Dating the Origin and Estimating the Transmission Rates of the Major HIV-1 Clusters in Greece: Evidence about the Earliest Subtype A1 Epidemic in Europe

Our aim was to estimate the date of the origin and the transmission rates of the major local clusters of subtypes A1 and B in Greece. Phylodynamic analyses were conducted in 14 subtype A1 and 31 subtype B clusters. The earliest dates of origin for subtypes A1 and B were in 1982.6 and in 1985.5, respectively. The transmission rate for the subtype A1 clusters ranged between 7.54 and 39.61 infections/100 person years (IQR: 9.39, 15.88), and for subtype B clusters between 4.42 and 36.44 infections/100 person years (IQR: 7.38, 15.04). Statistical analysis revealed that the average difference in the transmission rate between the PWID and the MSM clusters was 6.73 (95% CI: 0.86 to 12.60; p = 0.026). Our study provides evidence that the date of introduction of subtype A1 in Greece was the earliest in Europe. Transmission rates were significantly higher for PWID than MSM clusters due to the conditions that gave rise to an extensive PWID HIV-1 outbreak ten years ago in Athens, Greece. Transmission rate can be considered as a valuable measure for public health since it provides a proxy of the rate of epidemic growth within a cluster and, therefore, it can be useful for targeted HIV prevention programs.

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Public health in genetic spaces: a statistical framework to optimize cluster-based outbreak detection

Virus Evolution ◽

10.1093/ve/veaa011 ◽

2020 ◽

Vol 6 (1) ◽

Cited By ~ 1

Author(s):

Connor Chato ◽

Marcia L Kalish ◽

Art F Y Poon

Keyword(s):

Public Health ◽

Outbreak Detection ◽

Optimal Threshold ◽

Cluster Growth ◽

Clustering Method ◽

Transmission Rates ◽

Statistical Framework ◽

Subtype B ◽

Weighted Model ◽

Genetic Clustering

Abstract Genetic clustering is a popular method for characterizing variation in transmission rates for rapidly evolving viruses, and could potentially be used to detect outbreaks in ‘near real time’. However, the statistical properties of clustering are poorly understood in this context, and there are no objective guidelines for setting clustering criteria. Here, we develop a new statistical framework to optimize a genetic clustering method based on the ability to forecast new cases. We analysed the pairwise Tamura-Nei (TN93) genetic distances for anonymized HIV-1 subtype B pol sequences from Seattle (n = 1,653) and Middle Tennessee, USA (n = 2,779), and northern Alberta, Canada (n = 809). Under varying TN93 thresholds, we fit two models to the distributions of new cases relative to clusters of known cases: 1, a null model that assumes cluster growth is strictly proportional to cluster size, i.e. no variation in transmission rates among individuals; and 2, a weighted model that incorporates individual-level covariates, such as recency of diagnosis. The optimal threshold maximizes the difference in information loss between models, where covariates are used most effectively. Optimal TN93 thresholds varied substantially between data sets, e.g. 0.0104 in Alberta and 0.016 in Seattle and Tennessee, such that the optimum for one population would potentially misdirect prevention efforts in another. For a given population, the range of thresholds where the weighted model conferred greater predictive accuracy tended to be narrow (±0.005 units), and the optimal threshold tended to be stable over time. Our framework also indicated that variation in the recency of HIV diagnosis among clusters was significantly more predictive of new cases than sample collection dates (ΔAIC > 50). These results suggest that one cannot rely on historical precedence or convention to configure genetic clustering methods for public health applications, especially when translating methods between settings of low-level and generalized epidemics. Our framework not only enables investigators to calibrate a clustering method to a specific public health setting, but also provides a variable selection procedure to evaluate different predictive models of cluster growth.

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Research Synthesis, HIV Prevention Response, and Public Health: CDC’s HIV/AIDS Prevention Research Synthesis Project

Public Health Reports ◽

10.1177/0033354920988871 ◽

2021 ◽

pp. 003335492098887

Author(s):

Linda J. Koenig ◽

Cynthia M. Lyles ◽

Darrel Higa ◽

Mary M. Mullins ◽

Theresa A. Sipe

Keyword(s):

Public Health ◽

Hiv Prevention ◽

Systematic Reviews ◽

Research Synthesis ◽

Aids Prevention ◽

Prevention Programs ◽

The United States ◽

Research Translation ◽

Prevention Research ◽

Hiv Aids

Objective Research synthesis, through qualitative or quantitative systematic reviews, allows for integrating results of primary research to improve public health. We examined more than 2 decades of work in HIV prevention by the Centers for Disease Control and Prevention’s (CDC’s) HIV/AIDS Prevention Research Synthesis (PRS) Project. We describe the context and contributions of research synthesis, including systematic reviews and meta-analyses, through the experience of the PRS Project. Methods We reviewed PRS Project publications and products and summarized PRS contributions from 1996 to July 2020 in 4 areas: synthesis of interventions and epidemiologic studies, synthesis methods, prevention programs, and prevention policy. Results PRS Project publications summarized risk behaviors and effects of prevention interventions (eg, changing one’s perception of risk, teaching condom negotiation skills) across populations at risk for HIV infection and intervention approaches (eg, one-on-one or group meetings) as the HIV/AIDS epidemic and science evolved. We used the PRS Project cumulative database and intervention efficacy reviews to contribute to prevention programs and policies through identification of evidence-based interventions and development of program guidance. Subject matter experts and scientific evidence informed PRS Project products and contributions, which were implemented through strategic programmatic partnerships. Conclusions The contributions of the PRS Project to HIV prevention and public health efforts in the United States can be credited to CDC’s long-standing support of the project and its context within a federal prevention agency, where HIV programs and policies were developed and implemented. The effect of the PRS Project was likely facilitated by opportunities to directly influence program and policy because of connections with other research translation activities and program and policy decision making within CDC.

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Tracing the Impact of Public Health Interventions on HIV-1 Transmission in Portugal Using Molecular Epidemiology

The Journal of Infectious Diseases ◽

10.1093/infdis/jiz085 ◽

2019 ◽

Vol 220 (2) ◽

pp. 233-243 ◽

Cited By ~ 6

Author(s):

Tetyana I Vasylyeva ◽

Louis du Plessis ◽

Andrea C Pineda-Peña ◽

Denise Kühnert ◽

Philippe Lemey ◽

...

Keyword(s):

Public Health ◽

Highly Active Antiretroviral Therapy ◽

Hiv Transmission ◽

Scale Up ◽

Credible Interval ◽

Data Sets ◽

Highly Active ◽

Subtype B ◽

The Impact ◽

Hiv 1

Abstract Background Estimation of temporal changes in human immunodeficiency virus (HIV) transmission patterns can help to elucidate the impact of preventive strategies and public health policies. Methods Portuguese HIV-1 subtype B and G pol genetic sequences were appended to global reference data sets to identify country-specific transmission clades. Bayesian birth-death models were used to estimate subtype-specific effective reproductive numbers (Re). Discrete trait analysis (DTA) was used to quantify mixing among transmission groups. Results We identified 5 subtype B Portuguese clades (26–79 sequences) and a large monophyletic subtype G Portuguese clade (236 sequences). We estimated that major shifts in HIV-1 transmission occurred around 1999 (95% Bayesian credible interval [BCI], 1998–2000) and 2000 (95% BCI, 1998–2001) for subtypes B and G, respectively. For subtype B, Re dropped from 1.91 (95% BCI, 1.73–2.09) to 0.62 (95% BCI,.52–.72). For subtype G, Re decreased from 1.49 (95% BCI, 1.39–1.59) to 0.72 (95% BCI, .63–.8). The DTA suggests that people who inject drugs (PWID) and heterosexuals were the source of most (>80%) virus lineage transitions for subtypes G and B, respectively. Conclusions The estimated declines in Re coincide with the introduction of highly active antiretroviral therapy and the scale-up of harm reduction for PWID. Inferred transmission events across transmission groups emphasize the importance of prevention efforts for bridging populations.

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Appearance of Drug Resistance Mutations Among the Dominant HIV-1 Subtype, CRF01_AE in Maumere, Indonesia

Current HIV Research ◽

10.2174/1570162x16666180502114344 ◽

2018 ◽

Vol 16 (2) ◽

pp. 158-166 ◽

Cited By ~ 4

Author(s):

Dwi Wahyu Indriati ◽

Tomohiro Kotaki ◽

Siti Qamariyah Khairunisa ◽

Adiana Mutamsari Witaningrum ◽

Muhammad Qushai Yunifiar Matondang ◽

...

Keyword(s):

Drug Resistance ◽

Transmission Rate ◽

Sequencing Analysis ◽

Resistance Mutations ◽

Pr Genes ◽

Recombinant Viruses ◽

Subtype B ◽

Genes Encoding ◽

Major Drug ◽

Hiv 1

Background and Objectives:Human Immunodeficiency Virus (HIV) is still a major health issue in Indonesia. In recent years, the appearance of drug resistance-associated mutations has reduced the effectiveness of Antiretroviral Therapy (ART). We conducted genotypic studies, including the detection of drug resistance-associated mutations (from first-line regimen drugs), on HIV-1 genes derived from infected individuals in Maumere, West Nusa Tenggara. Maumere, a transit city in West Nusa Tenggara, which has a high HIV-1 transmission rate.Method:We collected 60 peripheral blood samples from 53 ART-experienced and 7 ART-naive individuals at TC Hillers Hospital, Maumere between 2014 and 2015. The amplification and a sequencing analysis of pol genes encoding protease (the PR gene) and reverse transcriptase (the RT gene) as well as the viral env and gag genes were performed. HIV-1 subtyping and the detection of drug resistance-associated mutations were then conducted.Results:Among 60 samples, 46 PR, 31 RT, 30 env, and 20 gag genes were successfully sequenced. The dominant HIV-1 subtype circulating in Maumere was CRF01_AE. Subtype B and recombinant viruses containing gene fragments of CRF01_AE, subtypes A, B, C, and/or G were also identified as minor populations. The major drug resistance-associated mutations, M184V, K103N, Y188L, and M230I, were found in the RT genes. However, no major drug resistance-associated mutations were detected in the PR genes.Conclusion:CRF01_AE was the major HIV-1 subtype prevalent in Maumere. The appearance of drug resistance-associated mutations found in the present study supports the necessity of monitoring the effectiveness of ART in Maumere.

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Public health in genetic spaces: a statistical framework to optimize cluster-based outbreak detection

10.1101/639997 ◽

2019 ◽

Author(s):

Connor Chato ◽

Marcia L. Kalish ◽

Art F. Y. Poon

Keyword(s):

Public Health ◽

Predictive Accuracy ◽

Outbreak Detection ◽

Optimal Threshold ◽

Clustering Method ◽

Transmission Rates ◽

Statistical Framework ◽

Subtype B ◽

Weighted Model ◽

Genetic Clustering

AbstractGenetic clustering is a popular method for characterizing variation in transmission rates for rapidly-evolving viruses, and could potentially be used to detect outbreaks in ‘near real time’. However, the statistical properties of clustering are poorly understood in this context, and there are no objective guidelines for setting clustering criteria. Here we develop a new statistical framework to optimize a genetic clustering method based on the ability to forecast new cases. We analyzed the pairwise Tamura-Nei (TN93) genetic distances for anonymized HIV-1 subtype B pol sequences from Seattle (n = 1, 653) and Middle Tennessee, USA (n = 2, 779), and northern Alberta, Canada (n = 809). Under varying TN93 thresholds, we fit two models to the distributions of new cases relative to clusters of known cases: (1) a null model that assumes cluster growth is strictly proportional to cluster size, i.e., no variation in transmission rates among individuals; and (2) a weighted model that incorporates individual-level covariates, such as recency of diagnosis. The optimal threshold maximizes the difference in information loss between models, where covariates are used most effectively. Optimal TN93 thresholds varied substantially between data sets, e.g., 0.0104 in Alberta and 0.016 in Seattle and Tennessee, such that the optimum for one population will potentially mis-direct prevention efforts in another. The range of thresholds where the weighted model conferred greater predictive accuracy tended to be narrow (±0.005 units), but the optimal threshold for a given population also tended to be stable over time. We also extended our method to demonstrate that variation in recency of HIV diagnosis among clusters was significantly more predictive of new cases than sample collection dates (ΔAIC> 50). These results demonstrate that one cannot rely on historical precedence or convention to configure genetic clustering methods for public health applications. Our framework not only provides an objective procedure to optimize a clustering method, but can also be used for variable selection in forecasting new cases.

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Heterosexual Transmission as a CRF01_AE HIV-1 Spread Factor in Mimika Papua

Journal of Health Science and Prevention ◽

10.29080/jhsp.v2i2.127 ◽

2018 ◽

Vol 2 (2) ◽

pp. 108-113

Author(s):

Mirna Widiyanti ◽

Reynold Ubra ◽

Evi Iriani

Keyword(s):

Transmission Rate ◽

Sexual Transmission ◽

Transmission Risk ◽

Heterosexual Transmission ◽

Cross Sectional ◽

Genetic Characteristics ◽

Subtype B ◽

Hiv Epidemic ◽

Spread Factor ◽

Hiv 1

The HIV epidemic has particular characteristic on each region. The genetic diversity of HIV-1 would affect variability of HIV virus that could potentially most virulent, pathogenic and high transmission rate. Thus it triggers the disease progresivity more rapidly and caused a new pandemic of HIV infection. The aim of the study is determine the genetic characteristics of HIV-1 on patient with heterosexual transmission based on gene fragment encoding the glycoprotein-41 (gp41) of HIV envelope. Descriptive analytic method and cross sectional design were attended on VCT clinic Mitra Masyarakat Mimika in March-May 2015. Samples of blood plasma from patient with HIV-1 sexual transmission wer amplified using RT-PCR and nested PCR. Genetic characteristics were analyzed with DNA Sequencing using software Bioedit and Mega 5. Identification using sequence analysis showed two subtypes of HIV patient in Mimika, which were CRF01_AE and B subtypes. There were 40 patients (87%) identified as having genetic characteristics CRF01_AE. Subtypes B was also identified in 6 heterosexual patients. This study suggest that CRF01_AE have heterosexual transmission risk higher than subtype B. Predominance of CRF01_AE contribute to the rapid spread of the HIV epidemic in Mimika

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Estimating Time-Dependent Disease Transmission Intensity using Reported Data: An Application to Ebola and Selected Public Health Problems

10.1101/2021.06.04.21258380 ◽

2021 ◽

Author(s):

Aditi Ghosh ◽

Anuj Mubayi ◽

Abhishek Pandey ◽

Christine Brasic ◽

Anamika Mubayi ◽

...

Keyword(s):

Public Health ◽

Infectious Diseases ◽

Disease Transmission ◽

Social Issues ◽

Transmission Rate ◽

Health Problems ◽

Time Dependent ◽

Transmission Rates ◽

Reported Data ◽

Over Time

Obtaining reasonable estimates for transmission rates from observed data is a challenge when using mathematical models to study the dynamics of infectious diseases, like Ebola. Most models assume the transmission rate of a contagion does not vary over time. However, these rates do vary during an epidemic due to environmental conditions, social behaviors, and public-health interventions deployed to control the disease. Therefore, obtaining time-dependent rates can aid in understanding the progression of disease through a population. We derive an analytical expression using a standard SIR-type mathematical model to compute time-dependent transmission rate estimates for an epidemic in terms of either incidence or prevalence type available data. We illustrate applicability of our method by applying data on various public health problems, including infectious diseases (Ebola, SARS, and Leishmaniasis) and social issues (obesity and alcohol drinking) to compute transmission rates over time. We show that transmission rate estimates can have a large variation over time, depending on the type of available data and other epidemiological parameters. Time-dependent estimation of transmission rates captures the dynamics of the problem and can be utilized to understand disease progression through population accurately. Alternatively, constant estimations may provide unacceptable results that could have major public health consequences.

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Analytical Estimation of Data-Motivated Time-Dependent Disease Transmission Rate: An Application to Ebola and Selected Public Health Problems

Tropical Medicine and Infectious Disease ◽

10.3390/tropicalmed6030141 ◽

2021 ◽

Vol 6 (3) ◽

pp. 141

Author(s):

Anuj Mubayi ◽

Abhishek Pandey ◽

Christine Brasic ◽

Anamika Mubayi ◽

Parijat Ghosh ◽

...

Keyword(s):

Public Health ◽

Infectious Diseases ◽

Disease Transmission ◽

Social Issues ◽

Transmission Rate ◽

Health Problems ◽

Time Dependent ◽

Transmission Rates ◽

Analytical Estimation ◽

Over Time

Obtaining reasonable estimates for transmission rates from observed data is a challenge when using mathematical models to study the dynamics of ?infectious? diseases, like Ebola. Most models assume the transmission rate of a contagion either does not vary over time or change in a fixed pre-determined adhoc ways. However, these rates do vary during an outbreak due to multitude of factors such as environmental conditions, social behaviors, and public-health interventions deployed to control the disease, which are in-part guided by changing size of an outbreak. We derive analytical estimates of time-dependent transmission rate for an epidemic in terms of either incidence or prevalence using a standard mathematical SIR-type epidemic model. We illustrate applicability of our method by applying data on various public health problems, including infectious diseases (Ebola, SARS, and Leishmaniasis) and social issues (obesity and alcohol drinking) to compute transmission rates over time. We show that time-dependent transmission rate estimates can have a large variation, depending on the type of available data and other epidemiological parameters. Time-dependent estimation of transmission rates captures the dynamics of the problem better and can be utilized to understand disease progression more accurately.

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