction. We used a mechanistic transmission model within a likelihood-based inference framework to carry out formal hypothesis testing. Irrespective of the source of data examined, we found that influenza infection increases the risk of pneumonia by ~100-fold. We found no support for enhanced transmission or severity impact of the interaction. For model-validation, we challenged our fitted model to make out-of-sample pneumonia predictions during pandemic and non-pandemic periods. The consistency in our inference tests carried out on several distinct datasets and the predictive skill of our model increase confidence in our overall conclusion that influenza infection substantially enhances the risk of pneumonia, though only for a short period.

BACKGROUND: Drug resistance poses a serious challenge for the control of tuberculosis in many settings. It is well established that the expected future trend in resistance depends on the reproductive fitness of drug-resistant Mycobacterium tuberculosis. However, the variability in fitness between strains with different resistance-conferring mutations has been largely ignored when making these predictions. METHODS: We developed a novel approach for incorporating the variable fitness costs of drug resistance-conferring mutations and for tracking this distribution of fitness costs over time within a transmission model. We used this approach to describe the effects of realistic fitness cost distributions on the future prevalence of drug-resistant tuberculosis. RESULTS: The shape of the distribution of fitness costs was a strong predictor of the long-term prevalence of resistance. While, as expected, lower average fitness costs of drug resistance-conferring mutations were associated with more severe epidemics of drug-resistant tuberculosis, fitness distributions with greater variance also led to higher levels of drug resistance. For example, compared to simulations in which the fitness cost of resistance was fixed, introducing a realistic amount of variance resulted in a 40% increase in prevalence of drug-resistant tuberculosis after 20 years. CONCLUSIONS: The differences in the fitness costs associated with drug resistance-conferring mutations are a key determinant of the future burden of drug-resistant tuberculosis. Future studies that can better establish the range of fitness costs associated with drug resistance-conferring mutations will improve projections and thus facilitate better public health planning efforts.

Classical life-history theory predicts that acute, immunizing pathogens should maximize between-host transmission. When such pathogens induce violent epi- demic outbreaks, however, a pathogen's short-term advan- tage at invasion may come at the expense of its ability to persist in the population over the long term. Here, we seek to understand how the classical and invasion-persistence trade-offs interact to shape pathogen life-history evolution as a function of the size and structure of the host popu- lation. We develop an individual-based infection model at three distinct levels of organization: within an individual host, among hosts within a local population, and among local populations within a metapopulation. We find a con- tinuum of evolutionarily stable pathogen strategies. At one end of the spectrum—in large well-mixed populations— pathogens evolve to greater acuteness to maximize between- host transmission: the classical trade-off theory applies in this regime. At the other end of the spectrum—when the host population is broken into many small patches— selection favors less acute pathogens, which persist longer within a patch and thereby achieve enhanced between- patch transmission: the invasion-persistence trade-off dom- inates in this regime. Between these extremes, we explore the effects of the size and structure of the host popula- tion in determining pathogen strategy. In general, pathogen strategies respond to evolutionary pressures arising at both scales.

Background. New first-line drug regimens for treatment of tuberculosis (TB) are in clinical trials: emergence of resistance is a key concern. Because population-level data on resistance cannot be collected in advance, epidemiological models are important tools for understanding the drivers and dynamics of resistance before novel drug regimens are launched. Methods. We developed a transmission model of TB after launch of a new drug regimen, defining drug-resistant TB (DR-TB) as resistance to the new regimen. The model is characterized by (1) the probability of acquiring resistance during treatment, (2) the transmission fitness of DR-TB relative to drug-susceptible TB (DS-TB), and (3) the probability of treatment success for DR-TB versus DS-TB. We evaluate the effect of each factor on future DR-TB prevalence, defined as the proportion of incident TB that is drug-resistant. Results. Probability of acquired resistance was the strongest predictor of the DR-TB proportion in the first 5 years after the launch of a new drug regimen. Over a longer term, however, the DR-TB proportion was driven by the resistant population's transmission fitness and treatment success rates. Regardless of uncertainty in acquisition probability and transmission fitness, high levels (>10%) of drug resistance were unlikely to emerge within 50 years if, among all cases of TB that were detected, 85% of those with DR-TB could be appropriately diagnosed as such and then successfully treated. Conclusions. Short-term surveillance cannot predict long-term drug resistance trends after launch of novel first-line TB regimens. Ensuring high treatment success of drug-resistant TB through early diagnosis and appropriate second-line therapy can mitigate many epidemiological uncertainties and may substantially slow the emergence of drug-resistant TB.

As novel diagnostics, therapies, and algorithms are developed to improve case finding, diagnosis, and clinical management of patients with TB, policymakers must make difficult decisions and choose among multiple new technologies while operating under heavy resource constrained settings. Mathematical modelling can provide helpful insight by describing the types of interventions likely to maximize impact on the population level and highlighting those gaps in our current knowledge that are most important for making such assessments. This review discusses the major contributions of TB transmission models in general, namely, the ability to improve our understanding of the epidemiology of TB. We focus particularly on those elements that are important to appropriately understand the role of TB diagnosis and treatment (i.e., what elements of better diagnosis or treatment are likely to have greatest population-level impact) and yet remain poorly understood at present. It is essential for modellers, decision-makers, and epidemiologists alike to recognize these outstanding gaps in knowledge and understand their potential influence on model projections that may guide critical policy choices (e.g., investment and scale-up decisions).

A significant fraction of seasonal and in particular pandemic influenza deaths are attributed to secondary bacterial infections. In animal models, influenza virus predisposes hosts to severe infection with both Streptococcus pneumoniae and Staphylococcus aureus . Despite its importance, the mechanistic nature of the interaction between influenza and pneumococci, its dependence on the timing and sequence of infections as well as the clinical and epidemiological consequences remain unclear. We explore an immune-mediated model of the viral–bacterial interaction that quantifies the timing and the intensity of the interaction. Taking advantage of the wealth of knowledge gained from animal models, and the quantitative understanding of the kinetics of pathogen-specific immunological dynamics, we formulate a mathematical model for immune-mediated interaction between influenza virus and S. pneumoniae in the lungs. We use the model to examine the pathogenic effect of inoculum size and timing of pneumococcal invasion relative to influenza infection, as well as the efficacy of antivirals in preventing severe pneumococcal disease. We find that our model is able to capture the key features of the interaction observed in animal experiments. The model predicts that introduction of pneumococcal bacteria during a 4–6 day window following influenza infection results in invasive pneumonia at significantly lower inoculum size than in hosts not infected with influenza. Furthermore, we find that antiviral treatment administered later than 4 days after influenza infection was not able to prevent invasive pneumococcal disease. This work provides a quantitative framework to study interactions between influenza and pneumococci and has the potential to accurately quantify the interactions. Such quantitative understanding can form a basis for effective clinical care, public health policies and pandemic preparedness.

The association between influenza virus and the bacterium Streptococcus pneumoniae (pneumococcus) has been proposed as a polymicrobial system, whereby transmission and pathogenicity of one pathogen (the bacterium) are affected by interactions with the other (the virus). However, studies focusing on different scales of resolution have painted an inconsistent picture: Individual-scale animal experiments have unequivocally demonstrated an association, whereas epidemiological support in human populations is, at best, inconclusive. We integrate weekly incidence reports and a mechanistic transmission model within a likelihood-based inference framework to characterize the nature, timing, and magnitude of this interaction. We find support for a strong but short-lived interaction, with influenza infection increasing susceptibility to pneumococcal pneumonia ~100-fold. We infer modest population-level impacts arising from strong processes at the level of an individual, thereby resolving the dichotomy in seemingly inconsistent observations across scales. An accurate characterization of the influenza-pneumococcal interaction can form a basis for more effective clinical care and public health measures for pneumococcal pneumonia.

Some of the most promising vaccines in the pipeline for tuberculosis (TB) target adolescents and adults. Unlike for childhood vaccines, high-coverage population-wide vaccination is significantly more challenging for adult vaccines. Here, we aimed to estimate the impact of vaccine delivery strategies that were targeted to high-incidence geographical ‘hotspots’ compared with randomly allocated vaccination. We developed a spatially explicit mathematical model of TB transmission that distinguished these hotspots from the general population. We evaluated the impact of targeted and untargeted vaccine delivery strategies in India—a country that bears more than 25% of global TB burden, and may be a potential early adopter of the vaccine. We collected TB notification data and conducted a demonstration study in the state of Gujarat to validate our estimates of heterogeneity in TB incidence. We then projected the impact of randomly vaccinating 8% of adults in a single mass campaign to a spatially targeted vaccination preferentially delivered to 80% of adults in the hotspots, with both strategies augmented by continuous adolescent vaccination. In consultation with vaccine developers, we considered a vaccine efficacy of 60%, and evaluated the population-level impact after 10 years of vaccination. Spatial heterogeneity in TB notification (per 100 000/year) was modest in Gujarat: 190 in the hotspots versus 125 in the remaining population. At this level of heterogeneity, the spatially targeted vaccination was projected to reduce TB incidence by 28% after 10 years, compared with a 24% reduction projected to achieve via untargeted vaccination—a 1.17-fold augmentation in the impact of vaccination by spatially targeting. The degree of the augmentation was robust to reasonable variation in natural history assumptions, but depended strongly on the extent of spatial heterogeneity and mixing between the hotspot and general population. Identifying high-incidence hotspots and quantifying spatial mixing patterns are critical to accurate estimation of the value of targeted intervention strategies.

Discoveries made during the 1918 influenza A pandemic and reports of severe disease associated with coinfection during the 2009 hemagglutinin type 1 and neuraminidase type 1 (commonly known as H1N1 or swine flu) pandemic have renewed interest in the role of coinfection in disease pathogenesis. The authors assessed how various timings of coinfection with influenza virus and pneumonia-causing bacteria could affect the severity of illness at multiple levels of interaction, including the biologic and population levels. Animal studies most strongly support a single pathway of coinfection with influenza inoculation occurring approximately 7 days before inoculation with Streptococcus pneumoniae, but less-examined pathways of infection also may be important for human disease. The authors discussed the implications of each pathway for disease prevention and what they would expect to see at the population level if there were sufficient data available. Lastly, the authors identified crucial gaps in the study of timing of coinfection and proposed related research questions. community-acquired infections; influenza, human; pneumococcal infections; pneumonia

Climate warming over the next century is expected to have a large impact on the interactions between pathogens and their animal and human hosts. Vector-borne diseases are particularly sensitive to warming because temperature changes can alter vector development rates, shift their geographical distribution and alter transmission dynamics. For this reason, African trypanosomiasis (sleeping sickness), a vector-borne disease of humans and animals, was recently identified as one of the 12 infectious diseases likely to spread owing to climate change. We combine a variety of direct effects of temperature on vector ecology, vector biology and vector–parasite interactions via a disease transmission model and extrapolate the potential compounding effects of projected warming on the epidemiology of African trypanosomiasis. The model predicts that epidemics can occur when mean temperatures are between 20.7°C and 26.1°C. Our model does not predict a large-range expansion, but rather a large shift of up to 60 per cent in the geographical extent of the range. The model also predicts that 46–77 million additional people may be at risk of exposure by 2090. Future research could expand our analysis to include other environmental factors that influence tsetse populations and disease transmission such as humidity, as well as changes to human, livestock and wildlife distributions. The modelling approach presented here provides a framework for using the climate-sensitive aspects of vector and pathogen biology to predict changes in disease prevalence and risk owing to climate change.

There is growing interest in understanding the nature and consequences of interactions among infectious agents. Pathogen interactions can be operational at different scales, either within a co-infected host or in host populations where they co-circulate, and can be either cooperative or competitive. The detection of interactions among pathogens has typically involved the study of synchrony in the oscillations of the protagonists, but as we show here, phase association provides an unreliable dynamical fingerprint for this task. We assess the capacity of a likelihood-based inference framework to accurately detect and quantify the presence and nature of pathogen interactions on the basis of realistic amounts and kinds of simulated data. We show that when epidemiological and demographic processes are well understood, noisy time series data can contain sufficient information to allow correct inference of interactions in multi-pathogen systems. The inference power is dependent on the strength and time-course of the underlying mechanism: stronger and longer-lasting interactions are more easily and more precisely quantified. We examine the limitations of our approach to stochastic temporal variation, under-reporting, and over-aggregation of data. We propose that likelihood shows promise as a basis for detection and quantification of the effects of pathogen interactions and the determination of their (competitive or cooperative) nature on the basis of population-level time-series data., Author Summary It is becoming increasingly evident that pathogens associated with infectious diseases interact amongst themselves. Pathogen interactions can occur in a co-infected host, or in host populations where they co-circulate, and they can be cooperative or competitive. Four serotypes of dengue virus, for example, can exhibit both forms of interactions – cross protection for a temporary period and followed by long-lasting enhancement. This bears important consequences for understanding the ecology and developing control and prevention measures. Detecting such interactions in a natural host population, though, can be tricky. We show that studying the phase relation of epidemic cycles, as it has been typically done, is unreliable. We assess the ability of a likelihood based method in detecting such interactions, and find that they are accurate and robust. We propose that this framework shows promise of serving as a basis for detecting and quantifying pathogen interactions.

We seek to understand the conditions favoring the evo- lution of acute, highly transmissible infections. Most work on the life-history evolution of pathogens has focused on the transmission- virulence trade-off. Here we focus on a distinct trade-off that op- erates, even among avirulent pathogens, between a pathogen's speed of invasion and its ability to persist in a finite host population. Other authors have shown how this invasion-persistence trade-off can lead to intermediate pathogen attack rates but have done so only by imposing trade-offs between the pathogen's transmissibility and the duration of the infectious period. Here we delve deeper, by linking a model of within-host pathogen dynamics—in which pathogen life- history parameters figure directly—to an epidemiological model at the population level. We find that a key determinant of the evolu- tionary trajectory is the shape of the dose-response curve that relates within-host pathogen load to between-host transmission. In partic- ular, under the usual assumption of proportionality we find that pathogens tend to evolve to the edge of their own extinction. Under more realistic assumptions, a critical host population size exists, above which highly acute pathogens are buffered from extinction. Our study is motivated by the emergence of acuteness in two human pathogens, Bordetella pertussis and Bordetella parapertussis, which in- dependently evolved from an ancestor, Bordetella bronchiseptica, char- acterized by chronic (nonacute) infection of wildlife. In contrast to the plethora of models that predict evolution of more aggressive pathogens in larger or denser populations, the invasion-persistence trade-off also operates for frequency-dependent pathogens.

Dengue, a mosquito-borne virus of humans, infects over 50 million people annually. Infection with any of the four dengue serotypes induces protective immunity to that serotype, but does not confer long-term protection against infection by other serotypes. The immunological interactions between serotypes are of central importance in understanding epidemiological dynamics and anticipating the impact of dengue vaccines. We analysed a 38-year time series with 12 197 serotyped dengue infections from a hospital in Bangkok, Thailand. Using novel mechanistic models to represent different hypothesized immune interactions between serotypes, we found strong evidence that infection with dengue provides substantial short-term cross-protection against other serotypes (approx. 1–3 years). This is the first quantitative evidence that short-term cross-protection exists since human experimental infection studies performed in the 1950s. These findings will impact strategies for designing dengue vaccine studies, future multi-strain modelling efforts, and our understanding of evolutionary pressures in multi-strain disease systems.