What data sources and models are most accurate in forecasting seasonal patterns of respiratory diseases?
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| Format: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901850035322880 |
|---|---|
| author | Tripdatabase |
| author_facet | Tripdatabase |
| contents | Forecasting models for respiratory diseases that integrate diverse data sources and utilize hybrid or machine learning techniques tend to be most accurate, although limitations in data quality and integration remain a challenge. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18916892 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | What data sources and models are most accurate in forecasting seasonal patterns of respiratory diseases? Tripdatabase data sources forecasting seasonal patterns respiratory diseases models accuracy public health preventive medicine Forecasting models for respiratory diseases that integrate diverse data sources and utilize hybrid or machine learning techniques tend to be most accurate, although limitations in data quality and integration remain a challenge. |
| title | What data sources and models are most accurate in forecasting seasonal patterns of respiratory diseases? |
| topic | data sources forecasting seasonal patterns respiratory diseases models accuracy public health preventive medicine |
| url | https://doi.org/10.5281/zenodo.18916892 |