Background: The COVID-19 pandemic has changed public health policies and human and community behaviors through lockdowns and mandates. Governments are rapidly evolving policies to increase hospital capacity and supply personal protective equipment and other equipment to mitigate disease spread in affected regions. Current models that predict COVID-19 case counts and spread are complex by nature and offer limited explainability and generalizability. This has highlighted the need for accurate and robust outbreak prediction models that balance model parsimony and performance. Objective: We sought to leverage readily accessible data sets extracted from multiple states to train and evaluate a parsimonious predictive model capable of identifying county-level risk of COVID-19 outbreaks on a day-to-day basis. Methods: Our modeling approach leveraged the following data inputs: COVID-19 case counts per county per day and county populations. We developed an outbreak gold standard across California, Indiana, and Iowa. The model utilized a per capita running 7-day sum of the case counts per county per day and the mean cumulative case count to develop baseline values. The model was trained with data recorded between March 1 and August 31, 2020, and tested on data recorded between September 1 and October 31, 2020. Results: The model reported sensitivities of 81%, 92%, and 90% for California, Indiana, and Iowa, respectively. The precision in each state was above 85% while specificity and accuracy scores were generally >95%. Conclusions: Our parsimonious model provides a generalizable and simple alternative approach to outbreak prediction. This methodology can be applied to diverse regions to help state officials and hospitals with resource allocation and to guide risk management, community education, and mitigation strategies.

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中文翻译：

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对简约 COVID-19 爆发预测模型的评估：使用公开数据集的启发式建模方法

背景：COVID-19 大流行通过封锁和强制措施改变了公共卫生政策以及人类和社区行为。各国政府正在迅速制定政策，以提高医院容量并提供个人防护设备和其他设备，以减轻疾病在受影响地区的传播。当前预测 COVID-19 病例数和传播的模型本质上很复杂，可解释性和普遍性有限。这凸显了对平衡模型简约性和性能的准确和强大的爆发预测模型的需求。目标：我们试图利用从多个州提取的易于访问的数据集来训练和评估能够每天识别县级 COVID-19 爆发风险的简约预测模型。方法：我们的建模方法利用了以下数据输入：每天每个县的 COVID-19 病例数和县人口。我们在加利福尼亚州、印第安纳州和爱荷华州制定了爆发黄金标准。该模型利用人均运行 7 天每个县每天的病例数总和和平均累积病例数来制定基线值。该模型使用 2020 年 3 月 1 日至 8 月 31 日期间记录的数据进行训练，并使用 2020 年 9 月 1 日至 10 月 31 日期间记录的数据进行测试。 结果：该模型报告的加利福尼亚州、印第安纳州的灵敏度分别为 81%、92% 和 90% ，和爱荷华州，分别。每个状态的精确度都在 85% 以上，而特异性和准确度得分一般 >95%。结论：我们的简约模型为爆发预测提供了一种通用且简单的替代方法。

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