Differentiated mental health patterns in pregnancy during COVID-19 first two waves in Sweden: a mixed methods study using digital phenotyping.

To utilize modern tools to assess depressive and anxiety symptoms, wellbeing and life conditions in pregnant women during the first two waves of the COVID-19 pandemic in Sweden. Pregnant women (n = 1577) were recruited through the mobile application Mom2B. Symptoms of depression, anxiety and wellbeing were assessed during January 2020-February 2021. Movement data was collected using the phone's sensor. Data on Google search volumes for "Corona" and Covid-related deaths were obtained. Qualitative analysis of free text responses regarding maternity care was performed. Two peaks were seen for depressive symptoms, corresponding to the two waves. Higher prevalence of anxiety was only noted during the first wave. A moderating effect of the two waves in the association of depression, anxiety, and well-being with Covid deaths was noted; positive associations during the first wave and attenuated or became negative during the second wave. Throughout, women reported on cancelled healthcare appointments and worry about partners not being allowed in hospital. The association of mental health outcomes with relevant covariates may vary during the different phases in a pandemic, possibly due to adaptation strategies on a personal and societal/healthcare level. Digital phenotyping can help healthcare providers and governmental bodies to in real time monitor high-risk groups during crises, and to adjust the support offered.

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

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.