Your browser doesn't support javascript.
Spatio-temporal predictions of COVID-19 test positivity in Uppsala County, Sweden: a comparative approach.
van Zoest, Vera; Varotsis, Georgios; Menzel, Uwe; Wigren, Anders; Kennedy, Beatrice; Martinell, Mats; Fall, Tove.
  • van Zoest V; Department of Information Technology, Uppsala University, P.O. Box 337, 751 05, Uppsala, Sweden. vera.van.zoest@it.uu.se.
  • Varotsis G; Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, 751 85, Uppsala, Sweden.
  • Menzel U; Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, 751 85, Uppsala, Sweden.
  • Wigren A; Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, 751 85, Uppsala, Sweden.
  • Kennedy B; Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, 751 85, Uppsala, Sweden.
  • Martinell M; Department of Public Health and Caring Sciences, Uppsala University, 751 22, Uppsala, Sweden.
  • Fall T; Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, 751 85, Uppsala, Sweden.
Sci Rep ; 12(1): 15176, 2022 09 07.
Article in English | MEDLINE | ID: covidwho-2008323
ABSTRACT
Previous spatio-temporal COVID-19 prediction models have focused on the prediction of subsequent number of cases, and have shown varying accuracy and lack of high geographical resolution. We aimed to predict trends in COVID-19 test positivity, an important marker for planning local testing capacity and accessibility. We included a full year of information (June 29, 2020-July 4, 2021) with both direct and indirect indicators of transmission, e.g. mobility data, number of calls to the national healthcare advice line and vaccination coverage from Uppsala County, Sweden, as potential predictors. We developed four models for a 1-week-window, based on gradient boosting (GB), random forest (RF), autoregressive integrated moving average (ARIMA) and integrated nested laplace approximations (INLA). Three of the models (GB, RF and INLA) outperformed the naïve baseline model after data from a full pandemic wave became available and demonstrated moderate accuracy. An ensemble model of these three models slightly improved the average root mean square error to 0.039 compared to 0.040 for GB, RF and INLA, 0.055 for ARIMA and 0.046 for the naïve model. Our findings indicate that the collection of a wide variety of data can contribute to spatio-temporal predictions of COVID-19 test positivity.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study / Randomized controlled trials Topics: Vaccines Limits: Humans Country/Region as subject: Europa Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-19155-y

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study / Randomized controlled trials Topics: Vaccines Limits: Humans Country/Region as subject: Europa Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-19155-y