Predicting COVID-19 and respiratory illness: results of the 2022-2023 Armed Forces Health Surveillance Division forecasting challenge.

Q3 Medicine
MSMR Pub Date : 2024-05-20
Mark L Bova, Sasha A McGee, Kathleen R Elliott, Juan I Ubiera
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引用次数: 0

Abstract

Since 2019, the Integrated Biosurveillance Branch of the Armed Forces Health Surveillance Division has conducted an annual forecasting challenge during influenza season to predict short-term respiratory disease activity among Military Health System beneficiaries. Weekly case and encounter observed data were used to generate 1- through 4-week advanced forecasts of disease activity. To create unified combinations of model inputs for evaluation across multiple spatial resolutions, 8 individual models were used to calculate 3 ensemble models. Forecast accuracy compared to the observed activity for each model was evaluated by calculating a weighted interval score. Weekly 1- through 4-week ahead forecasts for each ensemble model were generally higher than observed data, especially during periods of peak activity, with peaks in forecasted activity occurring later than observed peaks. The larger the forecasting horizon, the more pronounced the gap between forecasted peak and observed peak. The results showed that several models accurately predicted COVID-19 cases and respiratory encounters with enough lead time for public health response by senior leaders.

预测 COVID-19 和呼吸道疾病:2022-2023 年武装部队卫生监督处预测挑战的结果。
自 2019 年以来,武装部队健康监测司综合生物监测处每年都会在流感季节进行预测挑战,以预测军事卫生系统受益人的短期呼吸道疾病活动。每周的病例和就诊观察数据被用于生成 1 到 4 周的疾病活动高级预测。为了创建统一的模型输入组合,以便对多个空间分辨率进行评估,使用了 8 个单独模型来计算 3 个集合模型。通过计算加权区间得分来评估每个模型与观测活动相比的预测准确性。每个集合模式每周提前 1 到 4 周的预测值一般都高于观测数据,特别是在活动高峰期,预测活动峰值晚于观测峰值。预测范围越大,预测峰值与观测峰值之间的差距就越明显。结果表明,几个模型准确预测了 COVID-19 病例和呼吸道感染情况,为高层领导采取公共卫生应对措施提供了足够的准备时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
MSMR
MSMR Medicine-Public Health, Environmental and Occupational Health
CiteScore
2.30
自引率
0.00%
发文量
0
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