Are survey-based estimates of the burden of drug resistant TB too low? Insight from a simulation study

Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria

Individual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose using a Bayesian optimization framework employing Gaussian process or machine learning emulator functions to calibrate a complex malaria transmission simulator. We demonstrate our approach by optimizing over a high-dimensional parameter space with respect to a portfolio of multiple fitting objectives built from datasets capturing the natural history of malaria transmission and disease progression. Our approach quickly outperforms previous calibrations, yielding an improved final goodness of fit. Per-objective parameter importance and sensitivity diagnostics provided by our approach offer epidemiological insights and enhance trust in predictions through greater interpretability.

Population-level mathematical modeling of antimicrobial resistance: a systematic review

BACKGROUND

Mathematical transmission models are increasingly used to guide public health interventions for infectious diseases, particularly in the context of emerging pathogens; however, the contribution of modeling to the growing issue of antimicrobial resistance (AMR) remains unclear. Here, we systematically evaluate publications on population-level transmission models of AMR over a recent period (2006-2016) to gauge the state of research and identify gaps warranting further work.

METHODS

We performed a systematic literature search of relevant databases to identify transmission studies of AMR in viral, bacterial, and parasitic disease systems. We analyzed the temporal, geographic, and subject matter trends, described the predominant medical and behavioral interventions studied, and identified central findings relating to key pathogens.

RESULTS

We identified 273 modeling studies; the majority of which (> 70%) focused on 5 infectious diseases (human immunodeficiency virus (HIV), influenza virus, Plasmodium falciparum (malaria), Mycobacterium tuberculosis (TB), and methicillin-resistant Staphylococcus aureus (MRSA)). AMR studies of influenza and nosocomial pathogens were mainly set in industrialized nations, while HIV, TB, and malaria studies were heavily skewed towards developing countries. The majority of articles focused on AMR exclusively in humans (89%), either in community (58%) or healthcare (27%) settings. Model systems were largely compartmental (76%) and deterministic (66%). Only 43% of models were calibrated against epidemiological data, and few were validated against out-of-sample datasets (14%). The interventions considered were primarily the impact of different drug regimens, hygiene and infection control measures, screening, and diagnostics, while few studies addressed de novo resistance, vaccination strategies, economic, or behavioral changes to reduce antibiotic use in humans and animals.

CONCLUSIONS

The AMR modeling literature concentrates on disease systems where resistance has been long-established, while few studies pro-actively address recent rise in resistance in new pathogens or explore upstream strategies to reduce overall antibiotic consumption. Notable gaps include research on emerging resistance in Enterobacteriaceae and Neisseria gonorrhoeae; AMR transmission at the animal-human interface, particularly in agricultural and veterinary settings; transmission between hospitals and the community; the role of environmental factors in AMR transmission; and the potential of vaccines to combat AMR.

Optimally capturing latency dynamics in models of tuberculosis transmission

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

Epidemiology of Drug-Resistant Tuberculosis

As we move into the era of the Sustainable Development Goals (SDGs), the World Health Organization (WHO) has developed the End TB strategy 2016-2035 with a goal to end the global epidemic of tuberculosis (TB) by 2035. Achieving the targets laid out in the Strategy will require strengthening of the whole TB diagnosis and treatment cascade, including improved case detection, the establishment of universal drug susceptibility testing and rapid treatment initiation. An estimated 3.9% of new TB cases and 21% of previously treated cases had rifampicin-resistant (RR) or multidrug-resistant (MDR) TB in 2015. These levels have remained stable over time, although limited data are available from major high burden settings. In addition to the emergence of drug resistance due to inadequate treatment, there is growing evidence that direct transmission is a large contributor to the RR/MDR-TB epidemic. Only 340,000 of the estimated 580,000 incident cases of RR/MDR-TB were notified to WHO in 2015. Among these, only 125,000 were initiated on second-line treatment. RR/MDR-TB epidemics are likely to be driven by direct transmission. The most important risk factor for MDR-TB is a history of previous treatment. Other risk factors vary according to setting but can include hospitalisation, incarceration and HIV infection. Children have the same risk of MDR-TB as adults and represent a diagnostic and treatment challenge. Rapid molecular technologies have revolutionized the diagnosis of drug-resistant TB. Until capacity can be established to test every TB patient for rifampicin resistance, countries should focus on gradually expanding their coverage of testing. DNA sequencing technologies are being increasingly incorporated into patient management and drug resistance surveillance. They offer additional benefits over conventional culture-based phenotypic testing, including a faster turn-around time for results, assessment of resistance patterns to a range of drugs, and investigation of strain clustering and transmission.

Mathematical models of the epidemiology and control of drug-resistant TB

Recent reports of extensively drug-resistant TB in South Africa have renewed concerns that antibiotic resistance may undermine progress in TB control. We review three major questions for which mathematical models elucidate the epidemiology and control of drug-resistant TB. How is multiple drug-resistant Mycobacterium tuberculosis selected for in individuals exposed to combination chemotherapy? What factors determine the prevalence of drug-resistant TB? Which interventions to prevent the spread of drug-resistant TB are effective and feasible? Models offer insight into the acquisition and amplification of drug resistance, reveal the importance of distinguishing the intrinsic and extrinsic determinants of the reproductive capacity of drug-resistant M. tuberculosis, and demonstrate the cost effectiveness of interventions for drug-resistant TB. These models also highlight knowledge gaps for which new research will improve our ability to project trends of drug resistance and develop more effective policies for its control.

Linking surveillance with action against drug-resistant tuberculosis

The speed at which most countries with high burdens of multidrug-resistant tuberculosis (MDRTB) have scaled up their capacity to diagnose and treat individuals with these forms of TB has failed to keep pace with the problem. Limited availability of drug susceptibility testing, high costs and inefficiencies in the supply of second-line drugs, and inadequate capacity for the management of patients with MDRTB have contributed to the wide gap between the estimated need for and the delivery of MDRTB treatment. The most recent global estimates indicate that only about 1 in 20 individuals with incident MDRTB will be properly diagnosed; fewer still receive quality-assured treatment. As policy makers confront the threat of growing levels of drug-resistant TB, there is a clear role for improved surveillance methods that can facilitate more effective public health responses. In countries that cannot yet test all incident cases for drug resistance, analysis of programmatic data and use of periodic, efficient surveys can provide information to help prioritize the use of limited resources to geographic areas or population subgroups of greatest concern. We describe methods for the analysis of routinely collected data and alternative surveys that can help tighten the link between surveillance activities and interventions.

High prevalence of multidrug-resistant tuberculosis, Swaziland, 2009-2010

One third of previously treated patients had MDR TB.

In Africa, although emergence of multidrug-resistant (MDR) tuberculosis (TB) represents a serious threat in countries severely affected by the HIV epidemic, most countries lack drug-resistant TB data. This finding was particularly true in the Kingdom of Swaziland, which has the world’s highest HIV and TB prevalences. Therefore, we conducted a national survey in 2009–2010 to measure prevalence of drug-resistant TB. Of 988 patients screened, 420 new case-patients and 420 previously treated case-patients met the study criteria. Among culture-positive patients, 15.3% new case-patients and 49.5% previously treated case-patients harbored drug-resistant strains. MDR TB prevalence was 7.7% and 33.8% among new case-patients and previously treated case-patients, respectively. HIV infection and past TB treatment were independently associated with MDR TB. The findings assert the need for wide-scale intervention in resource-limited contexts such as Swaziland, where diagnostic and treatment facilities and health personnel are lacking.

Epidemiological models of Mycobacterium tuberculosis complex infections

The resurgence of tuberculosis in the 1990s and the emergence of drug-resistant tuberculosis in the first decade of the 21st century increased the importance of epidemiological models for the disease. Due to slow progression of tuberculosis, the transmission dynamics and its long-term effects can often be better observed and predicted using simulations of epidemiological models. This study provides a review of earlier study on modeling different aspects of tuberculosis dynamics. The models simulate tuberculosis transmission dynamics, treatment, drug resistance, control strategies for increasing compliance to treatment, HIV/TB co-infection, and patient groups. The models are based on various mathematical systems, such as systems of ordinary differential equations, simulation models, and Markov Chain Monte Carlo methods. The inferences from the models are justified by case studies and statistical analysis of TB patient datasets.

Discovery and validation of new antitubercular compounds as potential drug leads and probes

Increasing multidrug resistance in Mycobacterium tuberculosis continues to diminish the number of effective drugs available for treatment of active tuberculosis. Although there are four new products (representing three new chemical classes) in clinical development, an active, robust pipeline of new chemical entities is critical to discovery of medicines to dramatically improve or shorten length of therapy via new mechanisms of action. In the absence of major pharmaceutical industry activity in tuberculosis drug development, the National Institute of Allergy and Infectious Diseases (NIAID) has supported the development of a high throughput screen for growth inhibitors of M. tuberculosis using a chemically diverse commercial library, a compound library available through the NIH Roadmap, Molecular Libraries Screening Center Network, and other compound sources. The rationale for these screens and suggested approaches for follow-up studies to identify compounds for advanced preclinical studies and as chemical probes of critical functions in M. tuberculosis, are discussed.

High levels of multidrug resistant tuberculosis in new and treatment-failure patients from the Revised National Tuberculosis Control Programme in an urban metropolis (Mumbai) in Western India

BACKGROUND

India, China and Russia account for more than 62% of multidrug resistant tuberculosis (MDRTB) globally. Within India, locations like urban metropolitan Mumbai with its burgeoning population and high incidence of TB are suspected to be a focus for MDRTB. However apart from sporadic surveys at watched sites in the country, there has been no systematic attempt by the Revised National Tuberculosis Control Programme (RNTCP) of India to determine the extent of MDRTB in Mumbai that could feed into national estimates. Drug susceptibility testing (DST) is not routinely performed as a part of programme policy and public health laboratory infrastructure, is limited and poorly equipped to cope with large scale testing.

METHODS

From April 2004 to January 2007 we determined the extent of drug resistance in 724 {493 newly diagnosed, previously untreated and 231 first line treatment failures (sputum-smear positive at the fifth month after commencement of therapy)} cases of pulmonary tuberculosis drawn from the RNTCP in four suboptimally performing municipal wards of Mumbai. The observations were obtained using a modified radiorespirometric Buddemeyer assay and validated by the Swedish Institute for Infectious Disease Control, Stockholm, a supranational reference laboratory. Data was analyzed utilizing SPSS 10.0 and Epi Info 2002.

RESULTS

This study undertaken for the first time in RNTCP outpatients in Mumbai reveals a high proportion of MDRTB strains in both previously untreated (24%) and treatment-failure cases (41%). Amongst new cases, resistance to 3 or 4 drug combinations (amplified drug resistance) including isoniazid (H) and rifampicin (R), was greater (20%) than resistance to H and R alone (4%) at any point in time during the study. The trend for monoresistance was similar in both groups remaining highest to H and lowest to R. External quality control revealed good agreement for H and R resistance (k = 0.77 and 0.76 respectively).

CONCLUSION

Levels of MDRTB are much higher in both previously untreated and first line treatment-failure cases in the selected wards in Mumbai than those projected by national estimates. The finding of amplified drug resistance suggests the presence of a well entrenched MDRTB scenario. This study suggests that a wider set of surveillance sites are needed to obtain a more realistic view of the true MDRTB rates throughout the country. This would assist in the planning of an adequate response to the diagnosis and care of MDRTB.