Back

SARS-CoV-2 lineage-specific disease symptoms and disease severity in Sao Caetano do Sul city, Brazil

Sales, F. C. S.; Prete, C. A.; Abade, L.; Buss, L. F.; Candido, D. S.; Claro, I. M.; Moreira, F. R. R.; Manulli, E. R.; Capuani, L.; Silva, C. A. M.; Oliveira, B. A.; Coletti, T.; Paiao, H. G. O.; Costa, S. F.; Correa, M. C. M.; Leal, F. E.; Parag, K. V.; Nascimento, V. H.; Faria, N. R.; Sabino, E. C.

2023-10-20 epidemiology
10.1101/2023.10.19.23297252 medRxiv
Show abstract

BackgroundThe city of Sao Caetano do Sul, Brazil, established a web-based platform to provide primary care to suspected COVID-19 patients, integrating clinical and demographic data and sample metadata. Here we describe lineage-specific spatiotemporal dynamics of infections, clinical symptoms, and disease severity during the first year of the epidemic. MethodsWe selected and sequenced 879 PCR+ swab samples (8% of all reported cases), obtaining a spatially and temporally representative set of sequences. Daily lineage-specific prevalence was estimating using a moving-window approach, allowing inference of cumulative cases and symptom probability stratified by lineage using integrated data from the platform. ResultsMost infections were caused by B.1.1.28 (41.3%), followed by Gamma (31.7%), Zeta (9.6%) and B1.1.33 (9.0%). Gamma and Zeta were associated with larger prevalence of dyspnoea (respectively 81.3% and 78.5%) and persistent fever (84.7% and 61.1%) compared to B.1.1.28 and B.1.1.33. Ageusia, anosmia, and coryza were respectively 18.9%, 20.3% and 17.8% less commonly caused by Gamma, while altered mental status was 108.9% more common in Zeta. Case incidence was spatially heterogeneous and larger in poorer and younger districts. DiscussionOur study demonstrates that Gamma was associated with more severe disease, emphasising the role of its increased disease severity in the heightened mortality levels in Brazil.

Matching journals

The top 13 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.