Exogenous re-infection and the dynamics of tuberculosis epidemics: local effects in a network model of transmission

Mathematical modelling, analysis and numerical simulation of social media addiction and depression

We formulate a mathematical model of social media addiction and depression (SMAD) in this study. Key aspects, such as social media addiction and depression disease-free equilibrium point (SMADDFEP), social media addiction and depression endemic equilibrium point (SMADEEP), and basic reproduction number (R0), have been analyzed qualitatively. The results indicate that if R0 < 1, the SMADDFEP is locally asymptotically stable. The global asymptotic stability of the SMADDFEP has been established using the Castillo-Chavez theorem. On the other hand, if R0 > 1, the unique endemic equilibrium point (SMADEEP) is locally asymptotically stable by Lyapunov theorem, and the model exhibits a forward bifurcation at R0 = 1 according to the Center Manifold theorem. To examine the model's sensitivity, we calculated the normalized forward sensitivity index and conducted a Partial Rank Correlation Coefficient (PRCC) analysis to describe the influence of parameters on the SMAD. The numerical results obtained using the Fourth-order Runge-Kutta (RK-4) scheme show that increasing the number of addicted individuals leads to an increase in the number of depressed individuals.

Exploring Hopf-bifurcations and endemic bubbles in a tuberculosis model with behavioral changes and treatment saturation

To manage risks and minimize the transmission of contagious diseases, individuals may reduce their contact with each other and take other precautions as much as possible in their daily lives and workplaces. As a result, the transmission of the infection reduces due to the behavioral changes. These behavioral changes are incorporated into models by introducing saturation in disease incidence. In this article, we propose and analyze a tuberculosis model that incorporates saturated exogenous reinfection and treatment. The stability analysis of the model's steady states is rigorously examined. We observe that the disease-free equilibrium point and the endemic equilibrium point (EEP) are globally asymptotically stable if the basic reproduction number (R0) is less than 1 and greater than 1, respectively, only when exogenous reinfection is not present (p=0) and when treatment is available for all (ω=0). However, even when R0 is less than 1, tuberculosis may persist at a specific level in the presence of exogenous reinfection and treatment saturation, leading to a backward bifurcation in the system. The existence and direction of Hopf-bifurcations are also discussed. Furthermore, we numerically validate our analytical results using different parameter sets. In the numerical examples, we study Hopf-bifurcations for parameters such as β, p, α, and ω. In one example, we observe that increasing β leads to the loss of stability of the unique EEP through a forward Hopf-bifurcation. If β is further increased, the unique EEP restores its stability, and the bifurcation diagram exhibits an interesting structure known as an endemic bubble. The existence of an endemic bubble for the saturation constant ω is also observed.

Geographical evolutionary pathway of global tuberculosis incidence trends

BACKGROUNDS

Tuberculosis (TB) remains a serious public health and human development problem, especially in developing countries. Despite the effectiveness of directly observed therapy, short course programs in reducing transmission and progression of TB, poverty reduction and socioeconomic development remain crucial factors in decreasing TB incidence. However, the geographical pathway on the planet is not yet clear.

OBJECTIVES

This study was to reconstruct the geographical evolutionary process of TB in 173 countries and territories from 2010 to 2019 to analyze the socioeconomic determinants that impact the global TB epidemic. In addition, the TB incidence in 2030 was predicted.

METHODS

This study analyses TB incidence data from 173 countries and territories between 2010 and 2019. The Geotree model would be used to reconstruct the geographical evolutionary process of TB, which provides a simplified schema for geo-visualizing the trajectories of TB incidence and their socioeconomic drivers. Additionally, to estimate the future TB incidence in 2030, a multilevel model was utilized in conjunction with the hierarchical nature of the Geotree based on a stratified heterogeneity analysis.

RESULTS

Global TB incidence was found to be associated with the country type and development stages. Between 2010 and 2019, the average TB incidence rate in 173 countries and territories was -27.48%, with marked spatially stratified heterogeneity by country type and development stage. Low-income and lower-middle-income countries were most vulnerable to TB. Upper-middle-income countries experienced a faster decline in TB incidence than high-income countries, and TB incidence generally decreased as the development stage increased, except for the lower-middle development stage in 2019.The highest average rate of decline in TB incidence was observed in the upper-middle development stage of high-income countries, with a reduction of 45.24%. Meanwhile, 37 high-income countries in the high development stage demonstrated an average rate of change of -13.93%. Socioeconomic determinants, including gross domestic product per capita, urbanization rate, and sociodemographic index, were found to inhibit TB incidence. Based on current trends, the predicted average global TB incidence in 2030 is 91.581 per 100,000 population.

CONCLUSIONS

The trajectories of the global TB incidence have been reconstructed to formulate targeted public health responses. To eliminate TB, countries at similar development stage can draw on the experiences of countries at higher development stages that are tailored to their unique characteristics. By learning from successful TB control strategies, countries can take strategic steps toward eradicating TB and improving public health outcomes.

Transmission Dynamics of Tuberculosis with Age-specific Disease Progression

Demographic structure and latent phenomenon are two essential factors determining the rate of tuberculosis transmission. However, only a few mathematical models considered age structure coupling with disease stages of infectious individuals. This paper develops a system of delay partial differential equations to model tuberculosis transmission in a heterogeneous population. The system considers demographic structure coupling with the continuous development of disease stage, which is crucial for studying how aging affects tuberculosis dynamics and disease progression. Here, we determine the basic reproduction number, and several numerical simulations are used to investigate the influence of various progression rates on tuberculosis dynamics. Our results support that the aging effect on the disease progression rate contributes to tuberculosis permanence.

Mapping local hot spots with routine tuberculosis data: A pragmatic approach to identify spatial variability

Objective

To use routinely collected data, with the addition of geographic information and census data, to identify local hot spots of rates of reported tuberculosis cases.

Design

Residential locations of tuberculosis cases identified from eight public health facilities in Lima, Peru (2013–2018) were linked to census data to calculate neighborhood-level annual case rates. Heat maps of tuberculosis case rates by neighborhood were created. Local indicators of spatial autocorrelation, Moran’s I, were used to identify where in the study area spatial clusters and outliers of tuberculosis case rates were occurring. Age- and sex-stratified case rates were also assessed.

Results

We identified reports of 1,295 TB cases across 74 neighborhoods during the five-year study period, for an average annual rate of 124.2 reported TB cases per 100,000 population. In evaluating case rates by individual neighborhood, we identified a median rate of reported cases of 123.6 and a range from 0 to 800 cases per 100,000 population. Individuals aged 15–44 years old and men had higher case rates than other age groups and women. Locations of both hot and cold spots overlapped across age- and gender-specific maps.

Conclusions

There is significant geographic heterogeneity in rates of reported TB cases and evident hot and cold spots within the study area. Characterization of the spatial distribution of these rates and local hot spots may be one practical tool to inform the work of local coalitions to target TB interventions in their zones.

A Cluster-based Method to Quantify Individual Heterogeneity in Tuberculosis Transmission

BACKGROUND

Recent evidence suggests transmission of Mycobacterium tuberculosis (Mtb) may be characterized by extreme individual heterogeneity in secondary cases (i.e., few cases account for the majority of transmission). Such heterogeneity implies outbreaks are rarer but more extensive and has profound implications in infectious disease control. However, discrete person-to-person transmission events in tuberculosis (TB) are often unobserved, precluding our ability to directly quantify individual heterogeneity in TB epidemiology.

METHODS

We used a modified negative binomial branching process model to quantify the extent of individual heterogeneity using only observed transmission cluster size distribution data (i.e., the simple sum of all cases in a transmission chain) without knowledge of individual-level transmission events. The negative binomial parameter k quantifies the extent of individual heterogeneity (generally, indicates extensive heterogeneity, and as transmission becomes more homogenous). We validated the robustness of the inference procedure considering common limitations affecting cluster size data. Finally, we demonstrate the epidemiologic utility of this method by applying it to aggregate US molecular surveillance data from the US Centers for Disease Control and Prevention.

RESULTS

The cluster-based method reliably inferred k using TB transmission cluster data despite a high degree of bias introduced into the model. We found that the TB transmission in the United States was characterized by a high propensity for extensive outbreaks (; 95% confidence interval = 0.09, 0.10).

CONCLUSIONS

The proposed method can accurately quantify critical parameters that govern TB transmission using simple, more easily obtainable cluster data to improve our understanding of TB epidemiology.

Analysis of COVID-19 and comorbidity co-infection model with optimal control

In this work, we develop and analyze a mathematical model for the dynamics of COVID-19 with re-infection in order to assess the impact of prior comorbidity (specifically, diabetes mellitus) on COVID-19 complications. The model is simulated using data relevant to the dynamics of the diseases in Lagos, Nigeria, making predictions for the attainment of peak periods in the presence or absence of comorbidity. The model is shown to undergo the phenomenon of backward bifurcation caused by the parameter accounting for increased susceptibility to COVID-19 infection by comorbid susceptibles as well as the rate of reinfection by those who have recovered from a previous COVID-19 infection. Simulations of the cumulative number of active cases (including those with comorbidity), at different reinfection rates, show infection peaks reducing with decreasing reinfection of those who have recovered from a previous COVID-19 infection. In addition, optimal control and cost-effectiveness analysis of the model reveal that the strategy that prevents COVID-19 infection by comorbid susceptibles is the most cost-effective of all the control strategies for the prevention of COVID-19.

A Mathematical Model of the Tuberculosis Epidemic

Tuberculosis has continued to retain its title as "the captain among these men of death". This is evident as it is the leading cause of death globally from a single infectious agent. TB as it is fondly called has become a major threat to the achievement of the sustainable development goals (SDG) and hence require inputs from different research disciplines. This work presents a mathematical model of tuberculosis. A compartmental model of seven classes was used in the model formulation comprising of the susceptible S, vaccinated V, exposed E, undiagnosed infectious I₁, diagnosed infectious I₂, treated T and recovered R. The stability analysis of the model was established as well as the condition for the model to undergo backward bifurcation. With the existence of backward bifurcation, keeping the basic reproduction number less than unity [Formula: see text] is no more sufficient to keep TB out of the community. Hence, it is shown by the analysis that vaccination program, diagnosis and treatment helps to control the TB dynamics. In furtherance to that, it is shown that preference should be given to diagnosis over treatment as diagnosis precedes treatment. It is as well shown that at lower vaccination rate (0-20%), TB would still be endemic in the population. As such, high vaccination rate is required to send TB out of the community.

Are neighbourhoods of tuberculosis cases a high-risk population for active intervention? A protocol for tuberculosis active case finding

BACKGROUND

Indonesia has the second largest tuberculosis (TB) burden globally. Attempts to scale-up TB control efforts have focused on TB households. However, in most high burden settings, considerable Mycobacterium tuberculosis (Mtb) transmission occurs outside TB households. A better understanding of transmission dynamics in an urban setting in Indonesia will be crucial for the TB Control Program in scaling up efforts towards elimination of TB in a more targeted way. Therefore, the study aims to measure TB prevalence and incidence in household contacts and neighbourhoods in the vicinity of known TB cases and to assess their genomic and epidemiological relatedness.

METHODS AND ANALYSIS

Individuals (~1000) living in the same household as a case diagnosed with pulmonary TB (n = 250) or in a neighbouring household (~4500 individuals) will be screened for TB symptoms and by chest x-ray. Two sputum samples will be collected for microbiological analysis from anyone with a productive cough. Any person found to have TB will be treated by the National TB Control Program. All those with no evidence of TB disease will have a repeat screen at 12 months. Whole-genome sequencing (WGS) and social network analysis (SNA) will be conducted on Index cases and contacts diagnosed with TB.

Predicting the Effectiveness of Endemic Infectious Disease Control Interventions: The Impact of Mass Action versus Network Model Structure

BACKGROUND

Analyses of the effectiveness of infectious disease control interventions often rely on dynamic transmission models to simulate intervention effects. We aim to understand how the choice of network or compartmental model can influence estimates of intervention effectiveness in the short and long term for an endemic disease with susceptible and infected states in which infection, once contracted, is lifelong.

METHODS

We consider 4 disease models with different permutations of socially connected network versus unstructured contact (mass-action mixing) model and heterogeneous versus homogeneous disease risk. The models have susceptible and infected populations calibrated to the same long-term equilibrium disease prevalence. We consider a simple intervention with varying levels of coverage and efficacy that reduces transmission probabilities. We measure the rate of prevalence decline over the first 365 d after the intervention, long-term equilibrium prevalence, and long-term effective reproduction ratio at equilibrium.

RESULTS

Prevalence declined up to 10% faster in homogeneous risk models than heterogeneous risk models. When the disease was not eradicated, the long-term equilibrium disease prevalence was higher in mass-action mixing models than in network models by 40% or more. This difference in long-term equilibrium prevalence between network versus mass-action mixing models was greater than that of heterogeneous versus homogeneous risk models (less than 30%); network models tended to have higher effective reproduction ratios than mass-action mixing models for given combinations of intervention coverage and efficacy.

CONCLUSIONS

For interventions with high efficacy and coverage, mass-action mixing models could provide a sufficient estimate of effectiveness, whereas for interventions with low efficacy and coverage, or interventions in which outcomes are measured over short time horizons, predictions from network and mass-action models diverge, highlighting the importance of sensitivity analyses on model structure.

HIGHLIGHTS

• We calibrate 4 models-socially connected network versus unstructured contact (mass-action mixing) model and heterogeneous versus homogeneous disease risk-to 10% preintervention disease prevalence.• We measure the short- and long-term intervention effectiveness of all models using the rate of prevalence decline, long-term equilibrium disease prevalence, and effective reproduction ratio.• Generally, in the short term, prevalence declined faster in the homogeneous risk models than in the heterogeneous risk models.• Generally, in the long term, equilibrium disease prevalence was higher in the mass-action mixing models than in the network models, and the effective reproduction ratio was higher in network models than in the mass-action mixing models.

Protective impacts of household-based tuberculosis contact tracing are robust across endemic incidence levels and community contact patterns

There is an emerging consensus that achieving global tuberculosis control targets will require more proactive case finding approaches than are currently used in high-incidence settings. Household contact tracing (HHCT), for which households of newly diagnosed cases are actively screened for additional infected individuals is a potentially efficient approach to finding new cases of tuberculosis, however randomized trials assessing the population-level effects of such interventions in settings with sustained community transmission have shown mixed results. One potential explanation for this is that household transmission is responsible for a variable proportion of population-level tuberculosis burden between settings. For example, transmission is more likely to occur in households in settings with a lower tuberculosis burden and where individuals mix preferentially in local areas, compared with settings with higher disease burden and more dispersed mixing. To better understand the relationship between endemic incidence levels, social mixing, and the impact of HHCT, we developed a spatially explicit model of coupled household and community transmission. We found that the impact of HHCT was robust across settings of varied incidence and community contact patterns. In contrast, we found that the effects of community contact tracing interventions were sensitive to community contact patterns. Our results suggest that the protective benefits of HHCT are robust and the benefits of this intervention are likely to be maintained across epidemiological settings.

An SVEIRE Model of Tuberculosis to Assess the Effect of an Imperfect Vaccine and Other Exogenous Factors

This study extends a deterministic mathematical model for the dynamics of tuberculosis transmission to examine the impact of an imperfect vaccine and other exogenous factors, such as re-infection among treated individuals and exogenous re-infection. The qualitative behaviors of the model are investigated, covering many distinct aspects of the transmission of the disease. The proposed model is observed to show a backward bifurcation, even when Rv<1. As such, we assume that diminishing Rv to less than unity is not effective for the elimination of tuberculosis. Furthermore, the results reveal that an imperfect tuberculosis vaccine is always effective at reducing the spread of infectious diseases within the population, though the general effect increases with the increase in effectiveness and coverage. In particular, it is shown that a limited portion of people being vaccinated at steady-state and vaccine efficacy assume a equivalent role in decreasing disease burden. From the numerical simulation, it is shown that using an imperfect vaccine lead to effective control of tuberculosis in a population, provided that the efficacy of the vaccine and its coverage are reasonably high.

A simulation–optimization framework for optimizing response strategies to epidemics☆

Epidemics require dynamic response strategies that encompass a multitude of policy alternatives and that balance health, economic and societal considerations. We propose a simulation–optimization framework to aid policymakers select closure, protection and travel policies to minimize the total number of infections under a limited budget. The proposed framework combines a modified, age-stratified SEIR compartmental model to evaluate the health impact of response strategies and a Genetic Algorithm to effectively search for better strategies. We implemented our framework on a real case study in Nova Scotia to devise optimized response strategies to COVID-19 under different budget scenarios and found a clear trade-off between health and economic considerations. Closure policies seem to be the most sensitive to policy restrictions, followed by travel policies. On the other hand, results suggest that practising social distancing and wearing masks are necessary whenever their economic impacts are bearable. The framework is generic and can be extended to encompass vaccination policies and to use different epidemiological models and optimization methods.

Optimal control and differential game solutions for social distancing in response to epidemics of infectious diseases on networks

In this paper, the problem of social distancing in the spread of infectious diseases in the human network is extended by optimal control and differential game approaches. Hear, SEAIR model on simulation network is used. Total costs for both approaches are formulated as objective functions. SEAIR dynamics for group k that contacts with k individuals including susceptible, exposed, asymptomatically infected, symptomatically infected and improved or safe individuals is modeled. A novel random model including the concept of social distancing and relative risk of infection using Markov process is proposed. For each group, an aggregate investment is derived and computed using adjoint equations and maximum principle. Results show that for each group, investments in the differential game are less than investments in an optimal control approach. Although individuals' participation in investment for social distancing causes to reduce the epidemic cost, the epidemic cost according to the second approach is too much less than the first approach.

Investigating extradomiciliary transmission of tuberculosis: An exploratory approach using social network patterns of TB cases and controls and the genotyping of Mycobacterium tuberculosis

Extradomiciliary contacts have been overlooked in the study of TB transmission due to difficulties in identifying actual contacts in large populations. Complex network analysis provides a framework to model the structure of contacts, specially extradomiciliary ones. We conducted a study of incident sputum-positive TB cases and healthy controls occurring in a moderate TB burden city. Cases and controls were interviewed to obtain data regarding the usual locations of residence, work, study, and leisure. Mycobacterium tuberculosis isolated from sputum was genotyped. The collected data were used to build networks based on a framework of putative social interactions indicating possible TB transmission. A user-friendly open source environment (GraphTube) was setup to extract information from the collected data. Networks based on the likelihood of patient-patient, patient-healthy, and healthy-healthy contacts were setup, depending on a constraint of geographical distance of places attended by the volunteers. Using a threshold for the geographical distance of 300 m, the differences between TB cases and controls are revealed. Several clusters formed by social network nodes with high genotypic similarity were characterized. The developed framework provided consistent results and can be used to support the targeted search of potentially infected individuals and to help to understand the TB transmission.

The Importance of Heterogeneity to the Epidemiology of Tuberculosis

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.

Reconstructing the population dynamics of foreign residents in Japan to estimate the prevalence of infection with Mycobacterium tuberculosis

Among newly notified tuberculosis cases in Japan, both the number and the proportion of foreign-born cases have steadily increased over time. As Japan prepares to introduce pre-entry tuberculosis screening for foreign-born persons entering Japan, various epidemiological evidence is needed to evaluate its effectiveness, including the prevalence of tuberculosis among current foreign residents in Japan, by country of birth. Yet as of today, even the underlying population dynamics has yet to be quantified. The present study therefore aimed to firstly reconstruct the demographic prevalence of foreign residents by the length of stay in Japan and by country of birth, and secondly, to estimate the prevalence of infection from notification data among foreign residents in Japan. We employed the McKendrick partial differential equation model to reconstruct the dynamics among six Asian countries which account for 80% of foreign-born tuberculosis patients notified in Japan i.e. China, the Philippines, Vietnam, Nepal, Indonesia, and Myanmar. Compared with China and the Philippines, the recent remarkable increase in the number of residents who had arrived within 5 years from Myanmar and Vietnam was identified. Assuming that the risk of primary tuberculosis given infection is 5%, the estimated prevalence of infection ranged from 3.5% to 21.3%, and all the estimates were more than three times greater than the crude estimate that ignored the time since immigration. The proposed method may be used to further estimate the prevalence by age, sex and residential status, which could potentially provide critical evidence towards establishing policies to control tuberculosis among foreign-born persons in Japan, and also possibly among migrants globally.

Nonlinear adaptive control of tuberculosis with consideration of the risk of endogenous reactivation and exogenous reinfection

Tuberculosis is one of deadly diseases in many countries that attacks to the human body and causes damage to the lung, causing bloody coughing and if left untreated, it will kill half of the affected people. Tuberculosis bacteria can stay latent and reactivate after passing appropriate conditions. For this reason, control of this disease and treatment of infected people has a significant importance, and observing health issues can prevent the spread of it. In this paper, a nonlinear adaptive control method is proposed for the first time in order to control and treat tuberculosis outbreak subjected to the modeling uncertainty. To design a control system being robust against uncertainties, an adaptation law is defined to update values of estimated parameters and ensures the whole system stability. The treatment achievement and stability of the closed-loop system is proved by the Lyapunov theorem and confirmed by some simulations. The proposed strategy has the capability to control the tuberculosis outbreak by reducing the numbers of active infectious and persistent latent individuals based on their desired values in the society.

Mathematical analysis of a tuberculosis model with imperfect vaccine

Since 1921, the Bacille Calmette–Guerin (BCG) vaccine continues to be the most widely used vaccine for the prevention of Tuberculosis (TB). However, the immunity induced by BCG wanes out after some time making the vaccinated individual susceptible to TB infection. In this work, we formulate a mathematical model that incorporates the vaccination of newly born children and older susceptible individuals in the transmission dynamics of TB in a population, with a vaccine that can confer protection on older susceptible individuals. In the absence of disease-induced deaths, the model is shown to undergo the phenomenon of backward bifurcation where a stable disease-free equilibrium (DFE) co-exists with a stable positive (endemic) equilibrium when the associated reproduction number is less than unity. It is shown that this phenomenon does not exist in the absence of imperfect vaccine, exogenous reinfection, and reinfection of previously treated individuals. It is further shown that a special case of the model has a unique endemic equilibrium point (EEP), which is globally asymptotically stable when the associated reproduction number exceeds unity. Uncertainty and sensitivity analysis are carried out to identify key parameters that have the greatest influence on the transmission dynamics of TB in the population using the total population of latently infected individuals, total number of actively infected individuals, disease incidence, and the effective reproduction number as output responses. The analysis shows that the top five parameters of the model that have the greatest influence on the effective reproduction number of the model are the transmission rate, the fraction of fast disease progression, modification parameter which accounts for reduced likelihood to infection by vaccinated individuals due to imperfect vaccine, rate of progression from latent to active TB, and the treatment rate of actively infected individuals, with other key parameters influencing the outcomes of the other output responses. Numerical simulations suggest that with higher vaccination rate of older susceptible individuals, fewer new born children need to be vaccinated, in order to achieve disease eradication.

Effect of mixed infection on TB dynamics

Poor living conditions, overcrowding and strain diversity are some of the factors that influence mixed infection in Tuberculosis (TB) at the population level. We formulate a mathematical model for mixed infection in TB using nonlinear ordinary differential equations where such factors were represented as probabilities of acquiring mixed infection. A qualitative analysis of the model shows that it exhibits multiple endemic equilibria and backward bifurcation for certain parameter values. The reactivation rate and transmission rate of individuals with mixed infection were of importance as well as the probabilities for latent individuals to acquire mixed infection. We calculate the prevalence of mixed infection from the model and the effect of mixed infection on TB incidence, TB prevalence and Mycobacterium tuberculosis (MTB) infection rate. Numerical simulations show that mixed infection may explain high TB incidences in areas which have a high strain diversity, poor living conditions and are overcrowded even without HIV.

Mathematical modelling of the impact of treating latent tuberculosis infection in the elderly in a city with intermediate tuberculosis burden

Hong Kong is a high-income city with intermediate tuberculosis (TB) burden primarily driven by endogenous reactivations. A high proportion of remote latently infected people, particularly elderly, hinders the effectiveness of current strategies focusing on passive TB detection. In this study, we developed a mathematical model to evaluate the impact of treating latent TB infection (LTBI) in the elderly in addition to current TB control strategies. The model was calibrated using the annual age-stratified TB notifications from 1965-2013 in Hong Kong. Our results showed that at present, approximately 75% of annual new notifications were from reactivations. Given the present treatment completion rate, even if only a low to moderate proportion (approximately 20% to 40%) of elderly people were screened and treated for LTBI, the overall TB incidence could be reduced by almost 50%, to reach the 2025 milestone of the global End TB Strategy. Nevertheless, due to a high risk of hepatotoxicity in elderly population, benefit-risk ratios were mostly below unity; thus, intervention programs should be carefully formulated, including prioritising LTBI treatment for high-risk elderly groups who are closely monitored for possible adverse side effects.

Modelling heterogeneity in host susceptibility to tuberculosis and its effect on public health interventions

A tuberculosis (TB) model that accounts for heterogeneity in host susceptibility to tuberculosis is proposed, with the aim of investigating the implications this may have for the effectiveness of public health interventions. The model examines the possibility that recovered individuals treated from active TB and individuals treated with preventive therapy acquire different levels of immunity. This contrasts with recent studies that assume the two cohorts acquire the same level of immunity, and therefore both groups are reinfected at the same rate. The analysis presented here examines the impact of this assumption when designing intervention strategies. Comparison of reinfection rates between cohorts treated with preventive therapy and recovered individuals who were previously treated for active TB provides important epidemiological insights. It is found that the reinfection rate of the cohort treated with preventive therapy is the one that plays the key role in qualitative changes in TB dynamics. By contrast, the reinfection rate of recovered individuals (previously treated from active TB) plays a minor role. Moreover, the study shows that preventive treatment of individuals during early latency is always beneficial regardless of the level of susceptibility to reinfection. Further, if patients have greater immunity following treatment for late latent infection, then treatment is again beneficial. However, if susceptibility increases following treatment for late latent infection, the effect of treatment depends on the epidemiological setting. That is: (i) in (very) low burden settings, the effect on reactivation predominates and the burden declines with treatment; (ii) in moderate to high burden settings the effect of reinfection predominates and burden increases with treatment. The effect is most dominant between the two reinfection thresholds, RT2 and RT1, respectively associated with individuals being treated with preventive therapy and individuals with untreated late latent TB infection.

Dynamics and Determinants of HPV Infection: The Michigan HPV and Oropharyngeal Cancer (M-HOC) Study

INTRODUCTION

Human papillomavirus (HPV) is the primary cause of cervical and other anogenital cancers and is also associated with head and neck cancers. Incidence of HPV-related oropharyngeal squamous cell cancers (OPSCCs) is increasing, and HPV-related OPSCCs have surpassed cervical cancer as the most common HPV-related cancer in the USA. Given the multisite nature of HPV, there is strong interest in collecting data from both genital and oral sites, as well as associated data on social and sexual behaviours. The overarching goal of this study is to evaluate patterns of oral HPV infection incidence, clearance and persistence and their relationship to sexual behaviour history.

METHODS AND ANALYSIS

Participants are recruited from two populations: college students at a large public university and general population from the surrounding area. At the first study visit, participants complete a detailed sexual history, health and behaviour questionnaire. Follow-up visits occur every 3-4 months over 3 years, when participants complete an abbreviated questionnaire. All participants provide a saliva sample at each visit, and eligible participants may provide a cervicovaginal self-swab. Genetic material isolated from specimens is tested for 15 high-risk and 3 low-risk HPV types. Statistical analyses will examine outcome variables including HPV prevalence, incidence, persistence and clearance. Logistic regression models will be used to estimate odds ratios and 95% confidence intervals for associations between the outcomes of interest and demographic/behavioural variables collected in the questionnaires. The longitudinal HPV infection data and detailed sexual history data collected in the questionnaires will allow us to develop individual-based network models of HPV transmission and will be used to parameterise multiscale models of HPV-related OPSC carcinogenesis.

ETHICS AND DISSEMINATION

This study has been approved by the University of Michigan Institutional Review Board. All participants are consented in person by trained study staff. Study results will be disseminated through peer-reviewed publications.

Data Analysis and Forecasting of Tuberculosis Prevalence Rates for Smart Healthcare Based on a Novel Combination Model

In recent years, healthcare has attracted much attention, which is looking for more and more data analytics in healthcare to relieve medical problems in medical staff shortage, ageing population, people living alone, and quality of life. Data mining, analysis, and forecasting play a vital role in modern social and medical fields. However, how to select a proper model to mine and analyze the relevant medical information in the data is not only an extremely challenging problem, but also a concerning problem. Tuberculosis remains a major global health problem despite recent and continued progress in prevention and treatment. There is no doubt that the effective analysis and accurate forecasting of global tuberculosis prevalence rates lay a solid foundation for the construction of an epidemic disease warning and monitoring system from a global perspective. In this paper, the tuberculosis prevalence rate time series for four World Bank income groups are targeted. Kruskal–Wallis analysis of variance and multiple comparison tests are conducted to determine whether the differences of tuberculosis prevalence rates for different income groups are statistically significant or not, and a novel combined forecasting model with its weights optimized by a recently developed artificial intelligence algorithm—cuckoo search—is proposed to forecast the hierarchical tuberculosis prevalence rates from 2013 to 2016. Numerical results show that the developed combination model is not only simple, but is also able to satisfactorily approximate the actual tuberculosis prevalence rate, and can be an effective tool in mining and analyzing big data in the medical field.

Progression from latent infection to active disease in dynamic tuberculosis transmission models: a systematic review of the validity of modelling assumptions

Mathematical modelling is commonly used to evaluate infectious disease control policy and is influential in shaping policy and budgets. Mathematical models necessarily make assumptions about disease natural history and, if these assumptions are not valid, the results of these studies can be biased. We did a systematic review of published tuberculosis transmission models to assess the validity of assumptions about progression to active disease after initial infection (PROSPERO ID CRD42016030009). We searched PubMed, Web of Science, Embase, Biosis, and Cochrane Library, and included studies from the earliest available date (Jan 1, 1962) to Aug 31, 2017. We identified 312 studies that met inclusion criteria. Predicted tuberculosis incidence varied widely across studies for each risk factor investigated. For population groups with no individual risk factors, annual incidence varied by several orders of magnitude, and 20-year cumulative incidence ranged from close to 0% to 100%. A substantial proportion of modelled results were inconsistent with empirical evidence: for 10-year cumulative incidence, 40% of modelled results were more than double or less than half the empirical estimates. These results demonstrate substantial disagreement between modelling studies on a central feature of tuberculosis natural history. Greater attention to reproducing known features of epidemiology would strengthen future tuberculosis modelling studies, and readers of modelling studies are recommended to assess how well those studies demonstrate their validity.

Tuberculosis control interventions targeted to previously treated people in a high-incidence setting: a modelling study

BACKGROUND

In high-incidence settings, recurrent disease among previously treated individuals contributes substantially to the burden of incident and prevalent tuberculosis. The extent to which interventions targeted to this high-risk group can improve tuberculosis control has not been established. We aimed to project the population-level effect of control interventions targeted to individuals with a history of previous tuberculosis treatment in a high-incidence setting.

METHODS

We developed a transmission-dynamic model of tuberculosis and HIV in a high-incidence setting with a population of roughly 40 000 people in suburban Cape Town, South Africa. The model was calibrated to data describing local demography, TB and HIV prevalence, TB case notifications and treatment outcomes using a Bayesian calibration approach. We projected the effect of annual targeted active case finding in all individuals who had previously completed tuberculosis treatment and targeted active case finding combined with lifelong secondary isoniazid preventive therapy. We estimated the effect of these targeted interventions on local tuberculosis incidence, prevalence, and mortality over a 10 year period (2016-25).

FINDINGS

We projected that, under current control efforts in this setting, the tuberculosis epidemic will remain in slow decline for at least the next decade. Additional interventions targeted to previously treated people could greatly accelerate these declines. We projected that annual targeted active case finding combined with secondary isoniazid preventive therapy in those who previously completed tuberculosis treatment would avert 40% (95% uncertainty interval [UI] 21-56) of incident tuberculosis cases and 41% (16-55) of tuberculosis deaths occurring between 2016 and 2025.

INTERPRETATION

In this high-incidence setting, the use of targeted active case finding in combination with secondary isoniazid preventive therapy in previously treated individuals could accelerate decreases in tuberculosis morbidity and mortality. Studies to measure cost and resource implications are needed to establish the feasibility of this type of targeted approach for improving tuberculosis control in settings with high tuberculosis and HIV prevalence.

FUNDING

National Institutes of Health, German Research Foundation.

Mixed infections in tuberculosis: The missing part in a puzzle

The mixed strains infection phenomenon is a major problem posing serious challenges in control of tuberculosis (TB). In patients with mixed infection, several different strains of Mycobacterium tuberculosis can be isolated simultaneously. Although different genotyping methods and various molecular approaches can be employed for detection of mixed infection in clinical samples, the MIRU-VNTR technique is more sensitive with higher discriminative power than many widely used techniques. Furthermore, the recent introduction of whole genome sequencing (WGS) promises to reveal more details about mixed infection with high resolution. WGS has been used for detection of mixed infection with high sensitivity and discriminatory, but the technology is currently limited to developed countries. Mixed infection may involve strains with different susceptibility patterns, which may alter the treatment outcome. In this report, we review the current concepts of mixed strains infection and also infection involving strains with a different susceptibility pattern in TB. We evaluate the importance of identifying mixed infection for diagnosis as well as treatment and highlight the accuracy and clinical utility of direct genotyping of clinical specimens.

Seasonality of active tuberculosis notification from 2005 to 2014 in Xinjiang, China

OBJECTIVES

Xinjiang is one of the highest TB-burdened provinces of China. A time-series analysis was conducted to evaluate the trend, seasonality of active TB in Xinjiang, and explore the underlying mechanism of TB seasonality by comparing the seasonal variations of different subgroups.

METHODS

Monthly active TB cases from 2005 to 2014 in Xinjiang were analyzed by the X-12-ARIMA seasonal adjustment program. Seasonal amplitude (SA) was calculated and compared within the subgroups.

RESULTS

A total of 277,300 confirmed active TB cases were notified from 2005 to 2014 in Xinjiang, China, with a monthly average of 2311±577. The seasonality of active TB notification was peaked in March and troughed in October, with a decreasing SA trend. The annual 77.31% SA indicated an annual mean of additional TB cases diagnosed in March as compared to October. The 0-14-year-old group had significantly higher SA than 15-44-year-old group (P<0.05). Students had the highest SA, followed by herder and migrant workers (P<0.05). The pleural TB cases had significantly higher SA than the pulmonary cases (P <0.05). Significant associations were not observed between SA and sex, ethnic group, regions, the result of sputum smear microcopy, and treatment history (P>0.05).

CONCLUSION

TB notification in Xinjiang shows an apparent seasonal variation with a peak in March and trough in October. For the underlying mechanism of TB seasonality, our results hypothesize that winter indoor crowding increases the risk of TB transmission, and seasonality was mainly influenced by the recent exogenous infection rather than the endogenous reactivation.

Network theory may explain the vulnerability of medieval human settlements to the Black Death pandemic

Epidemics can spread across large regions becoming pandemics by flowing along transportation and social networks. Two network attributes, transitivity (when a node is connected to two other nodes that are also directly connected between them) and centrality (the number and intensity of connections with the other nodes in the network), are widely associated with the dynamics of transmission of pathogens. Here we investigate how network centrality and transitivity influence vulnerability to diseases of human populations by examining one of the most devastating pandemic in human history, the fourteenth century plague pandemic called Black Death. We found that, after controlling for the city spatial location and the disease arrival time, cities with higher values of both centrality and transitivity were more severely affected by the plague. A simulation study indicates that this association was due to central cities with high transitivity undergo more exogenous re-infections. Our study provides an easy method to identify hotspots in epidemic networks. Focusing our effort in those vulnerable nodes may save time and resources by improving our ability of controlling deadly epidemics.

A patchy model for the transmission dynamics of tuberculosis in sub-Saharan Africa

Tuberculosis (TB) spreads through contact between a susceptible person and smear positive pulmonary TB case (TPM+). The spread of TB is highly dependent on people migration between cities or regions that may have different contact rates and different environmental parameters, leading to different disease spread speed in the population. In this work, a metapopulation model, i.e., networks of populations connected by migratory flows, which overcomes the assumption of homogeneous mixing between different regions was constructed. The TB model was combined to a simple demographic structure for the population living in a multi-patch environment (cities, towns, regions or countries). The model consist of a system of differential equations coupling TB epidemic at different strength and mobility between the patches. Constant recruitment rate, slow and fast progression to the disease, effective chemoprophylaxis, diagnostic and treatment are taken into account to make the model including the reality of people in the sub-Saharan African countries. The basic reproduction number ( R 0 ) was computed and it was demonstrated that the disease-free equilibrium is globally asymptotically stable ifR 0 < 1 . WhenR 0 > 1 , the disease-free equilibrium is unstable and there exists one endemic equilibrium. Moreover, the impact of increasing migration rate between patches on the TB spread was quantified using numerical implementation of the model. Using an example on 15 inter-connected patches on the same road, we demonstrated that most people was most likely to get infected if the disease starts in a patch in the middle than in border patches.

The role of mobility and health disparities on the transmission dynamics of Tuberculosis

Background

The transmission dynamics of Tuberculosis (TB) involve complex epidemiological and socio-economical interactions between individuals living in highly distinct regional conditions. The level of exogenous reinfection and first time infection rates within high-incidence settings may influence the impact of control programs on TB prevalence. The impact that effective population size and the distribution of individuals’ residence times in different patches have on TB transmission and control are studied using selected scenarios where risk is defined by the estimated or perceive first time infection and/or exogenous re-infection rates.

Methods

This study aims at enhancing the understanding of TB dynamics, within simplified, two patch, risk-defined environments, in the presence of short term mobility and variations in reinfection and infection rates via a mathematical model. The modeling framework captures the role of individuals’ ‘daily’ dynamics within and between places of residency, work or business via the average proportion of time spent in residence and as visitors to TB-risk environments (patches). As a result, the effective population size of Patch i (home of i-residents) at time t must account for visitors and residents of Patch i, at time t.

Results

The study identifies critical social behaviors mechanisms that can facilitate or eliminate TB infection in vulnerable populations. The results suggest that short-term mobility between heterogeneous patches contributes to significant overall increases in TB prevalence when risk is considered only in terms of direct new infection transmission, compared to the effect of exogenous reinfection. Although, the role of exogenous reinfection increases the risk that come from large movement of individuals, due to catastrophes or conflict, to TB-free areas.

Conclusions

The study highlights that allowing infected individuals to move from high to low TB prevalence areas (for example via the sharing of treatment and isolation facilities) may lead to a reduction in the total TB prevalence in the overall population. The higher the population size heterogeneity between distinct risk patches, the larger the benefit (low overall prevalence) under the same “traveling” patterns. Policies need to account for population specific factors (such as risks that are inherent with high levels of migration, local and regional mobility patterns, and first time infection rates) in order to be long lasting, effective and results in low number of drug resistant cases.

Cost-Effectiveness of Community-Based TB/HIV Screening and Linkage to Care in Rural South Africa

South Africa has one of the highest burdens of TB worldwide, driven by the country's widespread prevalence of HIV, and further complicated by drug resistance. Active case finding within the community, particularly in rural areas where healthcare access is limited, can significantly improve diagnosis and treatment coverage in high-incidence settings. We evaluated the potential health and economic consequences of implementing community-based TB/HIV screening and linkage to care. Using a dynamic model of TB and HIV transmission over a time horizon of 10 years, we compared status quo TB/HIV control to community-based TB/HIV screening at frequencies of once every two years, one year, and six months. We also considered the impact of extending IPT from 36 months for TST positive and 12 months for TST negative or unknown patients (36/12) to lifetime use for all HIV-infected patients. We conducted a probabilistic sensitivity analysis to assess the effect of parameter uncertainty on the cost-effectiveness results. We identified four strategies that saved the most life years for a given outlay: status quo TB/HIV control with 36/12 months of IPT and TB/HIV screening strategies at frequencies of once every two years, one year, and six months with lifetime IPT. All of these strategies were very cost-effective at a threshold of $6,618 per life year saved (the per capita GDP of South Africa). Community-based TB/HIV screening with linkage to care is therefore very cost-effective in rural South Africa.

Reactivation or reinfection in adult tuberculosis: Is that the question?

Looking at the chapter on "natural history" in any tuberculosis (TB) reference book, there is a kind of certainty regarding TB in adults. That is the concept of "post-primary" TB described as the reactivation of dormant bacilli hidden in an old lesion developed during infancy due to a type of local immunosuppression. Intriguingly, this concept involves at least two major uncertainties: how can dormant bacilli remain for such a long period, almost a lifetime, in an old lesion, taking into account granuloma dynamism; and what sort of local immunosuppression is the one that facilitates reactivation? The controversy between reactivation and exogenous reinfection as the cause of active TB started very soon in TB research. Interestingly, this "balance" was disturbed in the 1960s when the "Unitary Concept" became very successful in supporting the reactivation dogma. The "Unitary Concept" was mainly based on the data of tuberculin surveillance during the pre-antibiotic era as well as the data obtained from experimental modelling in animals. At the same time, the "Three-risks model" appeared to explain the relationship between the risk of infection and TB incidence, granting reinfection a key role in adult TB together with primary infection. This role was reinforced by the studies of recurrence based on molecular epidemiology, and a better knowledge of the immune response, granuloma dynamics, and lung physiology. Now it is a matter of taking it into account when designing new prophylactic and therapeutic strategies and also reflecting it in text books to better illustrate to our students.

Emergence of mixed infection of Beijing/Non-Beijing strains among multi-drug resistant Mycobacterium tuberculosis in Pakistan

Tuberculosis (TB) remains as one of the deadliest diseases after HIV globally with 95 % of deaths confined to low-and-middle income countries. Pakistan is fifth among the 22 high-burden TB countries with the incidence rate of 230/100,000 persons, however, studies related to prevalent Mycobacterium tuberculosis strains and their spread, drug resistance pattern and evolutionary genetics are inadequate. The present study was undertaken to highlight the circulation of M. tuberculosis strains causing drug resistant TB in our community by targeting the molecular marker IS6110 and then characterization of these strains as Beijing and Non-Beijing genotypes. Sputum samples from 102 MDR TB suspects from different cities of Punjab were collected and their record was stored in a database. Sputum samples were evaluated by Ziehl Neelson staining and cultured on Lownstein Jensen medium by Modified Petroff’s method. DST was performed for first-line anti-mycobacterial drugs by indirect proportion method. Mycobacterium tuberculosis isolates were investigated for the presence of IS6110 and further identification as Beijing, Non-Beijing or mixed genotype. Percentage of male and female patients was found to be 58.8 and 41.2 % respectively. DST showed resistance of 93 % of isolates to isoniazid and rifampicin. All of the isolates showed positive results for IS6110 amplification. Based on PCR amplification of Beijing and non-Beijing primer sets 4.9 % of the patients showed infection with pure Beijing isolates, 14.7 % with both Beijing and non-Beijing isolates and 80.3 % with pure non-Beijing isolates. Analysis of IS6110 and Beijing sequences showed the presence of putative transposase conserved domain while non-Beijing sequences were epitomized with RAMP_I_III superfamily domain (CRISPR-associated protein family). TB in Pakistan is predominantly caused by Non-Beijing genotypes, but Beijing strains showed incessant circulation in our community as both single and mixed (co-infecting Non-Beijing and Beijing) strains.

Homo-psychologicus: Reactionary behavioural aspects of epidemics

We formulate an in silico model of pathogen avoidance mechanism and investigate its impact on defensive behavioural measures (e.g., spontaneous social exclusions and distancing, crowd avoidance and voluntary vaccination adaptation). In particular, we use SIR(B)S (e.g., susceptible-infected-recovered with additional behavioural component) model to investigate the impact of homo-psychologicus aspects of epidemics. We focus on reactionary behavioural changes, which apply to both social distancing and voluntary vaccination participations. Our analyses reveal complex relationships between spontaneous and uncoordinated behavioural changes, the emergence of its contagion properties, and mitigation of infectious diseases. We find that the presence of effective behavioural changes can impede the persistence of disease. Furthermore, it was found that under perfect effective behavioural change, there are three regions in the response factor (e.g., imitation and/or reactionary) and behavioural scale factor (e.g., global/local) factors ρ-α behavioural space. Mainly, (1) disease is always endemic even in the presence of behavioural change, (2) behavioural-prevalence plasticity is observed and disease can sometimes be eradication, and (3) elimination of endemic disease under permanence of permanent behavioural change is achieved. These results suggest that preventive behavioural changes (e.g., non-pharmaceutical prophylactic measures, social distancing and exclusion, crowd avoidance) are influenced by individual differences in perception of risks and are a salient feature of epidemics. Additionally, these findings indicates that care needs to be taken when considering the effect of adaptive behavioural change in predicting the course of epidemics, and as well as the interpretation and development of the public health measures that account for spontaneous behavioural changes.

Impact of tuberculosis treatment length and adherence under different transmission intensities

Tuberculosis (TB) is a leading cause of human mortality due to infectious disease. Treatment default is a relevant factor which reduces therapeutic success and increases the risk of resistant TB. In this work we analyze the relation between treatment default and treatment length along with its consequence on the disease spreading. We use a stylized model structure to explore, systematically, the effects of varying treatment duration and compliance. We find that shortening treatment alone may not reduce TB prevalence, especially in regions where transmission intensity is high, indicating the necessity of complementing this action with increased compliance. A family of default functions relating the proportion of defaulters to the treatment length is considered and adjusted to a particular dataset. We find that the epidemiological benefits of shorter treatment regimens are tightly associated with increases in treatment compliance and depend on the epidemiological background.

Integrating Community-Based Interventions to Reverse the Convergent TB/HIV Epidemics in Rural South Africa

The WHO recommends integrating interventions to address the devastating TB/HIV co-epidemics in South Africa, yet integration has been poorly implemented and TB/HIV control efforts need strengthening. Identifying infected individuals is particularly difficult in rural settings. We used mathematical modeling to predict the impact of community-based, integrated TB/HIV case finding and additional control strategies on South Africa’s TB/HIV epidemics. We developed a model incorporating TB and HIV transmission to evaluate the effectiveness of integrating TB and HIV interventions in rural South Africa over 10 years. We modeled the impact of a novel screening program that integrates case finding for TB and HIV in the community, comparing it to status quo and recommended TB/HIV control strategies, including GeneXpert, MDR-TB treatment decentralization, improved first-line TB treatment cure rate, isoniazid preventive therapy, and expanded ART. Combining recommended interventions averted 27% of expected TB cases (95% CI 18–40%) 18% HIV (95% CI 13–24%), 60% MDR-TB (95% CI 34–83%), 69% XDR-TB (95% CI 34–90%), and 16% TB/HIV deaths (95% CI 12–29). Supplementing these interventions with annual community-based TB/HIV case finding averted a further 17% of TB cases (44% total; 95% CI 31–56%), 5% HIV (23% total; 95% CI 17–29%), 8% MDR-TB (68% total; 95% CI 40–88%), 4% XDR-TB (73% total; 95% CI 38–91%), and 8% TB/HIV deaths (24% total; 95% CI 16–39%). In addition to increasing screening frequency, we found that improving TB symptom questionnaire sensitivity, second-line TB treatment delays, default before initiating TB treatment or ART, and second-line TB drug efficacy were significantly associated with even greater reductions in TB and HIV cases. TB/HIV epidemics in South Africa were most effectively curtailed by simultaneously implementing interventions that integrated community-based TB/HIV control strategies and targeted drug-resistant TB. Strengthening existing TB and HIV treatment programs is needed to further reduce disease incidence.

Mathematical Modelling and Tuberculosis: Advances in Diagnostics and Novel Therapies

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).

Age-specific risks of tuberculosis infection from household and community exposures and opportunities for interventions in a high-burden setting

We analyzed data from a large population-based prospective cohort study of household contacts of tuberculosis patients in Lima, Peru, to estimate the importance of within-household transmission relative to community-based transmission. We identified all adults (older than 15 years of age) who had incident pulmonary tuberculosis diagnosed at any of 106 public health centers in Lima from September 2009 to August 2012. A total of 14,041 household contacts of 3,446 index patients were assessed for tuberculosis infection and disease. We compared the prevalence of latent tuberculosis infection (LTBI) among persons who had received the Bacillus Calmette-Guérin vaccine in households with and without a microbiologically confirmed index case to estimate the age-specific risk of infection and excess risk of LTBI from household and community exposures. We found that the risk of infection from household and community sources increased from birth until 20 years of age. However, a large proportion of infections among child and young-adult household contacts could have been the result of household exposure. Excess infection risk associated with household exposure accounted for 58% (95% confidence interval: 47, 66) of LTBI prevalence among exposed children younger than 1 year of age, 48% (95% confidence interval: 39, 57) among 10-year-old children, and 44% (95% confidence interval: 34, 51) among 15-year-old adolescents. These findings suggest that expanded access to preventive therapy for older children and young-adult household contacts of known tuberculosis cases may be beneficial.

Cost-effectiveness analysis of optimal control measures for tuberculosis

We propose and analyze an optimal control problem where the control system is a mathematical model for tuberculosis that considers reinfection. The control functions represent the fraction of early latent and persistent latent individuals that are treated. Our aim was to study how these control measures should be implemented, for a certain time period, in order to reduce the number of active infected individuals, while minimizing the interventions implementation costs. The optimal intervention is compared along different epidemiological scenarios, by varying the transmission coefficient. The impact of variation of the risk of reinfection, as a result of acquired immunity to a previous infection for treated individuals on the optimal controls and associated solutions, is analyzed. A cost-effectiveness analysis is done, to compare the application of each one of the control measures, separately or in combination.

Dynamics of Mycobacterium and bovine tuberculosis in a human-buffalo population

A new model for the transmission dynamics of Mycobacterium tuberculosis and bovine tuberculosis in a community, consisting of humans and African buffalos, is presented. The buffalo-only component of the model exhibits the phenomenon of backward bifurcation, which arises due to the reinfection of exposed and recovered buffalos, when the associated reproduction number is less than unity. This model has a unique endemic equilibrium, which is globally asymptotically stable for a special case, when the reproduction number exceeds unity. Uncertainty and sensitivity analyses, using data relevant to the dynamics of the two diseases in the Kruger National Park, show that the distribution of the associated reproduction number is less than unity (hence, the diseases would not persist in the community). Crucial parameters that influence the dynamics of the two diseases are also identified. Both the buffalo-only and the buffalo-human model exhibit the same qualitative dynamics with respect to the local and global asymptotic stability of their respective disease-free equilibrium, as well as with respect to the backward bifurcation phenomenon. Numerical simulations of the buffalo-human model show that the cumulative number of Mycobacterium tuberculosis cases in humans (buffalos) decreases with increasing number of bovine tuberculosis infections in humans (buffalo).

Modeling the potential impact of host population survival on the evolution of M. tuberculosis latency

Tuberculosis (TB) is an infectious disease with a peculiar feature: Upon infection with the causative agent, Mycobacterium Tuberculosis (MTB), most hosts enter a latent state during which no transmission of MTB to new hosts occurs. Only a fraction of latently infected hosts develop TB disease and can potentially infect new hosts. At first glance, this seems like a waste of transmission potential and therefore an evolutionary suboptimal strategy for MTB. It might be that the human immune response keeps MTB in check in most hosts, thereby preventing it from achieving its evolutionary optimum. Another possible explanation is that long latency and progression to disease in only a fraction of hosts are evolutionary beneficial to MTB by allowing it to persist better in small host populations. Given that MTB has co-evolved with human hosts for millenia or longer, it likely encountered small host populations for a large share of its evolutionary history and had to evolve strategies of persistence. Here, we use a mathematical model to show that indeed, MTB persistence is optimal for an intermediate duration of latency and level of activation. The predicted optimal level of activation is above the observed value, suggesting that human co-evolution has lead to host immunity, which keeps MTB below its evolutionary optimum.

Interpreting measures of tuberculosis transmission: a case study on the Portuguese population

Background

Tuberculosis remains a high burden for Human society despite considerable investments in its control. Unique features in the history of infection and transmission dynamics of tuberculosis pose serious limitations on the direct interpretation of surveillance data and call for models that incorporate latent processes and simulate specific interventions.

Methods

A transmission model was adjusted to the dataset of active tuberculosis cases reported in Portugal between 2002 and 2009. We estimated key transmission parameters from the data (i.e. time to diagnosis, treatment length, default proportion, proportion of pulmonary TB cases). Using the adjusted model to the Portuguese case, we estimated the total burden of tuberculosis in Portugal. We further performed sensitivity analysis to heterogeneities in susceptibility to infection and exposure intensity.

Results

We calculated a mean time to diagnose of 2.81 months and treatment length of 8.80 months in Portugal. The proportion defaulting treatment was calculated as 0.04 and the proportion of pulmonary cases as 0.75. Using these values, we estimated a TB burden of 1.6 million infected persons, corresponding to more than 15% of the Portuguese population. We further described the sensitivity of these estimates to heterogeneity.

Conclusions

We showed that the model reproduces well the observed dynamics of the Portuguese data, thus demonstrating its adequacy for devising control strategies for TB and predicting the effects of interventions.

Clinical outcomes among persons with pulmonary tuberculosis caused by Mycobacterium tuberculosis isolates with phenotypic heterogeneity in results of drug-susceptibility tests

BACKGROUND

Patients with multidrug-resistant (MDR) tuberculosis may have phenotypic heterogeneity in results of drug-susceptibility tests (DSTs). However, the impact of this on clinical outcomes among patients treated for MDR tuberculosis is unknown.

METHODS

Phenotypic DST heterogeneity was defined as presence of at least 1 Mycobacterium tuberculosis isolate susceptible to rifampicin and isoniazid recovered <3 months after MDR tuberculosis treatment initiation from a patient with previous documented tuberculosis due to M. tuberculosis resistant to at least rifampicin and isoniazid. The primary outcome was defined as good (ie, cure or treatment completion) or poor (ie, treatment failure, treatment default, or death). A secondary outcome was time to culture conversion. Cox proportional hazard models were used to determine the association between phenotypic DST heterogeneity and outcomes.

RESULTS

Phenotypic DST heterogeneity was identified in 33 of 475 patients (7%) with MDR tuberculosis. Poor outcome occurred in 126 patients (28%). Overall, patients with MDR tuberculosis who had phenotypic DST heterogeneity were at greater risk of poor outcome than those with MDR tuberculosis but no phenotypic DST heterogeneity (adjusted hazard ratio [aHR], 2.1; 95% confidence interval [CI], 1.2-3.6). Among HIV-infected patients with MDR tuberculosis, the adjusted hazard for a poor outcome for those with phenotypic DST heterogeneity was 2.4 (95% CI, 1.3-4.2) times that for those without phenotypic DST heterogeneity, whereas among HIV-negative patients with MDR tuberculosis, the adjusted hazard for those with phenotypic DST heterogeneity was 1.5 (95% CI, .5-4.3) times that for those without phenotypic DST heterogeneity. HIV-infected patients with MDR tuberculosis with phenotypic DST heterogeneity also had a longer time to culture conversion than with HIV-infected patients with MDR tuberculosis without phenotypic DST heterogeneity (aHR, 2.9; 95% CI, 1.4-6.0).

CONCLUSIONS

Phenotypic DST heterogeneity among persons with HIV infection who are being treated for MDR tuberculosis is associated with poor outcomes and longer times to culture conversion.

Data needs for evidence-based decisions: a tuberculosis modeler's 'wish list'

Infectious disease models are important tools for understanding epidemiology and supporting policy decisions for disease control. In the case of tuberculosis (TB), such models have informed our understanding and control strategies for over 40 years, but the primary assumptions of these models--and their most urgent data needs--remain obscure to many TB researchers and control officers. The structure and parameter values of TB models are informed by observational studies and experiments, but the evidence base in support of these models remains incomplete. Speaking from the perspective of infectious disease modelers addressing the broader TB research and control communities, we describe the basic structure common to most TB models and present a 'wish list' that would improve the evidence foundation upon which these models are built. As a comprehensive TB research agenda is formulated, we argue that the data needs of infectious disease models--our primary long-term decision-making tools--should figure prominently.

Epidemiology of tuberculosis in an urban slum of Dhaka City, Bangladesh

Background

The objectives of this study were to assess the tuberculosis (TB) burden and to provide an insight into the type of circulating M. tuberculosis species in urban slums of Bangladesh. We also aimed to test the feasibility of a larger transmission study in this setting.

Methods

This cross-sectional study was conducted in an urban slum of Dhaka city. The household members were actively screened to assess the presence of TB-related signs and symptoms; cough ≥3 weeks and body mass index (BMI) <17 kg/m². Sputum specimens from suspects were collected for acid fast bacilli (AFB) microscopy, culture and drug susceptibility testing. Genotyping of M. tuberculosis was done using spoligotyping and variable number tandem repeats of mycobacterial interspersed repetitive units typing.

Results

Among 9,877 adult screened for pulmonary TB (PTB), 25 were positive for AFB on microscopy and/or culture and the prevalence of new PTB cases was estimated to be 253/100,000. Only one child TB case was diagnosed among 5,147 child screened. Out of 26 cases, 21(81%) had cough for several duration and 5(19%) did not present with cough at the time of screening. One multidrug resistant case was found. Fifty two percent of all TB cases had BMI <17 kg/m2 (p = <0.001). Among the 20 analyzed isolates, 13 different spoligotype patterns were identified in which 5 clusters contained 12 strains and 8 strains had unique pattern.

Conclusions

The study revealed high prevalence of TB in urban slums. Screening using low BMI can be beneficial among risk group population. It is important to conduct larger study to validate clinical variables like cough <3 weeks and low BMI to define TB suspect and also to investigate the transmission of TB in slum settings.

Field-isolated genotypes of Mycobacterium bovis vary in virulence and influence case pathology but do not affect outbreak size

Strains of many infectious agents differ in fundamental epidemiological parameters including transmissibility, virulence and pathology. We investigated whether genotypes of Mycobacterium bovis (the causative agent of bovine tuberculosis, bTB) differ significantly in transmissibility and virulence, combining data from a nine-year survey of the genetic structure of the M. bovis population in Northern Ireland with detailed records of the cattle population during the same period. We used the size of herd breakdowns as a proxy measure of transmissibility and the proportion of skin test positive animals (reactors) that were visibly lesioned as a measure of virulence. Average breakdown size increased with herd size and varied depending on the manner of detection (routine herd testing or tracing of infectious contacts) but we found no significant variation among M. bovis genotypes in breakdown size once these factors had been accounted for. However breakdowns due to some genotypes had a greater proportion of lesioned reactors than others, indicating that there may be variation in virulence among genotypes. These findings indicate that the current bTB control programme may be detecting infected herds sufficiently quickly so that differences in virulence are not manifested in terms of outbreak sizes. We also investigated whether pathology of infected cattle varied according to M. bovis genotype, analysing the distribution of lesions recorded at post mortem inspection. We concentrated on the proportion of cases lesioned in the lower respiratory tract, which can indicate the relative importance of the respiratory and alimentary routes of infection. The distribution of lesions varied among genotypes and with cattle age and there were also subtle differences among breeds. Age and breed differences may be related to differences in susceptibility and husbandry, but reasons for variation in lesion distribution among genotypes require further investigation.