Modelling the adjustment of COVID-19 response and exit from dynamic zero-COVID in China

Abstract

Background Since the initial Wuhan outbreak, China has been containing COVID-19 outbreaks through its "dynamic zero-COVID" policy. Striking a balance between sustainability and cost-benefit, China has recently begun to adjust its COVID-19 response strategies, e.g. by announcing the "20 measures" on 11 November and further the "10 measures" on 7 December 2022. Strategies for safely exiting from dynamic zero-COVID (i.e. without catastrophically overburdening health systems and/or incurring unacceptably excessive morbidity and mortality) are urgently needed. Methods We use simulations to assess the respective and combined effectiveness of fourth-dose heterologous boosting, large-scale antiviral treatment and public health and social measures (PHSMs) that might allow China to further adjust COVID-19 response and exit from zero-COVID safely after 7 December 2022. We also assess whether local health systems can cope with the surge of COVID-19 cases posed by reopening, given that chunyun, a 40-day period with extremely high mobility across China associated with Spring Festival, will begin on 7 January 2023. Findings Reopening against Omicron transmission should be supported by the following interventions: 1) fourth-dose heterologous boosting 30-60 days before reopening by vaccinating 4-8% of the population per week with ≥85% uptake across all ages; 2) timely antiviral treatment with ≥60% coverage; 3) moderate PHSMs to reduce transmissibility by 47-69%. With fourth-dose vaccination coverage of 85% and antiviral coverage of 60%, the cumulative mortality burden would be reduced by 26-35% to 448-503 per million, compared with reopening without any of these interventions. Simultaneously reopening all provinces under current PHSMs would still lead to hospitalization demand that are 1.5-2.5 times of surge hospital capacity (2.2 per 10,000 population per day). Interpretation Although the surge of disease burden posed by reopening in December 2022-January 2023 would likely overload many local health systems across the country, the combined effect of vaccination, antiviral treatment and PHSMs could substantially reduce COVID-19 morbidity and mortality as China transits from dynamic-zero to normality. Planning for such a nationwide, coordinated reopening should be an urgent priority as part of the global exit from the acute phase of the COVID-19 pandemic.

Estimates of COVID-19 deaths in Mainland China after abandoning zero COVID policy

“…Only 25% of people over 80 years old had been vaccinated by January 2022 in Hong Kong (23), while in China as of December 2022 overall vaccination coverage is 92% and 40% of people over 80 years old have received 3 vaccine doses (24). The ability to offer vaccine boosters to all the very high-risk people, the availability of effective treatments and the pragmatic effectiveness of the vaccines used in China (25-27) will further shape the eventual death toll. CoronaVac may be modestly less effective than mRNA vaccines (26,27).…”

“…The ability to offer vaccine boosters to all the very high-risk people, the availability of effective treatments and the pragmatic effectiveness of the vaccines used in China (25-27) will further shape the eventual death toll. CoronaVac may be modestly less effective than mRNA vaccines (26,27).…”

“…Vaccination coverage among the elderly as of end-2022 in China is better than the respective coverage of Hong Kong elderly and LTCF residents in early 2022 (only 25% of people over 80 years old had been vaccinated by January 2022) (21), but not necessarily better than South Korea in early 2022. The ability to offer vaccine boosters to very high-risk people, the availability of effective treatments and the pragmatic effectiveness of the vaccines used in China (22)(23)(24) will further shape the eventual death toll. CoronaVac may be modestly less effective than mRNA vaccines (22,23).…”

Modelling of outbreaks of SARS-CoV-2 Omicron variants after easing Dynamic Zero-COVID strategy in mainland China

“…In Fig s1, force of infection (λ) measures the risk of infection, 13 which is a function of transmission rate (η) and the prevalence of existing infectious individuals (I) among the population (N): λ=η•I/N. The transmission rate η in the discrete-time model can be defined as the average number of new infected individuals generated daily by an infected person.…”

“…15 The transition rate between model's compartments in infectious models is often assumed to be constant, calculated by 1/x, in which "x" is the average period that subjects remain before the transition to the model's next compartments. 13 Therefore, the infectious period in standard SIR or SEIR models is usually assumed to be exponentially distributed, with some limitations of the use of exponentially distributed infectious period. 16 17 In this study, we assumed that the transition probability between model's compartments are based on gamma distributed periods that individuals remain in a compartment.…”