Background

The Health Sentinel (Centinela de la Salud, CDS), a mobile crowdsourcing platform that includes the CDS app, was deployed to assess its utility as a tool for COVID-19 surveillance in San Luis Potosí, Mexico.

Methods

The CDS app allowed anonymized individual surveys of demographic features and COVID-19 risk of transmission and exacerbation factors from users of the San Luis Potosí Metropolitan Area (SLPMA). The platform’s data processing pipeline computed and geolocalized the risk index of each user and enabled the analysis of the variables and their association. Point process analysis identified geographic clustering patterns of users at risk and these were compared with the patterns of COVID-19 cases confirmed by the State Health Services.

Results

A total of 1554 COVID-19 surveys were administered through the CDS app. Among the respondents, 50.4 % were men and 49.6 % women, with an average age of 33.5 years. Overall risk index frequencies were, in descending order: no-risk 77.8 %, low risk 10.6 %, respiratory symptoms 6.7 %, medium risk 1.4 %, high risk 2.0 %, very high risk 1.5 %. Comorbidity was the most frequent vulnerability category (32.4 %), followed by the inability to keep home lockdown (19.2 %). Statistically significant risk clusters identified at a spatial scale between 5 and 730 m coincided with those in neighborhoods containing substantial numbers of confirmed COVID-19 cases.

Conclusions

The CDS platform enables the analysis of the sociodemographic features and spatial distribution of individual risk indexes of COVID-19 transmission and exacerbation. It is a useful epidemiological surveillance and early detection tool because it identifies statistically significant and consistent risk clusters in neighborhoods with a substantial number of confirmed COVID-19 cases.

中文翻译：

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Health Sentinel：一个用于自我报告调查的移动众包平台可及早发现墨西哥圣路易斯波托西的 COVID-19 集群

背景

Health Sentinel（Centinela de la Salud，CDS）是一个包含CDS应用程序的移动众包平台，在墨西哥圣路易斯波托西部署该平台以评估其作为 COVID-19 监测工具的效用。

方法

CDS应用程序允许对圣路易斯波托西都会区 (SLPMA) 用户的人口特征和 COVID-19 传播风险和恶化因素进行匿名个人调查。该平台的数据处理管道计算每个用户的风险指数并对其进行地理定位，并支持分析变量及其关联。点过程分析确定了处于风险中的用户的地理聚类模式，并将这些模式与国家卫生服务部门确认的 COVID-19 病例模式进行了比较。

结果

通过CDS应用程序共管理了 1554 项 COVID-19 调查。受访者中，男性占50.4%，女性占49.6%，平均年龄33.5岁。总体风险指数频率按降序排列为：无风险 77.8%，低风险 10.6%，呼吸道症状 6.7%，中等风险 1.4%，高风险 2.0%，极高风险 1.5%。合并症是最常见的脆弱性类别 (32.4%)，其次是无法保持居家隔离 (19.2%)。在 5 到 730 米的空间尺度上确定的具有统计学意义的风险集群与包含大量确诊 COVID-19 病例的社区中的风险集群一致。

结论

CDS平台可以分析 COVID-19 传播和恶化的个体风险指数的社会人口学特征和空间分布。它是一种有用的流行病学监测和早期检测工具，因为它可以在具有大量 COVID-19 确诊病例的社区中识别具有统计学意义且一致的风险群。