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Background <p>Declines in childhood vaccination in the U.S. have contributed to a resurgence of vaccine-preventable diseases, including a notable increase in pertussis cases. Traditional pertussis surveillance is limited by underdiagnosis and underreporting. Participatory surveillance systems such as Outbreaks Near Me (ONM) provide an additional population-level data stream by capturing self-reported symptoms. Although pertussis signals are difficult to detect due to low incidence and symptom overlap with other infections, ONM collects free-text descriptions that may contain pertussis-specific information. Advances in large language models (LLMs) enable the extraction of relevant signals from unstructured text to potentially improve forecasting.</p> Methods <p>We analyzed U.S. pertussis case data from the CDC and ONM reports from 2022 to 2025. ONM reports were filtered for prolonged cough without alternative diagnoses and further refined using a two-step GPT-4-based pipeline that summarized participant reports and excluded cases inconsistent with pertussis to enhance case specificity. Three datasets were created: CDC-only cases, CDC and ONM filtered cases, and CDC and ONM cases post-LLM processing. Aggregated time series were split into a training set (2022–2024) and a test set (2025, first 7 months). We trained multiple forecasting models (ARIMA, XG-Boost, and linear regression) on the 2022–2024 data, first using CDC-only data to establish a baseline. The best-performing model was then applied to the two datasets, incorporating the ONM participatory data. Performance was evaluated using Mean Absolute Error (MAE).</p> Results <p>CDC-reported pertussis cases totaled 862 in 2022, 2,512 in 2023, 11,276 in 2024, and 5,937 in the first seven months of 2025. Of 2,741 ONM-suspected cases, 957 remained after LLM refinement. XGBoost yielded the best baseline performance (MAE 26.65). Incorporating ONM data improved performance: MAE decreased to 25.60 with filtered ONM cases and 24.69 with LLM-processed cases.</p> Conclusions <p>Integrating LLM-processing of participatory surveillance data with traditional surveillance enhances the accuracy of pertussis outbreak forecasting. This approach introduces a novel way to leverage free-text data, offering a promising pathway to augment traditional public health surveillance systems.</p>

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Leveraging large language models on free-text symptoms from participatory surveillance enhances pertussis forecasting in the United States

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月7日

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  • Luigi De Angelis,
  • Samuele Tarallo,
  • Caterina Rizzo,
  • Autumn Gertz,
  • Francesco Baglivo,
  • John S. Brownstein,
  • Benjamin Rader

摘要

Background

Declines in childhood vaccination in the U.S. have contributed to a resurgence of vaccine-preventable diseases, including a notable increase in pertussis cases. Traditional pertussis surveillance is limited by underdiagnosis and underreporting. Participatory surveillance systems such as Outbreaks Near Me (ONM) provide an additional population-level data stream by capturing self-reported symptoms. Although pertussis signals are difficult to detect due to low incidence and symptom overlap with other infections, ONM collects free-text descriptions that may contain pertussis-specific information. Advances in large language models (LLMs) enable the extraction of relevant signals from unstructured text to potentially improve forecasting.

Methods

We analyzed U.S. pertussis case data from the CDC and ONM reports from 2022 to 2025. ONM reports were filtered for prolonged cough without alternative diagnoses and further refined using a two-step GPT-4-based pipeline that summarized participant reports and excluded cases inconsistent with pertussis to enhance case specificity. Three datasets were created: CDC-only cases, CDC and ONM filtered cases, and CDC and ONM cases post-LLM processing. Aggregated time series were split into a training set (2022–2024) and a test set (2025, first 7 months). We trained multiple forecasting models (ARIMA, XG-Boost, and linear regression) on the 2022–2024 data, first using CDC-only data to establish a baseline. The best-performing model was then applied to the two datasets, incorporating the ONM participatory data. Performance was evaluated using Mean Absolute Error (MAE).

Results

CDC-reported pertussis cases totaled 862 in 2022, 2,512 in 2023, 11,276 in 2024, and 5,937 in the first seven months of 2025. Of 2,741 ONM-suspected cases, 957 remained after LLM refinement. XGBoost yielded the best baseline performance (MAE 26.65). Incorporating ONM data improved performance: MAE decreased to 25.60 with filtered ONM cases and 24.69 with LLM-processed cases.

Conclusions

Integrating LLM-processing of participatory surveillance data with traditional surveillance enhances the accuracy of pertussis outbreak forecasting. This approach introduces a novel way to leverage free-text data, offering a promising pathway to augment traditional public health surveillance systems.