Objective: To explore the feasible and cost-effective intervention strategies to achieve the goal of dynamic COVID-Zero in China.
Design: A Susceptible-Exposed-Infectious-Recovered model combined economic evaluation was used to generate the number of infections, the time for dynamic COVID-Zero and calculate cost-effectiveness under different intervention strategies. The model simulated the 1 year spread of COVID-19 in mainland China after 100 initial infections were imported.
Interventions: According to close contact tracing degree from 80% to 100%, close contact tracing time from 2 days to 1 day, isolation time from 14 days to 7 days, scope of nucleic acid testing (NAT) from 10% to 100% and NAT frequency from weekly to every day, 720 scenarios were simulated.
Outcome Measure: Cumulative number of infections (CI), social COVID-Zero duration (SCD), total cost (TC) and incremental cost-effectiveness ratio.
Results: 205 of 720 scenarios could achieve the total COVID-Zero since the first case was reported. The fastest and most cost-effective strategy was Scenario 680, in which all close contacts were traced within 1 day, the isolation time was 14 days and 10% of the national population was randomly checked for NAT every day. In Scenario 680, the CI was 280 (100 initial infections) and the SCD was 13 days. The TC was ¥4126 hundred million and the cost of reducing one infection was ¥47 470. However, when the close contact tracing time was 2 days and the degree of close contact tracing was 80%-90%, the SCD would double to 24-101 days and the TCs increased by ¥16 505 to 37 134 hundred million compared with Scenario 680.
Conclusions: If all close contact was controlled within 1 day, the rapid social COVID-Zero can be achieved effectively and cost-effectively. Therefore, the future prevention and control of emerging respiratory infectious diseases can focus on enhancing the ability of close contact tracing.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401205 | PMC |
http://dx.doi.org/10.1136/bmjopen-2022-067294 | DOI Listing |
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