In the Corinth Gulf foreshock sequences occur as a rule within a time interval no longer than four months before the mainshock. If these precursory phenomena could be detected, then it would be utilized for the prediction of the mainshock. However, frequent swarms also characterize the Gulf of Corinth. Therefore, in a real time evaluation, the discrimination between swarms and foreshock sequences is of crucial importance. In this study we focus on establishing seismicity criteria to achieve such discrimination.
Objectives. To identify spatiotemporal patterns of epidemic spread at the community level. Methods. We extracted influenza cases reported between 2016 and 2019 and COVID-19 cases reported in March and April 2020 from a hospital network in Rhode Island. We performed a spatiotemporal hotspot analysis to simulate a real-time surveillance scenario. Results. We analyzed 6527 laboratory-confirmed influenza cases and identified microepidemics in more than 1100 neighborhoods, and more than half of the neighborhoods that had hotspots in a season became hotspots in the next season. We used data from 731 COVID-19 cases, and we found that a neighborhood was 1.90 times more likely to become a COVID-19 hotspot if it had been an influenza hotspot in 2018 to 2019. Conclusions. The use of readily available hospital data allows the real-time identification of spatiotemporal trends and hotspots of microepidemics. Public Health Implications. As local governments move to reopen the economy and ease physical distancing, the use of historic influenza hotspots could guide early prevention interventions, while the real-time identification of hotspots would enable the implementation of interventions that focus on small-area containment and mitigation.
OS3.6 Optical Biopsies in Neurosurgery: Raman Spectroscopy for the Real-time Identification of Tumours during Surgery
