Genomic assessment of invasion dynamics of SARS-CoV-2 Omicron BA.1.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) now arise in the context of heterogeneous human connectivity and population immunity. Through a large-scale phylodynamic analysis of 115,622 Omicron BA.1 genomes, we identified >6,000 introductions of the antigenically distinct VOC into England and analyzed their local transmission and dispersal history. We find that six of the eight largest English Omicron lineages were already transmitting when Omicron was first reported in southern Africa (22 November 2021). Multiple datasets show that importation of Omicron continued despite subsequent restrictions on travel from southern Africa as a result of export from well-connected secondary locations. Initiation and dispersal of Omicron transmission lineages in England was a two-stage process that can be explained by models of the country's human geography and hierarchical travel network. Our results enable a comparison of the processes that drive the invasion of Omicron and other VOCs across multiple spatial scales.

Genomic assessment of quarantine measures to prevent SARS-CoV-2 importation and transmission.

Mitigation of SARS-CoV-2 transmission from international travel is a priority. We evaluated the effectiveness of travellers being required to quarantine for 14-days on return to England in Summer 2020. We identified 4,207 travel-related SARS-CoV-2 cases and their contacts, and identified 827 associated SARS-CoV-2 genomes. Overall, quarantine was associated with a lower rate of contacts, and the impact of quarantine was greatest in the 16-20 age-group. 186 SARS-CoV-2 genomes were sufficiently unique to identify travel-related clusters. Fewer genomically-linked cases were observed for index cases who returned from countries with quarantine requirement compared to countries with no quarantine requirement. This difference was explained by fewer importation events per identified genome for these cases, as opposed to fewer onward contacts per case. Overall, our study demonstrates that a 14-day quarantine period reduces, but does not completely eliminate, the onward transmission of imported cases, mainly by dissuading travel to countries with a quarantine requirement.

Phylodynamic inference for structured epidemiological models.

Coalescent theory is routinely used to estimate past population dynamics and demographic parameters from genealogies. While early work in coalescent theory only considered simple demographic models, advances in theory have allowed for increasingly complex demographic scenarios to be considered. The success of this approach has lead to coalescent-based inference methods being applied to populations with rapidly changing population dynamics, including pathogens like RNA viruses. However, fitting epidemiological models to genealogies via coalescent models remains a challenging task, because pathogen populations often exhibit complex, nonlinear dynamics and are structured by multiple factors. Moreover, it often becomes necessary to consider stochastic variation in population dynamics when fitting such complex models to real data. Using recently developed structured coalescent models that accommodate complex population dynamics and population structure, we develop a statistical framework for fitting stochastic epidemiological models to genealogies. By combining particle filtering methods with Bayesian Markov chain Monte Carlo methods, we are able to fit a wide class of stochastic, nonlinear epidemiological models with different forms of population structure to genealogies. We demonstrate our framework using two structured epidemiological models: a model with disease progression between multiple stages of infection and a two-population model reflecting spatial structure. We apply the multi-stage model to HIV genealogies and show that the proposed method can be used to estimate the stage-specific transmission rates and prevalence of HIV. Finally, using the two-population model we explore how much information about population structure is contained in genealogies and what sample sizes are necessary to reliably infer parameters like migration rates.

Web-based peer-driven chain referrals for smoking cessation.

BACKGROUND: We are testing web-based respondent-driven sampling (RDS) chain referrals to recruit smokers to the Decide2Quit.org (D2Q) web-assisted tobacco intervention. METHODS: Using an online survey of smokers, we assessed the potential of recruiting 1200 smokers in 9 months using RDS chain referrals. RDS is a complex sample design, and many factors can influence its success. We conducted simulations to determine the design of optimal RDS chains. RESULTS: Smokers (n=48) were mostly female (72%) and between ages 30-60 (82%). Estimation of smokers in their network: 1-5 (40%), 6-10 (24%), and 10-20 (22%), with mean number of intimate family (2.2, SD=2.1) and close friend smokers (3.7, SD=3.8). Most smokers (82%) were willing to refer to D2Q and thought their friends (mean=5.0, SD=4.4, range=0-20) would be open to referral. Simulations suggested that with a quota of 3 and 10 seeds, 99.9% of the sample would be achieved in 107 days if the acceptance probability was 0.5. Acceptance probability of 25% would necessitate an increased quota. CONCLUSIONS: Our study suggests that it is possible to recruit smokers using RDS.

Using respondent-driven sampling in a hidden population at risk of HIV infection: who do HIV-positive recruiters recruit?

BACKGROUND: Respondent-driven sampling (RDS) is a network-based method used to recruit hidden populations. Since it is respondent-driven, RDS is prone to bias. However, these biases could facilitate recruitment of high-risk networks. We examined recruitment patterns of human immunodeficiency virus (HIV)-positive injection drug users (IDUs) and identified factors associated with being recruited by an HIV-positive IDU in a RDS-based study. METHODS: IDUs aged > or =18, who injected within the last month and resided in Tijuana, Mexico, were recruited using RDS and underwent interviews and testing for HIV, syphilis, and tuberculosis. Weighted logistic regression was used to identify predictors of being recruited by an HIV-positive IDU. RESULTS: Of 1056 IDUs, HIV-positive subjects comprised 4.4% of the sample and generated 4.7% of recruits, indicating that recruitment effectiveness did not vary by HIV-status. However, 10% of the subjects recruited by HIV-positive recruiters were infected with HIV as compared to 4.1% of subjects recruited by HIV-negative recruiters, (P = 0.06), a difference that, after controlling for whether the recruiter and recruit injected drugs together, attained statistical significance (P = 0.04), indicating that recruitment patterns differed by HIV-status. Factors independently associated with being recruited by an HIV-positive IDU included lifetime syphilis infection, ever having sex with an HIV-positive person, knowing someone with HIV/AIDS, being recruited at a shooting gallery, having recently used the local needle exchange program, and having a larger number of recent arrests for track marks. CONCLUSION: HIV-positive IDUs have different recruitment patterns than HIV-negative IDUs, with HIV-positive IDUs tending to recruit other HIV-positive IDUs. Social and environmental factors along with risk behaviors were independently associated with being the recruit of an HIV-positive IDU in Tijuana. Although the goal of this study was not to recruit HIV+ or other high-risk persons, our results suggest that RDS has the potential to successfully be used in the identification of HIV+ or other high risk individuals.