Retrospective Modeling of the Omicron Epidemic in Shanghai, China: Exploring the Timing and Performance of Control Measures

Contact patterns between index patients and their close contacts and assessing risk for COVID-19 transmission during different exposure time windows: a large retrospective observational study of 450 770 close contacts in Shanghai

ABSTRACT

Introduction

To characterise age-mixing patterns among index cases and contacts of COVID-19, and explore when patients are most infectious during the disease process.

Methods

This study examined all initial 90 885 confirmed index cases in Shanghai and their 450 770 close contacts. A generalised additive mixed model was used to analyse the associations of the number of close contacts with different demographic and clinical characteristics. The effect of different exposure time windows on the infection of close contacts was evaluated using a modified mixed-effects Poisson regression.

Results

Analysis of contacts indicated that 82 467 (18.29%; 95% CI 18.17%, 18.42%) were second-generation cases. Our result indicated the q-index was 0.300 (95% CI 0.298, 0.302) for overall contact matrix, and that assortativity was greatest for students (q-index=0.377; 95% CI 0.357, 0.396) and weakest for people working age not in the labour force (q-index=0.246; 95% CI 0.240, 0.252). The number of contacts was 4.96 individuals per index case (95% CI 4.86, 5.06). Contacts had a higher risk if they were exposed from 1 day before to 3 days after the onset of symptoms in the index patient, with a maximum at day 0 (adjusted relative risk (aRR)=1.52; 95% CI 1.30, 1.76). Contacts exposed from 3 days before to 3 days after an asymptomatic index case had a positive reverse transcriptase-PCR (RT-PCR) result had a higher risk, with a maximum on day 0 (aRR=1.48; 95% CI 1.37, 1.59).

Conclusions

The greatest assortativity was for students and weakest for people working age not in the labour force. Contact in the household was a significant contributor to the infection of close contacts. Contact tracing should focus on individuals who had contact soon before or soon after the onset of symptoms (or positive RT-PCR test) in the index case.

Public acceptance of COVID-19 control measures and associated factors during Omicron-dominant period in China: a cross-sectional survey

OBJECTIVES

This study aims to evaluate the public acceptance of coronavirus disease 2019 (COVID-19) control measures during the Omicron-dominant period and its associated factors.

METHODS

A cross-sectional design was conducted and 1391 study participants were openly recruited to participate in the questionnaire survey. Logistic regression model was performed to assess the association between the public acceptance and potential factors more specifically.

RESULTS

By August 26, 2022, 58.9% of the study participants were less acceptive of the control measures while 41.1% expressed higher acceptance. Factors associated with lower acceptance included young age, such as < 18 (OR = 8.251, 95% CI: 2.009 to 33.889) and 18-29 (OR = 2.349, 95% CI: 1.564 to 3.529), and household per capita monthly income lower than 5000 yuan (OR = 1.512, 95% CI: 1.085 to 2.105). Furthermore, individuals who perceived that the case fatality rate (CFR) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was very low (OR = 6.010, 95% CI: 2.475 to 14.595) and that the restrictions could be eased once the CFR dropped to 2-3 times of the influenza (OR = 2.792, 95% CI: 1.939 to 4.023) showed greater oppositional attitudes. Likewise, respondents who were dissatisfied with control measures (OR = 9.639, 95% CI: 4.425 to 20.998) or preferred fully relaxation as soon as possible (OR = 13.571, 95% CI: 7.751 to 23.758) had even lower acceptability. By contrast, rural residents (OR = 0.683, 95% CI: 0.473 to 0.987), students (OR = 0.510, 95% CI: 0.276 to 0.941), public (OR = 0.417, 95% CI: 0.240 to 0.727) and private (OR = 0.562, 95% CI: 0.320 to 0.986) employees, and vaccinated participants (OR = 0.393, 95% CI: 0.204 to 0.756) were more compliant with control measures.

CONCLUSION

More than half of the Chinese public were less supportive of COVID-19 control measures during Omicron-dominant period, which varied based on their different demographic characteristics, cognition and overall attitude towards SARS-CoV-2 infection. Control measures that struck a balance between public safety and individual freedom would be more acceptable during the pandemic.

An Explanation for Reports of Increased Prevalence of Olfactory Dysfunction With Omicron: Asymptomatic Infections

The prevalence of olfactory dysfunction (OD) in people infected with the Omicron variant is substantially reduced compared with previous variants. However, 4 recent studies reported a greatly increased prevalence of OD with Omicron. We provide a likely explanation for these outlier studies and reveal a major methodological flaw. When the proportion of asymptomatic infections is large, studies on the prevalence of OD will examine and report predominantly on nonrepresentative cohorts, those with symptomatic subjects, thereby artificially inflating the prevalence of OD by up to 10-fold. Estimation of the true OD prevalence requires representative cohorts that include relevant fractions of asymptomatic cases.

Understanding the implications of under-reporting, vaccine efficiency and social behavior on the post-pandemic spread using physics informed neural networks: A case study of China

In late 2019, the emergence of COVID-19 in Wuhan, China, led to the implementation of stringent measures forming the zero-COVID policy aimed at eliminating transmission. Zero-COVID policy basically aimed at completely eliminating the transmission of COVID-19. However, the relaxation of this policy in late 2022 reportedly resulted in a rapid surge of COVID-19 cases. The aim of this work is to investigate the factors contributing to this outbreak using a new SEIR-type epidemic model with time-dependent level of immunity. Our model incorporates a time-dependent level of immunity considering vaccine doses administered and time-post-vaccination dependent vaccine efficacy. We find that vaccine efficacy plays a significant role in determining the outbreak size and maximum number of daily infected. Additionally, our model considers under-reporting in daily cases and deaths, revealing their combined effects on the outbreak magnitude. We also introduce a novel Physics Informed Neural Networks (PINNs) approach which is extremely useful in estimating critical parameters and helps in evaluating the predictive capability of our model.

Response Strategies for Emerging Infectious Diseases: More Efforts Are Needed

In recent years, emerging infectious disease outbreaks have placed significant health and socioeconomic burdens upon the population [...].

Lessons drawn from Shanghai for controlling highly transmissible SARS-CoV-2 variants: insights from a modelling study

BACKGROUND

The continuous emergence of novel SARS-CoV-2 variants with markedly increased transmissibility presents major challenges to the zero-COVID policy in China. It is critical to adjust aspects of the policy about non-pharmaceutical interventions (NPIs) by searching for and implementing more effective ways. We use a mathematical model to mimic the epidemic pattern of the Omicron variant in Shanghai to quantitatively show the control challenges and investigate the feasibility of different control patterns in avoiding other epidemic waves.

METHODS

We initially construct a dynamic model with a core step-by-step release strategy to reveal its role in controlling the spread of COVID-19, including the city-based pattern and the district-based pattern. We used the least squares method and real reported case data to fit the model for Shanghai and its 16 districts, respectively. Optimal control theory was utilized to explore the quantitative and optimal solutions of the time-varying control strength (i.e., contact rate) to suppress the highly transmissible SARS-CoV-2 variants.

RESULTS

The necessary period for reaching the zero-COVID goal can be nearly 4 months, and the final epidemic size was 629,625 (95%CI: [608,049, 651,201]). By adopting the city-based pattern, 7 out of 16 strategies released the NPIs more or earlier than the baseline and ensured a zero-resurgence risk at the average cost of 10 to 129 more cases in June. By adopting the district-based pattern, a regional linked release can allow resumption of social activity to ~ 100% in the boundary-region group about 14 days earlier and allow people to flow between different districts without causing infection resurgence. Optimal solutions of the contact rate were obtained with various testing intensities, and higher diagnosis rate correlated with higher optimal contact rate while the number of daily reported cases remained almost unchanged.

CONCLUSIONS

Shanghai could have been bolder and more flexible in unleashing social activity than they did. The boundary-region group should be relaxed earlier and more attention should be paid to the centre-region group. With a more intensive testing strategy, people could return to normal life as much as possible but still ensure the epidemic was maintained at a relatively low level.

Epi-DNNs: Epidemiological priors informed deep neural networks for modeling COVID-19 dynamics

Differential equations-based epidemic compartmental models and deep neural networks-based artificial intelligence (AI) models are powerful tools for analyzing and fighting the transmission of COVID-19. However, the capability of compartmental models is limited by the challenges of parameter estimation, while AI models fail to discover the evolutionary pattern of COVID-19 and lack explainability. This paper aims to provide a novel method (called Epi-DNNs) by integrating compartmental models and deep neural networks (DNNs) to model the complex dynamics of COVID-19. In the proposed Epi-DNNs method, the neural network is designed to express the unknown parameters in the compartmental model and the Runge-Kutta method is implemented to solve the ordinary differential equations (ODEs) so as to give the values of the ODEs at a given time. Specifically, the discrepancy between predictions and observations is incorporated into the loss function, then the defined loss is minimized and applied to identify the best-fitted parameters governing the compartmental model. Furthermore, we verify the performance of Epi-DNNs on the real-world reported COVID-19 data on the Omicron epidemic in Shanghai covering February 25 to May 27, 2022. The experimental findings on the synthesized data have revealed its effectiveness in COVID-19 transmission modeling. Moreover, the inferred parameters from the proposed Epi-DNNs method yield a predictive compartmental model, which can serve to forecast future dynamics.

The effectiveness of control measures during the 2022 COVID-19 outbreak in Shanghai, China

BACKGROUND

In March 2022, the Omicron variant of SARS-CoV-2 spread rapidly in Shanghai, China. The city adopted strict non-pharmacological intervention (NPI) measures, including lockdown (implemented on March 28 in Pudong and April 1 in Puxi) and blanket PCR testing (April 4). This study aims to understand the effect of these measures.

METHODS

We tabulated daily case counts from official reports and fitted a two-patch stochastic SEIR model to the data for the period of March 19 to April 21. This model considered two regions in Shanghai, namely Pudong and Puxi, as the implementation of control measures in Shanghai was carried out on different dates in these regions. We verified our fitting results using the data from April 22 to June 26. Finally, we applied the point estimate of parameter values to simulate our model while varying the dates of control measure implementation, and studied the effectiveness of the control measures.

RESULTS

Our point estimate for the parameter values yields expected case counts that agree well the data for both the periods from March 19 to April 21 and from April 22 to June 26. Lockdown did not significantly reduce the intra-region transmission rates. Only about 21% cases were reported. The underlying basic reproduction number R0 was 1.7, and the control reproduction number with both lockdown and blanket PCR testing was 1.3. If both measures were implemented on March 19, only about 5.9% infections would be prevented.

CONCLUSIONS

Through our analysis, we found that NPI measures implemented in Shanghai were not sufficient to reduce the reproduction number to below unity. Thus, earlier intervention only has limited effect on reducing cases. The outbreak dies out because of only 27% of the population were active in disease transmission, possibly due to a combination of vaccination and lockdown.