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Comparing Objective Conjunctival Hyperemia Grading and the Ocular Surface Disease Index Score in Dry Eye Syndrome During COVID-19

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Density forecasting of conjunctivitis burden using high-dimensional environmental time series data.

Jue Tao Lim¹, Esther Li Wen Choo², A Janhavi²

¹Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.

|July 6, 2023

Summary

Forecasting acute conjunctivitis (eye infections) trends using environmental data improves public health planning. Complex models excel at predicting disease spread, aiding healthcare resource allocation.

Keywords:

Conjunctivitis Epidemics Forecasting Infectious diseases

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Area of Science:

Epidemiology
Environmental Health
Public Health Policy

Background:

Acute conjunctivitis is a common condition straining healthcare resources.
Accurate forecasting is crucial for public health policy and resource planning.
Understanding transmission factors is key to managing outbreaks.

Purpose of the Study:

To develop and evaluate novel forecasting approaches for conjunctivitis burden.
To assess the impact of environmental factors on conjunctivitis transmission.
To provide guidance for policymakers and healthcare resource planning.

Main Methods:

Utilized high-dimensional ambient air pollution and meteorological data (2012-2022).
Compared simple forecasting models with complex models optimizing predictive accuracy.
Employed post-selection inference for ecological analysis of environmental factors.

Main Results:

Simple models outperformed complex ones for point forecasts.
Complex models demonstrated superior density forecast performance.
Increased SO2, O3, and precipitation correlated with higher conjunctivitis attendance.

Conclusions:

Environmental data enhances conjunctivitis forecasting, particularly for probabilistic predictions.
The proposed methods offer valuable guidance for outbreak preparedness.
These approaches are adaptable to other infectious diseases and varying transmission patterns.