Incidence Trends for SARS-CoV-2 Alpha and Beta Variants, Finland, Spring 2021

Kant, Ravi; Nguyen, Phuoc Truong; Blomqvist, Soile; Erdin, Mert; Alburkat, Hussein; Suvanto, Maija T.; Zakham, Fathiah; Salminen, Veera; Olander, Viktor; Paloniemi, Minna; Huhti, Leena; Lehtinen, Sara; Luukinen, Bruno; Jarva, Hanna; Kallio‐Kokko, Hannimari; Kurkela, Satu; Lappalainen, Maija; Liimatainen, Hanna; Hannula, Sari; Halkilahti, Jani; Ikonen, Jonna; Ikonen, Niina; Helve, Otto; Gunell, Marianne; Vuorinen, Tytti; Plyusnin, Ilya; Lindh, Erika; Ellonen, Pekka; Sironen, Tarja; Savolainen-Kopra, Carita; Smura, Teemu; Vapalahti, Olli

doi:10.3201/eid2712.211631

Cited by 15 publications

(14 citation statements)

References 10 publications

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“…The incidence of the second wave peaked in early December, and after a brief descent, started to rise again in mid-January. This heralded the beginning of a third wave driven by the more transmissible alpha (B.1.1.7) and beta (B.1.351) SARS-CoV-2 variants ( Kant et al, 2021 ), which peaked in late March. This study focuses on the second and early third wave of the COVID-19 epidemic in the City of Helsinki from 28 th October 2020 to 24 th March 2021.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal clustering patterns and sociodemographic determinants of COVID-19 (SARS-CoV-2) infections in Helsinki, Finland

Siljander

Uusitalo

Pellikka

et al. 2022

Spatial and Spatio-temporal Epidemiology

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This study aims to elucidate the variations in spatiotemporal patterns and sociodemographic determinants of SARS-CoV-2 infections in Helsinki, Finland. Global and local spatial autocorrelation were inspected with Moran's I and LISA statistics, and Getis-Ord Gi* statistics was used to identify the hot spot areas. Space-time statistics were used to detect clusters of high relative risk and regression models were implemented to explain sociodemographic determinants for the clusters. The findings revealed the presence of spatial autocorrelation and clustering of COVID-19 cases. High–high clusters and high relative risk areas emerged primarily in Helsinki's eastern neighborhoods, which are socioeconomically vulnerable, with a few exceptions revealing local outbreaks in other areas. The variation in COVID-19 rates was largely explained by median income and the number of foreign citizens in the population. Furthermore, the use of multiple spatiotemporal analysis methods are recommended to gain deeper insights into the complex spatiotemporal clustering patterns and sociodemographic determinants of the COVID-19 cases.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal clustering patterns and sociodemographic determinants of COVID-19 (SARS-CoV-2) infections in Helsinki, Finland

Siljander

Uusitalo

Pellikka

et al. 2022

Spatial and Spatio-temporal Epidemiology

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“…In univariate analysis, the only variable strongly associated with failure to identify COVID-19 positive samples was the alpha variant ( table 5 ). Indeed, according with the epidemiological situation in our country, 20 the virus variants started to emerge only at the end of our validation sample collection period, with 59% of the positive samples in VAL6–7 confirmed to represent virus variants (mostly alpha) and only 3% the wild-type virus (the virus type of the others remained unknown). Importantly, the dogs had only been trained to detect samples of patients infected by the wild-type virus.…”

Section: Discussionmentioning

confidence: 86%

“…The initial RT-PCR samples were subjected to genomic sequencing and bioinformatics analysis as previously described. 19 Based on epidemiological data, 20 all samples obtained before 6 January 2020 were considered ‘wild-type,’ with reference to the D614G Wuhan-like strain.…”

Section: Methodsmentioning

confidence: 99%

Scent dogs in detection of COVID-19: triple-blinded randomised trial and operational real-life screening in airport setting

et al. 2022

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ObjectiveTo estimate scent dogs’ diagnostic accuracy in identification of people infected with SARS-CoV-2 in comparison with reverse transcriptase polymerase chain reaction (RT-PCR). We conducted a randomised triple-blinded validation trial, and a real-life study at the Helsinki-Vantaa International Airport, Finland.MethodsFour dogs were trained to detect COVID-19 using skin swabs from individuals tested for SARS-CoV-2 by RT-PCR. Our controlled triple-blinded validation study comprised four identical sets of 420 parallel samples (from 114 individuals tested positive and 306 negative by RT-PCR), randomly presented to each dog over seven trial sessions. In a real-life setting the dogs screened skin swabs from 303 incoming passengers all concomitantly examined by nasal swab SARS-CoV-2 RT-PCR. Our main outcomes were variables of diagnostic accuracy (sensitivity, specificity, positive predictive value, negative predictive value) for scent dog identification in comparison with RT-PCR.ResultsOur validation experiments had an overall accuracy of 92% (95% CI 90% to 93%), a sensitivity of 92% (95% CI 89% to 94%) and a specificity of 91% (95% CI 89% to 93%) compared with RT-PCR. For our dogs, trained using the wild-type virus, performance was less accurate for the alpha variant (89% for confirmed wild-type vs 36% for alpha variant, OR 14.0, 95% CI 4.5 to 43.4). In the real-life setting, scent detection and RT-PCR matched 98.7% of the negative swabs. Scant airport prevalence (0.47%) did not allow sensitivity testing; our only SARS-CoV-2 positive swab was not identified (alpha variant). However, ad hoc analysis including predefined positive spike samples showed a total accuracy of 98% (95% CI 97% to 99%).ConclusionsThis large randomised controlled triple-blinded validation study with a precalculated sample size conducted at an international airport showed that trained scent dogs screen airport passenger samples with high accuracy. One of our findings highlights the importance of continuous retraining as new variants emerge. Using scent dogs may present a valuable approach for high-throughput, rapid screening of large numbers of people.

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“…Recombinant SARS-CoV-2 genome sequences were observed in independent Illumina sequencing runs of surveillance samples in 2022 from week 3 onwards (sequencing and sequence analyses were conducted as described in [ 1 - 3 ]). Sequence analysis suggested a recombination breakpoint between nucleotide positions 13,296 and 15,240.…”

Section: Genomic Investigationmentioning

confidence: 99%

Genomic and epidemiological report of the recombinant XJ lineage SARS-CoV-2 variant, detected in northern Finland, January 2022

et al. 2022

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Recombinant sequences of the SARS-CoV-2 Omicron variant were detected in surveillance samples collected in north-western Finland in January 2022. We detected 191 samples with an identical genome arrangement in weeks 3 to 11, indicating sustained community transmission. The recombinant lineage has a 5’-end of BA.1, a recombination breakpoint between orf1a and orf1b (nucleotide position 13,296–15,240) and a 3’-end of BA.2 including the S gene. We describe the available genomic and epidemiological data about this currently circulating recombinant XJ lineage.

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Incidence Trends for SARS-CoV-2 Alpha and Beta Variants, Finland, Spring 2021

Cited by 15 publications

References 10 publications

Spatiotemporal clustering patterns and sociodemographic determinants of COVID-19 (SARS-CoV-2) infections in Helsinki, Finland

Spatiotemporal clustering patterns and sociodemographic determinants of COVID-19 (SARS-CoV-2) infections in Helsinki, Finland

Scent dogs in detection of COVID-19: triple-blinded randomised trial and operational real-life screening in airport setting

Genomic and epidemiological report of the recombinant XJ lineage SARS-CoV-2 variant, detected in northern Finland, January 2022

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