Nowcasting COVID-19 Deaths in England by Age and Region

Seaman, Shaun R.; Samartsidis, Pantelis; Kall, Meaghan; Angelis, Daniela De

doi:10.1111/rssc.12576

Cited by 5 publications

(7 citation statements)

References 21 publications

(38 reference statements)

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“…Therefore, nowcasting methods, which aim to predict the missing occurrences, are essential. There are several nowcasting methods in the epidemic modelling literature that are well principled and implemented in Bayesian frameworks [ 6 , 7 ]. However, this leads to them lacking flexibility to modify easily.…”

Section: Discussionmentioning

confidence: 99%

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Nowcasting the 2022 mpox outbreak in England

Overton,

Abbott,

Christie

et al. 2023

PLoS Comput Biol

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In May 2022, a cluster of mpox cases were detected in the UK that could not be traced to recent travel history from an endemic region. Over the coming months, the outbreak grew, with over 3000 total cases reported in the UK, and similar outbreaks occurring worldwide. These outbreaks appeared linked to sexual contact networks between gay, bisexual and other men who have sex with men. Following the COVID-19 pandemic, local health systems were strained, and therefore effective surveillance for mpox was essential for managing public health policy. However, the mpox outbreak in the UK was characterised by substantial delays in the reporting of the symptom onset date and specimen collection date for confirmed positive cases. These delays led to substantial backfilling in the epidemic curve, making it challenging to interpret the epidemic trajectory in real-time. Many nowcasting models exist to tackle this challenge in epidemiological data, but these lacked sufficient flexibility. We have developed a nowcasting model using generalised additive models that makes novel use of individual-level patient data to correct the mpox epidemic curve in England. The aim of this model is to correct for backfilling in the epidemic curve and provide real-time characteristics of the state of the epidemic, including the real-time growth rate. This model benefited from close collaboration with individuals involved in collecting and processing the data, enabling temporal changes in the reporting structure to be built into the model, which improved the robustness of the nowcasts generated. The resulting model accurately captured the true shape of the epidemic curve in real time.

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Section: Discussionmentioning

confidence: 99%

“…There are a range of nowcasting tools and packages available [6][7][8][9][10][11]. However, in any nowcasting problem, the unique characteristics of the local setting and data processing mean that a general tool may not be suitable.…”

Section: Introductionmentioning

confidence: 99%

Nowcasting the 2022 mpox outbreak in England

Overton,

Abbott,

Christie

et al. 2023

PLoS Comput Biol

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“…Though our approach is more computationally intensive than competitors, we believe that the run time is reasonable in a daily operational setting, allowing for potential errors and any need to run the MCMC for more iterations for convergence. Indeed, a model based on Stoner & Economou (2020) for nowcasting daily COVID-19 deaths by age and region in England (Seaman et al, 2022) is used operationally, providing information to the UK Scientific Pandemic Influenza Group on Modelling (SPI-M) on a weekly basis (MRC Biostatistics Unit, 2020). However, bigger data and model complexity (e.g., COVID-19 data at hospital trust level) could very easily result in run times in the order of days, so there is a need for either a more efficient implementation of the GDM or a new approach altogether which offers comparable predictive performance to the GDM and improved computational feasibility.…”

Section: Discussionmentioning

confidence: 99%

“…In subsequent months, community testing was available but not evenly distributed in space and time. These issues highlight the importance of COVID-19 deaths as a key indicator for informing government decision-making (Seaman et al, 2022).…”

Section: Application To Covid-19 Deathsmentioning

confidence: 99%

Correcting Delayed Reporting of COVID-19 Using the Generalized-Dirichlet-Multinomial Method

2022

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The COVID‐19 pandemic has highlighted delayed reporting as a significant impediment to effective disease surveillance and decision‐making. In the absence of timely data, statistical models which account for delays can be adopted to nowcast and forecast cases or deaths. We discuss the four key sources of systematic and random variability in available data for COVID‐19 and other diseases, and critically evaluate current state‐of‐the‐art methods with respect to appropriately separating and capturing this variability. We propose a general hierarchical approach to correcting delayed reporting of COVID‐19 and apply this to daily English hospital deaths, resulting in a flexible prediction tool which could be used to better inform pandemic decision‐making. We compare this approach to competing models with respect to theoretical flexibility and quantitative metrics from a 15‐month rolling prediction experiment imitating a realistic operational scenario. Based on consistent leads in predictive accuracy, bias, and precision, we argue that this approach is an attractive option for correcting delayed reporting of COVID‐19 and future epidemics.

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“…The methods have connections to insurance claims-reserving [7] and its epidemiological applications trace back to HIV modelling [8][9][10]. Nowcasting methods have been used in COVID-19 analysis for daily infections [11][12][13] and fatalities [14][15][16]. The foundation of our method is a Bayesian approach to nowcasting and was initially developed by Ho ¨hle and an der Heiden [5] and later extended by Gu ¨nther et al [17] and McGough et al [6].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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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Nowcasting COVID-19 Deaths in England by Age and Region

Cited by 5 publications

References 21 publications

Nowcasting the 2022 mpox outbreak in England

Nowcasting the 2022 mpox outbreak in England

Correcting Delayed Reporting of COVID-19 Using the Generalized-Dirichlet-Multinomial Method

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

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