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Competing Interests: We declare no competing interests.

Reliable forecast of COVID-19 hospital admissions in near-term horizons can help enable effective resource management which is vital in reducing pressure from healthcare services. The use of mobile network data has come to attention in response to COVID-19 pandemic leveraged on their ability in capturing people social behavior. Crucially, we show that there are latent features in irreversibly anonymized and aggregated mobile network data that carry useful information in relation to the spread of SARS-CoV-2 virus. We describe development of the forecast models using such features for prediction of COVID-19 hospital admissions in near-term horizons (21 days). In a case study, we verified the approach for two hospitals in Sweden, Sahlgrenska University Hospital and Södra Älvsborgs Hospital, working closely with the experts engaged in the hospital resource planning. Importantly, the results of the forecast models were used in year 2021 by logisticians at the hospitals as one of the main inputs for their decisions regarding resource management., (© 2022. The Author(s).)

The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Alterations in social mixing and subsequent changes in transmission dynamics eventually affect hospital admissions. We employ these observations to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the social mixing is assumed to depend on mobility data from public transport utilisation and locations for mobile phone usage. The results show that the model could capture the timing of the first and beginning of the second wave of the pandemic 3 weeks in advance without any additional assumptions about seasonality. Further, we show that for two major regions of Sweden, models with public transport data outperform models using mobile phone usage. We conclude that a model based on routinely collected mobility data makes it possible to predict future hospital admissions for COVID-19 3 weeks in advance., (© 2021. The Author(s).)

SARS-CoV-2 was discovered among humans in Wuhan, China in late 2019, and then spread rapidly, causing a global pandemic. The virus was found to be transmitted mainly by respiratory droplets from infected persons or by direct contact. It was also shown to be excreted in feces, why we investigated whether the virus could be detected in wastewater and if so, to which extent its levels reflects its spread in society. Samples of wastewater from the city of Gothenburg, and surrounding municipalities in Sweden were collected daily from mid-February until June 2020 at the Rya wastewater treatment plant. Flow proportional samples of wastewater were collected to ensure that comparable amounts were obtained for analysis. Daily samples were pooled into weekly samples. Virus was concentrated on a filter and analyzed by RT-qPCR. The amount of SARS-CoV-2 varied with peaks approximately every four week, preceding variations in number of newly hospitalized patients by 19-21 days. At that time virus testing for COVID-19 was limited to patients with severe symptoms. Local differences in viral spread was shown by analyzing weekly composite samples of wastewater from five sampling sites for four weeks. The highest amount of virus was found from the central, eastern, and northern parts of the city. SARS-CoV-2 was also found in the treated effluent wastewater from the WWTP discharged into the recipient, the Göta River, although with a reduction of 4-log 10. The viral peaks with regular temporal intervals indicated that SARS-CoV-2 may have a cluster spread, probably reflecting that the majority of infected persons only spread the disease during a few days. Our results are important for both the planning of hospital care and to rapidly identify and intervene against local spread of the virus., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier Ltd.)