School of Earth Sciences, University of Melbourne, Parkville, Victoria, Australia.
Melbourne Medical School, University of Melbourne, Parkville, Victoria, Australia; Department of Allergy and Immunology, Royal Melbourne Hospital, Parkville, Victoria, Australia.
Sci Total Environ. 2020 Jun 10;720:137351. doi: 10.1016/j.scitotenv.2020.137351. Epub 2020 Feb 22.
Seasonal allergic rhinitis (AR), also known as hay fever, is a common respiratory condition brought on by a range of environmental triggers. Previous work has characterised the relationships between community-level AR symptoms collected using mobile apps in two Australian cities, Canberra and Melbourne, and various environmental covariates including pollen. Here, we build on these relationships by assessing the skill of models that provide a next-day forecast of an individual's risk of developing AR and that nowcast ambient grass pollen concentrations using crowd-sourced AR symptoms as a predictor. Categorical grass pollen forecasts (low/moderate/high) were made based on binning mean daily symptom scores by corresponding categories. Models for an individual's risk were constructed by forward variable selection, considering environmental, demographic, behaviour and health-related inputs, with non-linear responses permitted. Proportional-odds logistic regression was then applied with the variables selected, modelling the symptom scores on their original five-point scale. AR symptom-based estimates of today's average grass pollen concentration were more accurate than those provided by two benchmark forecasting methods using various metrics for assessing accuracy. Predictions of an individual's next-day AR symptoms rated on a five-point scale were correct in 36% of cases and within one point on this scale in 82% of cases. Both outcomes were significantly better than chance. This large-scale AR symptoms measurement program shows that crowd-sourced symptom scores can be used to predict the daily average grass pollen concentration, as well as provide a personalised AR forecast.
季节性过敏性鼻炎（AR），也称为花粉症，是一种常见的呼吸道疾病，由多种环境诱因引起。之前的工作已经描述了使用移动应用程序在澳大利亚的两个城市堪培拉和墨尔本收集的社区水平 AR 症状与各种环境协变量（包括花粉）之间的关系。在这里，我们通过评估模型的技能来进一步研究这些关系，这些模型可以提供个体第二天患 AR 的风险预测，以及使用众包 AR 症状作为预测因子来即时预测环境草花粉浓度。基于每日症状评分的相应类别，对分类草花粉预测（低/中/高）进行了分类。通过向前变量选择来构建个体风险模型，考虑环境、人口统计学、行为和与健康相关的输入，允许非线性响应。然后应用比例优势逻辑回归，对选定的变量进行建模，在原始五分制上对症状评分进行建模。基于 AR 症状的今天平均草花粉浓度估计值比使用各种准确性评估指标的两种基准预测方法提供的估计值更准确。对个体在五分制上的第二天 AR 症状的预测，在 36%的情况下是正确的，在 82%的情况下是在这个范围内的一个点。这两种结果都明显好于随机结果。这个大规模的 AR 症状测量计划表明，众包症状评分可用于预测每日平均草花粉浓度，并提供个性化的 AR 预测。
