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.