Abstract
Non-pharmaceutical interventions, such as school closures and stay-at-home orders, have been implemented around the world to control the spread of SARS-CoV-2. Their effects on health-related outcomes have been the subject of numerous empirical studies. However, these studies show fairly large variation among methodologies in use, reflecting the absence of an established methodological framework. On the one hand, variation in methodologies may be desirable to assess the robustness of results; on the other hand, a lack of common standards can impede comparability among studies. To establish a comprehensive overview over the methodologies in use, we conducted a systematic review of studies assessing the effects of non-pharmaceutical interventions on health-related outcomes between January 1, 2020 and January 12, 2021 (n=248). We identified substantial variation in methodologies with respect to study setting, outcome, intervention, methodological approach, and effect assessment. On this basis, we point to shortcomings of existing studies and make recommendations for the design of future studies.
Dokumententyp: | Zeitschriftenartikel |
---|---|
Keywords: | Artificial Intelligence, AI, Künstliche Intelligenz, KI |
Fakultät: | Betriebswirtschaft > Institute of Artificial Intelligence (AI) in Management |
Themengebiete: | 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme |
URN: | urn:nbn:de:bvb:19-epub-94957-5 |
ISSN: | 0393-2990 |
Sprache: | Englisch |
Dokumenten ID: | 94957 |
Datum der Veröffentlichung auf Open Access LMU: | 08. Mrz. 2023, 13:58 |
Letzte Änderungen: | 08. Mrz. 2023, 13:58 |