Generalized log-normal chain-ladder

Kuang, D.; Nielsen, Bent

doi:10.1080/03461238.2019.1696885

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…To make the estimation strategy more compatible with existing out-of-the-box software, however, we propose pre-estimating π ts ( d ) and then fitting the disease model treating as a fixed offset in the mean model. Alternative distributional assumptions for N ts ( d ) may also be considered (e.g., lognormal as in [ 17 ]), but this offset-based method is limited to settings where mean counts are modeled using some type of log link function. Compartmental susceptible-infectious-recovered models, for example, are not compatible with this method.…”

Section: Methodsmentioning

confidence: 99%

Addressing delayed case reporting in infectious disease forecast modeling

2022

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Infectious disease forecasting is of great interest to the public health community and policymakers, since forecasts can provide insight into disease dynamics in the near future and inform interventions. Due to delays in case reporting, however, forecasting models may often underestimate the current and future disease burden. In this paper, we propose a general framework for addressing reporting delay in disease forecasting efforts with the goal of improving forecasts. We propose strategies for leveraging either historical data on case reporting or external internet-based data to estimate the amount of reporting error. We then describe several approaches for adapting general forecasting pipelines to account for under- or over-reporting of cases. We apply these methods to address reporting delay in data on dengue fever cases in Puerto Rico from 1990 to 2009 and to reports of influenza-like illness (ILI) in the United States between 2010 and 2019. Through a simulation study, we compare method performance and evaluate robustness to assumption violations. Our results show that forecasting accuracy and prediction coverage almost always increase when correction methods are implemented to address reporting delay. Some of these methods required knowledge about the reporting error or high quality external data, which may not always be available. Provided alternatives include excluding recently-reported data and performing sensitivity analysis. This work provides intuition and guidance for handling delay in disease case reporting and may serve as a useful resource to inform practical infectious disease forecasting efforts.

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Section: Methodsmentioning

confidence: 99%

Addressing delayed case reporting in infectious disease forecast modeling

2022

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“…To make the estimation strategy more compatible with existing out-of-the-box software, however, we propose pre-estimating π ts (d) and then fitting the disease model treating log (π ts (d)) as a fixed offset in the mean model. Alternative distributional assumptions for N ts (d) may also be considered (e.g., lognormal as in Kuang and Nielsen (2020)), but this offset-based method is limited to settings where mean counts are modeled using some type of log link function. Compartmental susceptible-infectious-recovered models, for example, are not compatible with this method.…”

Section: Modeling Real-time Data With Mean Model Offsetmentioning

confidence: 99%

Addressing delayed case reporting in infectious disease forecast modeling

Beesley¹,

Osthus²,

Valle³

2021

Preprint

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Infectious disease forecasting is of great interest to the public health community and policymakers, since forecasts can provide insight into disease dynamics in the near future and inform interventions. Due to delays in case reporting, however, forecasting models may often underestimate the current and future disease burden.In this paper, we propose a general framework for addressing reporting delay in disease forecasting efforts with the goal of improving forecasts. We propose strategies for leveraging either historical data on case reporting or external internet-based data to estimate the amount of reporting error. We then describe several approaches for adapting general forecasting pipelines to account for under-or over-reporting of cases. We apply these methods to address reporting delay in data on dengue fever cases in Puerto Rico from 1990 to 2009 and to reports of influenza-like illness (ILI) in the United States between 2010 and 2019. Through a simulation study, we compare method performance and evaluate robustness to assumption violations. Our results show that forecasting accuracy and prediction coverage almost always increase when correction methods are implemented to address reporting delay. Some of these methods required knowledge about the reporting error or high quality external data, which may not always be available. Provided alternatives include excluding recently-reported data and performing sensitivity analysis. This work provides intuition and guidance for handling delay in disease case reporting and may serve as a useful resource to inform practical infectious disease forecasting efforts.

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“…As implied by the name, a common feature of such models is that the mean of cumulative paid claims until the next development period is assumed to be a multiplication of the development factor (DF) and the most recent cumulative claim. Over the past several decades, various parametric models have been developed as stochastic extensions of the classic CL method (e.g., Wright, 1990;Mack & Venter, 2000;England & Verrall, 2002;Wüthrich & Merz, 2008;Kuang et al, 2015;Kuang & Nielsen, 2020;Gao et al, 2021). Many of these extensions are based on a generalized linear model (GLM) framework with a Poisson family distribution.…”

Section: Introductionmentioning

confidence: 99%

A semi-parametric claims reserving model with monotone splines

Chang,

Gao,

Shi

2024

Ann Oper Res

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Stochastic reserving models used in the insurance industry are usually based on an assumed distribution of claim amounts. Despite their popularity, such models may unavoidably be affected by the misspecification issue given that it is likely that the underlying distribution will be different from that assumed. In this paper, we incorporate monotone splines to ensure the expected monotonically increasing pattern of cumulative development factors (CDFs) to develop a new semi-parametric reserving model that does not require a density assumption. To allow the maximum utilization of available information, we also propose an enhanced sampling approach that greatly increases the size of unbiased CDFs, particularly in later development periods. Based on the enhanced samples, a bootstrap technique is employed in the estimation of monotone splines, from which incurred-but-not-reported (IBNR) reserves and prediction errors can be obtained. Associated technical features, such as the consistency of estimator, are discussed and demonstrated. Our simulation studies suggest that the new model improves the accuracy of IBNR reserving, compared with a range of classic competing models. A real data analysis produces many consistent findings, thus supporting the usefulness of the monotone spline model in actuarial and insurance practice.

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Generalized log-normal chain-ladder

Cited by 6 publications

References 27 publications

Addressing delayed case reporting in infectious disease forecast modeling

Addressing delayed case reporting in infectious disease forecast modeling

Addressing delayed case reporting in infectious disease forecast modeling

A semi-parametric claims reserving model with monotone splines

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