Etiology of Four Waves of the COVID-19 Pandemic in Ukraine according to the SARS-CoV-2 Virus Genome Sequencing Data: A Pilot Study

The COVID-19 pandemic in Ukraine, from March 2020 to June 2022, witnessed distinct waves, each characterized by an increase in cases and fatalities. Next-generation sequencing has been used to understand the impact of viral variants on the pandemic situation in Ukraine. We analyzed SARS-CoV-2 genome sequencing data to identify viral variants circulating during each wave. By integrating epidemiological information, we established associations between viral variants and disease spread. The adoption of next-generation sequencing for SARS-CoV-2 surveillance in Ukraine, despite limited resources, yielded adequate and trustworthy results, reflecting the pandemic situation. After the Russian military invasion of Ukraine in February 2022, a large number of refugees crossed the border with neighboring countries. Mutation analysis on sequencing data from Ukraine and Poland was used to estimate the exchange of SARS-CoV-2 variants between the countries during this period. Omicron subvariants detected in both countries were similar. The analysis of SARS-CoV-2 sequences from Poland and Ukraine revealed shared nucleotide mutations that can be used to identify the directions of spreading

The epidemiological signature of influenza B virus and its B/Victoria and B/Yamagata lineages in the 21st century

We describe the epidemiological characteristics, pattern of circulation, and geographical distribution of influenza B viruses and its lineages using data from the Global Influenza B Study. We included over 1.8 million influenza cases occurred in thirty-one countries during 2000-2018. We calculated the proportion of cases caused by influenza B and its lineages; determined the timing of influenza A and B epidemics; compared the age distribution of B/Victoria and B/Yamagata cases; and evaluated the frequency of lineage-level mismatch for the trivalent vaccine. The median proportion of influenza cases caused by influenza B virus was 23.4%, with a tendency (borderline statistical significance, p = 0.060) to be higher in tropical vs. temperate countries. Influenza B was the dominant virus type in about one every seven seasons. In temperate countries, influenza B epidemics occurred on average three weeks later than influenza A epidemics; no consistent pattern emerged in the tropics. The two B lineages caused a comparable proportion of influenza B cases globally, however the B/Yamagata was more frequent in temperate countries, and the B/Victoria in the tropics (p = 0.048). B/Yamagata patients were significantly older than B/Victoria patients in almost all countries. A lineage-level vaccine mismatch was observed in over 40% of seasons in temperate countries and in 30% of seasons in the tropics. The type B virus caused a substantial proportion of influenza infections globally in the 21st century, and its two virus lineages differed in terms of age and geographical distribution of patients. These findings will help inform health policy decisions aiming to reduce disease burden associated with seasonal influenza

Distribution of influenza virus types by age using case-based global surveillance data from twenty-nine countries, 1999-2014

Background: Influenza disease burden varies by age and this has important public health implications. We compared the proportional distribution of different influenza virus types within age strata using surveillance data from twenty-nine countries during 1999-2014 (N=358,796 influenza cases)

Temporal Patterns of Influenza A and B in Tropical and Temperate Countries: What Are the Lessons for Influenza Vaccination?

<div><p>Introduction</p><p>Determining the optimal time to vaccinate is important for influenza vaccination programmes. Here, we assessed the temporal characteristics of influenza epidemics in the Northern and Southern hemispheres and in the tropics, and discuss their implications for vaccination programmes.</p><p>Methods</p><p>This was a retrospective analysis of surveillance data between 2000 and 2014 from the Global Influenza B Study database. The seasonal peak of influenza was defined as the week with the most reported cases (overall, A, and B) in the season. The duration of seasonal activity was assessed using the maximum proportion of influenza cases during three consecutive months and the minimum number of months with ≥80% of cases in the season. We also assessed whether co-circulation of A and B virus types affected the duration of influenza epidemics.</p><p>Results</p><p>212 influenza seasons and 571,907 cases were included from 30 countries. In tropical countries, the seasonal influenza activity lasted longer and the peaks of influenza A and B coincided less frequently than in temperate countries. Temporal characteristics of influenza epidemics were heterogeneous in the tropics, with distinct seasonal epidemics observed only in some countries. Seasons with co-circulation of influenza A and B were longer than influenza A seasons, especially in the tropics.</p><p>Discussion</p><p>Our findings show that influenza seasonality is less well defined in the tropics than in temperate regions. This has important implications for vaccination programmes in these countries. High-quality influenza surveillance systems are needed in the tropics to enable decisions about when to vaccinate.</p></div

Distribution of influenza virus types by age using case-based global surveillance data from twenty-nine countries, 1999-2014

BACKGROUND : Influenza disease burden varies by age and this has important public health implications. We compared the proportional distribution of different influenza virus types within age strata using surveillance data from twenty-nine countries during 1999-2014 (N=358,796 influenza cases). METHODS : For each virus, we calculated a Relative Illness Ratio (defined as the ratio of the percentage of cases in an age group to the percentage of the country population in the same age group) for young children (0-4 years), older children (5-17 years), young adults (18-39 years), older adults (40-64 years), and the elderly (65+ years). We used random-effects meta-analysis models to obtain summary relative illness ratios (sRIRs), and conducted metaregression and sub-group analyses to explore causes of between-estimates heterogeneity. RESULTS : The influenza virus with highest sRIR was A(H1N1) for young children, B for older children, A(H1N1) pdm2009 for adults, and (A(H3N2) for the elderly. As expected, considering the diverse nature of the national surveillance datasets included in our analysis, between-estimates heterogeneity was high (I2>90%) for most sRIRs. The variations of countries’ geographic, demographic and economic characteristics and the proportion of outpatients among reported influenza cases explained only part of the heterogeneity, suggesting that multiple factors were at play. CONCLUSIONS : These results highlight the importance of presenting burden of disease estimates by age group and virus (sub)type.Table S1. Number of influenza cases caused by the difference influenza viruses that were included in the analysis. The Global Influenza B Study, 1999-2014.Figure S1. Forest plot of the Relative Illness Ratio for patients aged 0-4 years infected with A(H1N1) influenza virus. The Global Influenza B Study, 1999-2014. Figure S2. Forest plot of the Relative Illness Ratio for patients aged 5-17 years infected with A(H1N1) influenza virus. The Global Influenza B Study, 1999-2014. Figure S3. Forest plot of the Relative Illness Ratio for patients aged 18-39 years infected with A(H1N1) influenza virus. The Global Influenza B Study, 1999-2014. Figure S4. Forest plot of the Relative Illness Ratio for patients aged 40-64 years infected with A(H1N1) influenza virus. The Global Influenza B Study, 1999-2014. Figure S5. Forest plot of the Relative Illness Ratio for patients aged 65+ years infected with A(H1N1) influenza virus. The Global Influenza B Study, 1999-2014.Table S2. Summary Relative Illness Ratio (sRIR), 95% confidence intervals (95% CI) across age groups and influenza viruses by categories of country ageing index. The Global Influenza B Study, 1999- 2014. Table S3. Summary Relative Illness Ratio (sRIR), 95% confidence intervals (95% CI) across age groups and influenza viruses by percentage of outpatients among cases reported to the influenza surveillance system. The Global Influenza B Study, 1999-2014. Table S4. Summary Relative Illness Ratio (sRIR), 95% confidence intervals (95% CI) across age groups and influenza viruses by country latitude. The Global Influenza B Study, 1999-2014. Table S5. Summary Relative Illness Ratio (sRIR), 95% confidence intervals (95% CI) across age groups and influenza viruses by percentage of influenza cases caused by that influenza virus in the same season. The Global Influenza B Study, 1999-2014. Table S6. Summary Relative Illness Ratio (sRIR), 95% confidence intervals (95% CI) across age groups and influenza viruses by percentage of influenza cases caused by that influenza virus in the previous season. The Global Influenza B Study, 1999-2014. Table S7. Summary Relative Illness Ratio (sRIR), 95% confidence intervals (95% CI) across age groups and influenza viruses by categories of country gross domestic product (GDP) per capita. The Global Influenza B Study, 1999-2014.The Global Influenza B Study is funded by an unrestricted research grant from Sanofi Pasteur.https://bmcinfectdis.biomedcentral.comam2019Medical Virolog

Mean percentage of influenza cases by month (black diamonds) and number of times the peak of the influenza season took place in each month (pink squares) for countries in the inter-tropical belt.

<p>Mean percentage of influenza cases by month (black diamonds) and number of times the peak of the influenza season took place in each month (pink squares) for countries in the inter-tropical belt.</p

Influenza cases reported to the national influenza surveillance system by each participating country (from southern- to northern-most) and percentages of cases due to influenza type B virus.

<p>Influenza cases reported to the national influenza surveillance system by each participating country (from southern- to northern-most) and percentages of cases due to influenza type B virus.</p

Median percentage of influenza cases that occurred during the 3-month peak period and median number of months to have ≥80% of influenza cases during a season in countries of the Southern hemisphere, the inter-tropical belt, and the Northern hemisphere.

<p>Median percentage of influenza cases that occurred during the 3-month peak period and median number of months to have ≥80% of influenza cases during a season in countries of the Southern hemisphere, the inter-tropical belt, and the Northern hemisphere.</p

Median percentage of influenza cases that occurred during the 3-month peak period and median number of months to have ≥80% of influenza cases during a season by zone (Southern hemisphere, inter-tropical belt, Northern hemisphere) and proportion of influenza B.

<p>Median percentage of influenza cases that occurred during the 3-month peak period and median number of months to have ≥80% of influenza cases during a season by zone (Southern hemisphere, inter-tropical belt, Northern hemisphere) and proportion of influenza B.</p

Mean percentage of influenza cases by month (black diamonds) and number of times the peak of the influenza season took place in each month (pink squares) for countries in the Southern hemisphere.

<p>Mean percentage of influenza cases by month (black diamonds) and number of times the peak of the influenza season took place in each month (pink squares) for countries in the Southern hemisphere.</p