Department of Biomedical Engineering, Indian Institute of Technology - Hyderabad, Hyderabad, Telangana, India.
Department of Natural Sciences and Mathematics, The University of Texas at Dallas, Richardson, Texas, United States of America.
PLoS One. 2023 Mar 17;18(3):e0283081. doi: 10.1371/journal.pone.0283081. eCollection 2023.
With countries across the world facing repeated epidemic waves, it becomes critical to monitor, mitigate and prevent subsequent waves. Common indicators like active case numbers may not be sensitive enough in the presence of systemic inefficiencies like insufficient testing or contact tracing. Test positivity rates are sensitive to testing strategies and cannot estimate the extent of undetected cases. Reproductive numbers estimated from logarithms of new incidences are inaccurate in dynamic scenarios and not sensitive enough to capture changes in efficiencies. Systemic fatigue results in lower testing, inefficient tracing and quarantining thereby precipitating the onset of the epidemic wave. We propose a novel indicator for detecting the slippage of test-trace efficiency based on the number of deaths/hospitalizations resulting from known and hitherto unknown infections. This can also be used to forecast an epidemic wave that is advanced or exacerbated due to a drop in efficiency in situations where the testing has come down drastically and contact tracing is virtually nil as is prevalent currently. Using a modified SEIRD epidemic simulator we show that (i) Ratio of deaths/hospitalizations from an undetected infection to total deaths converges to a measure of systemic test-trace inefficiency. (ii) This index forecasts the slippage in efficiency earlier than other known metrics. (iii) Mitigation triggered by this index helps reduce peak active caseload and eventual deaths. Deaths/hospitalizations accurately track the systemic inefficiencies and detect latent cases. Based on these results we make a strong case that administrations use this metric in the ensemble of indicators. Further, hospitals may need to be mandated to distinctly register deaths/hospitalizations due to previously undetected infections. Thus the proposed metric is an ideal indicator of an epidemic wave that poses the least socio-economic cost while keeping the surveillance robust during periods of pandemic fatigue.
随着世界各国面临反复的疫情浪潮，监测、减轻和预防后续浪潮变得至关重要。在检测或接触者追踪等系统性效率低下的情况下，像活跃病例数量等常见指标可能不够敏感。阳性检出率对检测策略敏感，无法估计未检出病例的程度。从新发病例的对数估计的繁殖数在动态情况下不准确，并且对效率变化的敏感性不够。系统性疲劳导致检测次数减少、追踪和隔离效率低下，从而引发疫情浪潮的爆发。我们提出了一种新的指标，用于检测基于已知和未知感染导致的死亡/住院人数的检测-追踪效率的下降。这也可用于预测由于检测大幅下降和接触追踪几乎不存在（目前普遍存在这种情况）而导致效率下降导致的疫情提前或加剧。使用改良的 SEIRD 传染病模拟器，我们表明：(i) 未检出感染导致的死亡/住院人数与总死亡人数的比率收敛于系统性检测-追踪效率的衡量标准。(ii) 该指数比其他已知指标更早地预测效率的下降。(iii) 该指数触发的缓解措施有助于减少高峰活跃病例数和最终死亡人数。死亡/住院人数准确跟踪系统性效率低下，并检测潜在病例。基于这些结果，我们强烈建议行政部门在指标组合中使用该指标。此外，可能需要强制医院明确登记因先前未检出的感染而导致的死亡/住院人数。因此，该指标是一种理想的疫情浪潮指标，在大流行疲劳期间保持监测稳健的同时，将社会经济成本降至最低。
