Precise control balances epidemic mitigation and economic growth

The COVID-19 pandemic presented a significant challenge to policymakers globally, requiring a balance between public health measures and economic stability. Various intervention strategies were implemented, including nucleic acid testing and behavioral restrictions like mask-wearing, social distancing, and travel limitations. China's Health Code, a digital system classifying individuals based on health status and travel history, offered a unique approach to epidemic control. The Health Code, using a color-coded QR code (green, yellow, or red), facilitated targeted quarantine measures. While widely adopted, its impact on both public health and economic outcomes remained largely understudied. This research addresses this gap by analyzing the effectiveness of different control strategies—"No control," "Precise control" (using the Health Code), and "Extreme control" (city-wide lockdowns)—in curbing the spread of COVID-19 and their associated economic consequences. The study aims to assess the effectiveness of these strategies, provide short-term and long-term control recommendations based on Health Code data, and determine the threshold of viral contagiousness beyond which "Precise control" becomes ineffective.

Existing literature on COVID-19 control strategies has explored mathematical models to analyze the impact of various interventions. Studies like Yang and Wang's modeling of the Wuhan outbreak and Lemecha and Feyissa's work on optimal control strategies highlight the importance of minimizing exposed and infected populations while considering implementation costs. Research by Memon et al. and Firth et al. focused on the role of quarantine and isolation, contact tracing, and the combination of physical distancing with contact tracing. Nashebi et al. investigated the effects of various non-pharmaceutical interventions, including stay-at-home restrictions and reduced work hours. However, few studies have quantitatively assessed the impact of China's Health Code system itself on both infection rates and economic indicators, particularly regarding its capacity limits and optimal policy implementation.

The study employs an extended SEIR (Susceptible-Exposed-Infected-Recovered) model, termed the SLAIRD model, to simulate the spread of COVID-19. This model incorporates additional compartments to account for asymptomatic transmission, a key characteristic of the Omicron variant, and incorporates parameters for nucleic acid testing rate (r₁) and quarantine rate (r₂). The model was calibrated using real-world data from Shanghai, New York, and Los Angeles, validating its predictive accuracy. Three control strategies were simulated: "No control" (no intervention), "Precise control" (quarantining individuals with red or yellow Health Codes), and "Extreme control" (city-wide lockdown except for essential personnel). The economic analysis involved calculating the Gross Domestic Product (GDP) considering workforce participation and remote work rates. Costs were calculated by considering quarantine costs, medical treatment costs, and nucleic acid testing costs. The study also analyzes optimal resource allocation of nucleic acid tests (NAT), comparing "priority" (targeting individuals with yellow codes) and "random" allocation strategies. Finally, it assesses the limits of "Precise control" by determining the maximum basic reproduction number (R₀) that can be effectively suppressed under the current control strategy.

The "Precise control" strategy using the Health Code demonstrates comparable effectiveness to "Extreme control" in reducing infections and mortality while significantly boosting GDP compared to both "No control" and "Extreme control." Specifically, "Precise control" reduced deaths by 97% and increased GDP by 1% compared to "No control," while the costs were only 43% of those associated with "Extreme control." However, the study identifies a critical threshold: the effectiveness of "Precise control" diminishes when the R₀ value surpasses 16.5. The analysis of resource allocation reveals that prioritizing nucleic acid tests for high-risk individuals (yellow codes) significantly improves the detection rate. The study also reveals that switching from "Extreme control" to "Precise control" at the appropriate time (when the percentage of abnormal codes reaches 0.19%) avoids a resurgence of cases. This precise timing is crucial, as premature switching risks another peak, while delayed switching significantly impacts economic activity. Furthermore, the study finds that r2 (quarantine rate) is more effective in suppressing the epidemic than r1 (nucleic acid detection rate). The model's robustness was tested by varying parameters and simulating scenarios with NAT errors and Health Code assignment delays. These tests confirmed that while errors and delays increase the number of infected people, the control strategies still perform far better than "No control."

The findings underscore the importance of targeted, data-driven interventions in balancing public health and economic concerns during a pandemic. The Health Code system, when employed strategically through a "Precise control" approach, proves highly effective in mitigating the epidemic's impact while minimizing economic disruption. The identification of the R₀ threshold (16.5) provides crucial guidance for policymakers, signaling the point where targeted interventions become less effective, necessitating a shift to alternative control measures. The study’s emphasis on optimizing resource allocation through prioritized NAT testing highlights the importance of efficient resource management in public health crises. The optimal timing of transitioning from "Extreme control" to "Precise control" is vital to successfully containing the epidemic without prolonged economic repercussions.

This study demonstrates the effectiveness of China's Health Code system in balancing epidemic control and economic stability during the COVID-19 pandemic. The "Precise control" strategy, leveraging the Health Code for targeted quarantines, offers a superior alternative to both "No control" and "Extreme control." However, its effectiveness is limited by the contagiousness of the virus, with an R₀ threshold of 16.5 identified. Future research could explore the adaptability of this model to other infectious diseases and examine the long-term societal impact of digital health tracking systems. Further research should also focus on enhancing the privacy protections afforded by Health Code-like systems.

The study's limitations include the simplified nature of the epidemic model, which does not account for individual contact tracing and spatio-temporal variations in transmission probabilities. The use of simplified parameter values from existing literature might introduce some degree of inaccuracy into the precise quantitative results. Furthermore, the model focuses on the overall trends rather than highly precise values. Finally, the inherent privacy concerns associated with the collection and use of personal information in the Health Code system remain a relevant consideration.