Phylogenetic patterns recover known HIV epidemiological relationships and reveal common transmission of multiple variants.

The growth of human immunodeficiency virus (HIV) sequence databases resulting from drug resistance testing has motivated efforts using phylogenetic methods to assess how HIV spreads1-4. Such inference is potentially both powerful and useful for tracking the epidemiology of HIV and the allocation of resources to prevention campaigns. We recently used simulation and a small number of illustrative cases to show that certain phylogenetic patterns are associated with different types of epidemiological linkage5. Our original approach was later generalized for large next-generation sequencing datasets and implemented as a free computational pipeline6. Previous work has claimed that direction and directness of transmission could not be established from phylogeny because one could not be sure that there were no intervening or missing links involved7-9. Here, we address this issue by investigating phylogenetic patterns from 272 previously identified HIV transmission chains with 955 transmission pairs representing diverse geography, risk groups, subtypes, and genomic regions. These HIV transmissions had known linkage based on epidemiological information such as partner studies, mother-to-child transmission, pairs identified by contact tracing, and criminal cases. We show that the resulting phylogeny inferred from real HIV genetic sequences indeed reveals distinct patterns associated with direct transmission contra transmissions from a common source. Thus, our results establish how to interpret phylogenetic trees based on HIV sequences when tracking who-infected-whom, when and how genetic information can be used for improved tracking of HIV spread. We also investigate limitations that stem from limited sampling and genetic time-trends in the donor and recipient HIV populations

Systematic evaluation of the population-level effects of alternative treatment strategies on the basic reproduction number

An approach to estimate the influence of the treatment-type controls on the basic reproduction number, R 0 , is proposed and elaborated. The presented approach allows one to estimate the effect of a given treatment strategy or to compare a number of different treatment strategies on the basic reproduction number. All our results are valid for sufficiently small values of the control. However, in many cases it is possible to extend this analysis to larger values of the control as was illustrated by examples

Within-host phenotypic evolution and the population-level control of chronic viral infections by treatment and prophylaxis

Chronic viral infections can persist in an infected person for decades. From the perspective of the virus, a single infection can span thousands of generations, leading to a highly diverse population of viruses with its own complex evolutionary history. We propose a mathematical framework for understanding how the emergence of new viral strains and phenotype within infected persons affects the population-level control of those infections by both non-curative treatment and chemo-prophylactic measures. We consider the within-host emergence of new strains that lack phenotype novelty and also the evolution of variability in contagiousness, resistance to therapy, and efficacy of prophylaxis. Our framework balances the need for verisimilitude with our desire to retain a model that can be approached analytically. We show how to compute the population-level basic reproduction number accounting for the within-host evolutionary process where new phenotypes emerge and are lost in infected persons, which we also extend to include both treatment and prophylactic control efforts. This allows us to make clear statements about both the global and relative efficacy of different control efforts accounting for within-host phenotypic evolution. Finally, we give expressions for the endemic equilibrium of these models for certain constrained versions of the within-host evolutionary model providing a potential method for estimating within-host evolutionary parameters from population-level genetic sequence data

Numerical optimal control for HIV prevention with dynamic budget allocation

This paper is about numerical control of HIV propagation. The contribution of the paper is threefold: first, a novel model of HIV propagation is proposed; second, the methods from numerical optimal control are successfully applied to the developed model to compute optimal control profiles; finally, the computed results are applied to the real problem yielding important and practically relevant results.Comment: Submitted pape

Noise is not error : detecting parametric heterogeneity between epidemiologic time series

© Copyright © 2018 Romero-Severson, Ribeiro and Castro. This is an open-accessarticle distributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Mathematical models play a central role in epidemiology. For example, models unify heterogeneous data into a single framework, suggest experimental designs, and generate hypotheses. Traditional methods based on deterministic assumptions, such as ordinary differential equations (ODE), have been successful in those scenarios. However, noise caused by random variations rather than true differences is an intrinsic feature of the cellular/molecular/social world. Time series data from patients (in the case of clinical science) or number of infections (in the case of epidemics) can vary due to both intrinsic differences or incidental fluctuations. The use of traditional fitting methods for ODEs applied to noisy problems implies that deviation from some trend can only be due to error or parametric heterogeneity, that is noise can be wrongly classified as parametric heterogeneity. This leads to unstable predictions and potentially misguided policies or research programs. In this paper, we quantify the ability of ODEs under different hypotheses (fixed or random effects) to capture individual differences in the underlying data. We explore a simple (exactly solvable) example displaying an initial exponential growth by comparing state-of-the-art stochastic fitting and traditional least squares approximations. We also provide a potential approach for determining the limitations and risks of traditional fitting methodologies. Finally, we discuss the implications of our results for the interpretation of data from the 2014-2015 Ebola epidemic in Africa.This work was funded by NIH grants R01-AI087520 and R01-AI104373; grants FIS2013-47949-C2-2-P and FIS2016-78883-C2-2-P and PRX 16/00287 (Spain); and PIRSES-GA-2012-317893 (7th FP, EU).info:eu-repo/semantics/publishedVersio

Noise Is Not Error: Detecting Parametric Heterogeneity Between Epidemiologic Time Series

Mathematical models play a central role in epidemiology. For example, models unify heterogeneous data into a single framework, suggest experimental designs, and generate hypotheses. Traditional methods based on deterministic assumptions, such as ordinary differential equations (ODE), have been successful in those scenarios. However, noise caused by random variations rather than true differences is an intrinsic feature of the cellular/molecular/social world. Time series data from patients (in the case of clinical science) or number of infections (in the case of epidemics) can vary due to both intrinsic differences or incidental fluctuations. The use of traditional fitting methods for ODEs applied to noisy problems implies that deviation from some trend can only be due to error or parametric heterogeneity, that is noise can be wrongly classified as parametric heterogeneity. This leads to unstable predictions and potentially misguided policies or research programs. In this paper, we quantify the ability of ODEs under different hypotheses (fixed or random effects) to capture individual differences in the underlying data. We explore a simple (exactly solvable) example displaying an initial exponential growth by comparing state-of-the-art stochastic fitting and traditional least squares approximations. We also provide a potential approach for determining the limitations and risks of traditional fitting methodologies. Finally, we discuss the implications of our results for the interpretation of data from the 2014-2015 Ebola epidemic in Africa

Getting more from heterogeneous HIV-1 surveillance data in a high immigration country: estimation of incidence and undiagnosed population size using multiple biomarkers

BACKGROUND: Most HIV infections originate from individuals who are undiagnosed and unaware of their infection. Estimation of this quantity from surveillance data is hard because there is incomplete knowledge about (i) the time between infection and diagnosis (TI) for the general population, and (ii) the time between immigration and diagnosis for foreign-born persons. METHODS: We developed a new statistical method for estimating the incidence of HIV-1 and the number of undiagnosed people living with HIV (PLHIV), based on dynamic modelling of heterogeneous HIV-1 surveillance data. The methods consist of a Bayesian non-linear mixed effects model using multiple biomarkers to estimate TI of HIV-1-positive individuals, and a novel incidence estimator which distinguishes between endogenous and exogenous infections by modelling explicitly the probability that a foreign-born person was infected either before or after immigration. The incidence estimator allows for direct calculation of the number of undiagnosed persons. The new methodology is illustrated combining heterogeneous surveillance data from Sweden between 2003 and 2015. RESULTS: A leave-one-out cross-validation study showed that the multiple-biomarker model was more accurate than single biomarkers (mean absolute error 1.01 vs ≥1.95). We estimate that 816 [95% credible interval (CI) 775-865] PLHIV were undiagnosed in 2015, representing a proportion of 10.8% (95% CI 10.3-11.4%) of all PLHIV. CONCLUSIONS: The proposed methodology will enhance the utility of standard surveillance data streams and will be useful to monitor progress towards and compliance with the 90-90-90 UNAIDS target

Field trials reveal the complexities of deploying and evaluating the impacts of yeast-baited ovitraps on Aedes mosquito densities in Trinidad, West Indie

The use of lure-and-kill, large-volume ovitraps to control Aedes aegypti and Aedes albopictus populations has shown promise across multiple designs that target gravid females (adulticidal) or larvae post-oviposition (larvicidal). Here we report on a pilot trial to deploy 10 L yeast-baited ovitraps at select sites in Curepe, Trinidad, West Indies during July to December, 2019. Oviposition rates among ovitraps placed in three Treatment sites were compared to a limited number of traps placed in three Control areas (no Aedes management performed), and three Vector areas (subjected to standard Ministry of Health, Insect Vector Control efforts). Our goal was to gain baseline information on efforts to saturate the Treatment sites with ovitraps within 20-25 m of each other and compare oviposition rates at these sites with background oviposition rates in Control and Vector Areas. Although yeast-baited ovitraps were highly attractive to gravid Aedes females, a primary limitation encountered within the Treatment sites was the inability to gain access to residential compounds for trap placement, primarily due to residents being absent during the day. This severely limited our intent to saturate these areas with ovitraps, indicating that future studies must include plans to account for these inaccessible zones during trap placement

Evaluation of large volume yeast interfering RNA lure-and-kill ovitraps for attraction and control of Aedes mosquitoes

Aedes mosquitoes (Diptera: Culicidae), principle vectors of several arboviruses, typically lay eggs in man-made water-filled containers located near human dwellings. Given the widespread emergence of insecticide resistance, stable and biofriendly alternatives for mosquito larviciding are needed. Laboratory studies have demonstrated that inactivated yeast interfering RNA tablets targeting key larval developmental genes can be used to facilitate effective larvicidal activity while also promoting selective gravid female oviposition behaviour. Here we examined the efficacy of transferring this technology toward development of lure-and-kill ovitraps targeting Aedes aegypti (L.) and Aedes albopictus (Skuse) female mosquitoes. Insectary, simulated field and semi-field experiments demonstrated that two mosquito-specific yeast interfering RNA pesticides induce high levels of mortality among larvae of both species in treated large volume containers. Small-scale field trials conducted in Trinidad, West Indies demonstrated that large volume ovitrap containers baited with inactivated yeast tablets lure significantly more gravid females than traps containing only water and were highly attractive to both A. aegypti and A. albopictus females. These studies indicate that development of biorational yeast interfering RNA-baited ovitraps may represent a new tool for control of Aedes mosquitoes, including deployment in existing lure-and-kill ovitrap technologies or traditional container larviciding programs