Mortality associated with influenza infections in France before and during the COVID-19 pandemic, and mortality associated with Omicron infections, including mortality for cardiovascular disease and for mental and behavioral disorders (preprint)

Background: There is limited information on mortality associated with influenza and Omicron infections, as infections with those viruses are often not detected, or listed as contributing causes of death. Methods: We applied the previously developed methodology to estimate the contribution of influenza infections to all-cause mortality in France for the 2014/2015 through the 2018/2019 influenza seasons, and the contribution of both SARS-CoV-2 and influenza infections to all cause mortality between week 33, 2022 through week 12, 2023. Results: For the 2014/2015 through the 2018/2019 seasons, influenza was associated with an average of 15654 (95% CI (13013,18340)) deaths, while between week 33, 2022 through week 12, 2023, we estimated 7851 (5213,10463) influenza associated deaths and 32607 (20794,44496) SARS-CoV-2 associated deaths. The number of SARS-CoV2 associated deaths during the Omicron epidemic was significantly higher than the number of deaths with COVID19 listed on the death certificate or the hospitalization record; for example, between weeks 33-52 in 2022, we estimated 23983 (15307,32620) SARS-CoV-2-associated deaths in France, compared with 12811 deaths with COVID19 listed on the death certificate, and 8639 in-hospital deaths with COVID19 during the same period. While we do not have cause specific mortality data for France, in the US, compared to the SAR-CoV-2 epidemic in the Fall/Winter of 2021 - 2022, for the first Omicron epidemic wave in 2022 there were significantly higher increases in mortality with cardiovascular disease, as well as with mental/behavioral disorders listed on the death certificate and COVID-19 NOT listed on the death certificate, and smaller increases (compared to the 2020 - 2021 epidemic) in mortality with either cardiovascular disease or mental/behavioral disorders and COVID19 listed on the death certificate. Conclusions: The relatively high levels of mortality associated with Omicron infections (including deaths with COVID19 not listed on the death certificate) suggest the need for wider detection/treatment of Omicron infections (particularly in individuals with underlying health conditions such as mental/behavioral disorders and cardiac disease), as well as for an increase in COVID19 booster vaccination coverage in different population groups (including non-elderly individuals) to mitigate SARS-CoV-2 transmission in the community. Our results also suggest the need for boosting influenza vaccination coverage in different population groups (including children) in France to mitigate influenza transmission in the community, and for wider testing for influenza infection in respiratory hospitalizations (including pneumonia) in high risk individuals during influenza seasons in combination with the use of influenza antiviral medications.

On temporal changes in the role of different age groups in propagating the Omicron epidemic waves in England (preprint)

Background: There is limited information on the role of individuals in different age groups in the spread of infection during the Omicron epidemics, especially ones beyond the winter epidemic wave in 2021-2022. COVID-19 booster vaccination in England during the Autumn 2022 was restricted to persons aged over 50y, and persons in clinical risk groups. Methods: We used previously developed methodology to evaluate the role of individuals in different age groups in propagating the Spring, Summer, and Autumn waves of the Omicron epidemic in England. This methodology utilizes the relative risk (RR) statistic that measures the change in the proportion of cases in each age group among all COVID-19 cases in the population before the peak of an epidemic wave vs. after the peak of an epidemic wave. Higher values for the RR statistic represent age groups that experienced a disproportionate depletion of susceptible individuals during the ascent of the epidemic (due to increased contact rates and/or susceptibility to infection). Results: For the 2022 Spring wave, the highest RR estimate belonged to children aged 5 to 9y (RR=2.05 (95%CI (2.02,2.08)), followed by children aged 10 to 14y (RR=1.68 (1.66,1.7)) and children aged 0 to 4y (RR=1.38 (1.36,1.41)). For the Summer wave, the highest RR estimates belonged to persons aged 20 to 34y: (RR=1.09 (1.07,1.12) in aged 20 to 24y, RR=1.09 (1.07,1.11) in aged 25 to 29y, RR=1.09(1.07,1.11) in aged 30 to 34y). For the Autumn wave, the highest RR estimates belonged to those aged 70 to 74y (RR=1.10 (1.07,1.14)), followed by adults aged 35 to 39y (RR=1.09 (1.06,1.12)), adults aged 40 to 44y (RR=1.09 (1.06,1.12)), and adults aged 65 to 69y (RR=1.08 (1.05,1.11)). Conclusions: As time progressed, the greatest relative roles in propagating different waves of the Omicron epidemic in England shifted from school-age children to younger adults to adults aged 35 to 44y and 65 to 74y. Extending booster vaccination to all adults, and possibly to children should help limit the spread of Omicron infections in the community.

On the role of different age groups in propagating Omicron epidemics in France (preprint)

Background: There is limited information on the role of different age groups in propagating SARSCoV2 epidemics driven by the Omicron variants. Methods: We examined the role of individuals in different age groups in propagating the Spring, Summer, and Autumn waves of the Omicron epidemics in France using the previously developed methodology based on the relative risk (RR) statistic that measures the change in the proportion of an age group among all cases admitted to ICU for COVID19 before vs. after the peak of an epidemic wave. Higher value of the RR statistic for a given age group suggests a disproportionate depletion of susceptible individuals in that age group during the ascent of the epidemic (due to increased contact rates and/or susceptibility to infection). Results: For the Spring wave (March 14 through May 15), the highest RR estimate belonged to children aged 10 to 19y (RR=1.92 (95% CI (1.18,3.12)), followed by adults aged 40 to 49y (RR=1.45 (1.09,1.93)) and children aged 0 to 9y (RR=1.31 (0.98,1.74)). For the Summer wave (June 27 through Aug. 21), the highest RR estimate belonged to children aged 0 to 9y (RR=1.61 (1.12,2.3)) followed by children aged 10 to 19y (RR=1.46 (0.72,2.93)) and adults aged 20 to 29y (RR=1.42 (0.91,2.23)). For the Autumn wave (Sep. 18 through Nov. 12), the highest RR estimate belonged to children aged 10 to 19y (RR=1.63 (0.72,3.71)), followed by adults aged 30 to 34y (RR=1.34 (0.8,2.25)) and 20 to 24y (RR=1.20 (0.65,2.21)). Conclusions: Children aged 10 to 19y played the greatest relative role in propagating Omicron epidemics, particularly when schools were open, followed by children aged 0 to 9y and adults aged 20 to 29y, as well as adults aged 30 to 49y. Persons aged over 50y played a more limited role in propagating Omicron infection in the community. Additional efforts are needed to increase vaccination coverage in children aged 10 to 19y, as well as younger children and young adults to mitigate Omicron epidemics in the community.

SARS-CoV-2-associated mortality with a principal cause other than COVID-19 during the Omicron epidemic in France and detection and treatment of Omicron infections in associated complications (preprint)

BackgroundWith the emergence of the Omicron variant, an increasing proportion of SARS-CoV-2 associated deaths have a principal cause of death other than COVID-19. In France, between Nov. 1, 2021 -- July 31, 2022, in addition to 33,353 deaths with the principal cause of COVID-19, there were 9,638 deaths with a confirmed SARS-CoV-2 infection with a principal cause other than COVID-19 (as well as SARS-CoV-2-associated deaths with an undetected SARS-CoV-2 infection). MethodsWe examined the relation between mortality with a principal cause other than COVID-19 (non-COVID-19 mortality) in France during the pandemic and SARS-CoV-2-related ICU admissions in adults aged over 60y. ResultsThe number of non-COVID-19 deaths in France between July 2021-June 2022 was greater than the corresponding number between July 2020-June 2021 by 20,860 (95% CI (11241,30421)) after adjusting for pre-pandemic trends in mortality. During the period of Omicron circulation, there was a strong temporal association between the weekly rate of non-COVID-19 deaths in France and the rate of ICU admissions with a SARS-CoV-2 infection in adults aged under 60y during the previous week (correlation=0.90 (0.84,0.94)) for the period between Nov. 1, 2021 - Nov. 13. 2022). Proportions of ICU admissions for causes other than COVID-19 among all SARS-CoV-2-positive ICU admissions in older adults were lower during periods of active COVID-19 circulation in France, particularly the first wave of the Omicron epidemic. That pattern persisted for the later period as well, with correlations between the proportion of ICU admissions for causes other than COVID-19 among all SARS-CoV-2 positive ICU admissions and rates of ICU admissions for COVID-19 for the period between March 25, 2022 -- Dec. 5, 2022 ranging from -0.4 (-0.5,-0.3) in ages 70-79y to -0.53 (-0.61,-0.44) in ages over 90y. ConclusionsOur results suggest a significant contribution of Omicron infections to mortality with the principal cause other than COVID-19 in France. Our results also suggest temporal inconsistency in characterizing complications associated with Omicron infections, which may be related to both under-detection of Omicron infections and to temporal differences in the treatment of Omicron infections in associated complications.

Non-COVID-19 mortality in France, April 2020 - June 2022: reduction compared to pre-pandemic mortality patterns, relative increase during the Omicron period, and the importance of detecting SARS-CoV-2 infections (preprint)

Aims: We compared the number of non-COVID-19 deaths between April 2020 and June 2022 to the expected number of deaths based on the patterns observed in the five years prior to the pandemic in France with the aims of (a) estimating the reduction in non-COVID-19 mortality, particularly due to reduction in the circulation of other respiratory viruses during the pandemic; (b) examining the degree to which SARS-CoV-2 infection was detected and characterized as a cause of death during different periods of the pandemic. Methods: Using a previously developed regression model, we expressed weekly mortality rates in the 5-year period prior to the pandemic as a combination of influenza-associated mortality rates and baseline and a linear trend for the rates of non-influenza mortality. Estimates for the baseline and trend for non-influenza mortality together with estimates of influenza-related mortality prior to the pandemic were used to estimate expected mortality during the pandemic period. Results: The number of recorded non-COVID-19 deaths between week 15, 2020 and week 26, 2022 in France was less than the expected number of deaths by 49,623 (95% CI (20364,78837)). Additionally, rates of non-COVID-19 mortality increased during the later part of the study period, with the difference between the number of non-COVID-19 deaths and the expected number of deaths during the last 52 weeks of the study period being greater than the corresponding difference for the first 52 weeks of the study period by 28,954 (24979,32918) deaths. Conclusions: Our results suggest (a) the effectiveness of mitigation measures during the pandemic for reducing the rates of non-COVID-19 mortality, particularly mortality related to circulation of other respiratory viruses, including influenza (that was responsible for an annual average of 15,334 (12593,18077) deaths between 2015-2019 in France); (b) detection of a high proportion of SARS-CoV-2 infections leading to deaths in France, and characterization of those infections as the underlying cause of death. Additionally, while the increase in non-COVID-19 mortality during the later part of the study period is partly related to the temporal increase in the circulation of other respiratory viruses, there was an increase, particularly during the period of the circulation of the Omicron variant, in the proportion of hospitalizations with a SARS-CoV-2 infection in France that were coded as hospitalizations with COVID-19 (rather than COVID-19 hospitalizations), suggesting an increasing proportion of SARS-COV-2-associated deaths not being coded as COVID-19 deaths. All of this suggests the importance of timely detection of infections with SARS-CoV-2, particularly the Omicron variant (for which manifestations of disease complications are different compared to the earlier variants), and of providing the necessary treatment to patients to avoid progression to fatal outcomes.

Mortality associated with different influenza subtypes in France between 2015-2019 (preprint)

Background High levels of excess mortality during periods of active influenza circulation in France were observed in the years preceding the COVID-19 pandemic. Some of the factors that affect the rates of influenza associated mortality are influenza vaccination coverage levels in different population groups and practices for testing for influenza and related use of antiviral medications for various illness episodes (including pneumonia hospitalizations) during periods of active influenza circulation in the community. Methods Data on sentinel ILI surveillance and sentinel virological surveillance in France were combined in a framework of a previously developed regression model to estimate the number of deaths associated with the circulation of the major influenza subtypes (A/H3N2, A/H1N1, B/Yamagata and B/Victoria) in France between 2015-2019. Results Between week 3, 2015 and week 2, 2020, there were on average 15403 (95% CI (12591,18229)) annual influenza-associated deaths, of which 60.3% (49.9%,71.9%) were associated with influenza A/H3N2, and 29.5% (13.3%,45.5%) were associated with influenza B/Yamagata. During weeks when levels of ILI consultation in mainland France were above 50 per 100,000 persons, 7.9% (6.5%,9.4%) of all deaths in France were influenza-associated. Conclusions High rates of influenza-associated mortality in France prior to the COVID-19 pandemic suggest that boosting influenza vaccination coverage in different population groups and testing for influenza in respiratory illness episodes (including pneumonia hospitalizations) during periods of active influenza (particularly influenza A/H3N2) circulation in combination with the use of antiviral medications is needed to mitigate the impact of influenza epidemics.

Detectability of the novel coronavirus (SARS-CoV-2) infection and rates of mortality for COVID-19 in different regions of the Russian Federation (preprint)

BackgroundLaboratory diagnosis of the novel coronavirus (SARS-CoV-2) infection combined with tracing/quarantine for contacts of infected individuals affects the spread of SARS-CoV-2 in the community and the levels of related mortality. Moreover, not all cases of SARS-CoV-2 infection in the population are detected (laboratory diagnosed). Here, we examine the relation between detectability of SARS-CoV-2 infection (i.e. the percent of detected COVID-19 cases among all cases of SARS-CoV-2 infection in the population) and levels of mortality for COVID-19 for the 85 different regions (federal subjects) of the Russian Federation. MethodsLower case-fatality rate (CFR, the proportion of deaths among reported COVID-19 cases in the population) corresponds to higher detectability of the SARS-COV-2 infection. We used data from the Russian Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing (Rospotrebnadzor) on the number of detected COVID-19 cases and the number of deaths from COVID-19 in the 85 different regions of the Russian Federation to examine the correlation between case-fatality rates and rates of mortality for COVID-19 in different regions of the Russian Federation. ResultsThe correlation between case-fatality rates for cases/deaths reported by Sep. 17, 2020 and rates of mortality for COVID-19 per 100,000 for deaths reported by Sep. 17, 2020 in different regions of the Russian Federation is 0.68 (0.55,0.78). The region with both the highest COVID-19 mortality rate per 100,000 and the highest CFR (lowest detectability of SARS-CoV-2 infection) is the city of St. Petersburg. ConclusionsDetectability of SARS-CoV-2 infection is one of the factors that affects the levels of mortality for COVID-19 in Russia. Regions of the Russian Federation with relatively low detectability of SARS-CoV-2 infection (e.g. those regions for which the case-fatality rate is above the median value of 1.2% for the case-fatality rate in different regions of the Russian Federation on Sep. 17, 2020 [3]) ought to increase testing for SARS-CoV-2 in order to mitigate the spread of SARS-CoV-2 and diminish the related mortality.

Detectability of the novel coronavirus (SARS-CoV-2) infection and rates of mortality from the novel coronavirus infection in different regions of the Russian Federation (preprint)

Relevance: Laboratory diagnosis of the novel coronavirus (SARS-CoV-2) infection combined with quarantine for contacts of infected individuals affects the spread of SARS-CoV-2 infection and levels of related mortality. Practices for testing for SARS-CoV-2 infection vary geographically in Russia. For example, in the city of St. Petersburg, where mortality rate for COVID-19 is the highest in the Russian Federation on Oct. 25, 2020, every death for COVID-19 corresponds to 15.7 detected cases of COVID-19 in the population, while the corresponding number for the whole of Russia is 58.1, suggesting limited detection of mild/moderate cases of COVID-19 in St. Petersburg. Methods: More active testing for SARS-CoV-2 results in lower case-fatality ratio (i.e. the proportion of detected COVID-19 cases among all cases of SARS-CoV-2 infection in the population). We used data on COVID-19 cases and deaths to examine the correlation between case-fatality ratios and rates of mortality for COVID-19 in different regions of the Russian Federation. Results: The correlation between case-fatality ratios and rates of mortality for COVID-19 in different regions of the Russian Federation on Oct. 25, 2020 is 0.64 (0.50,0.75). For several regions of the Russian Federation, detectability of SARS-CoV-2 infection is relatively low, while rates of mortality for COVID-19 are relatively high. Conclusions: Detectability of the SARS-CoV-2 infection is one of the factors that affects the levels of mortality from COVID-19. To increase detectability, one ought to test all individuals with respiratory symptoms seeking medical care for SARS-CoV-2 infection, and to undertake additional measures to increase the volume of testing for SARS-CoV-2. Such measures, in combination with quarantine for infected cases and their close contacts help to mitigate the spread of the SARS-CoV-2 infection and diminish the related mortality.

On the effect of age on the transmission of SARS-CoV-2 in households, schools and the community (preprint)

BackgroundThere is limited information on the effect of age on the transmission of SARS-CoV-2 infection in different settings, including primary, secondary and high schools, households, and the whole community. We undertook a literature review of published studies/data on detection of SARS-CoV-2 infection in contacts of COVID-19 cases, as well as serological studies, and studies of infections in the school setting to examine those issues. ResultsOur literature review presents evidence for significantly lower susceptibility to infection for children aged under 10 years compared to adults given the same exposure, for elevated susceptibility to infection in adults aged over 60y compared to younger/middle aged adults, and for the risk of SARS-CoV-2 infection associated with sleeping close to an infected individual. Published serological studies also suggest that younger adults (particularly those aged under 35y) often have high cumulative rates of SARS-CoV-2 infection in the community. Additionally, there is some evidence of robust spread of SARS-CoV-2 in secondary/high schools, and there appears to be more limited spread in primary schools. Some countries with relatively large class sizes in primary schools (e.g.Chile and Israel) reported sizeable outbreaks in some of those schools, though routes of transmission of infection to both students and staff are not clear from current reports. ConclusionsOpening secondary/high schools is likely to contribute to the spread of SARS-CoV-2, and, if implemented, it should require both lower levels of community transmission and greater safeguards to reduce transmission. Compared to secondary/high schools, opening primary schools and daycare facilities may have a more limited effect on the spread of SARS-CoV-2 in the community, particularly under smaller class sizes and in the presence of mitigation measures. Efforts to avoid crowding in the classroom and other mitigation measures should be implemented, to the extent possible, when opening primary schools. Efforts should be undertaken to diminish the mixing in younger adults to mitigate the spread of the epidemic in the whole community.

Lockdown measures and relative changes in the age-specific incidence of SARS-CoV-2 in Spain (preprint)

BackgroundThe first months of the SARS-CoV-2 epidemic in Spain resulted in high incidence and mortality. A national sero-epidemiological survey suggests higher cumulative incidence of infection in older individuals than in younger individuals. However, little is known about the epidemic dynamics in different age groups, including the relative effect of the lockdown measures introduced on March 15, and strengthened on March 30 to April 14, 2020 when only essential workers continued to work. MethodsWe used data from the National Epidemiological Surveillance Network (RENAVE in Spanish) on the daily number of reported COVID-19 cases (by date of symptom onset) in eleven 5-year age groups: 15-19y through 65-69y. For each age group g, we computed the proportion E(g) of individuals in age group g among all reported cases aged 15-69y during the pre-lockdown period (March 1-10, 2020) and the corresponding proportion L(g) during two lockdown periods (March 25-April 3 and April 8-17, 2020). For each lockdown period, we computed the proportion ratios PR(g)= L(g)/E(g). For each pair of age groups g1,g2, PR(g1)>PR(g2) implies a relative increase in the incidence of detected SARS-CoV-2 infection in the age group g1 compared with g2 for the later vs. early period. ResultsFor the first lockdown period, the highest PR values were in age groups 50-54y (PR=1.21; 95% CI: 1.12,1.30) and 55-59y (PR=1.19; 1.11,1.27). For the second lockdown period, the highest PR values were in age groups 15-19y (PR=1.26; 0.95,1.68) and 50-54y (PR=1.20; 1.09,1.31). ConclusionsOur results suggest that different outbreak control measures led to different changes in the relative incidence by age group. During the first lockdown period, when non-essential work was allowed, individuals aged 40-64y, particularly those aged 50-59y presented with higher COVID-19 relative incidence compared to pre-lockdown period, while younger adults/older adolescents (together with persons aged 50-59y) had increased relative incidence during the later, strengthened lockdown. The role of different age groups during the epidemic should be considered when implementing future mitigation efforts.

On the increasing role of older adolescents and younger adults during the SARSCoV2 epidemic in Mexico, April 20 to May 24, 2020 (preprint)

Background: The SARSCoV2 epidemic in Mexico is growing, and there is uncertainty regarding the role that different age groups play in propagating the epidemic. Methods: We used data on hospitalizations with confirmed SARSCoV2 infection from the Mexican Ministry of Health in ten 5-year age groups: 10 to 14 through 55 to 59 years. For each age group g, we computed the proportion E(g) of individuals in that age group among all hospitalized cases aged 10-59 years during the early period (between April 20 to May 3, 2020), the corresponding proportion L(g) during the later periods (May 11 to 24), as well as the relative risk RR(g)= L(g)/E(g). For each pair of age groups g1,g2, RR(g1)>RR(g2) is interpreted as a relative increase in SARSCoV2 infections in the age group g1 compared with g2 for the later vs. early period. Results: The highest RR estimates belong to persons aged 15 to 19 years (RR=1.93(95% CI (1.19,3.12)) and 20 to 24 years (RR=1.40(1.07,1.83)). The RR estimates in persons aged over 30 years were significantly lower compared to persons aged 15-24 years. Conclusions: Our results suggest a temporal increase in the incidence of SARSCoV2 infection in older adolescents and younger adults compared to other age groups. Targeted interventions, particularly public health messaging at those age groups to increase knowledge and risk awareness may be considered.

Temporal rise in the proportion of both younger adults and older adolescents among COVID-19 cases in Germany: evidence of lesser adherence to social distancing practices? (preprint)

Background: There is uncertainty about the role of different age groups in propagating the SARS-CoV-2 epidemics in different countries, particularly under current social distancing practices. Methods: We used the Robert Koch Institute data on weekly COVID-19 cases in different age groups in Germany. To minimize the effect of changes in healthcare seeking behavior (e.g. for older adults) and testing practices, we included the following eight 5-year age groups in the analyses: 10-14y through 45-49y. For each age group g, we considered the proportion PL(g) of individuals in age group g among all detected cases aged 10-49y during weeks 13-14, 2020 (later period), as well as corresponding proportion PE(g) for weeks 10-11, 2020 (early period), and defined the relative risk RR(g) for the age group g to be the ratio RR(g)=PL(g)/PE(g). For each pair of age groups g1,g2, a higher value of RR(g1) compared to RR(g2), or, alternatively, a value above 1 for the odds ratio OR(g1,g2)=RR(g1)/RR(g2) for a COVID-19 case to be in group g1 vs. g2 for the later vs. early periods is interpreted as the relative increase in the population incidence of SARS-Cov-2 in the age group g1 compared to g2 for the later vs. early period. Results: The relative risk RR(g) was highest for individuals aged 20-24y (RR=1.4(95% CI (1.27,1.55))), followed by individuals aged 15-19y (RR=1.14(0.99,1.32)), aged 30-34y (RR= 1.07(0.99,1.16)), aged 25-29y (RR= 1.06(0.98,1.15)), aged 35-39y (RR=0.95(0.87,1.03)), aged 40-44y (RR=0.9(0.83,0.98)), aged 45-49y (RR=0.83(0.77,0.89)) and aged 10-14y (RR=0.78(0.64,0.95)). For the age group 20-24y, the odds ratio relative to any other age group for a case to be during the later vs. early period was significantly above 1. For the age group 15-19y, the odds ratio relative to any other age group either above 35y or 10-14y for a case to be during the later vs. early period was significantly above 1. Conclusions: The observed relative increase with time in the prevalence of individuals aged 15-34y (particularly those aged 20-24y) among detected COVID-19 cases in Germany is unlikely to be explained by increases in the likelihood of seeking medical care or the likelihood of being tested for individuals in those age groups compared to individuals aged 35-49y or 10-14y, and should be indicative of the actual increase in the prevalence of individuals aged 15-34y among SARS-CoV-2 infections in the German population. That increase likely reflects elevated mixing among individuals aged 15-34y (particularly those aged 20-24y) compared to other age groups, possibly due to lesser adherence to social distancing practices.

Temporal rise in the proportion of younger adults and older adolescents among COVID-19 cases in Germany: evidence of lesser adherence to social distancing practices? (preprint)

Background: There is uncertainty about the role of different age groups in propagating the SARS-CoV-2 epidemics in different countries. Methods: We used the Koch Institute data on COVID-19 cases in Germany. To minimize the effect of changes in healthcare seeking behavior and testing practices, we included the following 5-year age groups in the analyses: 10-14y through 45-49y. For each age group g, we considered the proportion PL(g) of individuals in age group g among all detected cases aged 10-49y during weeks 13-14, 2020 (later period), as well as corresponding proportion PE(g) for weeks 10-11, 2020 (early period), and the relative risk RR(g)=PL(g)/PE(g). For each pair of age groups g1,g2, a higher value of RR(g1) compared to RR(g2) is interpreted as the relative increase in the population incidence of SARS-Cov-2 for g1 compared to g2 for the later vs. early period. Results: The relative risk was highest for individuals aged 20-24y (RR=1.4(95% CI (1.27,1.55))), followed by individuals aged 15-19y (RR=1.14(0.99,1.32)), aged 30-34y (RR= 1.07(0.99,1.16)), aged 25-29y (RR= 1.06(0.98,1.15)), aged 35-39y (RR=0.95(0.87,1.03)), aged 40-44y (RR=0.9(0.83,0.98)), aged 45-49y (RR=0.83(0.77,0.89)) and aged 10-14y (RR=0.78(0.64,0.95)). Conclusions: The observed relative increase with time in the prevalence of individuals aged 15-34y (particularly those aged 20-24y) among COVID-19 cases is unlikely to be explained by increases in the likelihood of seeking medical care/being tested for individuals in those age groups compared to individuals aged 35-49y or 10-14y, suggesting an actual increase in the prevalence of individuals aged 15-34y among SARS-CoV-2 infections in the German population. That increase likely reflects elevated mixing among individuals aged 15-34y (particularly those aged 20-24y) compared to other age groups, possibly due to lesser adherence to social distancing practices.

Projecting the transmission dynamics of SARS-CoV-2 through the post-pandemic period (preprint)

There is an urgent need to project how transmission of the novel betacoronavirus SARS-CoV-2 will unfold in coming years. These dynamics will depend on seasonality, the duration of immunity, and the strength of cross-immunity to/from the other human coronaviruses. Using data from the United States, we measured how these factors affect transmission of human betacoronaviruses HCoV-OC43 and HCoV-HKU1. We then built a mathematical model to simulate transmission of SARS-CoV-2 through the year 2025. We project that recurrent wintertime outbreaks of SARS-CoV-2 will probably occur after an initial pandemic wave. We summarize the full range of plausible transmission scenarios and identify key data still needed to distinguish between them, most importantly longitudinal serological studies to determine the duration of immunity to SARS-CoV-2.