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Tuberculosis Epidemiology at the Country Scale: Self-Limiting Process and the HIV Effects

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TLDR
The endogenous view should be considered as a plausible hypothesis to model and explain tuberculosis dynamics and that future World Health Organization reports could include the endogenous/exogenous framework as a supplement to help to decipher the main drivers of TB dynamics and other diseases.
Abstract
Background The global spread of the human immunodeficiency virus (HIV) is the main hypothesis behind tuberculosis (TB) positive trends in the last decades, according to modeling studies and World Health Organization Reports (WHO). On one hand, TB (WHO) reports do not explicitly consider a modeling approach, but cover country and global TB trends. On the other hand, modeling studies usually do not cover the scale of WHO reports, because of the amount of parameters estimated to describe TB natural history. Here we combined these two principal sources of TB studies covering TB High Burden Countries (HBCs) dynamics. Our main goals were: (i) to detect the endogenous component of TB dynamics since 1974 for TB HBCs; and (ii) to explore the HIV exogenous effects on TB models`parameters. Methods and Findings We explored the relationship between the TB per capita population rate of change (RI) and the infectious class size following an endogenous/exogenous framework. RI can be affected by intra-population processes (i.e. competition, predation) and exogenous variables like HIV. We found that TB dynamics had always a strong endogenous component, represented by a negative correlation between TB population size and RI, which was captured by the discrete logistic model. Moreover, we explored the HIV exogenous effects on TB models`parameters. We found that overall the TB+HIV logistic model was more parsimonious than TB model alone, principally in the African region. Our results showed that HIV affected principally TB carrying capacity, as expected by the known HIV effects on TB natural-history. We also tested if DOTS (Directly Observed Treatment Short-Course Strategy), poverty levels and BCG (Bacillus Calmette-Guerin) coverage explained the models´ residuals variances, but they did not. Conclusions Since 1974, TB dynamics were categorized in distinct chronological domains, with different dynamics but nearly the same underlying mechanism: a negative relationship between RI and infected class size (i.e. self-limiting). In the last decades, not only HIV spread represented a new TB chronological domain, but it also has been pushing TB carrying capacity (K) to higher levels. TB has a complex natural-history and imposes real challenges to model its dynamics. Yet, we were able to explore and reveal the main drivers of TB dynamics for HBCs since 1974, through a simple approach. Based on our results, we suggest that the endogenous view should be considered as a plausible hypothesis to model and explain TB dynamics and that future World Health Organization reports could include the endogenous/exogenous framework as a supplement to help to decipher the main drivers of TB dynamics and other diseases.

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References
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Book

Infectious Diseases of Humans: Dynamics and Control

TL;DR: This book discusses the biology of host-microparasite associations, dynamics of acquired immunity heterogeneity within the human community indirectly transmitted helminths, and the ecology and genetics of hosts and parasites.
Book

Nonlinear Regression Analysis and Its Applications

TL;DR: This book offers a balanced presentation of the theoretical, practical, and computational aspects of nonlinear regression and provides background material on linear regression, including the geometrical development for linear and nonlinear least squares.
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Ecological effects of climate fluctuations.

TL;DR: This work reviews how two of the best-known climate phenomena—the North Atlantic Oscillation and the El Niño–Southern O oscillation—affect ecological patterns and processes in both marine and terrestrial systems.
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Mathematical Analysis of HIV-1 Dynamics in Vivo

TL;DR: It is shown how dynamical modeling and parameter estimation techniques have uncovered important features of HIV pathogenesis and impacted the way in which AIDS patients are treated with potent antiretroviral drugs.
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