Heterogeneity and effectiveness analysis of COVID-19 prevention and control in major cities in China through time-varying reproduction number estimation

Studying the impacts of variant evolution for a generalized age-group transmission model

The differences of SARS-CoV-2 variants brought the changes of transmission characteristics and clinical manifestations during the prevalence of COVID-19. In order to explore the evolution mechanisms of SARS-CoV-2 variants and the impacts of variant evolution, the classic SIR (Susceptible-Infected-Recovered) compartment model was modified to a generalized SVEIR (Susceptible-Vaccinated-Exposed-Infected-Recovered) compartment model with age-group and varying variants in this study. By using of the SVEIR model and least squares method, the optimal fittings against the surveillance data from Fujian Provincial Center for Disease Control and Prevention were performed for the five epidemics of Fujian Province. The main epidemiological characteristics such as basic reproduction number, effective reproduction number, sensitivity analysis, and cross-variant scenario investigations were extensively investigated during dynamic zero-COVID policy. The study results showed that the infectivities of the variants became fast from wild strain to the Delta variant, further to the Omicron variant. Meanwhile, the cross-variant investigations showed that the average incubation periods were shortened, and that the infection scales quickly enhanced. Further, the risk estimations with the new variants were performed without implements of the non-pharmaceutical interventions, based on the dominant variants XBB.1.9.1 and EG.5. The results of the risk estimations suggested that non-pharmaceutical interventions were necessary on the Chinese mainland for controlling severe infections and deaths, and also that the regular variant monitors were still workable against the aggressive variant evolution and the emergency of new transmission risks in the future.

D-dimer trends elaborate the heterogeneity of risk in hospitalized patients with COVID-19: A multi-national case series from different waves

Introduction

Variable D-dimer trends during hospitalization reportedly result in distinct in-hospital mortality. In this multinational case series from the first and second waves, we show the universality of such D-dimer trends.

Methods

We reviewed 405 patients with COVID-19 during the first wave admitted to three institutions in the United States, Italy, and Colombia, and 111 patients admitted to the U.S. site during the second wave and 55 patients during the third wave. D-dimer was serially followed during hospitalization.

Results

During the first wave, 66 (15%) patients had a persistently-low pattern, 33 (8%) had early-peaking, 70 (16%) had mid-peaking, 94 (22%) had fluctuating, 30 (7%) had late-peaking, and 112 (26%) had a persistently-high pattern. During the second and third waves, similar patterns were observed. D-dimer patterns were significantly different in terms of in-hospital mortality similarly in all waves. Patterns were then classified into low-risk patterns (persistently-low and early-peaking), where no deaths were observed in both waves, high-risk patterns (mid-peaking and fluctuating), and malignant patterns (late-peaking and persistently-high). Overall, D-dimer trends were associated with an increased risk for in-hospital mortality in the first wave (overall: HR: 1.73) and stayed the same during the second (HR: 1.67, p < 0.001) and the third (HR: 4.4, p = 0.001) waves.

Conclusion

D-dimer behavior during COVID-19 hospitalization yielded universal categories with distinct mortality risks that persisted throughout all studied waves of infection. Monitoring D-dimer behavior may be useful in the management of these patients.

The Association between Occupational Stress and Mental Health among Chinese Soccer Referees in the Early Stage of Reopening Soccer Matches during the COVID-19 Pandemic Outbreak: A Moderated Mediation Model

BACKGROUND

The sudden and unpredictable changes caused by the COVID-19 pandemic are a serious threat to the occupational stress and mental health of referees worldwide, which has not attracted widespread attention. The mental health of football referees has a certain influence on their job satisfaction or the accuracy of judgments.

METHODS

This study constructed a moderated mediation model to explore the buffer factors between occupational stress and mental health in Chinese soccer referees in the early stage of reopening soccer matches during the COVID-19 pandemic outbreak. Data from 317 Chinese soccer referees (aged 19-45) were collected through an online questionnaire in September-October 2022. Occupational stress, mental health, job burnout and perceived social support were measured, and moderated mediation model was analyzed.

RESULTS

The results of this study showed that occupational stress was negatively correlated with mental health through the mediating effect of job burnout and the moderated effect of perceived social support after controlling for demographic variables. Specifically, the association between occupational stress and mental health was weaker when perceived social support was higher and stronger.

CONCLUSIONS

The results demonstrate that job burnout and perceived social support played important roles in buffering the negative effects of occupational stress on the mental health of Chinese soccer referees in the early stage of reopening soccer matches during the COVID-19 pandemic outbreak. The findings provide implications for mental health interventions in soccer referees during the public health crises.

Heterogeneity in the onwards transmission risk between local and imported cases affects practical estimates of the time-dependent reproduction number

During infectious disease outbreaks, inference of summary statistics characterizing transmission is essential for planning interventions. An important metric is the time-dependent reproduction number (Rt), which represents the expected number of secondary cases generated by each infected individual over the course of their infectious period. The value of Rt varies during an outbreak due to factors such as varying population immunity and changes to interventions, including those that affect individuals' contact networks. While it is possible to estimate a single population-wide Rt, this may belie differences in transmission between subgroups within the population. Here, we explore the effects of this heterogeneity on Rt estimates. Specifically, we consider two groups of infected hosts: those infected outside the local population (imported cases), and those infected locally (local cases). We use a Bayesian approach to estimate Rt, made available for others to use via an online tool, that accounts for differences in the onwards transmission risk from individuals in these groups. Using COVID-19 data from different regions worldwide, we show that different assumptions about the relative transmission risk between imported and local cases affect Rt estimates significantly, with implications for interventions. This highlights the need to collect data during outbreaks describing heterogeneities in transmission between different infected hosts, and to account for these heterogeneities in methods used to estimate Rt. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Importation, Local Transmission, and Model Selection in Estimating the Transmissibility of COVID-19: The Outbreak in Shaanxi Province of China as a Case Study

Background: Since the emergence of the COVID-19 pandemic, many models have been applied to understand its epidemiological characteristics. However, the ways in which outbreak data were used in some models are problematic, for example, importation was mixed up with local transmission. Methods: In this study, five models were proposed for the early Shaanxi outbreak in China. We demonstrated how to select a reasonable model and correctly use the outbreak data. Bayesian inference was used to obtain parameter estimates. Results: Model comparison showed that the renewal equation model generates the best model fitting and the Susceptible-Exposed-Diseased-Asymptomatic-Recovered (SEDAR) model is the worst; the performance of the SEEDAR model, which divides the exposure into two stages and includes the pre-symptomatic transmission, and SEEDDAAR model, which further divides infectious classes into two equally, lies in between. The Richards growth model is invalidated by its continuously increasing prediction. By separating continuous importation from local transmission, the basic reproduction number of COVID-19 in Shaanxi province ranges from 0.45 to 0.61, well below the unit, implying that timely interventions greatly limited contact between people and effectively contained the spread of COVID-19 in Shaanxi. Conclusions: The renewal equation model provides the best modelling; mixing continuous importation with local transmission significantly increases the estimate of transmissibility.

Knowledge, attitude and practice regarding occupational protection against COVID-19 among midwives in China: A nationwide cross-sectional study

Midwives assume the roles in protecting perinatal women and newborns, meanwhile defending their own safety during the epidemic of COVID-19. Since there is currently no specific treatment available that targets the disease, strictly compliance with various infection prevention and control measures appears utmost important to achieve their occupational safety. We then explored the status quo and influencing factors of the knowledge, attitude and practice (KAP) of occupational protection against the COVID-19 among midwives in China. This online cross-sectional survey was conducted on 2663 midwives across the China during the early stages of the pandemic with a self-reported structured questionnaire. 97.4% and 92.9% of them were identified with positive attitude and appropriate practice, respectively, whereas only 6.4% showed good level of knowledge about the occupational protection toward the COVID-19. Midwives with older age, keeping on working during the breakout period, completing the training programs, caring the confirmed COVID-19 cases and having family members with cold-like symptoms were significantly associated with their KAP status. This study could provide valuable information not only for policy makers and administrators to optimize resource allocation and design education programs on targeted midwives, but also serve as a baseline for measuring changes in subsequent, post-intervention KAP studies.

COVID-19 heterogeneity in islands chain environment

Background

It is critical to capture data and modeling from the COVID-19 pandemic to understand as much as possible and prepare for future epidemics and possible pandemics. The Hawaiian Islands provide a unique opportunity to study heterogeneity and demographics in a controlled environment due to the geographically closed borders and mostly uniform pandemic-induced governmental controls and restrictions.

Objective

The goal of the paper is to quantify the differences and similarities in the spread of COVID-19 among different Hawaiian islands as well as several other archipelago and islands, which could potentially help us better understand the effect of differences in social behavior and various mitigation measures. The approach should be robust with respect to the unavoidable differences in time, as the arrival of the virus and promptness of mitigation measures may vary significantly among the chosen locations. At the same time, the comparison should be able to capture differences in the overall pandemic experience.

Methods

We examine available data on the daily cases, positivity rates, mobility, and employ a compartmentalized model fitted to the daily cases to develop appropriate comparison approaches. In particular, we focus on merge trees for the daily cases, normalized positivity rates, and baseline transmission rates of the models.

Results

We observe noticeable differences among different Hawaiian counties and interesting similarities between some Hawaiian counties and other geographic locations. The results suggest that mitigation measures should be more localized, that is, targeting the county level rather than the state level if the counties are reasonably insulated from one another. We also notice that the spread of the disease is very sensitive to unexpected events and certain changes in mitigation measures.

Conclusions

Despite being a part of the same archipelago and having similar protocols for mitigation measures, different Hawaiian counties exhibit quantifiably different dynamics of the spread of the disease. One potential explanation is that not sufficiently targeted mitigation measures are incapable of handling unexpected, localized outbreak events. At a larger-scale view of the general spread of the disease on the Hawaiian island counties, we find very interesting similarities between individual Hawaiian islands and other archipelago and islands.

The Geographical Distribution and Influencing Factors of COVID-19 in China

The study of the spatial differentiation of COVID-19 in cities and its driving mechanism is helpful to reveal the spatial distribution pattern, transmission mechanism and diffusion model, and evolution mechanism of the epidemic and can lay the foundation for constructing the spatial dynamics model of the epidemic and provide theoretical basis for the policy design, spatial planning and implementation of epidemic prevention and control and social governance. Geodetector (Origin version, Beijing, China) is a great tool for analysis of spatial differentiation and its influencing factors, and it provides decision support for differentiated policy design and its implementation in executing the city-specific policies. Using factor detection and interaction analysis of Geodetector, 15 indicators of economic, social, ecological, and environmental dimensions were integrated, and 143 cities were selected for the empirical research in China. The research shows that, first of all, risks of both infection and death show positive spatial autocorrelation, but the geographical distribution of local spatial autocorrelation differs significantly between the two. Secondly, the inequalities in urban economic, social, and residential environments interact with COVID-19 spatial heterogeneity, with stronger explanatory power especially when multidimensional inequalities are superimposed. Thirdly, the spatial distribution and spread of COVID-19 are highly spatially heterogeneous and correlated due to the complex influence of multiple factors, with factors such as Area of Urban Construction Land, GDP, Industrial Smoke and Dust Emission, and Expenditure having the strongest influence, the factors such as Area of Green, Number of Hospital Beds and Parks, and Industrial NOx Emissions having unignorable influence, while the factors such as Number of Free Parks and Industrial Enterprises, Per-GDP, and Population Density play an indirect role mainly by means of interaction. Fourthly, the factor interaction effect from the infected person's perspective mainly shows a nonlinear enhancement effect, that is, the joint influence of the two factors is greater than the sum of their direct influences; but from the perspective of the dead, it mainly shows a two-factor enhancement effect, that is, the joint influence of the two factors is greater than the maximum of their direct influences but less than their sum. Fifthly, some suggestions are put forward from the perspectives of building a healthy, resilient, safe, and smart city, providing valuable reference and decision basis for city governments to carry out differentiated policy design.

Epidemiology and Genetic Analysis of SARS-CoV-2 in Myanmar during the Community Outbreaks in 2020

We aimed to analyze the situation of the first two epidemic waves in Myanmar using the publicly available daily situation of COVID-19 and whole-genome sequencing data of SARS-CoV-2. From March 23 to December 31, 2020, there were 33,917 confirmed cases and 741 deaths in Myanmar (case fatality rate of 2.18%). The first wave in Myanmar from March to July was linked to overseas travel, and then a second wave started from Rakhine State, a western border state, leading to the second wave spreading countrywide in Myanmar from August to December 2020. The estimated effective reproductive number (R_t) nationwide reached 6–8 at the beginning of each wave and gradually decreased as the epidemic spread to the community. The whole-genome analysis of 10 Myanmar SARS-CoV-2 strains together with 31 previously registered strains showed that the first wave was caused by GISAID clade O or PANGOLIN lineage B.6 and the second wave was changed to clade GH or lineage B.1.36.16 with a close genetic relationship with other South Asian strains. Constant monitoring of epidemiological situations combined with SARS-CoV-2 genome analysis is important for adjusting public health measures to mitigate the community transmissions of COVID-19.

Is Curfew Effective in Limiting SARS-CoV-2 Progression? An Evaluation in France Based on Epidemiokinetic Analyses

BACKGROUND

Since late summer 2020, the French authorities implemented a curfew/lightened lockdown-alternating strategy instead of strict lockdown, to improve acceptability and limit socioeconomic consequences. However, data on curfew-related efficacy to control the epidemic are scarce.

OBJECTIVE

To investigate the effects on COVID-19 spread in France of curfew combined to local and/or nationwide restrictions from late summer 2020 to mid-February 2021.

DESIGN

We conducted a comparative evaluation using a susceptible-infected-recovered (SIR)-based model completed with epidemiokinetic tools.

MAIN MEASURES

We analyzed the time-course of epidemic progression rate under curfew in French Guyana and five metropolitan regions where additional restrictions were implemented at different times. Using linear regressions of the decay/increase rates in daily contaminations, we calculated the epidemic regression half-lives (t_1/2β) for each identified period.

KEY RESULTS

In French Guyana, two decay periods with rapid regression (t_1/2β of ~10 days) were observed under curfew, with slowing (t_1/2β of ~43 days) when curfew was lightened. During the 2-week pre-lockdown curfew (2020/10/17-2020/11/02) in Provence-Alpes-Côte-d'Azur, Auvergne-Rhône-Alpes, and Ile-de-France, the epidemic progression was unchanged. During the post-lockdown curfew (2020/12/15-2020/02/14), the epidemic slowly regressed in Grand-Est (t_1/2β of ~37 days), whereas its progression rate plateaued in Auvergne-Rhône-Alpes and increased immediately in Provence-Alpes-Côte-d'Azur, Ile-de-France, and Nouvelle-Aquitaine, whatever the curfew starting time was (06:00 or 08:00 pm). Interestingly, a delayed slow decay (17 days < t_1/2β < 64 days) occurred under curfew in all regions except Ile-de-France.

CONCLUSIONS

Curfew allowed the temporary control of SARS-CoV-2 epidemic, however variably in the French regions, without preventing lockdown necessity. To accelerate the epidemic regression such as observed in French Guyana, curfew should be implemented timely with additional restrictions.

Precise Transmission for COVID-19 Information: Based on China's Experience

Information on coronavirus disease 2019 (COVID-19) has been a significant focus for the global public since the outbreak of the disease. In response, data visualization has become the main form of media used to inform the public about the global pandemic's progress. This paper studies the example of China, the main country affected by the virus in the early stage of the pandemic, to explain the problems regarding the differences in time, knowledge, and technology for information transmission. This paper also tries to explain the causes behind the dissemination of rumors, misjudgment of the public, and the difficulties of perception regarding pandemic information based on the three aspects of information collection, processing, and presentation. We argue that comprehensive information transmission with direct and clear visual presentation could help the public better understand the development of the pandemic, relieve social panic, and help authorities promptly adjust public health policies to absorb the social and economic impacts of the pandemic. Based on a case study, we propose that hierarchical presentation, comprehensive descriptions, and accurate visualizations of pandemic data can effectively improve information transmission, thus providing helpful references for authorities and organizations to improve the effectiveness of pandemic information transmission.