Modelling Reporting Delays for Outbreak Detection in Infectious Disease Data

Noufaily, Angela; Ghebremichael‐Weldeselassie, Yonas; Enki, Doyo; Garthwaite, Paul H.; Andrews, Nicholas; Charlett, André; Farrington, Paddy

doi:10.1111/rssa.12055

Cited by 18 publications

(27 citation statements)

References 27 publications

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“…As a result, when reporting delays are time-varying, as is often the case in epidemics [17], we show that the NobBS approach is less accurate compared to its performance in a stable delay distribution, but still shows improvement over the HH approach likely because the NobBS approach is informed by the number of cases experienced in previous weeks, not just the delay distribution, making it more robust to larger fluctuations. As mentioned, the HH approach software includes the option to model a time-varying delay explicitly in the nowcast approach, but requires specifying the time at which the delay distribution is expected to change (the change-point), which is generally not known in real-time.…”

Section: Discussionmentioning

confidence: 81%

“…However, in the presence of simulated time-varying reporting delays, NobBS outperformed the HH in terms of confidence (NobBS average score = 0.06 vs. HH average score � 0), point estimates (NobBS rRMSE = 0.302 vs. HH rRMSE = 0.621), and accuracy of the predicted change (S3 Table). Such variations in the delay distribution are a reality in many epidemics [17].…”

Section: Nobbs Improves Nowcasting With Varying Reporting Delaysmentioning

confidence: 99%

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Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking

et al. 2020

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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. Author summaryAchieving accurate, real-time estimates of disease activity is challenged by delays in case reporting. However, approaches that seek to estimate cases in spite of reporting delays often do not consider the temporal relationship between cases during an outbreak, nor do PLOS COMPUTATIONAL BIOLOGY PLOS Computational Biology | https://doi..nigms.nih.gov/). The they identify characteristics of robust approaches that generalize to a wide range of surveillance contexts with very different reporting delays. Here, we present a smooth Bayesian nowcasting approach that produces accurate estimates that capture the time evolution of the epidemic curve and outperform a previously developed approach. We assess the performance for two diseases and show that relating cases between sequential time points contributes to NobBS's performance and robustness across surveillance settings. PLOS COMPUTATIONAL BIOLOGYNowcasting by Bayesian Smoothing PLOS Computational Biology | https://doi.org/10.

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Section: Discussionmentioning

confidence: 81%

Section: Nobbs Improves Nowcasting With Varying Reporting Delaysmentioning

confidence: 99%

Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking

et al. 2020

View full text Add to dashboard Cite

show abstract

“…However, in the presence of simulated time-varying reporting delays, NobBS outperformed the benchmark in terms of confidence (NobBS average score = 0.06 vs. benchmark average score ≈ 0), point estimates (NobBS rRMSE = 0.302 vs. benchmark rRMSE = 0.621), and accuracy of the predicted change (Table S3). Such variations are a reality in many epidemics (17).…”

Section: Nobbs Improves Nowcasting With Varying Reporting Delaysmentioning

confidence: 99%

Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking

McGough

Johansson²,

Lipsitch

et al. 2019

Preprint

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AbstractDelays in case reporting are common to disease surveillance systems, making it difficult to track diseases in real-time. “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 a single application, 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 package. 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.SignificanceAchieving accurate, real-time estimates of disease activity is challenged by delays in case reporting. However, approaches that seek to estimate cases in spite of reporting delays often do not consider the temporal relationship between cases during an outbreak, nor do they identify characteristics of robust approaches that generalize to a wide range of surveillance contexts with very different reporting delays. Here, we present a smooth Bayesian nowcasting approach that produces accurate estimates that capture the time evolution of the epidemic curve and outperform a previous approach in the literature. We assess the performance for two diseases to identify important features of the reporting delay distribution that contribute to the model’s performance and robustness across surveillance settings.

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“…() and Noufaily et al. () have analyzed reporting delays in European reporting systems, respectively, for Salmonella in France and 12 infections in the United Kingdom. Furthermore, Altmann et al.…”

Section: Introductionmentioning

confidence: 99%

“…This can be a hindrance to the timely detection of outbreaks. For example, Jones et al (2014) and Noufaily et al (2015) have analyzed reporting delays in European reporting systems, respectively, for Salmonella in France and 12 infections in the United Kingdom. Furthermore, Altmann et al (2011) and Höhle and an der Heiden (2014) have analyzed reporting delays during the German EHEC O104:H4 outbreak in particular.…”

Section: Introductionmentioning

confidence: 99%

Bayesian outbreak detection in the presence of reporting delays

et al. 2015

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One use of infectious disease surveillance systems is the statistical aberration detection performed on time series of counts resulting from the aggregation of individual case reports. However, inherent reporting delays in such surveillance systems make the considered time series incomplete, which can be an impediment to the timely detection and thus to the containment of emerging outbreaks. In this work, we synthesize the outbreak detection algorithms of Noufaily et al. (2013) and Manitz and Höhle (2013) while additionally addressing right truncation caused by reporting delays. We do so by considering the resulting time series as an incomplete two-way contingency table which we model using negative binomial regression. Our approach is defined in a Bayesian setting allowing a direct inclusion of all sources of uncertainty in the derivation of whether an observed case count is to be considered an aberration. The proposed algorithm is evaluated both on simulated data and on the time series of Salmonella Newport cases in Germany in 2011. Altogether, our method aims at allowing timely aberration detection in the presence of reporting delays and hence underlines the need for statistical modeling to address complications of reporting systems. An implementation of the proposed method is made available in the R package surveillance as the function "bodaDelay".

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Modelling Reporting Delays for Outbreak Detection in Infectious Disease Data

Cited by 18 publications

References 27 publications

Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking

Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking

Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking

Bayesian outbreak detection in the presence of reporting delays

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