TY - JOUR
T1 - The daily updated Dutch national database on COVID-19 epidemiology, vaccination and sewage surveillance
AU - National sewage surveillance group
AU - RIVM COVID-19 epidemiology, surveillance team
AU - Geubbels, E. L.P.E.
AU - Backer, J. A.
AU - Bakhshi-Raiez, F.
AU - van der Beek, R. F.H.J.
AU - van Benthem, B. H.B.
AU - van den Boogaard, J.
AU - Broekman, E. H.
AU - Dongelmans, D. A.
AU - Eggink, D.
AU - van Gaalen, R. D.
AU - van Gageldonk, A.
AU - Hahné, S.
AU - Hajji, K.
AU - Hofhuis, A.
AU - van Hoek, A. J.
AU - Kooijman, M. N.
AU - Kroneman, A.
AU - Lodder, W.
AU - van Rooijen, M.
AU - Roorda, W.
AU - Smorenburg, N.
AU - Zwagemaker, F.
AU - Beck, Yu Ling
AU - van Beugen, Dorothe
AU - van Boven, Michiel
AU - Breuning, Titus
AU - van Buuren, Chesley
AU - Dijkstra, Sipke
AU - Ding, Weiyi
AU - van der Drift, Anne Merel
AU - Grift, Ivo
AU - Haver, Auke
AU - Hetebrij, Wouter
AU - van de Hoef, Demi
AU - de Jong, Kim
AU - de Klijne, Arnoud
AU - Koelewijn, Jaap
AU - Kooij, Jannetje
AU - Korevaar, Jeroen
AU - Lynch, Gretta
AU - Nagelkerke, Erwin
AU - Nicanci, Süeda
AU - Peters, Noel
AU - Peterse, Céline
AU - van der Plaats, Rozemarijn
AU - Poorter, Elsa
AU - Raaijmakers, Gino
AU - van Rijckevorsel, Lars
AU - de Keizer, N. F.
AU - van den Hof, S.
N1 - Funding Information: We gratefully acknowledge the municipal health services hospitals, laboratories, Infectieradar participants, the RIVM, NICE and CoronIT data management teams, the OSIRIS registration committee, Dutch Water Authorities and their umbrella organization “Unie van Waterschappen”, Statistics Netherlands and the Ministry of Health, Welfare and Sport for their invaluable role in the collection, management and sharing of data, their assistance with data interpretation and their funding of surveillance systems. Publisher Copyright: © 2023, The Author(s).
PY - 2023/12
Y1 - 2023/12
N2 - The Dutch national open database on COVID-19 has been incrementally expanded since its start on 30 April 2020 and now includes datasets on symptoms, tests performed, individual-level positive cases and deaths, cases and deaths among vulnerable populations, settings of transmission, hospital and ICU admissions, SARS-CoV-2 variants, viral loads in sewage, vaccinations and the effective reproduction number. This data is collected by municipal health services, laboratories, hospitals, sewage treatment plants, vaccination providers and citizens and is cleaned, analysed and published, mostly daily, by the National Institute for Public Health and the Environment (RIVM) in the Netherlands, using automated scripts. Because these datasets cover the key aspects of the pandemic and are available at detailed geographical level, they are essential to gain a thorough understanding of the past and current COVID-19 epidemiology in the Netherlands. Future purposes of these datasets include country-level comparative analysis on the effect of non-pharmaceutical interventions against COVID-19 in different contexts, such as different cultural values or levels of socio-economic disparity, and studies on COVID-19 and weather factors.
AB - The Dutch national open database on COVID-19 has been incrementally expanded since its start on 30 April 2020 and now includes datasets on symptoms, tests performed, individual-level positive cases and deaths, cases and deaths among vulnerable populations, settings of transmission, hospital and ICU admissions, SARS-CoV-2 variants, viral loads in sewage, vaccinations and the effective reproduction number. This data is collected by municipal health services, laboratories, hospitals, sewage treatment plants, vaccination providers and citizens and is cleaned, analysed and published, mostly daily, by the National Institute for Public Health and the Environment (RIVM) in the Netherlands, using automated scripts. Because these datasets cover the key aspects of the pandemic and are available at detailed geographical level, they are essential to gain a thorough understanding of the past and current COVID-19 epidemiology in the Netherlands. Future purposes of these datasets include country-level comparative analysis on the effect of non-pharmaceutical interventions against COVID-19 in different contexts, such as different cultural values or levels of socio-economic disparity, and studies on COVID-19 and weather factors.
UR - http://www.scopus.com/inward/record.url?scp=85165443669&partnerID=8YFLogxK
U2 - https://doi.org/10.1038/s41597-023-02232-w
DO - https://doi.org/10.1038/s41597-023-02232-w
M3 - Article
C2 - 37474530
SN - 2052-4463
VL - 10
JO - Scientific Data
JF - Scientific Data
IS - 1
M1 - 469
ER -