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      Nowcasting COVID‐19 deaths in England by age and region

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          Abstract

          Understanding the trajectory of the daily number of COVID‐19 deaths is essential to decisions on how to respond to the pandemic, but estimating this trajectory is complicated by the delay between deaths occurring and being reported. In England the delay is typically several days, but it can be weeks. This causes considerable uncertainty about how many deaths occurred in recent days. Here we estimate the deaths per day in five age strata within seven English regions, using a Bayesian model that accounts for reporting‐day effects and longer‐term changes in the delay distribution. We show how the model can be computationally efficiently fitted when the delay distribution is the same in multiple strata, for example, over a wide range of ages.

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          Estimating the time-varying reproduction number of SARS-CoV-2 using national and subnational case counts

          Sam Abbott, Joel Hellewell, Robin Thompson … (2020)
          Background: Interventions are now in place worldwide to reduce transmission of the novel coronavirus. Assessing temporal variations in transmission in different countries is essential for evaluating the effectiveness of public health interventions and the impact of changes in policy. Methods: We use case notification data to generate daily estimates of the time-dependent reproduction number in different regions and countries. Our modelling framework, based on open source tooling, accounts for reporting delays, so that temporal variations in reproduction number estimates can be compared directly with the times at which interventions are implemented. Results: We provide three example uses of our framework. First, we demonstrate how the toolset displays temporal changes in the reproduction number. Second, we show how the framework can be used to reconstruct case counts by date of infection from case counts by date of notification, as well as to estimate the reproduction number. Third, we show how maps can be generated to clearly show if case numbers are likely to decrease or increase in different regions. Results are shown for regions and countries worldwide on our website ( https://epiforecasts.io/covid/ ) and are updated daily. Our tooling is provided as an open-source R package to allow replication by others. Conclusions: This decision-support tool can be used to assess changes in virus transmission in different regions and countries worldwide. This allows policymakers to assess the effectiveness of current interventions, and will be useful for inferring whether or not transmission will increase when interventions are lifted. As well as providing daily updates on our website, we also provide adaptable computing code so that our approach can be used directly by researchers and policymakers on confidential datasets. We hope that our tool will be used to support decisions in countries worldwide throughout the ongoing COVID-19 pandemic.
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            Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking

            Sarah F. McGough, Michael A Johansson, Marc Lipsitch … (2020)
            Achieving accurate, real-time estimates of disease activity is challenged by delays in case reporting. “Nowcast” approaches attempt to estimate the complete case counts for a given reporting date, using a time series of case reports that is known to be incomplete due to reporting delays. Modeling the reporting delay distribution is a common feature of nowcast approaches. However, many nowcast approaches ignore a crucial feature of infectious disease transmission—that future cases are intrinsically linked to past reported cases—and are optimized to one or two applications, which may limit generalizability. Here, we present a Bayesian approach, NobBS (Nowcasting by Bayesian Smoothing) capable of producing smooth and accurate nowcasts in multiple disease settings. We test NobBS on dengue in Puerto Rico and influenza-like illness (ILI) in the United States to examine performance and robustness across settings exhibiting a range of common reporting delay characteristics (from stable to time-varying), and compare this approach with a published nowcasting software package while investigating the features of each approach that contribute to good or poor performance. We show that introducing a temporal relationship between cases considerably improves performance when the reporting delay distribution is time-varying, and we identify trade-offs in the role of moving windows to accurately capture changes in the delay. We present software implementing this new approach (R package “NobBS”) for widespread application and provide practical guidance on implementation.
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              Programming with models: writing statistical algorithms for general model structures with NIMBLE

              P. de Valpine, D. Turek, CJ PACIOREK … (2017)
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                Author and article information

                Contributors
                Shaun R. Seaman: shaun.seaman@mrc-bsu.cam.ac.uk
                Journal
                Journal ID (nlm-ta): J R Stat Soc Ser C Appl Stat
                Journal ID (iso-abbrev): J R Stat Soc Ser C Appl Stat
                Journal ID (doi): 10.1111/(ISSN)1467-9876
                Journal ID (publisher-id): RSSC
                Title: Journal of the Royal Statistical Society. Series C, Applied Statistics
                Publisher: John Wiley and Sons Inc. (Hoboken )
                ISSN (Print): 0035-9254
                ISSN (Electronic): 1467-9876
                Publication date (Electronic): 15 June 2022
                Publication date PMC-release: 15 June 2022
                Electronic Location Identifier: 10.1111/rssc.12576
                Affiliations
                [ 1 ] MRC Biostatistics Unit University of Cambridge Cambridge Cambridgeshire UK
                [ 2 ] COVID‐19 National Epidemiology Cell UK Health Security Agency London UK
                [ 3 ] Statistics, Modelling and Economics Department, Data, Analytics and Surveillance UK Health Security Agency London UK
                Author notes
                [*] [* ] Correspondence

                Shaun R. Seaman, MRC Biostatistics Unit, University of Cambridge, East Forvie Building, Forvie Site, Robinson Way, Cambridge, Cambridgeshire CB2 0SR, UK.

                Email:  shaun.seaman@ 123456mrc-bsu.cam.ac.uk

                Article
                Publisher ID: RSSC12576
                DOI: 10.1111/rssc.12576
                PMC ID: 9349735
                PubMed ID: 35942006
                SO-VID: 0d53e0fc-915b-4d34-96c1-5fa530761b25
                Copyright © © 2022 The Authors. Journal of the Royal Statistical Society: Series C (Applied Statistics) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society.
                License:

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 30 November 2020
                Date accepted : 11 May 2022
                Page count
                Figures: 4, Tables: 1, Pages: 16, Words: 7083
                Categories
                Subject: Original Article
                Subject: Original Articles
                Custom metadata
                source-schema-version-number 2.0
                edited-state corrected-proof
                details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.7 mode:remove_FC converted:04.08.2022

                ScienceOpen disciplines: Statistics
                Keywords: epidemic monitoring,generalised dirichlet,reporting delay,right‐truncation
                Data availability:
                ScienceOpen disciplines: Statistics
                Keywords: epidemic monitoring, generalised dirichlet, reporting delay, right‐truncation

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