Monitoring COVID-19 and Influenza: the added-value of a Severe Acute Respiratory Infection surveillance system in Portugal (preprint)

Background: Severe Acute Respiratory Infections (SARI) surveillance is recommended to assess severity of respiratory infections disease. In 2021, the National Institute of Health Doutor Ricardo Jorge, in collaboration with two central hospitals, implemented a SARI sentinel surveillance system based on electronic health registries. We describe its application in the 2021/2022 season and compare the evolution of SARI cases with the COVID-19 and influenza activity in two regions of Portugal. Methods We identified SARI cases based on ICD-10 codes for influenza-like illness, cardiovascular diagnosis, respiratory diagnosis and respiratory infection. Pearson correlation and cross-correlations between weekly SARI cases, weekly COVID-19 cases and the number of weekly positive samples for influenza were estimated. Results A high correlation between SARI cases or hospitalizations due to respiratory infection and COVID-19 incidence was obtained (ρ = 0.78 and ρ = 0.82, respectively). Weekly SARI hospitalizations detected the COVID-19 epidemic peak a week earlier. A weak correlation was observed between SARI cases and the number of positive samples for influenza (ρ = -0.20). However, if restricted to hospitalizations due to cardiovascular diagnosis, a moderate correlation was observed (ρ = 0.37). Moreover, hospitalizations due to cardiovascular diagnosis detected the increase of influenza epidemic activity a week earlier. Conclusion In the 2021/2022 season, the Portuguese SARI sentinel surveillance system pilot was able to early detect the 5th COVID-19 epidemic wave and the increase of influenza activity. Establishing complementary virological inpatient surveillance is vital to aid in understanding the relationship between respiratory virus epidemics and disease severity.

Dynamics of a dual SARS-CoV-2 strain co-infection on a prolonged viral shedding COVID-19 case: insights into clinical severity and disease duration (preprint)

ObjectivesA few molecularly proven SARS-CoV-2 cases of symptomatic reinfection are currently known worldwide, with a resolved first infection followed by a second infection after a 48 to 142-day intervening period. We report a multiple-component study of a clinically severe and prolonged viral shedding COVID-19 case in a teenager Portuguese female. She had two hospitalisations, a total of 19 RT-PCR tests, mostly positive, and criteria for releasing from home isolation at the end of 97 days. MethodsThe viral genome was sequenced in seven serial samples and in the diagnostic sample from an infected close relative. A human genome-wide array (>900K) was screened on the seven samples, and in vitro culture was conducted on isolates from three late samples. ResultsThe patient had co-infection by two SARS-CoV-2 strains, affiliated in distinct clades and diverging by six variants. The 20A lineage was absolute at the diagnosis (shared with a cohabitating relative), but nine days later the 20B lineage had 3% frequency, and two months later the 20B lineage had 100% frequency. The 900K profiles confirmed the identity of the patient in the serial samples, and allowed us to infer that she had polygenic risk scores for hospitalization and severe respiratory disease within the normal distributions for a Portuguese population cohort. ConclusionsThe early-on dynamic co-infection was the probable cause for the severity of COVID-19 in this otherwise healthy young patient, and for her prolonged SARS-CoV-2 shedding profile.

Analysis of 46,046 SARS-CoV-2 whole-genomes leveraging principal component analysis (PCA) (preprint)

Since the beginning of the global SARS-CoV-2 pandemic, there have been a number of efforts to understand the mutations and clusters of genetic lines of the SARS-CoV-2 virus. Until now, phylogenetic analysis methods have been used for this purpose. Here we show that Principal Component Analysis (PCA), which is widely used in population genetics, can not only help us to understand existing findings about the mutation processes of the virus, but can also provide even deeper insights into these processes while being less sensitive to sequencing gaps. Here we describe a comprehensive analysis of a 46,046 SARS-CoV-2 genome sequence dataset downloaded from the GISAID database in June of this year. SummaryPCA provides deep insights into the analysis of large data sets of SARS-CoV-2 genomes, revealing virus lineages that have thus far been unnoticed.