Department of Medicine Nazarbayev University School of Medicine, Nazarbayev University, Astana, 020000, Kazakhstan.
Department of Mathematics, Statistics and Computer Sciences, University of Illinois at Chicago, Chicago, IL, 60607, USA.
Adv Exp Med Biol. 2024;1457:373-384. doi: 10.1007/978-3-031-61939-7_21.
The COVID-19 pandemic is ongoing worldwide, and various case and death numbers are being reported to track its spread. However, the number of actual cases is uncertain due to under-reporting. Using mortality data as a more reliable indicator, this study in Kazakhstan evaluated the extent of under-reporting and under-detection of COVID-19 cases from March 2020 to September 2022 using back-casting and capture-recapture methods. The results indicate that official case reporting in Kazakhstan significantly underestimates the number of infections by at least 50%. The study also suggests that improved testing capabilities may have led to a decrease in the percentage of unreported cases, however, early in the pandemic, Kazakhstan faced significant testing shortages. The study presents a mathematical model based on mortality data that highlights the severe under-reporting of COVID-19 cases in Kazakhstan and argues that understanding the true estimate of actual cases could aid in making informed decisions to end the pandemic.
全球范围内的 COVID-19 疫情仍在持续，为了追踪其传播，各国报告了各种病例和死亡人数。然而，由于漏报，实际病例数量并不确定。本研究利用死亡率数据作为更可靠的指标，通过回溯和捕获-再捕获方法，评估了 2020 年 3 月至 2022 年 9 月期间哈萨克斯坦 COVID-19 病例漏报和漏检的程度。结果表明，官方病例报告严重低估了哈萨克斯坦的感染人数，至少低估了 50%。该研究还表明，检测能力的提高可能导致未报告病例的比例下降，但在疫情早期，哈萨克斯坦面临着严重的检测短缺。该研究提出了一个基于死亡率数据的数学模型，突出了哈萨克斯坦 COVID-19 病例严重漏报的情况，并认为了解实际病例的真实估计可以帮助做出明智的决策，以结束疫情。
