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<p>Vector-borne diseases, such as dengue and chikungunya, along with air-borne diseases like influenza and COVID-19, are prone to epidemics, which increases the demand for real-time outbreak monitoring. Developing such systems requires harmonized datasets for calibration and validation. In this study, we created a dataset containing official disease notifications for dengue, chikungunya, and severe acute respiratory infections (SARI) across Brazilian states for over a decade. The dataset integrates Google Trends search data for each disease and all associated symptoms, organized by the corresponding epidemiological week. By providing this integrated resource, we aim to encourage the use of alternative online data to explore associations between digital search behavior and official disease incidence, thereby supporting the development of nowcasting models for early outbreak detection to inform timely public health responses and decision-making.</p>

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Epidemiological and digital syndromic surveillance data on dengue, chikungunya, and SARI in Brazil

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月20日

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  • Marcelo E. Borges,
  • Cláudia T. Codeço,
  • Dalila Machado,
  • Alexandra Almeida

摘要

Vector-borne diseases, such as dengue and chikungunya, along with air-borne diseases like influenza and COVID-19, are prone to epidemics, which increases the demand for real-time outbreak monitoring. Developing such systems requires harmonized datasets for calibration and validation. In this study, we created a dataset containing official disease notifications for dengue, chikungunya, and severe acute respiratory infections (SARI) across Brazilian states for over a decade. The dataset integrates Google Trends search data for each disease and all associated symptoms, organized by the corresponding epidemiological week. By providing this integrated resource, we aim to encourage the use of alternative online data to explore associations between digital search behavior and official disease incidence, thereby supporting the development of nowcasting models for early outbreak detection to inform timely public health responses and decision-making.