Artikel ; Online: Density forecasting of conjunctivitis burden using high-dimensional environmental time series data.
2023 Band 44, Seite(n) 100694
Abstract: As one of the most common eye conditions being presented at clinics, acute conjunctivitis puts substantial strain on primary health resources. To reduce this public health burden, it is important to forecast and provide forward guidance to policymakers ... ...
Abstract | As one of the most common eye conditions being presented at clinics, acute conjunctivitis puts substantial strain on primary health resources. To reduce this public health burden, it is important to forecast and provide forward guidance to policymakers by estimating conjunctivitis trends, taking into account factors which influence transmission. Using a high-dimensional set of ambient air pollution and meteorological data, this study describes new approaches to point and probabilistic forecasting of conjunctivitis burden which can be readily translated to other infectious diseases. Over the period of 2012 - 2022, we show that simple models without environmental data provided better point forecasts but the more complex models which optimized predictive accuracy and combined multiple predictors demonstrated superior density forecast performance. These results were shown to be consistent over periods with and without structural breaks in transmission. Furthermore, ecological analysis using post-selection inference showed that increases in SO |
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Mesh-Begriff(e) | Humans ; Time Factors ; Air Pollution/analysis ; Communicable Diseases/epidemiology ; Disease Outbreaks ; Conjunctivitis/epidemiology |
Sprache | Englisch |
Erscheinungsdatum | 2023-06-03 |
Erscheinungsland | Netherlands |
Dokumenttyp | Journal Article ; Research Support, Non-U.S. Gov't |
ZDB-ID | 2467993-8 |
ISSN | 1878-0067 ; 1755-4365 |
ISSN (online) | 1878-0067 |
ISSN | 1755-4365 |
DOI | 10.1016/j.epidem.2023.100694 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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