Department of Community Medicine, Information and Decision in Health, Faculty of Medicine, University of Porto, Porto, Portugal.
Center for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal.
JMIR Public Health Surveill. 2023 Apr 6;9:e43836. doi: 10.2196/43836.
Contact tracing is a fundamental intervention in public health. When systematically applied, it enables the breaking of chains of transmission, which is important for controlling COVID-19 transmission. In theoretically perfect contact tracing, all new cases should occur among quarantined individuals, and an epidemic should vanish. However, the availability of resources influences the capacity to perform contact tracing. Therefore, it is necessary to estimate its effectiveness threshold. We propose that this effectiveness threshold may be indirectly estimated using the ratio of COVID-19 cases arising from quarantined high-risk contacts, where higher ratios indicate better control and, under a threshold, contact tracing may fail and other restrictions become necessary.
This study assessed the ratio of COVID-19 cases in high-risk contacts quarantined through contact tracing and its potential use as an ancillary pandemic control indicator.
We built a 6-compartment epidemiological model to emulate COVID-19 infection flow according to publicly available data from Portuguese authorities. Our model extended the usual susceptible-exposed-infected-recovered model by adding a compartment Q with individuals in mandated quarantine who could develop infection or return to the susceptible pool and a compartment P with individuals protected from infection because of vaccination. To model infection dynamics, data on SARS-CoV-2 infection risk (IR), time until infection, and vaccine efficacy were collected. Estimation was needed for vaccine data to reflect the timing of inoculation and booster efficacy. In total, 2 simulations were built: one adjusting for the presence and absence of variants or vaccination and another maximizing IR in quarantined individuals. Both simulations were based on a set of 100 unique parameterizations. The daily ratio of infected cases arising from high-risk contacts (q estimate) was calculated. A theoretical effectiveness threshold of contact tracing was defined for 14-day average q estimates based on the classification of COVID-19 daily cases according to the pandemic phases and was compared with the timing of population lockdowns in Portugal. A sensitivity analysis was performed to understand the relationship between different parameter values and the threshold obtained.
An inverse relationship was found between the q estimate and daily cases in both simulations (correlations >0.70). The theoretical effectiveness thresholds for both simulations attained an alert phase positive predictive value of >70% and could have anticipated the need for additional measures in at least 4 days for the second and fourth lockdowns. Sensitivity analysis showed that only the IR and booster dose efficacy at inoculation significantly affected the q estimates.
We demonstrated the impact of applying an effectiveness threshold for contact tracing on decision-making. Although only theoretical thresholds could be provided, their relationship with the number of confirmed cases and the prediction of pandemic phases shows the role as an indirect indicator of the efficacy of contact tracing.
接触者追踪是公共卫生的一项基本干预措施。当系统应用时，它可以打破传播链，这对于控制 COVID-19 的传播很重要。在理论上完美的接触者追踪中，所有新病例都应该出现在被隔离的个体中，疫情应该消失。然而，资源的可获得性影响了进行接触者追踪的能力。因此，有必要估计其有效性阈值。我们提出，这个有效性阈值可以通过接触追踪中隔离的高风险接触者的 COVID-19 病例比例来间接估计，较高的比例表明控制得更好，而且在一定阈值以下，接触者追踪可能失败，需要采取其他限制措施。
本研究评估了通过接触追踪隔离的高风险接触者中的 COVID-19 病例比例及其作为辅助大流行控制指标的潜力。
我们构建了一个 6 compartment 流行病学模型，根据葡萄牙当局公开提供的数据模拟 COVID-19 感染流。我们的模型通过添加一个带有在强制隔离中可能感染或返回易感池的个体的 Q compartment 和一个由于接种疫苗而免受感染的个体的 P compartment，扩展了通常的易感-暴露-感染-恢复模型。为了模拟感染动态，收集了 SARS-CoV-2 感染风险 (IR)、感染时间和疫苗效力的数据。需要对疫苗数据进行估计，以反映接种时间和加强针效力。总共进行了 2 次模拟：一次调整了变体或疫苗接种的存在，另一次则最大限度地提高了隔离个体的 IR。这两个模拟都是基于 100 个独特的参数化方案构建的。计算了源自高风险接触者的感染病例的每日比例（q 估计）。根据 COVID-19 每日病例的分类，根据大流行阶段定义了接触追踪的理论有效性阈值，并与葡萄牙的人口封锁时间进行了比较。进行了敏感性分析，以了解不同参数值与获得的阈值之间的关系。
在这两个模拟中，q 估计值与每日病例之间都存在负相关关系（相关性>0.70）。这两个模拟的理论有效性阈值在 14 天的平均 q 估计值上达到了>70%的阳性预测值，并且至少可以提前 4 天预测到需要采取额外措施，这与第二次和第四次封锁相吻合。敏感性分析表明，只有 IR 和加强剂量在接种时的效力对 q 估计值有显著影响。
我们展示了应用接触追踪有效性阈值对决策的影响。尽管只能提供理论阈值，但它们与确诊病例数量的关系以及对大流行阶段的预测表明，它们可以作为接触追踪有效性的间接指标。
