Despite the consistent recommendation to scale-up the testing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), comprehensive analysis on determining the desirable testing capacity (TC) is limited. This study aims to investigate the daily TC and the percentage of positive cases over the tested population (PPCTP) to evaluate the novel coronavirus disease 2019 (COVID-19) trajectory phase and generate benchmarks on desirable TC. Data were retrieved from government facilities, including 101 countries and 55 areas in the USA. We have divided the pandemic situations of investigated areas into four phases, i.e., low-level, suppressing, widespread, or uncertain transmission phase. Findings indicate each country should increase TC to roughly two tests per thousand people each day. Additionally, based on TC, a susceptible-unconfirmed-confirmed-recovered (SUCR) model, which can capture the dynamic growth of confirmed cases and estimate the group size of unconfirmed cases in a country or area, is proposed. We examined our proposed SUCR model for 55 areas in the USA. Results show that the SUCR model can accurately capture the dynamic growth of confirmed cases in each area. By increasing TC by five times and applying strict control measures, the total number of COVID-19 patients would reduce to 33%.

尽管一致建议扩大严重急性呼吸系统综合症冠状病毒 2 (SARS-CoV-2) 的检测，但确定理想检测能力 ( TC ) 的综合分析仍然有限。本研究旨在调查每日TC和阳性病例占测试人群的百分比 ( PPCTP )，以评估新型冠状病毒病 2019 (COVID-19) 的轨迹阶段并生成理想TC的基准. 数据取自政府设施，包括美国的 101 个国家和 55 个地区。我们将调查地区的疫情分为四个阶段，即低级别、抑制阶段、广泛传播阶段和不确定传播阶段。调查结果表明，每个国家/地区都应将 TC 增加到每天每千人大约两次测试。此外，基于TC，提出了一种易感-未确诊-确诊-恢复（SUCR）模型，该模型可以捕捉确诊病例的动态增长并估计一个国家或地区未确诊病例的群体规模。我们针对美国的 55 个地区检查了我们提出的 SUCR 模型。结果表明，SUCR模型能够准确捕捉到各地区确诊病例的动态增长情况。通过增加TC增加五倍并采取严格的控制措施，COVID-19 患者总数将减少到 33%。