Public Health benefits by implementing digital symptom diaries for COVID patients from Cologne : Barbara Gruene

Background High rate of people infected with SARS-CoV-2 and their contacts in Cologne, Germany required innovative tools for notification, monitoring and reporting. The digital tool for COVID19 (DiKoMa) provides self-service symptom diaries allowing (a) the stratification for prioritized telephone contact by the health authority and (b) training a machine learning (ML) model that predicts infections with prevailing dominant variant (PDV) from early symptom profiles (SP). Methods Pseudononymized SP covering the first week of diary recordings were included for training (16646 index, 11582 contacts). A balanced random forest (BRF) model was trained to differentiate early predictive symptom patterns of different PDV and contact persons. Model evaluation was performed using sex and age stratified cross validation (CV), the model was validated on SP recorded from days 1 and 6. Results From 03/20 to 02/22, 90478 indeces and 75444 contact persons reported symptoms and health status, covering 46% and 42% of all reported cases, respectively. Diaries contained between 1-52 entries (566791, median 2). Daily analysis of entries, prioritized according to age, prevalent co-morbidities and detoriation of symptoms allowed risk adjusted follow up even during phases with high case notification rates. The top 5 predictive factors of the BRF were immunization, cough, dysgeusia and dysnosmia, fatigue, and sniffles to differentiate infection between wildtype, three PDV and contact persons (CV AUC 80.6%, Validation AUC 77.1%). Conclusions The use of digital symptom diary surveillance helps to provide appropriate medical support for patients on a large scale. Machine learning shows potential for symptom based risk assessment to differentiate PDV for future outbreaks and can thus become a valuable tool alongside specific laboratory diagnostics. Key messages • Digital symptom diaries are a powerful and widely accepted tool to attend COVID19 patients in isolation. They allow risk stratification for follow up and are a low-threshold service. • Machine learning supports index case identification by symptom analysis and can thus become a valuable tool alongside specific laboratory diagnostics.

Coping strategies during legally enforced quarantine and their association to psychological distress level: a cross-sectional study.

OBJECTIVES: The non-pharmacological measures to contain the COVID-19 pandemic may lead to considerable psychological distress. The aim of the CoCo-Fakt study was to investigate possible coping strategies and their effects on psychological distress during legally enforced quarantine of infected persons (IPs) and their close contacts (CPs). STUDY DESIGN: This was a cross-sectional cohort study. METHODS: From 12 December 2020 to 6 January 2021, all IPs and their CPs (n = 8232) registered by the public health department (Cologne, Germany) were surveyed online. Psychosocial distress and coping were measured using sum scores; free-text answers related to specific strategies were subsequently categorised. RESULTS: Psychosocial distress was higher in IPs than in CPs (P < .001). Although the mean coping score did not differ between both groups, it was influenced by the reason for quarantine (IP vs CP) besides gender, age, socio-economic status, living situation, psychological distress, resilience, physical activity and eating behaviour. This final regression model explained 25.9% of the variance. Most participants used active coping strategies, such as contact with the social environment, a positive attitude and hobbies. CONCLUSIONS: Although psychological distress was higher in IPs than in CPs during the quarantine period, the mean coping score did not differ. The strategies most frequently used by IPs and CPs were activating social networks, a healthy lifestyle and professional support systems, such as the health department helpline. Appropriate advice should be implemented to prevent long-term psychological consequences when supporting affected people.

Breakthrough infections with the SARS-CoV-2 Delta variant: vaccinations halved transmission risk.

OBJECTIVES: The SARS-CoV-2 Delta variant (B.1.617.2) is associated with increased infectivity. Data on breakthrough SARS-CoV-2 Delta variant infections in vaccinated individuals and transmission risk are limited. The aim of this study was to provide estimates of transmission risk in Delta variant breakthrough infections. STUDY DESIGN: A matched case-control study was performed.. METHODS: To analyse onward transmission of fully vaccinated individuals infected with B.1.617.2, we compared 85 patients (vaccination group [VG]) with an age- and sex-matched unvaccinated control group (CG; n = 85). RESULTS: Transmission of B.1.617.2 was significantly reduced (halved) in the VG. The number of infected contacts to total number of contacts per infected person was 0.26 ± 0.40 in the VG vs 0.56 ± 0.45 in the CG (P = .001). Similarly, fully vaccinated contacts were less likely to be infected by fully vaccinated infected persons (IPs) than by unvaccinated IPs (20.0% vs 37.5%), although this association was not significant. CONCLUSIONS: Fully vaccinated contacts had 50% less transmissions than unvaccinated individuals. These findings must be verified in larger sample populations, and it is especially important to investigate the role of vaccination status of close contacts.