Background: The structural environment of urban slums, including physical, demographic, and socioeconomic attributes, renders inhabitants more vulnerable to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Yet, little is known about the specific determinants that contribute to high transmission within these communities. We therefore aimed to investigate SARS-CoV-2 seroprevalence in an urban slum in Brazil. Methods and findings: We performed a cross-sectional serosurvey of an established cohort of 2,041 urban slum residents from the city of Salvador, Brazil between November 2020 and February 2021, following the first Coronavirus Disease 2019 (COVID-19) pandemic wave in the country and during the onset of the second wave. The median age in this population was 29 years (interquartile range [IQR] 16 to 44); most participants reported their ethnicity as Black (51.5%) or Brown (41.7%), and 58.5% were female. The median size of participating households was 3 (IQR 2 to 4), with a median daily per capita income of 2.32 (IQR 0.33–5.15) US Dollars. The main outcome measure was presence of IgG against the SARS-CoV-2 spike protein. We implemented multilevel models with random intercepts for each household to estimate seroprevalence and associated risk factors, adjusting for the sensitivity and specificity of the assay, and the age and gender distribution of our study population. We identified high seroprevalence (47.9%, 95% confidence interval [CI] 44.2% to 52.1%), particularly among female residents (50.3% [95% CI 46.3% to 54.8%] versus 44.6% [95% CI 40.1% to 49.4%] among male residents, p < 0.01) and among children (54.4% [95% CI 49.6% to 59.3%] versus 45.4% [95% CI 41.5% to 49.7%] among adults, p < 0.01). Adults residing in households with children were more likely to be seropositive (48.6% [95% CI 44.8% to 52.3%] versus 40.7% [95% CI 37.2% to 44.3%], p < 0.01). Women who were unemployed and living below the poverty threshold (daily per capita household income <$1.25) were more likely to be seropositive compared to men with the same employment and income status (53.9% [95% CI 47.0% to 60.6%] versus 32.9% [95% CI 23.2% to 44.3%], p < 0.01). Participation in the study was voluntary, which may limit the generalizability of our findings. Conclusions: Prior to the peak of the second wave of the COVID-19 pandemic, cumulative incidence as assessed by serology approached 50% in a Brazilian urban slum population. In contrast to observations from industrialized countries, SARS-CoV-2 incidence was highest among children, as well as women living in extreme poverty. These findings emphasize the need for targeted interventions that provide safe environments for children and mitigate the structural risks posed by crowding and poverty for the most vulnerable residents of urban slum communities.

Arboviruses, including the Zika virus, have recently emerged as one of the most important threats to human health. The use of metagenomics-based approaches has already proven valuable to aid surveillance of arboviral infections, and the ability to reconstruct complete viral genomes from metatranscriptomics data is key to the development of new control strategies for these diseases. Herein, we used RNA-based metatranscriptomics associated with Ion Torrent deep sequencing to allow for the high-quality reconstitution of an outbreak-related Zika virus (ZIKV) genome (10,739 nt), with extended 5′-UTR and 3′-UTR regions, using a newly-implemented bioinformatics approach. Besides allowing for the assembly of one of the largest complete ZIKV genomes to date, our strategy also yielded high-quality complete genomes of two arthropod-infecting viruses co-infecting C6/36 cell lines, namely: Alphamesonivirus 1 strain Salvador (20,194 nt) and Aedes albopictus totivirus-like (4618 nt); the latter likely represents a new viral species. Altogether, our results demonstrate that our bioinformatics approach associated with Ion Torrent sequencing allows for the high-quality reconstruction of known and unknown viral genomes, overcoming the main limitation of RNA deep sequencing for virus identification.