Real time surveillance of COVID-19 space and time clusters during the summer 2020 in Spain
| Autor: | Marina Peñuelas, María Guerrero-Vadillo, Nicolás Rosillo, Clara Mazagatos, Rebeca Ramis, Javier Del-Águila-Mejía, Ayelén Rojas-Benedicto, Diana Gómez-Barroso, Jordi Segú-Tell |
|---|---|
| Přispěvatelé: | Instituto de Salud Carlos III |
| Rok vydání: | 2021 |
| Předmět: |
2019-20 coronavirus outbreak
Coronavirus disease 2019 (COVID-19) Scan statistic 030231 tropical medicine Distribution (economics) Disease distribution law.invention Disease Outbreaks Clusters 03 medical and health sciences 0302 clinical medicine law Cluster (physics) Medicine Humans 030212 general & internal medicine Prospective Studies Surveillance business.industry SARS-CoV-2 Research Public Health Environmental and Occupational Health Spatial analysis Outbreak COVID-19 Transmission (mechanics) Spain Public aspects of medicine RA1-1270 business Cartography |
| Zdroj: | Repisalud Instituto de Salud Carlos III (ISCIII) BMC Public Health, Vol 21, Iss 1, Pp 1-11 (2021) BMC Public Health |
| Popis: | Background On June 21st de-escalation measures and state-of-alarm ended in Spain after the COVID-19 first wave. New surveillance and control strategy was set up to detect emerging outbreaks. Aim To detect and describe the evolution of COVID-19 clusters and cases during the 2020 summer in Spain. Methods A near-real time surveillance system to detect active clusters of COVID-19 was developed based on Kulldorf’s prospective space-time scan statistic (STSS) to detect daily emerging active clusters. Results Analyses were performed daily during the summer 2020 (June 21st – August 31st) in Spain, showing an increase of active clusters and municipalities affected. Spread happened in the study period from a few, low-cases, regional-located clusters in June to a nationwide distribution of bigger clusters encompassing a higher average number of municipalities and total cases by end-August. Conclusion STSS-based surveillance of COVID-19 can be of utility in a low-incidence scenario to help tackle emerging outbreaks that could potentially drive a widespread transmission. If that happens, spatial trends and disease distribution can be followed with this method. Finally, cluster aggregation in space and time, as observed in our results, could suggest the occurrence of community transmission. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|