Different levels of host-pathogen interactions and consequences for the pathogen life history evolution.

For pathogens associated with acute and rapidly transmitting infections, their incidence, abundance and fitness depend not only on their ability to spread through a host population but also on their ability to maintain circulation. This dissertation will explore the conditions required for persistence of acute pathogens and the consequences for the pathogen's life history evolution by constructing models that abstract salient features of infection, transmission and competition dynamics. We begin with a mechanistic model of within-host dynamics and link it to epidemiological models of disease transmission. The epidemic patterns and population level extinction risk for the pathogen can result in a trade-off, an invasion-persistence trade-off, between pathogen's abilities to spread in the host population and to maintain circulation in the host population. Depending on the shape of the dose-response curve and the host population size, we observe contrasting results --- pathogen evolution driving itself to the brink of extinction or existence of evolutionarily stable intermediate pathogen growth rate. Two independent emergences of acute and short-lasting species of Bordetellae, B. pertussis and B. parapertussis as human pathogen, from milder but persistent emph{B. bronchiseptica}, can be understood in the light of this trade-off. In an individual based framework, we extend this model to include structure in the host population, stochasticity in demographic and transmission processes, and explicit competition between pathogen strains. We find that the evolutionary dynamics are strongly influenced by the structure of the host population. When the host interactions are localized, pathogen traits that optimize the recruitment of susceptibles, are no longer favored. Instead, pathogens that produce larger and longer-lasting epidemics in their local communities are favored. We further construct an analytical model that abstracts meta-population epidemic dynamics, and within-patch strain competition. For acute pathogens that reside in locally small host populations that exhibit episodic behavior, a pathogen's ability to compete for susceptibles and its ability to colonize patches are in conflict. We find that migration rate and patch-size affect the meta-population epidemic dynamics, and consequently the frequencies of competition and colonization interactions --- the proportion of which determines the evolutionarily stable pathogen traits.Ph.D.Applied and Interdisciplinary MathematicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64648/1/sourya_1.pd

Drivers and trajectories of resistance to new first-line drug regimens for tuberculosis.

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

Quantifying the potential epidemiological impact of a 2-year active case finding for tuberculosis in rural Nepal: a model-based analysis

Objectives: Active case finding (ACF) is an important tuberculosis (TB) intervention in high-burden settings. However, empirical evidence garnered from field data has been equivocal about the long-term community-level impact, and more data at a finer geographic scale and data-informed methods to quantify their impact are necessary. Methods: Using village development committee (VDC)-level data on TB notification and demography between 2016 and 2017 in four southern districts of Nepal, where ACF activities were implemented as a part of the IMPACT-TB study between 2017 and 2019, we developed VDC-level transmission models of TB and ACF. Using these models and ACF yield data collected in the study, we estimated the potential epidemiological impact of IMPACT-TB ACF and compared its efficiency across VDCs in each district. Results: Cases were found in the majority of VDCs during IMPACT-TB ACF, but the number of cases detected within VDCs correlated weakly with historic case notification rates. We projected that this ACF intervention would reduce the TB incidence rate by 14% (12–16) in Chitwan, 8.6% (7.3–9.7) in Dhanusha, 8.3% (7.3–9.2) in Mahottari and 3% (2.5–3.2) in Makwanpur. Over the next 10 years, we projected that this intervention would avert 987 (746–1282), 422 (304–571), 598 (450–782) and 197 (172–240) cases in Chitwan, Dhanusha, Mahottari and Makwanpur, respectively. There was substantial variation in the efficiency of ACF across VDCs: there was up to twofold difference in the number of cases averted in the 10 years per case detected. Conclusion: ACF data confirm that TB is widely prevalent, including in VDCs with relatively low reporting rates. Although ACF is a highly efficient component of TB control, its impact can vary substantially at local levels and must be combined with other interventions to alter TB epidemiology significantly

Impact of Targeted Tuberculosis Vaccination Among a Mining Population in South Africa: A Model-Based Study.

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

Spotting the old foe-revisiting the case definition for TB.

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

Statistical Inference for Multi-Pathogen Systems

There is growing interest in understanding the nature and consequences of interactions among infectious agents. Pathogen interactions can be operational at different scales, either within a co-infected host or in host populations where they co-circulate, and can be either cooperative or competitive. The detection of interactions among pathogens has typically involved the study of synchrony in the oscillations of the protagonists, but as we show here, phase association provides an unreliable dynamical fingerprint for this task. We assess the capacity of a likelihood-based inference framework to accurately detect and quantify the presence and nature of pathogen interactions on the basis of realistic amounts and kinds of simulated data. We show that when epidemiological and demographic processes are well understood, noisy time series data can contain sufficient information to allow correct inference of interactions in multi-pathogen systems. The inference power is dependent on the strength and time-course of the underlying mechanism: stronger and longer-lasting interactions are more easily and more precisely quantified. We examine the limitations of our approach to stochastic temporal variation, under-reporting, and over-aggregation of data. We propose that likelihood shows promise as a basis for detection and quantification of the effects of pathogen interactions and the determination of their (competitive or cooperative) nature on the basis of population-level time-series data

Dataset for the article: Quantifying the potential epidemiological impact of a two-year active case finding for tuberculosis in rural Nepal: A model-based analysis

Includes data and codes for the manuscript entitled: Quantifying the potential epidemiological impact of a two-year active case finding for tuberculosis in rural Nepal: A model-based analysi

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)

Holistic Approach to Tuberculosis Detection, Treatment and Prevention: Emerging Evidence and Strategies from the Field

The global fight against tuberculosis (TB) has gained momentum since the adoption of the ‘End TB Strategy’ in 2014 [...