ESPIDAM Stochastic epidemic models with inference, sommarskola 2024

SISMID Stochastic epidemic models with inference, sommarskola 2023 and 2024.

Statistisk databehandling (MT4007), HT 2020 och 2021.

Introduktion till maskininlärning (DA4004), VT 2021. 

I urval från Stockholms universitets publikationsdatabas

• Bayesian nowcasting with leading indicators applied to COVID-19 fatalities in Sweden

  2022. Fanny Bergström (et al.). PloS Computational Biology 18 (12)

  Artikel

  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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