# Quantifying Error Growth to Improve Infectious Disease Forecast Accuracy

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2022 · $649,085

## Abstract

PROJECT SUMMARY/ABSTRACT
Over the last decade, infectious disease forecasting has advanced considerably. Using methods derived from
dynamic modeling, statistical inference and numerical weather prediction, forecast systems have been
developed for diseases such as influenza, SARS-CoV-2, dengue and Ebola. These systems have generated
probabilistic forecasts of future epidemic outcomes with quantifiable accuracy and lead times up to 3 months,
and in some instances, have been operationalized to deliver forecasts in real time. Such forecast information
can be used to help manage the timing and distribution of medical countermeasures, to plan hospital and clinic
staffing, and to allocate healthcare supplies in anticipation of patient surges. Ongoing research is needed to
further improve the accuracy of these disease forecasts so that the decisions and actions that are based on
this information are more soundly motivated. To this end, it is vital that the sources of error in infectious
disease forecasts are better understood, that the growth of error during forecast is quantified, and that methods
are developed to control and optimize that error growth in order to improve forecast accuracy. The aim of this
project is to leverage methods that have been employed to understand and quantify error growth in weather
forecasting models and to improve weather forecasting accuracy, and to apply these methods to infectious
disease forecasting systems. Specifically, we will: 1) quantify the nonlinear growth of error within a diversity of
infectious disease forecasting models and then develop methods to optimize that error growth during
forecasting, thus improving forecast accuracy; we hypothesize that the fastest growing mode within disease
forecasting models can be identified using singular vector analysis (SVA); quantified error growth can then be
exploited using optimal perturbation methods, in conjunction with observations and data assimilation
approaches, to generate a more calibrated ensemble forecast that produces more accurate probabilistic
predictions; 2) apply SVA and optimal perturbation methods to a recently validated, spatially explicit model of
influenza in order to understand how uncertainty propagates when observations are missing and to identify
which locations are critical for accurate forecasting throughout the network; we hypothesize these findings can
be used to identify improved, more optimal disease surveillance networks; and 3) develop models to forecast
and project the continued spread of influenza and SARS-CoV-2 internationally; here, we will develop multi-
country spatially-explicit networked metapopulation models capable of accurate simulation and forecasting of
the transmission and spread of seasonal influenza and SARS-CoV-2 within and between countries; we
hypothesize that the intra- and inter-country spread of these diseases can be forecast more accurately with
systems that utilize network model structures. The findings from this pro...

## Key facts

- **NIH application ID:** 10424587
- **Project number:** 5R01AI163023-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** JEFFREY L SHAMAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $649,085
- **Award type:** 5
- **Project period:** 2021-06-09 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10424587

## Citation

> US National Institutes of Health, RePORTER application 10424587, Quantifying Error Growth to Improve Infectious Disease Forecast Accuracy (5R01AI163023-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10424587. Licensed CC0.

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