Arbovirus risk in Brazil: Despite the existence of an effective vaccine for yellow fever, there are still almost 80,000 fatalities from this infection each year. Since 2016, there has been a resurgence of cases in Africa and South America—and this at a time when the vaccine is in short supply. The worry is that yellow fever will spread from the forests to the cities, because its vector, Aedes spp. mosquitoes, are globally ubiquitous. Faria et al. integrate genomic, epidemiological, and case distribution data from Brazil to estimate patterns of geographic spread, the risks of virus exposure, and the contributions of rural versus urban transmission (see the Perspective by Barrett). Currently, the yellow fever epidemic in Brazil seems to be driven by infections acquired while visiting forested areas and indicates spillover from susceptible wild primates. Science , this issue p. 894 ; see also p. 847

Understanding the circumstances that lead to pandemics is important for their prevention. We analyzed the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted “A” and “B.” Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were most probably the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October to 8 December), and the separate introduction of lineage A likely occurred within weeks of this event. These findings indicate that it is unlikely that SARS-CoV-2 circulated widely in humans before November 2019 and define the narrow window between when SARS-CoV-2 first jumped into humans and when the first cases of COVID-19 were reported. As with other coronaviruses, SARS-CoV-2 emergence likely resulted from multiple zoonotic events.

Introduction: In light of the COVID‐19 pandemic, considerable effort is going into identifying and protecting those at risk. Criminalization, stigmatization and the psychological, physical, behavioural and economic consequences of substance use make people who inject drugs (PWID) extremely vulnerable to many infectious diseases. While relationships between drug use and blood‐borne and sexually transmitted infections are well studied, less attention has been paid to other infectious disease outbreaks among PWID.Discussion: COVID‐19 is likely to disproportionally affect PWID due to a high prevalence of comorbidities that make the disease more severe, unsanitary and overcrowded living conditions, stigmatization, common incarceration, homelessness and difficulties in adhering to quarantine, social distancing or self‐isolation mandates. The COVID‐19 pandemic also jeopardizes essential for PWID services, such as needle exchange or substitution therapy programmes, which can be affected both in a short‐ and a long‐term perspective. Importantly, there is substantial evidence of other infectious disease outbreaks in PWID that were associated with factors that enable COVID‐19 transmission, such as poor hygiene, overcrowded living conditions and communal ways of using drugs.Conclusions: The COVID‐19 crisis might increase risks of homelessnes, overdoses and unsafe injecting and sexual practices for PWID. In order to address existing inequalities, consultations with PWID advocacy groups are vital when designing inclusive health response to the COVID‐19 pandemic.

Ukraine has one of the largest HIV epidemics in Europe, historically driven by people who inject drugs (PWID). The epidemic showed signs of stabilization in 2012, but the recent war in eastern Ukraine may be reigniting virus spread. We investigated the movement of HIV-infected people within Ukraine before and during the conflict. We analyzed HIV-1 subtype-A pol nucleotide sequences sampled during 2012–2015 from 427 patients of 24 regional AIDS centers and used phylogeographic analysis to reconstruct virus movement among different locations in Ukraine. We then tested for correlations between reported PWID behaviors and reconstructed patterns of virus spread. Our analyses suggest that Donetsk and Lugansk, two cities not controlled by the Ukrainian government in eastern Ukraine, were significant exporters of the virus to the rest of the country. Additional analyses showed that viral dissemination within the country changed after 2013. Spearman correlation analysis showed that incoming virus flow was correlated with the number of HIV-infected internally displaced people. Additionally, there was a correlation between more intensive virus movement and locations with a higher proportion of PWID practicing risky sexual behaviors. Our findings suggest that effective prevention responses should involve internally displaced people and people who frequently travel to war-affected regions. Scale-up of harm reduction services for PWID will be an important factor in preventing new local HIV outbreaks in Ukraine.

Early treatment, soon after infection, reduces HIV transmissions and benefits patients. The Transmission Reduction Intervention Project (TRIP) evaluated a network intervention to detect individuals recently infected (in the past 6 months). TRIP was conducted in Greece (2013–2015) and focused on drug injector networks. Based on HIV status, testing history, and the results of an assay to detect recent infections, TRIP classified drug injector “Seeds” into groups: Recent Seeds (RS), and Control Seeds with Long-term HIV infection (LCS). The network members of RS and LCS were traced for two steps. The analysis included 23 RS, 171 network members of the RS, 19 LCS, and 65 network members of the LCS. The per-seed number of recents detected in the network of RS was 5 times the number in the network of LCS (Ratio RS vs. LCS: 5.23; 95% Confidence Interval (CI): 1.54–27.61). The proportion of recents among HIV positives in the network of RS (27%) was approximately 3 times (Ratio RS vs. LCS: 3.30; 95% CI: 1.04–10.43) that in the network of LCS (8%). Strategic network tracing that starts with recently infected persons could support public health efforts to find and treat people early in their HIV infection.

Birth-death processes have given biologists a model-based framework to answer questions about changes in the birth and death rates of lineages in a phylogenetic tree. Therefore birth-death models are central to macroevolutionary as well as phylodynamic analyses. Early approaches to studying temporal variation in birth and death rates using birth-death models faced difficulties due to the restrictive choices of birth and death rate curves through time. Sufficiently flexible time-varying birth-death models are still lacking. We use a piecewise-constant birth-death model, combined with both Gaussian Markov random field (GMRF) and horseshoe Markov random field (HSMRF) prior distributions, to approximate arbitrary changes in birth rate through time. We implement these models in the widely used statistical phylogenetic software platform RevBayes, allowing us to jointly estimate birth-death process parameters, phylogeny, and nuisance parameters in a Bayesian framework. We test both GMRF-based and HSMRF-based models on a variety of simulated diversification scenarios, and then apply them to both a macroevolutionary and an epidemiological dataset. We find that both models are capable of inferring variable birth rates and correctly rejecting variable models in favor of effectively constant models. In general the HSMRF-based model has higher precision than its GMRF counterpart, with little to no loss of accuracy. Applied to a macroevolutionary dataset of the Australian gecko family Pygopodidae (where birth rates are interpretable as speciation rates), the GMRF-based model detects a slow decrease whereas the HSMRF-based model detects a rapid speciation-rate decrease in the last 12 million years. Applied to an infectious disease phylodynamic dataset of sequences from HIV subtype A in Russia and Ukraine (where birth rates are interpretable as the rate of accumulation of new infections), our models detect a strongly elevated rate of infection in the 1990s.