ArticlePLoS computational biology2022
Bayesian nowcasting with leading indicators applied to COVID-19 fatalities in Sweden.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 19 citations in OpenAlex.
- Evaluation of respiratory disease hospitalisation forecasts using synthetic outbreak data.Communications medicine · 2026Article
- Joint Bayesian Nowcasting of Severe Acute Respiratory Illness and COVID-19 Positives in Brazil.Statistics in medicine · 2026Article
- Dengue nowcasting in Brazil by combining official surveillance data and Google Trends information.PLoS neglected tropical diseases · 2025Article
- A renewal-equation approach to estimatingPhilosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Nowcasting reported covid-19 hospitalizations using de-identified, aggregated medical insurance claims data.PLoS computational biology · 2025Article
- An application of nowcasting methods: Cases of norovirus during the winter 2023/2024 in England.PLoS computational biology · 2025Article
- Baseline nowcasting methods for handling delays in epidemiological data.Wellcome open research · 2025Article
- Generative Bayesian modeling to nowcast the effective reproduction number from line list data with missing symptom onset dates.PLoS computational biology · 2024Article
- Increasing situational awareness through nowcasting of the reproduction number.Frontiers in public health · 2024Article
- Nowcasting the 2022 mpox outbreak in England.PLoS computational biology · 2023Article
- Collaborative nowcasting of COVID-19 hospitalization incidences in Germany.PLoS computational biology · 2023Article
- Nowcasting COVID-19 deaths in England by age and region.Journal of the Royal Statistical Society. Series C, Applied statistics · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The real-time analysis of infectious disease surveillance data is essential in obtaining situational awareness about the current dynamics of a major public health event such as the COVID-19 pandemic. This analysis of e.g., time-series of reported cases or fatalities is complicated by reporting delays that lead to under-reporting of the complete number of events for the most recent time points. This can lead to misconceptions by the interpreter, for instance the media or the public, as was the case with the time-series of reported fatalities during the COVID-19 pandemic in Sweden. Nowcasting methods provide real-time estimates of the complete number of events using the incomplete time-series of currently reported events and information about the reporting delays from the past. In this paper we propose a novel Bayesian nowcasting approach applied to COVID-19-related fatalities in Sweden. We incorporate additional information in the form of time-series of number of reported cases and ICU admissions as leading signals. We demonstrate with a retrospective evaluation that the inclusion of ICU admissions as a leading signal improved the nowcasting performance of case fatalities for COVID-19 in Sweden compared to existing methods.
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