Inferring population dynamics of HIV-1 subtype C epidemics in Eastern Africa and Southern Brazil applying different Bayesian phylodynamics approaches

Molecular Epidemiology and Transmission Dynamics of the HIV-1 Epidemic in Ethiopia: Epidemic Decline Coincided With Behavioral Interventions Before ART Scale-Up

Background

Ethiopia is one of the sub-Saharan countries hit hard by the HIV epidemic. Previous studies have shown that subtype C dominates the Ethiopian HIV-1 epidemic, but the evolutionary and temporal dynamics of HIV-1 in Ethiopia have not been closely scrutinized. Understanding the evolutionary and epidemiological pattern of HIV is vital to monitor the spread, evaluate and implement HIV prevention strategies.

Methods

We analyzed 1,276 Ethiopian HIV-1 subtype C polymerase (pol sequences), including 144 newly generated sequences, collected from different parts of the country from 1986 to 2017. We employed state-of-art maximum likelihood and Bayesian phylodynamic analyses to comprehensively describe the evolutionary dynamics of the HIV-1 epidemic in Ethiopia. We used Bayesian phylodynamic models to estimate the dynamics of the effective population size (Ne) and reproductive numbers (Re) through time for the HIV epidemic in Ethiopia.

Results

Our analysis revealed that the Ethiopian HIV-1 epidemic originated from two independent introductions at the beginning of the 1970s and 1980s from eastern and southern African countries, respectively, followed by epidemic growth reaching its maximum in the early 1990s. We identified three large clusters with a majority of Ethiopian sequences. Phylodynamic analyses revealed that all three clusters were characterized by high transmission rates during the early epidemic, followed by a decline in HIV-1 transmissions after 1990. Re was high (4–6) during the earlier time of the epidemic but dropped significantly and remained low (Re < 1) after the mid-1990. Similarly, with an expected shift in time, the effective population size (Ne) steadily increased until the beginning of 2000, followed by a decline and stabilization until recent years. The phylodynamic analyses corroborated the modeled UNAIDS incidence and prevalence estimates.

Conclusion

The rapid decline in the HIV epidemic took place a decade before introducing antiretroviral therapy in Ethiopia and coincided with early behavioral, preventive, and awareness interventions implemented in the country. Our findings highlight the importance of behavioral interventions and antiretroviral therapy scale-up to halt and maintain HIV transmissions at low levels (Re < 1). The phylodynamic analyses provide epidemiological insights not directly available using standard surveillance and may inform the adjustment of public health strategies in HIV prevention in Ethiopia.

Dynamic Dispersion of HIV-1 Subtype C Toward Brazilian Northeastern Region

The subtype C accounts for >50% of HIV type 1 (HIV-1) infections worldwide and it is currently the predominant viral form in South Brazil. Subtype C has been reported in all Brazilian regions; however, the phylogenetic relationship among strains circulating in those regions still remains unclear. This study aimed to investigate the origin and dynamic dispersion of HIV-1 subtype C toward Northeast Brazil. Our phylogenetic analysis suggests that most subtype C strains circulating in Brazil (99%) are descendant from the main lineage whose entrance in the country was previously described in the 1970s. According to the literature, additional introductions of subtype C were reported in the country through the Southeast region and in this study we identified another entry event that occurred most likely through the North region. Furthermore, our analysis suggests that the spread of subtype C to Brazilian Northeastern states occurred through multiple independent introductions of the main lineage that originated in South Brazil between mid-1980s and late 1990s. Despite the observation of eventual new HIV-1 subtype C introductions, our results highlight the predominance of a single lineage of this subtype in Brazil and the importance of South region in its dissemination throughout the country.

Fundamental Identifiability Limits in Molecular Epidemiology

Viral phylogenies provide crucial information on the spread of infectious diseases, and many studies fit mathematical models to phylogenetic data to estimate epidemiological parameters such as the effective reproduction ratio (Re) over time. Such phylodynamic inferences often complement or even substitute for conventional surveillance data, particularly when sampling is poor or delayed. It remains generally unknown, however, how robust phylodynamic epidemiological inferences are, especially when there is uncertainty regarding pathogen prevalence and sampling intensity. Here, we use recently developed mathematical techniques to fully characterize the information that can possibly be extracted from serially collected viral phylogenetic data, in the context of the commonly used birth-death-sampling model. We show that for any candidate epidemiological scenario, there exists a myriad of alternative, markedly different, and yet plausible "congruent" scenarios that cannot be distinguished using phylogenetic data alone, no matter how large the data set. In the absence of strong constraints or rate priors across the entire study period, neither maximum-likelihood fitting nor Bayesian inference can reliably reconstruct the true epidemiological dynamics from phylogenetic data alone; rather, estimators can only converge to the "congruence class" of the true dynamics. We propose concrete and feasible strategies for making more robust epidemiological inferences from viral phylogenetic data.

The Role of Phylogenetics in Discerning HIV-1 Mixing among Vulnerable Populations and Geographic Regions in Sub-Saharan Africa: A Systematic Review

To reduce global HIV-1 incidence, there is a need to understand and disentangle HIV-1 transmission dynamics and to determine the geographic areas and populations that act as hubs or drivers of HIV-1 spread. In Sub-Saharan Africa (sSA), the region with the highest HIV-1 burden, information about such transmission dynamics is sparse. Phylogenetic inference is a powerful method for the study of HIV-1 transmission networks and source attribution. In this review, we assessed available phylogenetic data on mixing between HIV-1 hotspots (geographic areas and populations with high HIV-1 incidence and prevalence) and areas or populations with lower HIV-1 burden in sSA. We searched PubMed and identified and reviewed 64 studies on HIV-1 transmission dynamics within and between risk groups and geographic locations in sSA (published 1995-2021). We describe HIV-1 transmission from both a geographic and a risk group perspective in sSA. Finally, we discuss the challenges facing phylogenetic inference in mixed epidemics in sSA and offer our perspectives and potential solutions to the identified challenges.

Phylogenetics applied to the human immunodeficiency virus type 1 (HIV-1): from the cross-species transmissions to the contact network inferences

Phylogenetic analyses were crucial to elucidate the origin and spread of the pandemic human immunodeficiency virus type 1 (HIV-1) group M virus, both during the pre-epidemic period of cryptic dissemination in human populations as well as during the epidemic phase of spread. The use of phylogenetics and phylodynamics approaches has provided important insights to track the founder events that resulted in the spread of HIV-1 strains across vast geographic areas, specific countries and within geographically restricted communities. In the recent years, the use of phylogenetic analysis combined with the huge availability of HIV sequences has become an increasingly important approach to reconstruct HIV transmission networks and understand transmission dynamics in concentrated and generalised epidemics. Significant efforts to obtain viral sequences from newly HIV-infected individuals could certainly contribute to detect rapidly expanding HIV-1 lineages, identify key populations at high-risk and understand what public health interventions should be prioritised in different scenarios.

A Fast Likelihood Method to Reconstruct and Visualize Ancestral Scenarios

The reconstruction of ancestral scenarios is widely used to study the evolution of characters along phylogenetic trees. One commonly uses the marginal posterior probabilities of the character states, or the joint reconstruction of the most likely scenario. However, marginal reconstructions provide users with state probabilities, which are difficult to interpret and visualize, whereas joint reconstructions select a unique state for every tree node and thus do not reflect the uncertainty of inferences. We propose a simple and fast approach, which is in between these two extremes. We use decision-theory concepts (namely, the Brier score) to associate each node in the tree to a set of likely states. A unique state is predicted in tree regions with low uncertainty, whereas several states are predicted in uncertain regions, typically around the tree root. To visualize the results, we cluster the neighboring nodes associated with the same states and use graph visualization tools. The method is implemented in the PastML program and web server. The results on simulated data demonstrate the accuracy and robustness of the approach. PastML was applied to the phylogeography of Dengue serotype 2 (DENV2), and the evolution of drug resistances in a large HIV data set. These analyses took a few minutes and provided convincing results. PastML retrieved the main transmission routes of human DENV2 and showed the uncertainty of the human-sylvatic DENV2 geographic origin. With HIV, the results show that resistance mutations mostly emerge independently under treatment pressure, but resistance clusters are found, corresponding to transmissions among untreated patients.

Phylogeographic Analyses Reveal the Early Expansion and Frequent Bidirectional Cross-Border Transmissions of Non-pandemic HIV-1 Subtype B Strains in Hispaniola

The human immunodeficiency virus-type 1 (HIV-1) subtype B has probably been circulating on the island of Hispaniola since the 1960s, but information about the early viral history on this Caribbean island is scarce. In this study, we reconstruct the dissemination dynamics of early divergent non-pandemic subtype B lineages (designated B_CAR) on Hispaniola by analyzing a country-balanced dataset of HIV-1 B_CARpol sequences from Haiti (n = 103) and the Dominican Republic (n = 123). Phylogenetic analyses supported that B_CAR strains from Haiti and the Dominican Republic were highly intermixed between each other, although the null hypothesis of completely random mixing was rejected. Bayesian phylogeographic analyses placed the ancestral B_CAR virus in Haiti and the Dominican Republic with the same posterior probability support. These analyses estimate frequent viral transmissions between Haiti and the Dominican Republic since the early 1970s onwards, and the presence of local B_CAR transmission networks in both countries before first AIDS cases was officially recognized. Demographic reconstructions point that the B_CAR epidemic in Hispaniola grew exponentially until the 1990s. These findings support that the HIV-1 epidemics in Haiti and the Dominican Republic have been connected by a recurrent bidirectional viral flux since the initial phase, which poses a great challenge in tracing the geographic origin of the B_CAR epidemic within Hispaniola using only genetic data. These data also reinforce the notion that prevention programs have successfully reduced the rate of new HIV-1 transmissions in Hispaniola since the end of the 1990s.

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.

Phylogeography of HIV-1 suggests that Ugandan fishing communities are a sink for, not a source of, virus from general populations

Although fishing communities (FCs) in Uganda are disproportionately affected by HIV-1 relative to the general population (GP), the transmission dynamics are not completely understood. We earlier found most HIV-1 transmissions to occur within FCs of Lake Victoria. Here, we test the hypothesis that HIV-1 transmission in FCs is isolated from networks in the GP. We used phylogeography to reconstruct the geospatial viral migration patterns in 8 FCs and 2 GP cohorts and a Bayesian phylogenetic inference in BEAST v1.8.4 to analyse the temporal dynamics of HIV-1 transmission. Subtype A1 (pol region) was most prevalent in the FCs (115, 45.1%) and GP (177, 50.4%). More recent HIV transmission pairs from FCs were found at a genetic distance (GD) <1.5% than in the GP (Fisher’s exact test, p = 0.001). The mean time depth for pairs was shorter in FCs (5 months) than in the GP (4 years). Phylogeographic analysis showed strong support for viral migration from the GP to FCs without evidence of substantial viral dissemination to the GP. This suggests that FCs are a sink for, not a source of, virus strains from the GP. Targeted interventions in FCs should be extended to include the neighbouring GP for effective epidemic control.