for Health and Population Studies

Abstract Introduction: Despite recent breakthroughs in the fight against the HIV/AIDS epidemic within South Africa, the transmission of the virus continues at alarmingly high rates. It is possible, with the use of phylogenetic methods, to uncover transmission events of HIV amongst local communities in order to identify factors that may contribute to the sustained transmission of the virus. The aim of this study was to uncover transmission events of HIV amongst the infected population of Cape Town

Understanding the Transmission Dynamics of the Chikungunya Virus in Africa.

The Chikungunya virus (CHIKV) poses a significant global public health concern, especially in Africa. Since its first isolation in Tanzania in 1953, CHIKV has caused recurrent outbreaks, challenging healthcare systems in low-resource settings. Recent outbreaks in Africa highlight the dynamic nature of CHIKV transmission and the challenges of underreporting and underdiagnosis. Here, we review the literature and analyse publicly available cases, outbreaks, and genomic data, providing insights into the epidemiology, genetic diversity, and transmission dynamics of CHIKV in Africa. Our analyses reveal the circulation of geographically distinct CHIKV genotypes, with certain regions experiencing a disproportionate burden of disease. Phylogenetic analysis of sporadic outbreaks in West Africa suggests repeated emergence of the virus through enzootic spillover, which is markedly different from inferred transmission dynamics in East Africa, where the virus is often introduced from Asian outbreaks, including the recent reintroduction of the Indian Ocean lineage from the Indian subcontinent to East Africa. Furthermore, there is limited evidence of viral movement between these two regions. Understanding the history and transmission dynamics of outbreaks is crucial for effective public health planning. Despite advances in surveillance and research, diagnostic and surveillance challenges persist. This review and secondary analysis highlight the importance of ongoing surveillance, research, and collaboration to mitigate the burden of CHIKV in Africa and improve public health outcomes

Increasing HIV-1 Drug Resistance Between 2010 and 2012 in Adults Participating in Population-Based HIV Surveillance in Rural KwaZulu-Natal, South Africa.

As more human immunodeficiency virus (HIV)-infected patients access combination antiretroviral therapy (cART), higher proportions of newly infected patients may be infected with drug-resistant viruses. Regular surveillance of transmitted drug resistance (TDR) is required in southern Africa where high rates of transmission persist despite rapid expansion of ART. Dried blood spot samples from cART-naive participants from two rounds of an annual population-based HIV surveillance program in rural KwaZulu-Natal were tested for HIV RNA, and samples with HIV RNA >10,000 copies/ml were genotyped for drug resistance. The 2009 surveillance of drug resistance mutation (SDRM) list was used for drug resistance interpretation. The data were added to previously published data from the same program, and the χ(2) test for trend was used to test for trend in estimated prevalence of any TDR. Seven hundred and one participants' data were analyzed: 67 (2010), 381 (2011), and 253 (2012). No TDR was detected in 2010. Years 2011 and 2012 had 18 participants with SDRMs 4.7% and 7.1%, respectively (p = .02, χ(2) test for trend). The nonnucleoside reverse transcriptase inhibitor mutation, K103N, was the most common mutation, occurring in 27 (3.8%) of the participants, while nucleoside reverse transcriptase inhibitor (NRTI) SDRMs were detected in 10 (1.4%) of the participants, of whom eight had only a single NRTI SDRM. The increase in levels of drug resistance observed in this population could be a signal of increasing transmission of drug-resistant HIV. Thus, continued surveillance is critical to inform public health policies around HIV treatment and prevention

Combining Phylogenetic and Network Approaches to Identify HIV-1 Transmission Links in San Mateo County, California

The HIV epidemic in San Mateo County is sustained by multiple overlapping risk groups and is an important hub for HIV transmission in northern California. Limited access to care has led historically to delayed clinical presentation, higher rates of opportunistic infections, and an increased prevalence of antiretroviral drug resistance. The virologic and clinical consequences of treatment within these multiple ethnic and behavioral groups are poorly understood, highlighting the need for efficient surveillance strategies that are able to elucidate transmission networks and drug resistance patterns. We obtained sequence data from a group of 316 HIV-positive individuals in the San Mateo AIDS Program over a 14-year period and integrated epidemiologic, phylogenetic, and network approaches to characterize transmission clusters, risk factors and drug resistance. Drug resistance mutations were identified using the Stanford HIV Drug Resistance Database. A maximum likelihood tree was inferred in RAxML and subjected to clustering analysis in Cluster Picker. Network analysis using pairwise genetic distances was performed in HIV-TRACE. Participants were primarily male (60%), white Hispanics and non-Hispanics (32%) and African American (20.6%). The most frequent behavior risk factor was male-male sex (33.5%), followed by heterosexual (23.4%) and injection drug use (9.5%). Nearly all sequences were subtype B (96%) with subtypes A, C, and CRF01_AE also observed. Sequences from 65% of participants had at least one drug resistance mutation. Clustered transmissions included a higher number of women when compared to non-clustered individuals and were more likely to include heterosexual or people who inject drugs (PWID). Detailed analysis of the largest network (N = 47) suggested that PWID played a central role in overall transmission of HIV-1 as well as bridging men who have sex with men (MSM) transmission with heterosexual/PWID among primarily African American men. Combined phylogenetic and network analysis of HIV sequence data identified several overlapping risk factors in the epidemic, including MSM, heterosexual and PWID transmission with a disproportionate impact on African Americans and a high prevalence of drug resistance

Identifying Recent HIV Infections: From Serological Assays to Genomics

In this paper, we review serological and molecular based methods to identify HIV infection recency. The accurate identification of recent HIV infection continues to be an important research area and has implications for HIV prevention and treatment interventions. Longitudinal cohorts that follow HIV negative individuals over time are the current gold standard approach, but they are logistically challenging, time consuming and an expensive enterprise. Methods that utilize cross-sectional testing and biomarker information have become an affordable alternative to the longitudinal approach. These methods use well-characterized biological makers to differentiate between recent and established HIV infections. However, recent results have identified a number of limitations in serological based assays that are sensitive to the variability in immune responses modulated by HIV subtypes, viral load and antiretroviral therapy. Molecular methods that explore the dynamics between the timing of infection and viral evolution are now emerging as a promising approach. The combination of serological and molecular methods may provide a good solution to identify recent HIV infection in cross-sectional data. As part of this review, we present the advantages and limitations of serological and molecular based methods and their potential complementary role for the identification of HIV infection recency

Urgent need for a non-discriminatory and non-stigmatizing nomenclature for monkeypox virus

Free PMC article: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9451062/We propose a novel, non-discriminatory classification of monkeypox virus diversity. Together with the World Health Organization, we named three clades (I, IIa and IIb) in order of detection. Within IIb, the cause of the current global outbreak, we identified multiple lineages (A.1, A.2, A.1.1 and B.1) to support real-time genomic surveillance.info:eu-repo/semantics/publishedVersio

Sampling bias and incorrect rooting make phylogenetic network tracing of SARS-COV-2 infections unreliable.

There is obvious interest in gaining insights into the epidemiology and evolution of the virus that has recently emerged in humans as the cause of the coronavirus disease 2019 (COVID-19) pandemic. The recent paper by Forster et al. (1), analyzed 160 SARS-CoV-2 full genomes available (https://www.gisaid.org/) in early March 2020. The central claim is the identification of three main SARS-CoV-2 types, named A, B, and C, circulating in different proportions among Europeans and Americans (types A and C) and East Asian (type B). According to a median-joining network analysis, variant A is proposed to be the ancestral type because it links to the sequence of a coronavirus from bats, used as an outgroup to trace the ancestral origin of the human strains. The authors further suggest that the “ancestral Wuhan B-type virus is immunologically or environmentally adapted to a large section of the East Asian population, and may need to mutate to overcome resistance outside East Asia”. There are several serious flaws with their findings and interpretation. First, and most obviously, the sequence identity between SARS-CoV-2 and the bat virus is only 96.2%, implying that these viral genomes (which are nearly 30,000 nucleotides long) differ by more than 1,000 mutations. Such a distant outgroup is unlikely to provide a reliable root for the network. Yet, strangely, the branch to the bat virus, in Figure 1 of the paper, is only 16 or 17 mutations in length. Indeed, the network seems to be mis-rooted because (see Supplementary Figure 4) a virus from Wuhan from week 0 (24th December 2019) is portrayed as a descendant of a clade of viruses collected in weeks 1-9 (presumably from many places outside China), which makes no evolutionary (2), nor epidemiological sense (3).N