Post-lockdown changes of age-specific susceptibility and its correlation with adherence to social distancing measures

Droplet Micro-Sensor and Detection of Respiratory Droplet Transmission

Droplet transmission is the primary infection route for respiratory diseases like COVID-19 and influenza, but small and low-cost wearable droplet detection devices are a significant challenge. Herein, a respiratory droplet micro-sensor based on graphene oxide quantum dots (GOQDs) assembled onto SiO2 microspheres by the nebulized natural deposition is presented. Benefiting from the energy dissipation of the microsphere to droplets, the sensor can detect droplets as far as 2 m from coughing. With this sensor, droplet signal variations caused by some factors like distance, speech, angles, and wind directions are explored, and the effectiveness of different protective measures in preventing droplet transmission is evaluated. This droplet detection technology is expected to be utilized for the development of personal detection and protection devices against infectious respiratory diseases.

Exploration of the COVID-19 pandemic at the neighborhood level in an intra-urban setting

The COVID-19 pandemic represents a worldwide threat to health. Since its onset in 2019, the pandemic has proceeded in different phases, which have been shaped by a complex set of influencing factors, including public health and social measures, the emergence of new virus variants, and seasonality. Understanding the development of COVID-19 incidence and its spatiotemporal patterns at a neighborhood level is crucial for local health authorities to identify high-risk areas and develop tailored mitigation strategies. However, analyses at the neighborhood level are scarce and mostly limited to specific phases of the pandemic. The aim of this study was to explore the development of COVID-19 incidence and spatiotemporal patterns of incidence at a neighborhood scale in an intra-urban setting over several pandemic phases (March 2020-December 2021). We used reported COVID-19 case data from the health department of the district Berlin-Neukölln, Germany, additional socio-demographic data, and text documents and materials on implemented public health and social measures. We examined incidence over time in the context of the measures and other influencing factors, with a particular focus on age groups. We used incidence maps and spatial scan statistics to reveal changing spatiotemporal patterns. Our results show that several factors may have influenced the development of COVID-19 incidence. In particular, the far-reaching measures for contact reduction showed a substantial impact on incidence in Neukölln. We observed several age group-specific effects: school closures had an effect on incidence in the younger population (< 18 years), whereas the start of the vaccination campaign had an impact primarily on incidence among the elderly (> 65  years). The spatial analysis revealed that high-risk areas were heterogeneously distributed across the district. The location of high-risk areas also changed across the pandemic phases. In this study, existing intra-urban studies were supplemented by our investigation of the course of the pandemic and the underlying processes at a small scale over a long period of time. Our findings provide new insights for public health authorities, community planners, and policymakers about the spatiotemporal development of the COVID-19 pandemic at the neighborhood level. These insights are crucial for guiding decision-makers in implementing mitigation strategies.