Density forecasting of conjunctivitis burden using high-dimensional environmental time series data
Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved..
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 SO2, O3 surface concentration and total precipitation were associated to increased conjunctivitis attendance. The methods proposed can provide rich and informative forward guidance for outbreak preparedness and help guide healthcare resource planning in both stable periods of transmission and periods where structural breaks in data occur.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:44 |
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| Enthalten in: |
Epidemics - 44(2023) vom: 05. Sept., Seite 100694 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lim, Jue Tao [VerfasserIn] |
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| Links: |
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| Themen: |
Conjunctivitis |
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| Anmerkungen: |
Date Completed 08.09.2023 Date Revised 26.09.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.epidem.2023.100694 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM359150020 |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved. | ||
| 520 | |a 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 SO2, O3 surface concentration and total precipitation were associated to increased conjunctivitis attendance. The methods proposed can provide rich and informative forward guidance for outbreak preparedness and help guide healthcare resource planning in both stable periods of transmission and periods where structural breaks in data occur | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Conjunctivitis | |
| 650 | 4 | |a Epidemics | |
| 650 | 4 | |a Forecasting | |
| 650 | 4 | |a Infectious diseases | |
| 700 | 1 | |a Choo, Esther Li Wen |e verfasserin |4 aut | |
| 700 | 1 | |a Janhavi, A |e verfasserin |4 aut | |
| 700 | 1 | |a Tan, Kelvin Bryan |e verfasserin |4 aut | |
| 700 | 1 | |a Abisheganaden, John |e verfasserin |4 aut | |
| 700 | 1 | |a Dickens, Borame |e verfasserin |4 aut | |
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