Department of Economics, University of Zurich, Zurich, Switzerland.
Data Science for Social Impact and Sustainability, ISI Foundation, Turin, Italy.
JMIR Public Health Surveill. 2023 Apr 26;9:e44517. doi: 10.2196/44517.
The ongoing COVID-19 pandemic has emphasized the necessity of a well-functioning surveillance system to detect and mitigate disease outbreaks. Traditional surveillance (TS) usually relies on health care providers and generally suffers from reporting lags that prevent immediate response plans. Participatory surveillance (PS), an innovative digital approach whereby individuals voluntarily monitor and report on their own health status via web-based surveys, has emerged in the past decade to complement traditional data collection approaches.
This study compared novel PS data on COVID-19 infection rates across 9 Brazilian cities with official TS data to examine the opportunities and challenges of using PS data, and the potential advantages of combining the 2 approaches.
The TS data for Brazil are publicly accessible on GitHub. The PS data were collected through the Brazil Sem Corona platform, a Colab platform. To gather information on an individual's health status, each participant was asked to fill out a daily questionnaire on symptoms and exposure in the Colab app.
We found that high participation rates are key for PS data to adequately mirror TS infection rates. Where participation was high, we documented a significant trend correlation between lagged PS data and TS infection rates, suggesting that PS data could be used for early detection. In our data, forecasting models integrating both approaches increased accuracy up to 3% relative to a 14-day forecast model based exclusively on TS data. Furthermore, we showed that PS data captured a population that significantly differed from a traditional observation.
In the traditional system, the new recorded COVID-19 cases per day are aggregated based on positive laboratory-confirmed tests. In contrast, PS data show a significant share of reports categorized as potential COVID-19 cases that are not laboratory confirmed. Quantifying the economic value of PS system implementation remains difficult. However, scarce public funds and persisting constraints to the TS system provide motivation for a PS system, making it an important avenue for future research. The decision to set up a PS system requires careful evaluation of its expected benefits, relative to the costs of setting up platforms and incentivizing engagement to increase both coverage and consistent reporting over time. The ability to compute such economic tradeoffs might be key to have PS become a more integral part of policy toolkits moving forward. These results corroborate previous studies when it comes to the benefits of an integrated and comprehensive surveillance system, and shed light on its limitations and on the need for additional research to improve future implementations of PS platforms.
持续的 COVID-19 大流行强调了建立一个运作良好的监测系统以发现和减轻疾病爆发的必要性。传统监测（TS）通常依赖于医疗保健提供者，并且通常存在报告延迟，这阻碍了立即制定应对计划。参与式监测（PS）是一种创新的数字方法，通过网络调查，个体自愿监测和报告自身健康状况，在过去十年中出现，以补充传统的数据收集方法。
本研究比较了巴西 9 个城市新型 PS 数据与官方 TS 数据在 COVID-19 感染率方面的差异，以考察使用 PS 数据的机会和挑战，以及结合这两种方法的潜在优势。
巴西的 TS 数据可在 GitHub 上公开获取。PS 数据是通过巴西 Sem Corona 平台（一个 Colab 平台）收集的。为了获取个体健康状况的信息，每位参与者都被要求在 Colab 应用程序中填写一份关于症状和暴露的每日问卷。
我们发现，高参与率是 PS 数据充分反映 TS 感染率的关键。在参与度高的情况下，我们记录到滞后 PS 数据与 TS 感染率之间存在显著的趋势相关性，表明 PS 数据可用于早期发现。在我们的数据中，整合这两种方法的预测模型相对于仅基于 TS 数据的 14 天预测模型，准确性提高了 3%。此外，我们表明 PS 数据捕捉到的人群与传统观察结果有显著差异。
在传统系统中，每天新记录的 COVID-19 病例是基于阳性实验室确诊检测的结果进行汇总的。相比之下，PS 数据显示出大量未经过实验室确诊的潜在 COVID-19 病例报告。量化 PS 系统实施的经济价值仍然很困难。然而，公共资金匮乏以及 TS 系统持续存在的限制为 PS 系统提供了动力，使其成为未来研究的重要途径。建立 PS 系统的决策需要仔细评估其预期收益，相对于建立平台和激励参与以随着时间的推移提高覆盖范围和一致性报告的成本。计算这些经济权衡的能力可能是使 PS 成为未来政策工具包的一个更重要组成部分的关键。这些结果与综合全面监测系统的优势相一致，同时也揭示了其局限性，并需要进一步研究以改进 PS 平台的未来实施。
