Forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast combinations.
Upper respiratory tract infections (URTIs) represent a large strain on primary health resources. To mitigate URTI transmission and public health burdens, it is important to pre-empt and provide forward guidance on URTI burden, while taking into account various facets which influence URTI transmissio...
| Published in: | PLoS Computational Biology |
|---|---|
| Main Authors: | , , , |
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-02-01
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| Online Access: | https://doi.org/10.1371/journal.pcbi.1010892 |
