Background: Italy has experienced severe consequences (ie, hospitalizations and deaths) during the COVID-19 pandemic. Online decision support systems (DSS) and self-triage applications have been used in several settings to supplement health authority recommendations to prevent and manage COVID-19. A digital Italian health tech startup, Paginemediche, developed a noncommercial, online DSS with a chat user interface to assist individuals in Italy manage their potential exposure to COVID-19 and interpret their symptoms since early in the pandemic. Objective: This study aimed to compare the trend in online DSS sessions with that of COVID-19 cases reported by the national health surveillance system in Italy, from February 2020 to March 2021. Methods: We compared the number of sessions by users with a COVID-19–positive contact and users with COVID-19–compatible symptoms with the number of cases reported by the national surveillance system. To calculate the distance between the time series, we used the dynamic time warping algorithm. We applied Symbolic Aggregate approXimation (SAX) encoding to the time series in 1-week periods. We calculated the Hamming distance between the SAX strings. We shifted time series of online DSS sessions 1 week ahead. We measured the improvement in Hamming distance to verify the hypothesis that online DSS sessions anticipate the trends in cases reported to the official surveillance system. Results: We analyzed 75,557 sessions in the online DSS; 65,207 were sessions by symptomatic users, while 19,062 were by contacts of individuals with COVID-19. The highest number of online DSS sessions was recorded early in the pandemic. Second and third peaks were observed in October 2020 and March 2021, respectively, preceding the surge in notified COVID-19 cases by approximately 1 week. The distance between sessions by users with COVID-19 contacts and reported cases calculated by dynamic time warping was 61.23; the distance between sessions by symptomatic users was 93.72. The time series of users with a COVID-19 contact was more consistent with the trend in confirmed cases. With the 1-week shift, the Hamming distance between the time series of sessions by users with a COVID-19 contact and reported cases improved from 0.49 to 0.46. We repeated the analysis, restricting the time window to between July 2020 and December 2020. The corresponding Hamming distance was 0.16 before and improved to 0.08 after the time shift. Conclusions: Temporal trends in the number of online COVID-19 DSS sessions may precede the trend in reported COVID-19 cases through traditional surveillance. The trends in sessions by users with a contact with COVID-19 may better predict reported cases of COVID-19 than sessions by symptomatic users. Data from online DSS may represent a useful supplement to traditional surveillance and support the identification of early warning signals in the COVID-19 pandemic.

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中文翻译：

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通过在线决策支持工具对意大利 COVID-19 疫情一年多的情况进行数字监控：观察性研究


背景：意大利在 COVID-19 大流行期间经历了严重后果（即住院和死亡）。在线决策支持系统 (DSS) 和自我分类应用程序已在多种环境中使用，以补充卫生当局预防和管理 COVID-19 的建议。意大利数字健康科技初创公司 Paginemediche 开发了一种带有聊天用户界面的非商业在线 DSS，以帮助意大利的个人管理他们可能接触的 COVID-19 并解释他们自大流行初期以来的症状。目的：本研究旨在将 2020 年 2 月至 2021 年 3 月期间在线 DSS 会话趋势与意大利国家健康监测系统报告的 COVID-19 病例趋势进行比较。方法：我们比较了患有 COVID 的用户的会话数量-19 – 阳性接触者和具有 COVID-19 兼容症状的用户与国家监测系统报告的病例数。为了计算时间序列之间的距离，我们使用了动态时间扭曲算法。我们将符号聚合近似 (SAX) 编码应用于 1 周周期的时间序列。我们计算了 SAX 字符串之间的汉明距离。我们将在线 DSS 会议的时间序列提前 1 周。我们测量了汉明距离的改进，以验证在线 DSS 会话预测向官方监测系统报告的病例趋势的假设。结果：我们分析了在线 DSS 中的 75,557 个会话； 65,207 次会议由有症状的用户进行，而 19,062 次会议由 COVID-19 患者的接触者进行。在线 DSS 会话数量最高记录是在大流行初期。 第二次和第三次高峰分别出现在 2020 年 10 月和 2021 年 3 月，比通报的 COVID-19 病例激增大约 1 周。通过动态时间规整计算出的有 COVID-19 接触者的用户会话与报告病例之间的距离为 61.23；有症状用户的会话间隔为 93.72。与 COVID-19 接触过的用户的时间序列与确诊病例的趋势更加一致。经过 1 周的轮班，有 COVID-19 接触者的用户会话时间序列与报告病例之间的汉明距离从 0.49 改善到 0.46。我们重复分析，将时间窗口限制在2020年7月至2020年12月之间。对应的汉明距离之前为0.16，时移后提高到0.08。结论：在线 COVID-19 DSS 会话数量的时间趋势可能先于通过传统监测报告的 COVID-19 病例趋势。与有症状的用户的会话相比，与 COVID-19 接触过的用户的会话趋势可以更好地预测报告的 COVID-19 病例。来自在线 DSS 的数据可能是对传统监测的有用补充，并支持识别 COVID-19 大流行中的早期预警信号。


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