Prevalence of SARS-CoV-2 antibodies in France: results from nationwide serological surveillance

[Seroprevalence of SARS-CoV-2 among children and adolescents in Germany-an overview]

Hintergrund

SARS-CoV-2-Antikörperstudien ergänzen und erweitern die Erkenntnisse aus der Meldestatistik laborbestätigter COVID-19-Fälle um Informationen zu unentdeckten Fällen.

Ziel der Arbeit

Der vorliegende Beitrag fasst bisherige Ergebnisse zur SARS-CoV-2-Prävalenz aus seroepidemiologischen Studien in Deutschland zusammen, die sich auf Kinder und Jugendliche konzentrieren, und ergänzt die bereits vorliegende Übersicht zur Seroprävalenz bei Erwachsenen und speziell bei Blutspendenden in Deutschland.

Material und Methoden

Die Ergebnisse der Übersichtsarbeit beruhen auf einer fortlaufenden systematischen Recherche in Studienregistern, Literaturdatenbanken, von Preprint-Veröffentlichungen und Medienberichten seroepidemiologischer Studien in Deutschland sowie deren Ergebnissen.

Ergebnisse

Mit Stand 17.09.2021 sind uns 16 deutsche seroepidemiologische Studien, die sich auf Kinder und Jugendliche konzentrieren, bekannt geworden. Für 9 dieser Studien liegen Ergebnisse vor. Für fast alle untersuchten Settings lag die SARS-CoV-2-Seroprävalenz für Kinder im Kita- und Grundschulalter in der ersten COVID-19-Welle deutlich unter 1 % und für Jugendliche unter 2 %. Im Verlauf der Pandemie wurden höhere Seroprävalenzen von bis zu 8 % für Kinder im Grundschulalter ermittelt.

Diskussion

Ergebnisse von SARS-CoV-2-Antikörperstudien bei Kindern und Jugendlichen in Deutschland liegen bislang erst in geringem Umfang und basierend auf lokal-regionalen, nichtrepräsentativen Stichproben vor. In künftigen Studien gilt es, einerseits abzuschätzen, welcher Anteil der Kinder und Jugendlichen bereits eine Infektion hatte oder geimpft ist. Zum anderen gilt es, die Verbreitung körperlicher und psychischer Beeinträchtigungen im Nachgang einer Infektion zu untersuchen.

Incorporating false negative tests in epidemiological models for SARS-CoV-2 transmission and reconciling with seroprevalence estimates

Susceptible-Exposed-Infected-Removed (SEIR)-type epidemiologic models, modeling unascertained infections latently, can predict unreported cases and deaths assuming perfect testing. We apply a method we developed to account for the high false negative rates of diagnostic RT-PCR tests for detecting an active SARS-CoV-2 infection in a classic SEIR model. The number of unascertained cases and false negatives being unobservable in a real study, population-based serosurveys can help validate model projections. Applying our method to training data from Delhi, India, during March 15-June 30, 2020, we estimate the underreporting factor for cases at 34-53 (deaths: 8-13) on July 10, 2020, largely consistent with the findings of the first round of serosurveys for Delhi (done during June 27-July 10, 2020) with an estimated 22.86% IgG antibody prevalence, yielding estimated underreporting factors of 30-42 for cases. Together, these imply approximately 96-98% cases in Delhi remained unreported (July 10, 2020). Updated calculations using training data during March 15-December 31, 2020 yield estimated underreporting factor for cases at 13-22 (deaths: 3-7) on January 23, 2021, which are again consistent with the latest (fifth) round of serosurveys for Delhi (done during January 15-23, 2021) with an estimated 56.13% IgG antibody prevalence, yielding an estimated range for the underreporting factor for cases at 17-21. Together, these updated estimates imply approximately 92-96% cases in Delhi remained unreported (January 23, 2021). Such model-based estimates, updated with latest data, provide a viable alternative to repeated resource-intensive serosurveys for tracking unreported cases and deaths and gauging the true extent of the pandemic.

Anatomy of digital contact tracing: Role of age, transmission setting, adoption, and case detection

The efficacy of digital contact tracing against coronavirus disease 2019 (COVID-19) epidemic is debated: Smartphone penetration is limited in many countries, with low coverage among the elderly, the most vulnerable to COVID-19. We developed an agent-based model to precise the impact of digital contact tracing and household isolation on COVID-19 transmission. The model, calibrated on French population, integrates demographic, contact and epidemiological information to describe exposure and transmission of COVID-19. We explored realistic levels of case detection, app adoption, population immunity, and transmissibility. Assuming a reproductive ratio R = 2.6 and 50% detection of clinical cases, a ~20% app adoption reduces peak incidence by ~35%. With R = 1.7, >30% app adoption lowers the epidemic to manageable levels. Higher coverage among adults, playing a central role in COVID-19 transmission, yields an indirect benefit for the elderly. These results may inform the inclusion of digital contact tracing within a COVID-19 response plan.

Anatomy of digital contact tracing: Role of age, transmission setting, adoption, and case detection

The efficacy of digital contact tracing against coronavirus disease 2019 (COVID-19) epidemic is debated: Smartphone penetration is limited in many countries, with low coverage among the elderly, the most vulnerable to COVID-19. We developed an agent-based model to precise the impact of digital contact tracing and household isolation on COVID-19 transmission. The model, calibrated on French population, integrates demographic, contact and epidemiological information to describe exposure and transmission of COVID-19. We explored realistic levels of case detection, app adoption, population immunity, and transmissibility. Assuming a reproductive ratio R = 2.6 and 50% detection of clinical cases, a ~20% app adoption reduces peak incidence by ~35%. With R = 1.7, >30% app adoption lowers the epidemic to manageable levels. Higher coverage among adults, playing a central role in COVID-19 transmission, yields an indirect benefit for the elderly. These results may inform the inclusion of digital contact tracing within a COVID-19 response plan.

Impact of the lockdown on the burden of COVID-19 in outpatient care in France, spring 2020

BACKGROUND

To limit the spread of SARS-CoV-2 several countries implemented measures to reduce the number of contacts such as a national lockdown. We estimated the impact of the first lockdown on the burden of COVID-19 in the community in France.

METHODS

Physicians participating in the French Sentinelles network reported the number of patients with an acute respiratory infection (ARI) seen in consultation and performed nasopharyngeal swabs in a sample of these patients (first patient of the week). The swabs were tested by RT-PCR for the presence of SARS-CoV-2. Clinical and virological data were combined to estimate ARI incidence attributable to SARS-CoV-2 from 17 March to 10 May 2020.

RESULTS

The incidence of ARI attributable to COVID-19 decreased after the second week of the lockdown period from 142 (95%CI [101; 183]) to 41 (95%CI [21; 60]) per 100,000 population. A decrease was observed in all areas in metropolitan France. The youngest age groups (<15-years-old) were least affected with a cumulated incidence estimated to 14 per 100,000 population during the study period.

CONCLUSIONS

The data collected in primary care suggests that the first lockdown implemented in France during spring 2020 significantly reduced the incidence of acute respiratory infections including COVID-19 in France and limited the geographic spread of SARS-CoV-2.

Underdetection of cases of COVID-19 in France threatens epidemic control

As countries in Europe gradually relaxed lockdown restrictions after the first wave, test-trace-isolate strategies became critical to maintain the incidence of coronavirus disease 2019 (COVID-19) at low levels^1,2. Reviewing their shortcomings can provide elements to consider in light of the second wave that is currently underway in Europe. Here we estimate the rate of detection of symptomatic cases of COVID-19 in France after lockdown through the use of virological³ and participatory syndromic⁴ surveillance data coupled with mathematical transmission models calibrated to regional hospitalizations². Our findings indicate that around 90,000 symptomatic infections, corresponding to 9 out 10 cases, were not ascertained by the surveillance system in the first 7 weeks after lockdown from 11 May to 28 June 2020, although the test positivity rate did not exceed the 5% recommendation of the World Health Organization (WHO)⁵. The median detection rate increased from 7% (95% confidence interval, 6-8%) to 38% (35-44%) over time, with large regional variations, owing to a strengthening of the system as well as a decrease in epidemic activity. According to participatory surveillance data, only 31% of individuals with COVID-19-like symptoms consulted a doctor in the study period. This suggests that large numbers of symptomatic cases of COVID-19 did not seek medical advice despite recommendations, as confirmed by serological studies^6,7. Encouraging awareness and same-day healthcare-seeking behaviour of suspected cases of COVID-19 is critical to improve detection. However, the capacity of the system remained insufficient even at the low epidemic activity achieved after lockdown, and was predicted to deteriorate rapidly with increasing incidence of COVID-19 cases. Substantially more aggressive, targeted and efficient testing with easier access is required to act as a tool to control the COVID-19 pandemic. The testing strategy will be critical to enable partial lifting of the current restrictive measures in Europe and to avoid a third wave.

First Wave of COVID-19 in French Patients with Cystic Fibrosis

Viral infections are known to lead to serious respiratory complications in cystic fibrosis (CF) patients. Hypothesizing that CF patients were a population at high risk for severe respiratory complications from SARS-CoV-2 infection, we conducted a national study to describe the clinical expression of COVID-19 in French CF patients. This prospective observational study involves all 47 French CF centers caring for approximately 7500 CF patients. Between March 1st and June 30th 2020, 31 patients were diagnosed with COVID-19: 19 had positive SARS-CoV-2 RT-PCR in nasopharyngeal swabs; 1 had negative RT-PCR but typical COVID-19 signs on a CT scan; and 11 had positive SARS-CoV-2 serology. Fifteen were males, median (range) age was 31 (9-60) years, and 12 patients were living with a lung transplant. The majority of the patients had CF-related diabetes (n = 19, 61.3%), and a mild lung disease (n = 19, 65%, with percent-predicted forced expiratory volume in 1 s (ppFEV1) > 70). Three (10%) patients remained asymptomatic. For the 28 (90%) patients who displayed symptoms, most common symptoms at admission were fever (n = 22, 78.6%), fatigue (n = 14, 50%), and increased cough (n = 14, 50%). Nineteen were hospitalized (including 11 out of the 12 post-lung transplant patients), seven required oxygen therapy, and four (3 post-lung transplant patients) were admitted to an Intensive Care Unit (ICU). Ten developed complications (including acute respiratory distress syndrome in two post-lung transplant patients), but all recovered and were discharged home without noticeable short-term sequelae. Overall, French CF patients were rarely diagnosed with COVID-19. Further research should establish whether they were not infected or remained asymptomatic upon infection. In diagnosed cases, the short-term evolution was favorable with rare acute respiratory distress syndrome and no death. Post-lung transplant patients had more severe outcomes and should be monitored more closely.