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.

App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden.

The app-based COVID Symptom Study was launched in Sweden in April 2020 to contribute to real-time COVID-19 surveillance. We enrolled 143,531 study participants (≥18 years) who contributed 10.6 million daily symptom reports between April 29, 2020 and February 10, 2021. Here, we include data from 19,161 self-reported PCR tests to create a symptom-based model to estimate the individual probability of symptomatic COVID-19, with an AUC of 0.78 (95% CI 0.74-0.83) in an external dataset. These individual probabilities are employed to estimate daily regional COVID-19 prevalence, which are in turn used together with current hospital data to predict next week COVID-19 hospital admissions. We show that this hospital prediction model demonstrates a lower median absolute percentage error (MdAPE: 25.9%) across the five most populated regions in Sweden during the first pandemic wave than a model based on case notifications (MdAPE: 30.3%). During the second wave, the error rates are similar. When we apply the same model to an English dataset, not including local COVID-19 test data, we observe MdAPEs of 22.3% and 19.0% during the first and second pandemic waves, respectively, highlighting the transferability of the prediction model.

In-Flight Transmission of a SARS-CoV-2 Lineage B.1.617.2 Harbouring the Rare S:E484Q Immune Escape Mutation.

We describe a flight-associated infection scenario of seven individuals with a B.1.617.2 (Delta) lineage, harbouring an S:E484Q point mutation. In Sweden, at least 10% of all positive SARS-CoV-2 samples were sequenced in each county; the B.1.717.2 + S:E484Q combination was not detected in Sweden before and was imported within the scenario described in this report. The high transmission rate of the delta lineage combined with the S:E484Q mutation, associated with immune escape in other lineages, makes this specific genetic combination a possible threat to the global fight against the COVID-19 pandemic. Even within the Omicron wave, the B.1.617.2 + S:E484Q variant appeared in community samples in Sweden, as it seems that this combination has an evolutionary gain compared to other B.1.617.2 lineages. The here described genomic combination was not detectable with the common fasta file-based Pango-lineage analysis, hence increasing the probability of the true global prevalence to be higher.