Corrigendum: Transmissibility, hospitalization, and intensive care admissions due to omicron compared to delta variants of SARS-CoV-2 in Catalonia: A cohort study and ecological analysis

Mathematical modeling of SARS-CoV-2 variant substitutions in European countries: transmission dynamics and epidemiological insights

Background

Countries across Europe have faced similar evolutions of SARS-CoV-2 variants of concern, including the Alpha, Delta, and Omicron variants.

Materials and methods

We used data from GISAID and applied a robust, automated mathematical substitution model to study the dynamics of COVID-19 variants in Europe over a period of more than 2 years, from late 2020 to early 2023. This model identifies variant substitution patterns and distinguishes between residual and dominant behavior. We used weekly sequencing data from 19 European countries to estimate the increase in transmissibility  ( Δ β )   between consecutive SARS-CoV-2 variants. In addition, we focused on large countries with separate regional outbreaks and complex scenarios of multiple competing variants.

Results

Our model accurately reproduced the observed substitution patterns between the Alpha, Delta, and Omicron major variants. We estimated the daily variant prevalence and calculated Δ β between variants, revealing that: ( i ) Δ β increased progressively from the Alpha to the Omicron variant; ( i i ) Δ β showed a high degree of variability within Omicron variants; ( i i i ) a higher Δ β was associated with a later emergence of the variant within a country; ( i v ) a higher degree of immunization of the population against previous variants was associated with a higher Δ β for the Delta variant; ( v ) larger countries exhibited smaller Δ β ,  suggesting regionally diverse outbreaks within the same country; and finally ( v i ) the model reliably captures the dynamics of competing variants, even in complex scenarios.

Conclusion

The use of mathematical models allows for precise and reliable estimation of daily cases of each variant. By quantifying Δ β ,  we have tracked the spread of the different variants across Europe, highlighting a robust increase in transmissibility trend from Alpha to Omicron. Additionally, we have shown that the geographical characteristics of a country, as well as the timing of new variant entrances, can explain some of the observed differences in variant substitution dynamics across countries.

A mixture of mobility and meteorological data provides a high correlation with COVID-19 growth in an infection-naive population: a study for Spanish provinces

Introduction

We use Spanish data from August 2020 to March 2021 as a natural experiment to analyze how a standardized measure of COVID-19 growth correlates with asymmetric meteorological and mobility situations in 48 Spanish provinces. The period of time is selected prior to vaccination so that the level of susceptibility was high, and during geographically asymmetric implementation of non-pharmacological interventions.

Methods

We develop reliable aggregated mobility data from different public sources and also compute the average meteorological time series of temperature, dew point, and UV radiance in each Spanish province from satellite data. We perform a dimensionality reduction of the data using principal component analysis and investigate univariate and multivariate correlations of mobility and meteorological data with COVID-19 growth.

Results

We find significant, but generally weak, univariate correlations for weekday aggregated mobility in some, but not all, provinces. On the other hand, principal component analysis shows that the different mobility time series can be properly reduced to three time series. A multivariate time-lagged canonical correlation analysis of the COVID-19 growth rate with these three time series reveals a highly significant correlation, with a median R-squared of 0.65. The univariate correlation between meteorological data and COVID-19 growth is generally not significant, but adding its two main principal components to the mobility multivariate analysis increases correlations significantly, reaching correlation coefficients between 0.6 and 0.98 in all provinces with a median R-squared of 0.85. This result is robust to different approaches in the reduction of dimensionality of the data series.

Discussion

Our results suggest an important effect of mobility on COVID-19 cases growth rate. This effect is generally not observed for meteorological variables, although in some Spanish provinces it can become relevant. The correlation between mobility and growth rate is maximal at a time delay of 2-3 weeks, which agrees well with the expected 5?10 day delays between infection, development of symptoms, and the detection/report of the case.

Comparing COVID-19 severity in patients hospitalized for community-associated Delta, BA.1 and BA.4/5 variant infection

Background

Despite decreasing COVID-19 disease severity during the Omicron waves, a proportion of patients still require hospitalization and intensive care.

Objective

To compare demographic characteristics, comorbidities, vaccination status, and previous infections in patients hospitalized for community-associated COVID-19 (CAC) in predominantly Delta, Omicron BA.1 and BA.4/5 SARS-CoV-2 waves.

Methods

Data were extracted from three national databases-the National COVID-19 Database, National Vaccination Registry and National Registry of Hospitalizations.

Results

Among the hospitalized CAC patients analyzed in this study, 5,512 were infected with Delta, 1,120 with Omicron BA.1, and 1,143 with the Omicron BA.4/5 variant. The age and sex structure changed from Delta to BA.4/5, with the proportion of women (9.5% increase), children and adolescents (10.4% increase), and octa- and nonagenarians increasing significantly (24.5% increase). Significantly more patients had comorbidities (measured by the Charlson Comorbidity Index), 30.3% in Delta and 43% in BA.4/5 period. The need for non-invasive ventilatory support (NiVS), ICU admission, mechanical ventilation (MV), and in-hospital mortality (IHM) decreased from Delta to Omicron BA.4/5 period for 12.6, 13.5, 11.5, and 6.3%, respectively. Multivariate analysis revealed significantly lower odds for ICU admission (OR 0.68, CI 0.54-0.84, p < 0.001) and IHM (OR 0.74, CI 0.58-0.93, p = 0.011) during the Delta period in patients who had been fully vaccinated or boosted with a COVID-19 vaccine within the previous 6 months. In the BA.1 variant period, patients who had less than 6 months elapsed between the last vaccine dose and SARS-CoV-2 positivity had lower odds for MV (OR 0.38, CI 0.18-0.72, p = 0.005) and IHM (OR 0.56, CI 0.37- 0.83, p = 0.005), but not for NIVS or ICU admission.

Conclusion

The likelihood of developing severe CAC in hospitalized patients was higher in those with the Delta and Omicron BA.1 variant compared to BA.4/5.