Quantitative assessment of the role of undocumented infection in the 2019 novel coronavirus (COVID-19) pandemic
Yong-Shang Long, Zheng-Meng Zhai, Li-Lei Han, Jie Kang, Yi-Lin Li,, Zhao-Hua Lin, Lang Zeng, Da-Yu Wu, Chang-Qing Hao, Ming Tang, Zonghua Liu,, and Ying-Cheng Lai

TL;DR
This study develops a five-state model to quantify the impact of undocumented COVID-19 infections on the pandemic's progression, highlighting the importance of testing and strict policies to prevent long-term outbreaks.
Contribution
The paper introduces a novel five-state model that accurately predicts COVID-19 spread and assesses the role of undocumented infections across different countries.
Findings
Undocumented infections significantly influence COVID-19 spread.
Insufficient testing leads to large hidden populations and prolonged outbreaks.
Strict government measures can prevent future hidden outbreaks.
Abstract
An urgent problem in controlling COVID-19 spreading is to understand the role of undocumented infection. We develop a five-state model for COVID-19, taking into account the unique features of the novel coronavirus, with key parameters determined by the government reports and mathematical optimization. Tests using data from China, South Korea, Italy, and Iran indicate that the model is capable of generating accurate prediction of the daily accumulated number of confirmed cases and is entirely suitable for real-time prediction. The drastically disparate testing and diagnostic standards/policies among different countries lead to large variations in the estimated parameter values such as the duration of the outbreak, but such uncertainties have little effect on the occurrence time of the inflection point as predicted by the model, indicating its reliability and robustness. Model prediction…
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Taxonomy
TopicsCOVID-19 epidemiological studies · COVID-19 Pandemic Impacts · COVID-19 diagnosis using AI
