Immediate and long-term changes in infectious diseases in China at the "First-level-response", "Normalized-control" and "Dynamic-COVID-zero" stages from 2020 to 2022: a multistage interrupted-time-series-analysis.

BACKGROUND: From January 2020 to December 2022, China implemented "First-level-response", "Normalized-control" and "Dynamic-COVID-zero" to block the COVID-19 epidemic; however, the immediate and long-term impact of three strategies on other infectious diseases and the difference in their impact is currently unknown. We aim to provide a more comprehensive understanding of the impact of non-pharmacological interventions (NPIs) on infectious diseases in China. METHODS: We collected data on the monthly case count of infectious diseases in China from January 2015 to July 2022. After considering long-term trends using the Cox-Stuart test, we performed the two ratio Z tests to preliminary analyze the impact of three strategies on infectious diseases. Next, we used a multistage interrupted-time-series analysis fitted by the Poisson regression to evaluate and compare the immediate and long-term impact of three strategies on infectious diseases in China. RESULTS: Compared to before COVID-19, the incidence of almost all infectious diseases decreased immediately at stages 1, 2, and 3; meanwhile, the slope in the incidence of many infectious diseases also decreased at the three stages. However, the slope in the incidence of all sexually transmitted diseases increased at stage 1, the slope in the incidence of all gastrointestinal infectious diseases increased at stage 2, and the slope in the incidence of some diseases such as pertussis, influenza, and brucellosis increased at stage 3. The immediate and long-term limiting effects of "Normalized-control" on respiratory-transmitted diseases were weaker than "First-level-response" and the long-term limiting effects of "Dynamic-COVID-zero" on pertussis, influenza, and hydatid disease were weaker than "Normalized-control". CONCLUSIONS: Three COVID-19 control strategies in China have immediate and long-term limiting effects on many infectious diseases, but there are differences in their limiting effects. Evidence from this study shows that pertussis, influenza, brucellosis, and hydatid disease began to recover at stage 3, and relaxation of NPIs may lead to the resurgence of respiratory-transmitted diseases and vector-borne diseases.

Undoing elimination: Modelling Australia's way out of the COVID-19 pandemic.

In the middle of 2020, with its borders tightly closed to the rest of the world, Australia almost achieved the local elimination of COVID-19 and subsequently maintained 'COVID-zero' in most parts of the country for the following year. Australia has since faced the relatively unique challenge of deliberately 'undoing' these achievements by progressively easing restrictions and reopening. Exploring the role of mathematical modelling in navigating a course through the pandemic through qualitative interviews with modellers and others working closely with modelling, we argue that each of these two significant phases of Australia's COVID-19 experience can be understood as distinct forms of 'model society'. This refers at once to the society enacted through the governance of risk, and to the visions of societal outcomes - whether to be sought or to be avoided - that are offered up by models. Each of the two model societies came about through a reflexive engagement with risk facilitated by models, and the iterative relationship between the representations of society enacted within models and the possibilities that these representations generate in the material world beyond them.

A model simulation on the SARS-CoV-2 Omicron variant containment in Beijing, China.

Objective: The Omicron variant of SARS-COV-2 is replacing previously circulating variants around the world in 2022. Sporadic outbreaks of the Omicron variant into China have posed a concern how to properly response to battle against evolving coronavirus disease 2019 (COVID-19). Methods: Based on the epidemic data from website announced by Beijing Center for Disease Control and Prevention for the recent outbreak in Beijing from April 22nd to June 8th in 2022, we developed a modified SEPIR model to mathematically simulate the customized dynamic COVID-zero strategy and project transmissions of the Omicron epidemic. To demonstrate the effectiveness of dynamic-changing policies deployment during this outbreak control, we modified the transmission rate into four parts according to policy-changing dates as April 22nd to May 2nd, May 3rd to 11st, May 12th to 21st, May 22nd to June 8th, and we adopted Markov chain Monte Carlo (MCMC) to estimate different transmission rate. Then we altered the timing and scaling of these measures used to understand the effectiveness of these policies on the Omicron variant. Results: The estimated effective reproduction number of four parts were 1.75 (95% CI 1.66-1.85), 0.89 (95% CI 0.79-0.99), 1.15 (95% CI 1.05-1.26) and 0.53 (95% CI 0.48 -0.60), respectively.  In the experiment, we found that till June 8th the cumulative cases would rise to 132,609 (95% CI 59,667-250,639), 73.39 times of observed cumulative cases number 1,807 if no policy were implemented on May 3rd, and would be 3,235 (95% CI 1,909 - 4,954), increased by 79.03% if no policy were implemented on May 22nd. A 3-day delay of the implementation of policies would led to increase of cumulative cases by 58.28% and a 7-day delay would led to increase of cumulative cases by 187.00%. On the other hand, taking control measures 3 or 7 days in advance would result in merely 38.63% or 68.62% reduction of real cumulative cases. And if lockdown implemented 3 days before May 3rd, the cumulative cases would be 289 (95% CI 211-378), reduced by 84%, and the cumulative cases would be 853 (95% CI 578-1,183), reduced by 52.79% if lockdown implemented 3 days after May 3rd. Conclusion: The dynamic COVID-zero strategy might be able to effectively minimize the scale of the transmission, shorten the epidemic period and reduce the total number of infections.

Pandemic modelling for regions implementing an elimination strategy.

During the COVID-19 pandemic, some countries, such as Australia, China, Iceland, New Zealand, Thailand, and Vietnam successfully implemented an elimination strategy, enacting strict border control and periods of lockdowns to end community transmission. Atlantic Canada and Canada's territories implemented similar policies, and reported long periods with no community cases. In Newfoundland and Labrador (NL), Nova Scotia, and Prince Edward Island a median of 80% or more of daily reported cases were travel-related from July 1, 2020 to May 31, 2021. With increasing vaccination coverage, it may be appropriate to exit an elimination strategy, but most existing epidemiological frameworks are applicable only to situations where most cases occur in the community, and are not appropriate for regions that have implemented an elimination strategy. To inform the pandemic response in regions that are implementing an elimination strategy, we extend importation modelling to consider post-arrival travel restrictions, and pharmaceutical and non-pharmaceutical interventions in the local community. We find that shortly after the Omicron variant had begun spreading in Canada, the expected daily number of spillovers, infections spread to NL community members from travellers and their close contacts, was higher than any time previously in the pandemic. By December 24, 2021, the expected number of spillovers was 44% higher than the previous high, which occurred in late July 2021 shortly after travel restrictions were first relaxed. We develop a method to assess the characteristics of potential future community outbreaks in regions that are implementing an elimination strategy. We apply this method to predict the effect of variant and vaccination coverage on the size of hypothetical community outbreaks in Mount Pearl, a suburb of the St. John's metropolitan area in NL. Our methodology can be used to evaluate alternative plans to relax public health restrictions when vaccine coverage is high in regions that have implemented an elimination strategy. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Pandemic control - do's and don'ts from a control theory perspective.

Managing a pandemic is a difficult task. Pandemics are part of the dynamics of nonlinear systems with multiple different interactive features that co-adapt to each other (such as humans, animals, and pathogens). The target of controlling such a nonlinear system is best achieved using the control system theory developed in engineering and applied in systems biology. But is this theory and its principles actually used in controlling the current coronavirus disease-19 pandemic? We review the evidence for applying principles in different aspects of pandemic control related to different goals such as disease eradication, disease containment, and short- or long-term economic loss minimization. Successful policies implement multiple measures in concordance with control theory to achieve a robust response. In contrast, unsuccessful policies have numerous failures in different measures or focus only on a single measure (only testing, vaccines, etc.). Successful approaches rely on predictions instead of reactions to compensate for the costs of time delay, on knowledge-based analysis instead of trial-and-error, to control complex nonlinear systems, and on risk assessment instead of waiting for more evidence. Iran is an example of the effects of delayed response due to waiting for evidence to arrive instead of a proper risk analytical approach. New Zealand, Australia, and China are examples of appropriate application of basic control theoretic principles and focusing on long-term adaptive strategies, updating measures with the evolution of the pandemic.

The role of vaccines in COVID-19 control strategies in Singapore and China.

Objectives: In this article, we critically review the development and implementation of COVID-19 vaccination in Singapore and China during the pandemic. Methods: We collect and analyze data from a range of sources, including scholarly articles, statistics and documents from national governments in the two countries, and reports from international organizations. Results: There are important differences in the two countries' approaches to the evolving pandemic, and thus the roles that COVID-19 vaccination plays in the overall response strategies in these two countries. Conclusions: Whereas Singapore adopted a "living with the virus" strategy, China continued to pursue a COVID-zero strategy. The overall COVID-19 response strategy of Singapore was largely shared by many countries in the world, while that of China was more unique and hardly imitated elsewhere. Nevertheless, vaccination played a significant role in both countries' responses to the pandemic. A comparison and contrast between the vaccination processes in these two countries thus shed important light on the drivers and outcomes of COVID-19 vaccination in different settings.