Identification of Hotspots of Currently Emerging HIV Infections in Kazakhstan Using an Integrated Phylodynamic-Epidemiologic Approach

Collaborative Research Program 2023-2025

Central Asia and the Caucasus (CAC) is the sole area in the world where HIV incidence has risen over the last decade. Deterioration of socio-economic conditions in the former Soviet republics, and massive migrations across CAC facilitated the transmission of infectious diseases within and across CAC countries. Large-scale availability of opioids and economic dislocation led to a rise in injecting drug use and in the late 1990s HIV subtype A variant AFSU, (now subtype A6), started to spread rapidly amongst persons who inject drugs (PWID). More recently, rapid expansion of Circulating Recombinant Form CRF02AG has occurred in Kazakhstan and Kyrgyzstan. Recent studies now demonstrate a steady expansion of heterosexual transmission as well as bridging of this epidemic into countries outside the region. Key populations transmitting the infections are poorly understood or not effectively linked to harm reduction services, highlighting failures in current surveillance and/or harm reduction approaches.
In recent years, use of molecular epidemiology to reconstruct viral transmission networks has been recognized widely as a crucial tool for successful intervention to curtail outbreaks of viral infections. For HIV, this approach forms an explicit part of the Ending the Epidemic (EHE) in the US program; for COVID, it has played a critical role in the detection of variants of concern. As part of a National Institute on Drug Abuse (NIDA)-funded project we have been analyzing HIV pol sequences obtained from >1,400 people living with HIV (PLWH) through our longstanding collaboration with Republican Center for Prophylactic and Control of AIDS (RCPC-AIDS) in Nur-Sultan, Kazakhstan. Phylodynamic and phylogeographic analysis using the NextStrain platform has allowed us to tentatively identify recent transmission events in PWID communities and the patterns of transmission of antiretroviral drug resistance mutations (see Preliminary Results).
In the study we now propose, we will combine phylodynamic and phylogeographic analyses with adaptive respondent-driven sampling (aRDS) to increase the rate of identifying recent infections to focus on communities where the recent infections are concentrated. We will employ state-of-the-art Bayesian time-resolved phylogenetic analyses to reconstruct the history of the epidemics and the relationship of the strains within and across these countries, identifying communities at greatest risk of uncontrolled HIV spread. From deep analysis of phylogenetic clusters (see Preliminary Results) we will estimate the growth rate of the epidemic (Rt) and its network properties, and characterize hotspots of currently emerging infections (HCEI). Our specific aims are to:

Aim 1. Perform deep cluster analysis of sequences generated as part of the R03 study to tentatively identify HCEI amongst those samples. This extends our ongoing HIV sequence analysis in Kazakhstan. These HCEI samples will be further analyzed using next-generation sequencing (NGS) to confirm recency of infection. The samples thus confirmed as HCEI and other PLWH with characteristics similar to HCEI will help in identifying the social networks and location of HCEI, and will be used as initial seeds for our aRDS.

Aim 2. Implement aRDS in Kazakhstan to sample within-HCEI-associated social networks to obtain blood samples and demographic surveys from PLWH. We will focus on recruiting members of high-risk groups and apply a testing algorithm that includes a validated antibody assay for recent infections to enhance the sample for individuals from HCEI. Using NGS, phylogenetically clustered HIV(+) sequences will be analyzed to confirm their HCEI status. Combining these data with socio-demographic, travel history, and behavioral data pertinent to sex- and injection-related transmission will help to home in on the social networks and locations of HCEI.

Aim 3.To involve stakeholders in Kazakhstan with our research team from Yale and Nazarbayev universities, including the republic’s AIDS Center, academic institutions, healthcare facilities, NGOs, and media. Disseminating information obtained from our analyses we will promote awareness about current trends in HIV transmission and advise local health officials where their intervention efforts are most likely to reduce HIV spread. These analyses will be expedited by establishing the real-time NextStrain platform to generate data on drug resistance, subtypes, and potential phylogenetic clusters to promote awareness about current population, spatial, and drug resistance trends in HIV transmission.