Spatio-temporal predictions of COVID-19 test positivity in Uppsala County, Sweden: a comparative approach.

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).

SUBMITTER: van Zoest V 

PROVIDER: S-EPMC9450842 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature