[Paper Review] Quantitative assessment of the role of undocumented infection in the 2019 novel coronavirus (COVID-19) pandemic

Yongshang Long, Zheng-Meng Zhai+10 more|arXiv (Cornell University)|Mar 26, 2020

COVID-19 epidemiological studies2 references19 citations

TL;DR

This study develops a five-state epidemiological model to quantitatively assess the impact of undocumented infections on the COVID-19 pandemic, using real-time data from China, South Korea, Italy, and Iran. The model reveals that undetected asymptomatic carriers can sustain transmission even after apparent epidemic control, increasing the risk of future outbreaks if public health measures are relaxed, highlighting the need for universal testing to identify hidden cases.

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 for Italy suggests that insufficient government action leading to a large fraction of undocumented infection plays an important role in the abnormally high mortality in that country. With the data currently available from United Kingdom, our model predicts catastrophic epidemic scenarios in the country if the government did not impose strict travel and social distancing restrictions. A key finding is that, if the percentage of undocumented infection exceeds a threshold, a non-negligible hidden population can exist even after the the epidemic has been deemed over, implying the likelihood of future outbreaks should the currently imposed strict government actions be relaxed. This could make COVID-19 evolving into a long-term epidemic or a community disease a real possibility, suggesting the necessity to conduct universal testing and monitoring to identify the hidden individuals.

Motivation & Objective

• To quantify the role of undocumented infections in sustaining SARS-CoV-2 transmission during the early pandemic.
• To assess how varying testing and diagnostic policies across countries affect epidemic predictions.
• To evaluate the risk of future outbreaks due to hidden, asymptomatic carriers after apparent epidemic control.
• To provide a predictive model for real-time assessment of epidemic trends under different government interventions.
• To inform public health policy by identifying the threshold at which undocumented infections become a major driver of sustained transmission.

Proposed method

• A five-state compartmental model is developed to track susceptible, exposed, symptomatic, asymptomatic (undocumented), and recovered individuals.
• Key parameters such as transmission rates and incubation periods are calibrated using official government reports and data from China, South Korea, Italy, and Iran.
• Mathematical optimization is applied to fit model outputs to observed cumulative case data, enhancing predictive accuracy.
• The model incorporates time-varying government interventions, including travel restrictions and social distancing, as dynamic inputs.
• Sensitivity analysis is performed to assess robustness of predictions under varying testing standards and policy responses.
• The model predicts the inflection point of the epidemic curve and evaluates the persistence of hidden infections post-peak.

Experimental results

Research questions

• RQ1 What is the quantitative contribution of undocumented infections to the overall transmission dynamics of SARS-CoV-2?
• RQ2 How do differences in national testing and diagnostic policies affect the estimation of key epidemic parameters?
• RQ3 At what threshold does the proportion of undocumented infections lead to a persistent hidden reservoir of infection?
• RQ4 Can the model reliably predict the inflection point and epidemic peak despite variations in data quality and policy implementation?
• RQ5 To what extent can hidden asymptomatic carriers sustain transmission after apparent epidemic control, posing a risk of future outbreaks?

Key findings

• The model accurately predicts the daily cumulative number of confirmed cases in China, South Korea, Italy, and Iran, demonstrating strong predictive power.
• Despite large variations in testing policies, the model's prediction of the epidemic inflection point remains robust and reliable across countries.
• In Italy, insufficient government action and high levels of undocumented infection are identified as key contributors to the country’s unusually high mortality rate.
• The model predicts catastrophic epidemic scenarios in the UK if strict travel and social distancing measures were not enforced.
• If the percentage of undocumented infections exceeds a critical threshold, a non-negligible hidden population of infectious individuals can persist even after the epidemic appears to be over.
• The study provides quantitative evidence that COVID-19 may evolve into a long-term or endemic disease due to persistent hidden transmission, necessitating universal testing to identify and isolate asymptomatic carriers.