The epidemiological fitness cost of drug resistance in Mycobacterium tuberculosis

Combating antimicrobial resistance in malaria, HIV and tuberculosis

Antimicrobial resistance poses a significant threat to the sustainability of effective treatments against the three most prevalent infectious diseases: malaria, human immunodeficiency virus (HIV) infection and tuberculosis. Therefore, there is an urgent need to develop novel drugs and treatment protocols capable of reducing the emergence of resistance and combating it when it does occur. In this Review, we present an overview of the status and underlying molecular mechanisms of drug resistance in these three diseases. We also discuss current strategies to address resistance during the research and development of next-generation therapies. These strategies vary depending on the infectious agent and the array of resistance mechanisms involved. Furthermore, we explore the potential for cross-fertilization of knowledge and technology among these diseases to create innovative approaches for minimizing drug resistance and advancing the discovery and development of new anti-infective treatments. In conclusion, we advocate for the implementation of well-defined strategies to effectively mitigate and manage resistance in all interventions against infectious diseases.

Estimating mutation rates in a Markov branching process using approximate Bayesian computation

Estimating microbial mutation rates is an essential task in evolutionary biology, with wide range applications in related fields such as virology, epidemiology, clinic and public health, and antibiotic research. Significant progress has been made on this research since 1943 when Luria-Delbrück fluctuation analysis was first introduced. However, existing estimators of mutation rates are heavily reliant on model assumptions in fluctuation analysis, and become less applicable to real microbial experiments which deviate from the model assumptions. To overcome this difficulty, we propose to model fluctuation experimental data by a two-type Markov branching process (MBP) and use approximate Bayesian computation (ABC) to estimate the mutation probability parameters. Such an ABC-based mutation rate estimator is based on intensive simulations from the mutation process, thereby taking advantage of modern computing power. Most importantly, its likelihood-free feature allows more complex and realistic setups of the mutation process, especially when the distribution of the number of mutants cannot be easily derived. To further improve computation efficiency, we use a Gaussian process surrogate to substitute the simulator in the ABC algorithm, and call the resulting estimator GPS-ABC. Simulation studies show that, when used to estimate constant mutation rate in MBP, ABC-based estimators generally outperform traditional moment or likelihood-based estimators. When mutations occur in two stages, i.e., in MBP with a piece-wise constant mutation rate function, traditional mutation rate estimators become not applicable, yet GPS-ABC still achieves reasonable estimates. Finally, the proposed GPS-ABC estimator is used to analyze real fluctuation experimental datasets for studying drug resistance.

Discrepancy in the transmissibility of multidrug-resistant Mycobacterium tuberculosis in urban and rural areas in China

The fitness of multidrug-resistant tuberculosis (MDR-TB) is thought to be an important determinant of a strain's ability to be transmitted and cause outbreaks. Studies in the laboratory have demonstrated that MDR-TB strains have reduced fitness but the relative transmissibility of MDR-TB versus drug-susceptible (DS) TB strains in human populations remains unresolved. We used data on genomic clustering from our previous molecular epidemiological study in Songjiang (2011-2020) and Wusheng (2009-2020), China, to compare the relative transmissibility of MDR-TB versus DS-TB. Genomic clusters were defined with a threshold distance of 12-single-nucleotide-polymorphisms and the risk for MDR-TB clustering was analyzed by logistic regression. In total, 2212 culture-positive pulmonary TB patients were enrolled in Songjiang and 1289 in Wusheng. The clustering rates of MDR-TB and DS-TB strains were 19.4% (20/103) and 26.3% (509/1936), respectively in Songjiang, and 43.9% (29/66) and 26.0% (293/1128) in Wusheng. The risk of MDR-TB clustering was 2.34 (95% CI 1.38-3.94) times higher than DS-TB clustering in Wusheng and 0.64 (95% CI 0.38-1.06) times lower in Songjiang. Neither lineage 2, compensatory mutations nor rpoB S450L were significantly associated with MDR-TB transmission, and katG S315T increased MDR-TB transmission only in Wusheng (OR 5.28, 95% CI 1.42-19.21). MDR-TB was not more transmissible than DS-TB in either Songjiang or Wusheng. It appears that the different transmissibility of MDR-TB in Songjiang and Wusheng is likely due to differences in the quality of the local TB control programs. These results suggest that the most effective way to control MDR-TB is by improving local TB control programs.

Three enigmatic BioH isoenzymes are programmed in the early stage of mycobacterial biotin synthesis, an attractive anti-TB drug target

Tuberculosis (TB) is one of the leading infectious diseases of global concern, and one quarter of the world’s population are TB carriers. Biotin metabolism appears to be an attractive anti-TB drug target. However, the first-stage of mycobacterial biotin synthesis is fragmentarily understood. Here we report that three evolutionarily-distinct BioH isoenzymes (BioH1 to BioH3) are programmed in biotin synthesis of Mycobacterium smegmatis. Expression of an individual bioH isoform is sufficient to allow the growth of an Escherichia coli ΔbioH mutant on the non-permissive condition lacking biotin. The enzymatic activity in vitro combined with biotin bioassay in vivo reveals that BioH2 and BioH3 are capable of removing methyl moiety from pimeloyl-ACP methyl ester to give pimeloyl-ACP, a cognate precursor for biotin synthesis. In particular, we determine the crystal structure of dimeric BioH3 at 2.27Å, featuring a unique lid domain. Apart from its catalytic triad, we also dissect the substrate recognition of BioH3 by pimeloyl-ACP methyl ester. The removal of triple bioH isoforms (ΔbioH1/2/3) renders M. smegmatis biotin auxotrophic. Along with the newly-identified Tam/BioC, the discovery of three unusual BioH isoforms defines an atypical ‘BioC-BioH(3)’ paradigm for the first-stage of mycobacterial biotin synthesis. This study solves a long-standing puzzle in mycobacterial nutritional immunity, providing an alternative anti-TB drug target.

Delays, Masks, the Elderly, and Schools: First Covid-19 Wave in the Czech Republic

Running across the globe for nearly 2 years, the Covid-19 pandemic keeps demonstrating its strength. Despite a lot of understanding, uncertainty regarding the efficiency of interventions still persists. We developed an age-structured epidemic model parameterized with epidemiological and sociological data for the first Covid-19 wave in the Czech Republic and found that (1) starting the spring 2020 lockdown 4 days earlier might prevent half of the confirmed cases by the end of lockdown period, (2) personal protective measures such as face masks appear more effective than just a realized reduction in social contacts, (3) the strategy of sheltering just the elderly is not at all effective, and (4) leaving schools open is a risky strategy. Despite vaccination programs, evidence-based choice and timing of non-pharmaceutical interventions remains an effective weapon against the Covid-19 pandemic.

Decreased thermal niche breadth as a trade-off of antibiotic resistance

Evolutionary theory predicts that adaptations, including antibiotic resistance, should come with associated fitness costs; yet, many resistance mutations seemingly contradict this prediction by inducing no growth rate deficit. However, most growth assays comparing sensitive and resistant strains have been performed under a narrow range of environmental conditions, which do not reflect the variety of contexts that a pathogenic bacterium might encounter when causing infection. We hypothesized that reduced niche breadth, defined as diminished growth across a diversity of environments, can be a cost of antibiotic resistance. Specifically, we test whether chloramphenicol-resistant Escherichia coli incur disproportionate growth deficits in novel thermal conditions. Here we show that chloramphenicol-resistant bacteria have greater fitness costs at novel temperatures than their antibiotic-sensitive ancestors. In several cases, we observed no resistance cost in growth rate at the historic temperature but saw diminished growth at warmer and colder temperatures. These results were consistent across various genetic mechanisms of resistance. Thus, we propose that decreased thermal niche breadth is an under-documented fitness cost of antibiotic resistance. Furthermore, these results demonstrate that the cost of antibiotic resistance shifts rapidly as the environment changes; these context-dependent resistance costs should select for the rapid gain and loss of resistance as an evolutionary strategy.

Markov genealogy processes

We construct a family of genealogy-valued Markov processes that are induced by a continuous-time Markov population process. We derive exact expressions for the likelihood of a given genealogy conditional on the history of the underlying population process. These lead to a nonlinear filtering equation which can be used to design efficient Monte Carlo inference algorithms. We demonstrate these calculations with several examples. Existing full-information approaches for phylodynamic inference are special cases of the theory.

Quantifying transmission fitness costs of multi-drug resistant tuberculosis

As multi-drug resistant tuberculosis (MDR-TB) continues to spread, investigating the transmission potential of different drug-resistant strains becomes an ever more pressing topic in public health. While phylogenetic and transmission tree inferences provide valuable insight into possible transmission chains, phylodynamic inference combines evolutionary and epidemiological analyses to estimate the parameters of the underlying epidemiological processes, allowing us to describe the overall dynamics of disease spread in the population. In this study, we introduce an approach to Mycobacterium tuberculosis (M. tuberculosis) phylodynamic analysis employing an existing computationally efficient model to quantify the transmission fitness costs of drug resistance with respect to drug-sensitive strains. To determine the accuracy and precision of our approach, we first perform a simulation study, mimicking the simultaneous spread of drug-sensitive and drug-resistant tuberculosis (TB) strains. We analyse the simulated transmission trees using the phylodynamic multi-type birth-death model (MTBD, (Kühnert et al., 2016)) within the BEAST2 framework and show that this model can estimate the parameters of the epidemic well, despite the simplifying assumptions that MTBD makes compared to the complex TB transmission dynamics used for simulation. We then apply the MTBD model to an M. tuberculosis lineage 4 dataset that primarily consists of MDR sequences. Some of the MDR strains additionally exhibit resistance to pyrazinamide - an important first-line anti-tuberculosis drug. Our results support the previously proposed hypothesis that pyrazinamide resistance confers a transmission fitness cost to the bacterium, which we quantify for the given dataset. Importantly, our sensitivity analyses show that the estimates are robust to different prior distributions on the resistance acquisition rate, but are affected by the size of the dataset - i.e. we estimate a higher fitness cost when using fewer sequences for analysis. Overall, we propose that MTBD can be used to quantify the transmission fitness cost for a wide range of pathogens where the strains can be appropriately divided into two or more categories with distinct properties.

New Developments and Insights in the Improvement of Mycobacterium tuberculosis Vaccines and Diagnostics Within the End TB Strategy

PURPOSE OF REVIEW

The alignment of sustainable development goals (SDGs) with the End Tuberculosis (TB) strategy provides an integrated roadmap to implement key approaches towards TB elimination. This review summarizes current social challenges for TB control, and yet, recent developments in TB diagnosis and vaccines in the context of the End TB strategy and SDGs to transform global health.

RECENT FINDINGS

Advances in non-sputum based TB biomarkers and whole genome sequencing technologies could revolutionize TB diagnostics. Moreover, synergistic novel technologies such as mRNA vaccination, nanovaccines and promising TB vaccine models are key promising developments for TB prevention and control.

SUMMARY

The End TB strategy depends on novel developments in point-of-care TB diagnostics and effective vaccines. However, despite outstanding technological developments in these fields, TB elimination will be unlikely achieved if TB social determinants are not fully addressed. Indeed, the End TB strategy and SDGs emphasize the importance of implementing sustainable universal health coverage and social protection.

Delays, masks, the elderly, and schools: first COVID-19 wave in the Czech Republic

Running across the globe for more than a year, the COVID-19 pandemic keeps demonstrating its strength. Despite a lot of understanding, uncertainty regarding the efficiency of interventions still persists. We developed an age-structured epidemic model parameterized with sociological data for the Czech Republic and found that (1) delaying the spring 2020 lockdown by four days produced twice as many confirmed cases by the end of the lockdown period, (2) personal protective measures such as face masks appear more effective than just a reduction of social contacts, (3) only sheltering the elderly is by no means effective, and (4) leaving schools open is a risky strategy. Despite the onset of vaccination, an evidence-based choice and timing of non-pharmaceutical interventions still remains the most important weapon against the COVID-19 pandemic.

Treating symptomatic infections and the co-evolution of virulence and drug resistance

Antimicrobial therapeutic treatments are by definition applied after the onset of symptoms, which tend to correlate with infection severity. Using mathematical epidemiology models, I explore how this link affects the coevolutionary dynamics between the virulence of an infection, measured via host mortality rate, and its susceptibility to chemotherapy. I show that unless resistance pre-exists in the population, drug-resistant infections are initially more virulent than drug-sensitive ones. As the epidemic unfolds, virulence is more counter-selected in drug-sensitive than in drug-resistant infections. This difference decreases over time and, eventually, the exact shape of genetic trade-offs govern long-term evolutionary dynamics. Using adaptive dynamics, I show that two types of evolutionary stable strategies (ESS) may be reached in the context of this simple model and that, depending on the parameter values, an ESS may only be locally stable. In general, the more the treatment rate increases with virulence, the lower the ESS value. Overall, both on the short-term and long-term, having treatment rate depend on infection virulence tend to favour less virulent strains in drug-sensitive infections. These results highlight the importance of the feedbacks between epidemiology, public health policies and parasite evolution, and have implications for the monitoring of virulence evolution.

The Biotin Biosynthetic Pathway in Mycobacterium tuberculosis is a Validated Target for the Development of Antibacterial Agents

Mycobacterium tuberculosis, responsible for Tuberculosis (TB), remains the leading cause of mortality among infectious diseases worldwide from a single infectious agent, with an estimated 1.7 million deaths in 2016. Biotin is an essential cofactor in M. tuberculosis that is required for lipid biosynthesis and gluconeogenesis. M. tuberculosis relies on de novo biotin biosynthesis to obtain this vital cofactor since it cannot scavenge sufficient biotin from a mammalian host. The biotin biosynthetic pathway in M. tuberculosis has been well studied and rigorously genetically validated providing a solid foundation for medicinal chemistry efforts. This review examines the mechanism and structure of the enzymes involved in biotin biosynthesis and ligation, summarizes the reported genetic validation studies of the pathway, and then analyzes the most promising inhibitors and natural products obtained from structure-based drug design and phenotypic screening.

Treatment as prevention and other interventions to reduce transmission of multidrug-resistant tuberculosis

Drug-resistant tuberculosis (DR-TB) represents a major programmatic challenge at the national and global levels. Only ∼30% of patients with multidrug-resistant TB (MDR-TB) were diagnosed, and ∼25% were initiated on treatment for MDR-TB in 2016. Increasing evidence now points towards primary transmission of DR-TB, rather than inadequate treatment, as the main driver of the DR-TB epidemic. The cornerstone of DR-TB transmission prevention should be earlier diagnosis and prompt initiation of effective treatment for all patients with DR-TB. Despite the extensive scale-up of Xpert^® MTB/RIF testing, major implementation barriers continue to limit its impact. Although there is longstanding evidence in support of the rapid impact of treatment on patient infectiousness, delays in the initiation of effective DR-TB treatment persist, resulting in ongoing transmission. However, it is also imperative to address the burden of latent drug-resistant tuberculous infection because it is estimated that many DR-TB patients will become infectious before seeking care and encounter various diagnostic delays before treatment. Addressing latent DR-TB primarily consists of identifying, treating and following the contacts of patients with MDR-TB, typically through household contact evaluation. Adjunctive measures, such as improved ventilation and use of germicidal ultraviolet technology can further reduce TB transmission in high-risk congregate settings. Although many gaps remain in our biological understanding of TB transmission, implementation barriers to early diagnosis and rapid initiation of effective DR-TB treatment can and must be overcome if we are to impact DR-TB incidence in the short and long term.

On the probability of strain invasion in endemic settings: Accounting for individual heterogeneity and control in multi-strain dynamics

•

Endemic infection can insulate host populations from invasion by mutant variants.

•

The timing of control implementation strongly influences its efficacy.

•

Controls that exacerbate host heterogeneity outperform those that curtail it.

•

Differential control can facilitate strain invasion and eventual replacement.

Pathogen evolution is an imminent threat to global health that has warranted, and duly received, considerable attention within the medical, microbiological and modelling communities. Outbreaks of new pathogens are often ignited by the emergence and transmission of mutant variants descended from wild-type strains circulating in the community. In this work we investigate the stochastic dynamics of the emergence of a novel disease strain, introduced into a population in which it must compete with an existing endemic strain. In analogy with past work on single-strain epidemic outbreaks, we apply a branching process approximation to calculate the probability that the new strain becomes established. As expected, a critical determinant of the survival prospects of any invading strain is the magnitude of its reproduction number relative to that of the background endemic strain. Whilst in most circumstances this ratio must exceed unity in order for invasion to be viable, we show that differential control scenarios can lead to less-fit novel strains invading populations hosting a fitter endemic one. This analysis and the accompanying findings will inform our understanding of the mechanisms that have led to past instances of successful strain invasion, and provide valuable lessons for thwarting future drug-resistant strain incursions.

Heterogeneous infectiousness in mathematical models of tuberculosis: A systematic review

TB mathematical models employ various assumptions and approaches in dealing with the heterogeneous infectiousness of persons with active TB. We reviewed existing approaches and considered the relationship between them and existing epidemiological evidence. We searched the following electronic bibliographic databases from inception to 9 October 2018: MEDLINE, EMBASE, Biosis, Global Health and Scopus. Two investigators extracted data using a standardised data extraction tool. We included in the review any transmission dynamic model of M. tuberculosis transmission explicitly simulating heterogeneous infectiousness of person with active TB. We extracted information including: study objective, model structure, number of active TB compartments, factors used to stratify the active TB compartment, relative infectiousness of each active TB compartment and any intervention evaluated in the model. Our search returned 1899 unique references, of which the full text of 454 records were assessed for eligibility, and 99 studies met the inclusion criteria. Of these, 89 used compartmental models implemented with ordinary differential equations, while the most common approach to stratification of the active TB compartment was to incorporate two levels of infectiousness. However, various clinical characteristics were used to stratify the active TB compartments, and models differed as to whether they permitted transition between these states. Thirty-four models stratified the infectious compartment according to sputum smear status or pulmonary involvement, while 18 models stratified based on health care-related factors. Variation in infectiousness associated with drug-resistant M. tuberculosis was the rationale for stratifying active TB in 33 models, with these models consistently assuming that drug-resistant active TB cases were less infectious. Given the evidence of extensive heterogeneity in infectiousness of individuals with active TB, an argument exists for incorporating heterogeneous infectiousness, although this should be considered in light of the objectives of the study and the research question. PROSPERO Registration: CRD42019111936.

Transition bias influences the evolution of antibiotic resistance in Mycobacterium tuberculosis

Transition bias, an overabundance of transitions relative to transversions, has been widely reported among studies of the rates and spectra of spontaneous mutations. However, demonstrating the role of transition bias in adaptive evolution remains challenging. In particular, it is unclear whether such biases direct the evolution of bacterial pathogens adapting to treatment. We addressed this challenge by analyzing adaptive antibiotic-resistance mutations in the major human pathogen Mycobacterium tuberculosis (MTB). We found strong evidence for transition bias in two independently curated data sets comprising 152 and 208 antibiotic-resistance mutations. This was true at the level of mutational paths (distinct adaptive DNA sequence changes) and events (individual instances of the adaptive DNA sequence changes) and across different genes and gene promoters conferring resistance to a diversity of antibiotics. It was also true for mutations that do not code for amino acid changes (in gene promoters and the 16S ribosomal RNA gene rrs) and for mutations that are synonymous to each other and are therefore likely to have similar fitness effects, suggesting that transition bias can be caused by a bias in mutation supply. These results point to a central role for transition bias in determining which mutations drive adaptive antibiotic resistance evolution in a key pathogen.

Some types of mutations occur more frequently than expected. This study shows that such bias —an excess of transitions over transversions—influences the evolution of antibiotic resistance in a key global pathogen, Mycobacterium tuberculosis.

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.

Whole genome sequencing identifies bacterial factors affecting transmission of multidrug-resistant tuberculosis in a high-prevalence setting

Whole genome sequencing (WGS) can elucidate Mycobacterium tuberculosis (Mtb) transmission patterns but more data is needed to guide its use in high-burden settings. In a household-based TB transmissibility study in Peru, we identified a large MIRU-VNTR Mtb cluster (148 isolates) with a range of resistance phenotypes, and studied host and bacterial factors contributing to its spread. WGS was performed on 61 of the 148 isolates. We compared transmission link inference using epidemiological or genomic data and estimated the dates of emergence of the cluster and antimicrobial drug resistance (DR) acquisition events by generating a time-calibrated phylogeny. Using a set of 12,032 public Mtb genomes, we determined bacterial factors characterizing this cluster and under positive selection in other Mtb lineages. Four of the 61 isolates were distantly related and the remaining 57 isolates diverged ca. 1968 (95%HPD: 1945-1985). Isoniazid resistance arose once and rifampin resistance emerged subsequently at least three times. Emergence of other DR types occurred as recently as within the last year of sampling. We identified five cluster-defining SNPs potentially contributing to transmissibility. In conclusion, clusters (as defined by MIRU-VNTR typing) may be circulating for decades in a high-burden setting. WGS allows for an enhanced understanding of transmission, drug resistance, and bacterial fitness factors.

Evolutionary epidemiology models to predict the dynamics of antibiotic resistance

The evolution of resistance to antibiotics is a major public health problem and an example of rapid adaptation under natural selection by antibiotics. The dynamics of antibiotic resistance within and between hosts can be understood in the light of mathematical models that describe the epidemiology and evolution of the bacterial population. "Between-host" models describe the spread of resistance in the host community, and in more specific settings such as hospitalized hosts (treated by antibiotics at a high rate), or farm animals. These models make predictions on the best strategies to limit the spread of resistance, such as reducing transmission or adapting the prescription of several antibiotics. Models can be fitted to epidemiological data in the context of intensive care units or hospitals to predict the impact of interventions on resistance. It has proven harder to explain the dynamics of resistance in the community at large, in particular because models often do not reproduce the observed coexistence of drug-sensitive and drug-resistant strains. "Within-host" models describe the evolution of resistance within the treated host. They show that the risk of resistance emergence is maximal at an intermediate antibiotic dose, and some models successfully explain experimental data. New models that include the complex host population structure, the interaction between resistance-determining loci and other loci, or integrating the within- and between-host levels will allow better interpretation of epidemiological and genomic data from common pathogens and better prediction of the evolution of resistance.

Host-targeted therapy for tuberculosis: Time to revisit the concept

Tuberculosis (TB) is an important cause of morbidity and mortality worldwide. Every year millions of people die due to TB. Drug resistance has been a major factor that has obstructed successful control and treatment of TB. As the rate of spread of drug-resistant TB outpaces the rate of discovery of new anti-tubercular drugs, targeted therapy may provide a new approach to TB cure. In a scenario where drug resistance is spreading rapidly, and existing drugs regimens seem to be dwindling away, this review summarizes the concept of host-targeted therapy which may be the ray of hope for the effective management and control of the rapidly spreading drug-resistant TB (multidrug resistant and extensively drug resistant).

Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in Python

Fitting complex models to epidemiological data is a challenging problem: methodologies can be inaccessible to all but specialists, there may be challenges in adequately describing uncertainty in model fitting, the complex models may take a long time to run, and it can be difficult to fully capture the heterogeneity in the data. We develop an adaptive approximate Bayesian computation scheme to fit a variety of epidemiologically relevant data with minimal hyper-parameter tuning by using an adaptive tolerance scheme. We implement a novel kernel density estimation scheme to capture both dispersed and multi-dimensional data, and directly compare this technique to standard Bayesian approaches. We then apply the procedure to a complex individual-based simulation of lymphatic filariasis, a human parasitic disease. The procedure and examples are released alongside this article as an open access library, with examples to aid researchers to rapidly fit models to data. This demonstrates that an adaptive ABC scheme with a general summary and distance metric is capable of performing model fitting for a variety of epidemiological data. It also does not require significant theoretical background to use and can be made accessible to the diverse epidemiological research community.

Genetics and roadblocks of drug resistant tuberculosis

Considering the extensive evolutionary history of Mycobacterium tuberculosis, anti-Tuberculosis (TB) drug therapy exerts a recent selective pressure. However, in a microorganism devoid of horizontal gene transfer and with a strictly clonal populational structure such as M. tuberculosis the usual, but not sole, path to overcome drug susceptibility is through de novo mutations on a relatively strict set of genes. The possible allelic diversity that can be associated with drug resistance through several mechanisms such as target alteration or target overexpression, will dictate how these genes can become associated with drug resistance. The success demonstrated by this pathogenic microbe in this latter process and its ability to spread is currently one of the major obstacles to an effective TB elimination. This article reviews the action mechanism of the more important anti-TB drugs, including bedaquiline and delamanid, along with new findings on specific resistance mechanisms. With the development, validation and endorsement of new in vitro molecular tests for drug resistance, knowledge on these resistance mechanisms and microevolutionary dynamics leading to the emergence and fixation of drug resistance mutations within the host is highly important. Additionally, the fitness toll imposed by resistance development is also herein discussed together with known compensatory mechanisms. By elucidating the possible mechanisms that enable one strain to reacquire the original fitness levels, it will be theoretically possible to make more informed decisions and develop novel strategies that can force M. tuberculosis microevolutionary trajectory down through a path of decreasing fitness levels.

The impact of within-host ecology on the fitness of a drug-resistant parasite

Background and objectives

The rate of evolution of drug resistance depends on the fitness of resistant pathogens. The fitness of resistant pathogens is reduced by competition with sensitive pathogens in untreated hosts and so enhanced by competitive release in drug-treated hosts. We set out to estimate the magnitude of those effects on a variety of fitness measures, hypothesizing that competitive suppression and competitive release would have larger impacts when resistance was rarer to begin with.

Methodology

We infected mice with varying densities of drug-resistant Plasmodium chabaudi malaria parasites in a fixed density of drug-sensitive parasites and followed infection dynamics using strain-specific quantitative PCR.

Results

Competition with susceptible parasites reduced the absolute fitness of resistant parasites by 50–100%. Drug treatment increased the absolute fitness from 2- to >10 000-fold. The ecological context and choice of fitness measure was responsible for the wide variation in those estimates. Initial population growth rates poorly predicted parasite abundance and transmission probabilities.

Conclusions and implications

(i) The sensitivity of estimates of pathogen fitness to ecological context and choice of fitness measure make it difficult to derive field-relevant estimates of the fitness costs and benefits of resistance from experimental settings. (ii) Competitive suppression can be a key force preventing resistance from emerging when it is rare, as it is when it first arises. (iii) Drug treatment profoundly affects the fitness of resistance. Resistance evolution could be slowed by developing drug use policies that consider in-host competition.

Have compensatory mutations facilitated the current epidemic of multidrug-resistant tuberculosis?

Compensatory mutations have been suggested to promote multidrug-resistant tuberculosis (MDR-TB) transmission, but their role in facilitating the recent transmission of MDR-TB is unclear. To investigate the epidemiological significance of compensatory mutations, we analyzed a four-year population-based collection of MDR-TB strains from Shanghai (the most populous city in China) and 1346 published global MDR-TB strains. We report that MDR-TB strains with compensatory mutations in the rpoA, rpoB, or rpoC genes were neither more frequently clustered nor found in larger clusters than those without compensatory mutations. Our results suggest that compensatory mutations are not a major contributor to the current epidemic of MDR-TB.

Opportunities and challenges for modelling epidemiological and evolutionary dynamics in a multihost, multiparasite system: Zoonotic hybrid schistosomiasis in West Africa

Multihost multiparasite systems are evolutionarily and ecologically dynamic, which presents substantial trans-disciplinary challenges for elucidating their epidemiology and designing appropriate control. Evidence for hybridizations and introgressions between parasite species is gathering, in part in line with improvements in molecular diagnostics and genome sequencing. One major system where this is becoming apparent is within the Genus Schistosoma, where schistosomiasis represents a disease of considerable medical and veterinary importance, the greatest burden of which occurs in sub-Saharan Africa. Interspecific hybridizations and introgressions bring an increased level of complexity over and above that already inherent within multihost, multiparasite systems, also representing an additional source of genetic variation that can drive evolution. This has the potential for profound implications for the control of parasitic diseases, including, but not exclusive to, widening host range, increased transmission potential and altered responses to drug therapy. Here, we present the challenging case example of haematobium group Schistosoma spp. hybrids in West Africa, a system involving multiple interacting parasites and multiple definitive hosts, in a region where zoonotic reservoirs of schistosomiasis were not previously considered to be of importance. We consider how existing mathematical model frameworks for schistosome transmission could be expanded and adapted to zoonotic hybrid systems, exploring how such model frameworks can utilize molecular and epidemiological data, as well as the complexities and challenges this presents. We also highlight the opportunities and value such mathematical models could bring to this and a range of similar multihost, multi and cross-hybridizing parasites systems in our changing world.

Reconciling Pasteur and Darwin to control infectious diseases

The continual emergence of new pathogens and the increased spread of antibiotic resistance in bacterial populations remind us that microbes are living entities that evolve at rates that impact public health interventions. Following the historical thread of the works of Pasteur and Darwin shows how reconciling clinical microbiology, ecology, and evolution can be instrumental to understanding pathology, developing new therapies, and prolonging the efficiency of existing ones.

Antimicrobial resistance in Mycobacterium tuberculosis: mechanistic and evolutionary perspectives

Antibiotic-resistant Mycobacterium tuberculosis strains are threatening progress in containing the global tuberculosis epidemic. Mycobacterium tuberculosis is intrinsically resistant to many antibiotics, limiting the number of compounds available for treatment. This intrinsic resistance is due to a number of mechanisms including a thick, waxy, hydrophobic cell envelope and the presence of drug degrading and modifying enzymes. Resistance to the drugs which are active against M. tuberculosis is, in the absence of horizontally transferred resistance determinants, conferred by chromosomal mutations. These chromosomal mutations may confer drug resistance via modification or overexpression of the drug target, as well as by prevention of prodrug activation. Drug resistance mutations may have pleiotropic effects leading to a reduction in the bacterium's fitness, quantifiable e.g. by a reduction in the in vitro growth rate. Secondary so-called compensatory mutations, not involved in conferring resistance, can ameliorate the fitness cost by interacting epistatically with the resistance mutation. Although the genetic diversity of M. tuberculosis is low compared to other pathogenic bacteria, the strain genetic background has been demonstrated to influence multiple aspects in the evolution of drug resistance. The rate of resistance evolution and the fitness costs of drug resistance mutations may vary as a function of the genetic background.

Reversing resistance: different routes and common themes across pathogens

Resistance spreads rapidly in pathogen or pest populations exposed to biocides, such as fungicides and antibiotics, and in many cases new biocides are in short supply. How can resistance be reversed in order to prolong the effectiveness of available treatments? Some key parameters affecting reversion of resistance are well known, such as the fitness cost of resistance. However, the population biological processes that actually cause resistance to persist or decline remain poorly characterized, and consequently our ability to manage reversion of resistance is limited. Where do susceptible genotypes that replace resistant lineages come from? What is the epidemiological scale of reversion? What information do we need to predict the mechanisms or likelihood of reversion? Here, we define some of the population biological processes that can drive reversion, using examples from a wide range of taxa and biocides. These processes differ primarily in the origin of revertant genotypes, but also in their sensitivity to factors such as coselection and compensatory evolution that can alter the rate of reversion, and the likelihood that resistance will re-emerge upon re-exposure to biocides. We therefore argue that discriminating among different types of reversion allows for better prediction of where resistance is most likely to persist.

Current Affairs, Future Perspectives of Tuberculosis and Antitubercular Agents

Tuberculosis (TB) is the major threat for humans from past several decades. Even after advent of several antitubercular drugs, researchers are still struggling for the mycobacterial infections in humans are TB and leprosy. Chronic infections caused by Mycobacterium tuberculosis and Mycobacterium leprae. A particular problem with both of these organisms is that they can survive inside macrophages after phagocytosis, unless these cells are activated by cytokines produced by T-lymphocytes, because of this researchers are not yet succeeded in finding effective treatment on TB. In recent years TB has spread globally and became the major issue for world healthcare organizations. Some compounds like benzothiazinones shown promising activity against mycobacterium, few compounds are in pipeline which may exhibit improved pharmacological effect. Decaprenylphosphoryl-d-ribose 2'-epimerase (DprE1) is the vulnerable target for antitubercular drug discovery. DprE1 is a flavoprotein that along with decaprenylphosphoryl-2-keto-ribose reductase catalyses epimerization of decaprenylphosphoryl-d-ribose to decaprenylphosphoryl-d-arabinose through an intermediate formation of decaprenylphosphoryl-2-keto-ribose. This conversion makes DprE1 a potential drug target. Further research requires to tackle the biggest hurdles in Tuberculosis treatment, i.e. multi drug and extensively drug resistance.

Transmissible Mycobacterium tuberculosis Strains Share Genetic Markers and Immune Phenotypes

RATIONALE

Successful transmission of tuberculosis depends on the interplay of human behavior, host immune responses, and Mycobacterium tuberculosis virulence factors. Previous studies have been focused on identifying host risk factors associated with increased transmission, but the contribution of specific genetic variations in mycobacterial strains themselves are still unknown.

OBJECTIVES

To identify mycobacterial genetic markers associated with increased transmissibility and to examine whether these markers lead to altered in vitro immune responses.

METHODS

Using a comprehensive tuberculosis registry (n = 10,389) and strain collection in the Netherlands, we identified a set of 100 M. tuberculosis strains either least or most likely to be transmitted after controlling for host factors. We subjected these strains to whole-genome sequencing and evolutionary convergence analysis, and we repeated this analysis in an independent validation cohort. We then performed immunological experiments to measure in vitro cytokine production and neutrophil responses to a subset of the original strains with or without the identified mutations associated with increased transmissibility.

MEASUREMENTS AND MAIN RESULTS

We identified the loci espE, PE-PGRS56, Rv0197, Rv2813-2814c, and Rv2815-2816c as targets of convergent evolution among transmissible strains. We validated four of these regions in an independent set of strains, and we demonstrated that mutations in these targets affected in vitro monocyte and T-cell cytokine production, neutrophil reactive oxygen species release, and apoptosis.

CONCLUSIONS

In this study, we identified genetic markers in convergent evolution of M. tuberculosis toward enhanced transmissibility in vivo that are associated with altered immune responses in vitro.

Genomic Insight into Mechanisms of Reversion of Antibiotic Resistance in Multidrug Resistant Mycobacterium tuberculosis Induced by a Nanomolecular Iodine-Containing Complex FS-1

Drug induced reversion of antibiotic resistance is a promising way to combat multidrug resistant infections. However, lacking knowledge of mechanisms of drug resistance reversion impedes employing this approach in medicinal therapies. Induction of antibiotic resistance reversion by a new anti-tuberculosis drug FS-1 has been reported. FS-1 was used in this work in combination with standard anti-tuberculosis antibiotics in an experiment on laboratory guinea pigs infected with an extensively drug resistant (XDR) strain Mycobacterium tuberculosis SCAID 187.0. During the experimental trial, genetic changes in the population were analyzed by sequencing of M. tuberculosis isolates followed by variant calling. In total 11 isolates obtained from different groups of infected animals at different stages of disease development and treatment were sequenced. It was found that despite the selective pressure of antibiotics, FS-1 caused a counter-selection of drug resistant variants that speeded up the recovery of the infected animals from XDR tuberculosis. Drug resistance mutations reported in the genome of the initial strain remained intact in more sensitive isolates obtained in this experiment. Variant calling in the sequenced genomes revealed that the drug resistance reversion could be associated with a general increase in genetic heterogeneity of the population of M. tuberculosis. Accumulation of mutations in PpsA and PpsE subunits of phenolpthiocerol polyketide synthase was observed in the isolates treated with FS-1 that may indicate an increase of persisting variants in the population. It was hypothesized that FS-1 caused an active counter-selection of drug resistant variants from the population by aggravating the cumulated fitness cost of the drug resistance mutations. Action of FS-1 on drug resistant bacteria exemplified the theoretically predicted induced synergy mechanism of drug resistance reversion. An experimental model to study the drug resistance reversion phenomenon is hereby introduced.

Selectable Markers for Use in Genetic Manipulation of Extensively Drug-Resistant (XDR) Acinetobacter baumannii HUMC1

Multidrug-resistant (MDR), extensively drug-resistant (XDR), and pan-drug-resistant (PDR) strains of Acinetobacter baumannii have frequently been characterized. The ability of A. baumannii to develop resistance to antibiotics is a key reason this organism has been difficult to study using genetic and molecular biology approaches. Here we report selectable markers that are not only useful but necessary for the selection of drug-resistant transformants in the setting of drug-resistant backgrounds. Use of these selectable markers can be applied to a variety of genetic and molecular techniques such as mutagenesis and transformation. These selectable markers will help promote genetic and molecular biology studies of otherwise onerous drug-resistant strains, while avoiding the generation of pathogenic organisms that are resistant to clinically relevant antibiotics.

Acinetobacter baumannii is one of the most antibiotic-resistant pathogens in clinical medicine, and extensively drug-resistant (XDR) strains are commonly isolated from infected patients. Such XDR strains are already resistant to traditional selectable genetic markers, limiting the ability to conduct pathogenesis research by genetic disruption. Optimization of selectable markers is therefore critical for the advancement of fundamental molecular biology techniques to use in these strains. We screened 23 drugs that constitute a broad array of antibiotics spanning multiple drug classes against HUMC1, a highly virulent and XDR A. baumannii clinical blood and lung isolate. HUMC1 is resistant to all clinically useful antibiotics that are reported by the clinical microbiology laboratory, except for colistin. Ethical concerns about intentionally establishing pan-resistance, including to the last-line agent, colistin, in a clinical isolate made identification of other markers desirable. We screened additional antibiotics that are in clinical use and those that are useful only in a lab setting to identify selectable markers that were effective at selecting for transformants in vitro. We show that supraphysiological levels of tetracycline can overcome innate drug resistance displayed by this XDR strain. Last, we demonstrate that transformation of the tetA (tetracycline resistance) and Sh ble (zeocin resistance), but not pac (puromycin resistance), resistance cassettes allow for selection of drug-resistant transformants. These results make the genetic manipulation of XDR A. baumannii strains easily achieved.

IMPORTANCE Multidrug-resistant (MDR), extensively drug-resistant (XDR), and pan-drug-resistant (PDR) strains of Acinetobacter baumannii have frequently been characterized. The ability of A. baumannii to develop resistance to antibiotics is a key reason this organism has been difficult to study using genetic and molecular biology approaches. Here we report selectable markers that are not only useful but necessary for the selection of drug-resistant transformants in the setting of drug-resistant backgrounds. Use of these selectable markers can be applied to a variety of genetic and molecular techniques such as mutagenesis and transformation. These selectable markers will help promote genetic and molecular biology studies of otherwise onerous drug-resistant strains, while avoiding the generation of pathogenic organisms that are resistant to clinically relevant antibiotics.

Mathematical Modeling Tools to Study Preharvest Food Safety

This article provides an overview of the emerging field of mathematical modeling in preharvest food safety. We describe the steps involved in developing mathematical models, different types of models, and their multiple applications. The introduction to modeling is followed by several sections that introduce the most common modeling approaches used in preharvest systems. We finish the chapter by outlining potential future directions for the field.

Tradeoffs in Introduction Policies for the Anti-Tuberculosis Drug Bedaquiline: A Model-Based Analysis

BACKGROUND

New drugs for the treatment of tuberculosis (TB) are becoming available for the first time in over 40 y. Optimal strategies for introducing these drugs have not yet been established. The objective of this study was to compare different strategies for introducing the new TB drug bedaquiline based on patients' resistance patterns.

METHODS AND FINDINGS

We created a Markov decision model to follow a hypothetical cohort of multidrug-resistant (MDR) TB patients under different bedaquiline use strategies. The explored strategies included making bedaquiline available to all patients with MDR TB, restricting bedaquiline usage to patients with MDR plus additional resistance and withholding bedaquiline introduction completely. We compared these strategies according to life expectancy, risks of acquired resistance, and the expected number and health outcomes of secondary cases. For our simulated cohort, the mean (2.5th, 97.5th percentile) life expectancy from time of initiation of MDR TB treatment at age 30 was 36.0 y (33.5, 38.7) assuming all patients with MDR TB received bedaquiline, 35.1 y (34.4, 35.8) assuming patients with pre-extensively drug-resistant (PreXDR) and extensively drug-resistant (XDR) TB received bedaquiline, and 34.9 y (34.6, 35.2) assuming only patients with XDR TB received bedaquiline. Although providing bedaquiline to all MDR patients resulted in the highest life expectancy for our initial cohort averaged across all parameter sets, for parameter sets in which bedaquiline conferred high risks of added mortality and only small reductions in median time to culture conversion, the optimal strategy would be to withhold use even from patients with the most extensive resistance. Across all parameter sets, the most liberal bedaquiline use strategies consistently increased the risk of bedaquiline resistance but decreased the risk of resistance to other MDR drugs. In almost all cases, more liberal bedaquiline use strategies reduced the expected number of secondary cases and resulting life years lost. The generalizability of our results is limited by the lack of available data about drug effects among individuals with HIV co-infection, drug interactions, and other sources of heterogeneity, as well as changing recommendations for MDR TB treatment.

CONCLUSIONS

If mortality benefits can be empirically verified, our results provide support for expanding bedaquiline access to all patients with MDR TB. Such expansion could improve patients' health, protect background MDR TB drugs, and decrease transmission, but would likely result in greater resistance to bedaquiline.

Forecasting Epidemiological and Evolutionary Dynamics of Infectious Diseases

Mathematical models have been powerful tools in developing mechanistic understanding of infectious diseases. Furthermore, they have allowed detailed forecasting of epidemiological phenomena such as outbreak size, which is of considerable public-health relevance. The short generation time of pathogens and the strong selection they are subjected to (by host immunity, vaccines, chemotherapy, etc.) mean that evolution is also a key driver of infectious disease dynamics. Accurate forecasting of pathogen dynamics therefore calls for the integration of epidemiological and evolutionary processes, yet this integration remains relatively rare. We review previous attempts to model and predict infectious disease dynamics with or without evolution and discuss major challenges facing the development of the emerging science of epidemic forecasting.

The Distribution of Fitness Costs of Resistance-Conferring Mutations Is a Key Determinant for the Future Burden of Drug-Resistant Tuberculosis: A Model-Based Analysis

BACKGROUND

Drug resistance poses a serious challenge for the control of tuberculosis in many settings. It is well established that the expected future trend in resistance depends on the reproductive fitness of drug-resistant Mycobacterium tuberculosis. However, the variability in fitness between strains with different resistance-conferring mutations has been largely ignored when making these predictions.

METHODS

We developed a novel approach for incorporating the variable fitness costs of drug resistance-conferring mutations and for tracking this distribution of fitness costs over time within a transmission model. We used this approach to describe the effects of realistic fitness cost distributions on the future prevalence of drug-resistant tuberculosis.

RESULTS

The shape of the distribution of fitness costs was a strong predictor of the long-term prevalence of resistance. While, as expected, lower average fitness costs of drug resistance-conferring mutations were associated with more severe epidemics of drug-resistant tuberculosis, fitness distributions with greater variance also led to higher levels of drug resistance. For example, compared to simulations in which the fitness cost of resistance was fixed, introducing a realistic amount of variance resulted in a 40% increase in prevalence of drug-resistant tuberculosis after 20 years.

CONCLUSIONS

The differences in the fitness costs associated with drug resistance-conferring mutations are a key determinant of the future burden of drug-resistant tuberculosis. Future studies that can better establish the range of fitness costs associated with drug resistance-conferring mutations will improve projections and thus facilitate better public health planning efforts.

New agents for the treatment of drug-resistant Mycobacterium tuberculosis

Inadequate dosing and incomplete treatment regimens, coupled with the ability of the tuberculosis bacilli to cause latent infections that are tolerant of currently used drugs, have fueled the rise of multidrug-resistant tuberculosis (MDR-TB). Treatment of MDR-TB infections is a major clinical challenge that has few viable or effective solutions; therefore patients face a poor prognosis and years of treatment. This review focuses on emerging drug classes that have the potential for treating MDR-TB and highlights their particular strengths as leads including their mode of action, in vivo efficacy, and key medicinal chemistry properties. Examples include the newly approved drugs bedaquiline and delaminid, and other agents in clinical and late preclinical development pipeline for the treatment of MDR-TB. Herein, we discuss the challenges to developing drugs to treat tuberculosis and how the field has adapted to these difficulties, with an emphasis on drug discovery approaches that might produce more effective agents and treatment regimens.

Epidemiology and Clinical Characteristics of Pediatric Drug-Resistant Tuberculosis in Chongqing, China

To gain insight into the epidemiology of childhood drug resistant tuberculosis (DR-TB) in China that has the second largest burden of TB and the largest number of multidrug resistant (MDR) TB cases in the world, we performed the cross-sectional study to investigate drug resistance of four first-line anti-TB drugs (isoniazid, rifampicin, streptomycin and ethambutol) using Mycobacterium tuberculosis isolates from 196 culture-confirmed pediatric TB cases diagnosed in the Children's Hospital of Chongqing Medical University, China during 2008-2013. Univariate and multivariate logistic regression analyses were performed to assess the associations between patient demographic and clinical characteristics and DR-and MDR-TB, respectively. Twenty-eight percent (56/196) of the study patients exhibited resistance to at least one of the four first-line anti-TB drugs tested. MDR was found in 4.6% (9/196) of the study patients. More than half (5/9, 55.6%) of the MDR cases were from a single county of Chongqing. A significant association was found between being acid-fast bacilli-smear negative and DR-TB (adjusted OR, 2.33; 95% CI, 1.13-4.80) and between having concurrent thoracic-extrathoracic involvement and MDR-TB (adjusted OR, 9.49; 95% CI, 1.05-85.92), respectively. The findings of this study indicate that the rate of DR is high among pediatric TB patients in Chongqing and suggest an urgent need for studies to identify MDR transmission hotspots in Chongqing, thereby contributing to the control DR- and MDR-TB epidemics in China. The study also generates new insight into the pathogenesis of DR and MDR M. tuberculosis strains and highlights the importance of studying childhood TB to the goal of global TB control.

Blood stream infections due to multidrug-resistant organisms among spinal cord-injured patients, epidemiology over 16 years and associated risks: a comparative study

STUDY DESIGN

Retrospective study.

OBJECTIVES

We aimed to describe the epidemiology of multidrug-resistant organisms (MDROs) during bloodstream infection (BSI) and identify associated risks of MDROs among patients with spinal cord injury (SCI).

SETTING

A teaching hospital, expert center in disability, in France.

METHODS

We studied a retrospective cohort of all BSIs occurring in SCI patients hospitalized over 16 years. We described the prevalence of MDRO BSI among this population and its evolution over time and compared the BSI population due to MDROs and due to non-MDROs.

RESULTS

A total of 318 BSIs occurring among 256 patients were included in the analysis. The most frequent primary sites of infection were urinary tract infection (34.0%), pressure sore (25.2%) and catheter line-associated bloodstream infection (11.3%). MDROs were responsible for 41.8% of BSIs, and this prevalence was stable over 16 years. No significant associated factor for MDRO BSI could be identified concerning sociodemographic and clinical characteristics, primary site of infection and bacterial species in univariate and multivariate analyses. BSI involving MDROs was not associated with initial severity of sepsis compared with infection without MDROs (43.8 vs 43.6%, respectively) and was not associated either with 30th-day mortality (6.2 vs 9%, respectively).

CONCLUSION

During BSI occurrence in an SCI population, MDROs are frequent but remain stable over years. No associated risk can be identified that would help optimize antibiotic treatment. Neither the severity of the episode nor the mortality is significantly different when an MDRO is involved.

Estimating wildlife disease dynamics in complex systems using an Approximate Bayesian Computation framework

Emerging infectious diseases of wildlife are of increasing concern to managers and conservation policy makers, but are often difficult to study and predict due to the complexity of host-disease systems and a paucity of empirical data. We demonstrate the use of an Approximate Bayesian Computation statistical framework to reconstruct the disease dynamics of bovine tuberculosis in Kruger National Park's lion population, despite limited empirical data on the disease's effects in lions. The modeling results suggest that, while a large proportion of the lion population will become infected with bovine tuberculosis, lions are a spillover host and long disease latency is common. In the absence of future aggravating factors, bovine tuberculosis is projected to cause a lion population decline of ~3% over the next 50 years, with the population stabilizing at this new equilibrium. The Approximate Bayesian Computation framework is a new tool for wildlife managers. It allows emerging infectious diseases to be modeled in complex systems by incorporating disparate knowledge about host demographics, behavior, and heterogeneous disease transmission, while allowing inference of unknown system parameters.

Outbreak of multidrug-resistant tuberculosis in two secondary schools

OBJECTIVES

To describe an outbreak of multidrug-resistant tuberculosis (MDR-TB) in two schools

METHODS

This was a prospective, observational study of an outbreak of MDR-TB in 2 schools located in the towns of Onda and Nules, in the Spanish province of Castellon, from the moment of detection in November 2008 until November 2014, including patient follow-up and contact tracing.

RESULTS

Five cases of MDR-TB were diagnosed. Overall attack rate was 0.9%, and among the contacts traced, 66 had latent tuberculous infection, with an infection rate of 14.4%. Molecular characterization of the 5M. tuberculosis isolates was performed by restriction fragment length polymorphism (RFLP) analysis of the IS6110 sequence. In all 5 patients, cultures were negative at 4-month follow-up, showing the efficacy of the treatment given. No recurrence has been reported to date.

CONCLUSIONS

In the context of globalization and the increased prevalence of MDR-TB, outbreaks such as the one presented here are only to be expected. Contact tracing, strict follow-up of confirmed cases, the availability of fast diagnostic techniques to avoid treatment delay, and chemoprophylaxis, together with the molecular characterization of strains, are still essential.

Cost-effectiveness of adding bedaquiline to drug regimens for the treatment of multidrug-resistant tuberculosis in the UK

OBJECTIVE

To evaluate the cost-effectiveness of adding bedaquiline to a background regimen (BR) of drugs for multidrug-resistant tuberculosis (MDR-TB) in the United Kingdom (UK).

METHODS

A cohort-based Markov model was developed to estimate the incremental cost-effectiveness ratio of bedaquiline plus BR (BBR) versus BR alone (BR) in the treatment of MDR-TB, over a 10-year time horizon. A National Health Service (NHS) and personal social services perspective was considered. Cost-effectiveness was evaluated in terms of Quality-Adjusted Life Years (QALYs) and Disability-Adjusted Life Years (DALYs). Data were sourced from a phase II, placebo-controlled trial, NHS reference costs, and the literature; the US list price of bedaquiline was used and converted to pounds (£18,800). Costs and effectiveness were discounted at a rate of 3.5% per annum. Probabilistic and deterministic sensitivity analysis was conducted.

RESULTS

The total discounted cost per patient (pp) on BBR was £106,487, compared with £117,922 for BR. The total discounted QALYs pp were 5.16 for BBR and 4.01 for BR. The addition of bedaquiline to a BR resulted in a cost-saving of £11,434 and an additional 1.14 QALYs pp over a 10-year period, and is therefore considered to be the dominant (less costly and more effective) strategy over BR. BBR remained dominant in the majority of sensitivity analyses, with a 81% probability of being dominant versus BR in the probabilistic analysis.

CONCLUSIONS

In the UK, bedaquiline is likely to be cost-effective and cost-saving, compared with the current MDR-TB standard of care under a range of scenarios. Cost-savings over a 10-year period were realized from reductions in length of hospitalization, which offset the bedaquiline drug costs. The cost-benefit conclusions held after several sensitivity analyses, thus validating assumptions made, and suggesting that the results would hold even if the actual price of bedaquiline in the UK were higher than in the US.

Trade-offs between and within scales: environmental persistence and within-host fitness of avian influenza viruses

Trade-offs between different components of a pathogen's replication and transmission cycle are thought to be common. A number of studies have identified trade-offs that emerge across scales, reflecting the tension between strategies that optimize within-host proliferation and large-scale population spread. Most of these studies are theoretical in nature, with direct experimental tests of such cross-scale trade-offs still rare. Here, we report an analysis of avian influenza A viruses across scales, focusing on the phenotype of temperature-dependent viral persistence. Taking advantage of a unique dataset that reports both environmental virus decay rates and strain-specific viral kinetics from duck challenge experiments, we show that the temperature-dependent environmental decay rate of a strain does not impact within-host virus load. Hence, for this phenotype, the scales of within-host infection dynamics and between-host environmental persistence do not seem to interact: viral fitness may be optimized on each scale without cross-scale trade-offs. Instead, we confirm the existence of a temperature-dependent persistence trade-off on a single scale, with some strains favouring environmental persistence in water at low temperatures while others reduce sensitivity to increasing temperatures. We show that this temperature-dependent trade-off is a robust phenomenon and does not depend on the details of data analysis. Our findings suggest that viruses might employ different environmental persistence strategies, which facilitates the coexistence of diverse strains in ecological niches. We conclude that a better understanding of the transmission and evolutionary dynamics of influenza A viruses probably requires empirical information regarding both within-host dynamics and environmental traits, integrated within a combined ecological and within-host framework.