Practical aspects of backward bifurcation in a mathematical model for tuberculosis.

In this work, we examine practical aspects of backward bifurcation for a data-based model of tuberculosis that incorporates multiple features which have previously been shown to produce backward bifurcation (e.g. exogenous reinfection and imperfect vaccination) and new considerations such as the treatment of latent TB infection (LTBI) and the BCG vaccine's interference with detecting LTBI. Understanding the interplay between these multiple factors and backward bifurcation is particularly timely given that new diagnostic tests for LTBI detection could dramatically increase rates of both LTBI detection and vaccination in the coming decades. By establishing analytic thresholds for the existence of backward bifurcation, we identify those aspects of TB's complicated pathology that make backward bifurcation more or less likely to occur. We also examine the magnitude of the backward bifurcation produced by the model and its sensitivity to various model parameters. We find that backward bifurcation is unlikely to occur. While increased vaccine coverage and/or increased detection and treatment of LTBI can push the threshold for backward bifurcation into the region of biological plausibility, the resulting bifurcations may still be too small to have any noticeable epidemiological impact.

Trade-off between BCG vaccination and the ability to detect and treat latent tuberculosis.

Policies regarding the use of the Bacille Calmette-Guérin (BCG) vaccine for tuberculosis vary greatly throughout the international community. In several countries, consideration of discontinuing universal vaccination programs is currently under way. The arguments against mass vaccination are that the effectiveness of BCG in preventing tuberculosis is uncertain and that BCG vaccination can interfere with the detection and treatment of latent tuberculosis. In this work, we pose a dynamical systems model for the population-level dynamics of tuberculosis in order to study the trade-off which occurs between vaccination and detection/treatment of latent tuberculosis. We assume that latent infection in vaccinated individuals is completely undetectable. For the case of a country with very low levels of tuberculosis, we establish analytic thresholds, via stability analysis and the basic reproductive number, which determine the optimal vaccination policy, given the effectiveness of the vaccine and the detection/treatment rate of latent tuberculosis. The results of this work suggest that it is unlikely that a country detects and treats latent tuberculosis at a high enough rate to justify the discontinuation of mass vaccination from this perspective.

An SEIQR model for childhood diseases.

It has been shown that the inclusion of an isolated class in the classical SIR model for childhood diseases can be responsible for self-sustained oscillations. Hence, the recurrent outbreaks of such diseases can be caused by autonomous, deterministic factors. We extend the model to include a latent class (i.e. individuals who are infected with the disease, but are not yet able to pass the disease to others) and study the resulting dynamics. The existence of Hopf bifurcations is shown for the model, as well as a homoclinic bifurcation for a perturbation to the model. For historical data on scarlet fever in England, our model agrees with the epidemiological data much more closely than the model without the latent class. For other childhood diseases, our model suggests that isolation is unlikely to be a major factor in sustained oscillations.

An exact approach to calibrating infectious disease models to surveillance data: The case of HIV and HSV-2.

When mathematical models of infectious diseases are used to inform health policy, an important first step is often to calibrate a model to disease surveillance data for a specific setting (or multiple settings). It is increasingly common to also perform sensitivity analyses to demonstrate the robustness, or lack thereof, of the modeling results. Doing so requires the modeler to find multiple parameter sets for which the model produces behavior that is consistent with the surveillance data. While frequently overlooked, the calibration process is nontrivial at best and can be inefficient, poorly communicated and a major hurdle to the overall reproducibility of modeling results. In this work, we describe a general approach to calibrating infectious disease models to surveillance data. The technique is able to match surveillance data to high accuracy in a very efficient manner as it is based on the Newton-Raphson method for solving nonlinear systems. To demonstrate its robustness, we use the calibration technique on multiple models for the interacting dynamics of HIV and HSV-2.

Using geospatial modelling to optimize the rollout of antiretroviral-based pre-exposure HIV interventions in Sub-Saharan Africa.

Antiretroviral (ARV)-based pre-exposure HIV interventions may soon be rolled out in resource-constrained Sub-Saharan African countries, but rollout plans have yet to be designed. Here we use geospatial modelling and optimization techniques to compare two rollout plans for ARV-based microbicides in South Africa: a utilitarian plan that minimizes incidence by using geographic targeting, and an egalitarian plan that maximizes geographic equity in access to interventions. We find significant geographic variation in the efficiency of interventions in reducing HIV transmission, and that efficiency increases disproportionately with increasing incidence. The utilitarian plan would result in considerable geographic inequity in access to interventions, but (by exploiting geographic variation in incidence) could prevent ~40% more infections than the egalitarian plan. Our results show that the geographic resource allocation decisions made at the beginning of a rollout, and the location where the rollout is initiated, will be crucial in determining the success of interventions in reducing HIV epidemics.