Title: Addressing delayed case reporting in infectious disease forecast modeling

Abstract

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 bemore » 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.« less

Authors:

Beesley, Lauren J. ;

• Search DOE PAGES for author "Beesley, Lauren J."
• Search DOE PAGES for ORCID "0000-0002-3788-5944"

Osthus, Dave ;

• Search DOE PAGES for author "Osthus, Dave"
• Search DOE PAGES for ORCID "0000-0002-4681-091X"

Del Valle, Sara Y. ;

• Search DOE PAGES for author "Del Valle, Sara Y."
• Search DOE PAGES for ORCID "0000-0002-0159-1952"

Perkins, ed., Alex

Publication Date:

Fri Jun 03 00:00:00 EDT 2022

Research Org.:

Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Org.:

USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program; National Institutes of Health/National Institute of General Medical Sciences (NIH/NIGMS)

OSTI Identifier:

1872480

Alternate Identifier(s):

OSTI ID: 1871044; OSTI ID: 1894842

Report Number(s):

LA-UR-21-30640
Journal ID: ISSN 1553-7358; 10.1371/journal.pcbi.1010115

Grant/Contract Number:  

20210761PRD1; 89233218CNA000001; R01GM130668-01

Resource Type:

Published Article

Journal Name:

PLoS Computational Biology (Online)

Additional Journal Information:

Journal Name: PLoS Computational Biology (Online) Journal Volume: 18 Journal Issue: 6; Journal ID: ISSN 1553-7358

Publisher:

Public Library of Science (PLoS)

Country of Publication:

United States

Language:

English

Subject:

59 BASIC BIOLOGICAL SCIENCES; mathematics; simulation and modeling; infectious diseases; forecasting; influenza