Not only COVID-19 disease impacts ambulance emergency demands but also lockdowns and quarantines.

BACKGROUND: The pandemic has impacted both patients infected by the SARS-CoV-2 virus and patients who seek emergency assistance due to other health issues. Changes in emergency demands are expected to have occurred during the pandemic, the objective of this investigation is to characterize the changes in ambulance emergency demands during the first year of the COVID-19 pandemic in the Vaud State of Switzerland. The goal of this research is to identify the collateral effects of the COVID-19 pandemic on emergency demands. To do so, this study quantifies the differences in health issues, level of severity, and patients' sociodemographic characteristics (age, location, gender) prior to and during the outbreak. METHOD: This is a retrospective, descriptive and comparative statistical analysis of all ambulance emergency missions from 2018 to 2020 (n = 107,150) in the State of Vaud in Switzerland. Variables analyzed were the number of ambulance missions, patient age and gender, health issues, severity (NACA scores), number of non-transports, mission times and locations. Variables were compared between prepandemic and pandemic situations across years and months. Comparative analysis used bivariate analysis, χ2 test, Student's t test, and Mann‒Whitney U test. RESULTS: The pandemic has had two major impacts on the population's emergency demands. The first appears to be due to COVID-19, with an increase in respiratory distress cases that doubled in November 2020. The second relates to the implementation of lockdown and quarantine measures for the population and the closures of restaurants and bars. These might explain the decrease in both the number of traumas and intoxications, reaching more than 25% and 28%, respectively. An increase in prehospital emergency demands by the older population, which accounted for 53% of all demands in 2020, is measured. CONCLUSION: Collateral effects occurred during 2020 and were not only due to the pandemic but also due to protective measures deployed relative to the population. This work suggests that more targeted reflections and interventions concerning the most vulnerable group, the population of people 65 and older, should be of high priority. Gaining generalizable knowledge from the COVID-19 pandemic in prehospital settings is critical for the management of future pandemics or other unexpected disasters.

Detection of SARS-CoV-2 infection clusters: The useful combination of spatiotemporal clustering and genomic analyses.

Background: The need for effective public health surveillance systems to track virus spread for targeted interventions was highlighted during the COVID-19 pandemic. It spurred an interest in the use of spatiotemporal clustering and genomic analyses to identify high-risk areas and track the spread of the SARS-CoV-2 virus. However, these two approaches are rarely combined in surveillance systems to complement each one's limitations; spatiotemporal clustering approaches usually consider only one source of virus transmission (i.e., the residential setting) to detect case clusters, while genomic studies require significant resources and processing time that can delay decision-making. Here, we clarify the differences and possible synergies of these two approaches in the context of infectious disease surveillance systems by investigating to what extent geographically-defined clusters are confirmed as transmission clusters based on genome sequences, and how genomic-based analyses can improve the epidemiological investigations associated with spatiotemporal cluster detection. Methods: For this purpose, we sequenced the SARS-CoV-2 genomes of 172 cases that were part of a collection of spatiotemporal clusters found in a Swiss state (Vaud) during the first epidemic wave. We subsequently examined intra-cluster genetic similarities and spatiotemporal distributions across virus genotypes. Results: Our results suggest that the congruence between the two approaches might depend on geographic features of the area (rural/urban) and epidemic context (e.g., lockdown). We also identified two potential superspreading events that started from cases in the main urban area of the state, leading to smaller spreading events in neighboring regions, as well as a large spreading in a geographically-isolated area. These superspreading events were characterized by specific mutations assumed to originate from Mulhouse and Milan, respectively. Our analyses propose synergistic benefits of using two complementary approaches in public health surveillance, saving resources and improving surveillance efficiency.

Size and duration of COVID-19 clusters go along with a high SARS-CoV-2 viral load: A spatio-temporal investigation in Vaud state, Switzerland.

To understand the geographical and temporal spread of SARS-CoV-2 during the first documented wave of infection in the state of Vaud, Switzerland, we analyzed clusters of positive cases using the precise residential location of 33,651 individuals tested (RT-PCR) between January 10 and June 30, 2020. We used a prospective Poisson space-time scan statistic (SaTScan) and a Modified Space-Time Density-Based Spatial Clustering of Application with Noise (MST-DBSCAN) to identify both space-time and transmission clusters, and estimated cluster duration, transmission behavior (emergence, growth, reduction, etc.) and relative risk. For each cluster, we computed the number of individuals, the median age of individuals and their viral load. Among the 1684 space-time clusters identified, 457 (27.1%) were significant (p ≤ 0.05), such that they harbored a higher relative risk of infection within the cluster than compared to regions outside the cluster. Clusters lasted a median of 11 days (IQR 7-13) and included a median of 12 individuals per cluster (IQR 5-20). The majority of significant clusters (n = 260; 56.9%) had at least one person with an extremely high viral load (>1 billion copies/ml). Those clusters were considerably larger (median of 17 infected individuals, p < 0.001) than clusters with individuals showing a viral load below 1 million copies/ml (median of three infected individuals). The highest viral loads were found in clusters with the lowest average age group considered in the investigation, while clusters with the highest average age had low to middle viral load. In 20 significant clusters, the viral load of the three first cases was below 100,000 copies/ml, suggesting that subjects with fewer than 100,000 copies/ml may still be contagious. Notably, the dynamics of transmission clusters made it possible to identify three diffusion zones, which predominantly differentiated between rural and urban areas, the latter being more prone to persistence and expansion, which may result in the emergence of new clusters nearby. The use of geographic information is key for public health decision makers in mitigating the spread of the SARS-CoV-2 virus. This study suggests that early localization of clusters may help implement targeted protective measures limiting the spread of the virus.