Mechanisms of recurrent outbreak of COVID-19: a model-based study

The neural oscillatory mechanism underlying human brain fingerprint recognition using a portable EEG acquisition device

In recent years, brainprint recognition has emerged as a novel method of personal identity verification. Although studies have demonstrated the feasibility of this technology, some limitations hinder its further development into the society, such as insufficient efficiency (extended wear time for multi-channel EEG cap), complex experimental paradigms (more time in learning and completing experiments), and unclear neurobiological characteristics (lack of intuitive biomarkers and an inability to eliminate the impact of noise on individual differences). Overall, these limitations are due to the incomplete understanding of the underlying neural mechanisms. Therefore, this study aims to investigate the neural mechanisms behind brainwave recognition and simplify the operation process. We recorded prefrontal resting-state EEG data from 40 participants, which is followed up over nine months using a single-channel portable brainwave device. We found that portable devices can effectively and stably capture the characteristics of different subjects in the alpha band (8-13Hz) over long periods, as well as capturing their individual differences (no alpha peak, 1 alpha peak, or 2 alpha peaks). Through correlation analysis, alpha-band activity can reveal the uniqueness of the subjects compared to others within one minute. We further used a descriptive model to dissect the oscillatory and non-oscillatory components in the alpha band, demonstrating the different contributions of fine oscillatory features to individual differences (especially amplitude and bandwidth). Our study validated the feasibility of portable brainwave devices in brainwave recognition and the underlying neural oscillation mechanisms. The fine characteristics of various alpha oscillations will contribute to the accuracy of brainwave recognition, providing new insights for the development of future brainwave recognition technology.

Exploring COVID-19 Phobia among International Chinese College Students in South Korea Before Ending COVID-19 Restrictions

BACKGROUND

College students, considered to be the driving force of society, are highly vulnerable to COVID-19. At a time when facing a new pandemic wave in 2022, China's policy was in contrast with that of Korea. We investigated the phobia levels of international Chinese college students studying in Korea.

OBJECTIVE

This study aimed to investigate the relationship between the frequency of use and trust of information sources, and COVID-19 phobia (C19P) among Chinese college students studying in Korea before ending related restrictions.

METHODS

This study employed a cross-sectional design, conducting an online survey among Chinese college students studying in Korea from April 8-15, 2022 (before Korea ended the limitations due to COVID-19). Data about 319 respondents were analyzed, including socio-demographics, information variables, knowledge, attitudes, practices (KAP), and C19P. Hierarchical regression analysis with different models was used to examine the relationship between information trust, KAP, and C19P.

RESULTS

Results showed that students performed well in knowledge and preventive practices, had diverse sources of getting information related to COVID-19, and highly depended on the internet and news. Students who perceived a higher severity of infection showed higher levels of COVID-19 phobia. The tendency to wear masks with family/friends, avoid crowded places, and not agree with Korean government mitigation policies reported higher levels of COVID-19 phobia.

CONCLUSIONS

More authority and proactive communication strategies, such as consultations or education programs, are needed for international students to alleviate their phobias and psychological stress.

Temporal features of goals, substitutions, and fouls in football games in the five major European league from 2018 to 2021

The "Big Five" European football leagues, comprising England's Premier League, Germany's Bundesliga, Spain's La Liga, Italy's Serie A, and France's Ligue 1, command significant attention. While the occurrence of goals, substitutions, and fouls in football games is often considered random, of the presence of an inherent inevitability is unclear. To investigate, we analyzed a public dataset detailing timing of goals, substitutions, and yellow cards in regular time from WhoScored across three seasons (2018-2019, 2019-2020, 2020-2021) in the top five European football leagues. We employed various mathematical descriptive models (including linear, sigmoid, and gaussian functions) to measure the temporal tendency of goals, substitutions, and yellow cards. Our results indicate that, whether in the first or second half of the match, the temporal distribution of these elements exhibits evenness a (indicative of randomness). However, specific characteristics were discerned through distinct model parameters, capturing novel phenomena that were intuitively illustrated. Furthermore, we explored the interaction of the timing of goals, substitutions, and yellow cards. In this analysis we found that scoring in the second half leads to more substitutions and yellow cards. Changing players in the second half corresponded with more goals, while the impact of yellow card fouls showed no differences in goals in the first and second halves. Our research is the first to systematically study the laws of modern football matches, providing valuable guidance and reference for many football coaches.

Temporal dynamic effects of meteorological factors and air quality on the physical health of the older adults in Shenzhen, China

Introduction

Meteorological and environmental factors can affect people's lives and health, which is crucial among the older adults. However, it is currently unclear how they specifically affect the physical condition of older adults people.

Methods

We collected and analyzed the basic physical examination indicators of 41 older adults people for two consecutive years (2021 and 2022), and correlated them with meteorological and environmental factors. Partial correlation was also conducted to exclude unrelated factors as well.

Results

We found that among the physical examination indicators of the older adults for two consecutive years, five indicators (HB, WBC, HbAlc, CB, LDL-C) showed significant differences across the population, and they had significantly different dynamic correlation patterns with six meteorological (air pressure, temperature, humidity, precipitation, wind speed, and sunshine duration) and seven air quality factors (NO2, SO2, PM10, O3-1h, O3-8h, CO, PM2.5).

Discussion

Our study has discovered for the first time the dynamic correlation between indicators in normal basic physical examinations and meteorological factors and air quality indicators, which will provide guidance for the future development of policies that care for the healthy life of the older adults.

An agent-based model with antibody dynamics information in COVID-19 epidemic simulation

Accurate prediction of the temporal and spatial characteristics of COVID-19 infection is of paramount importance for effective epidemic prevention and control. In order to accomplish this objective, we incorporated individual antibody dynamics into an agent-based model and devised a methodology that encompasses the dynamic behaviors of each individual, thereby explicitly capturing the count and spatial distribution of infected individuals with varying symptoms at distinct time points. Our model also permits the evaluation of diverse prevention and control measures. Based on our findings, the widespread employment of nucleic acid testing and the implementation of quarantine measures for positive cases and their close contacts in China have yielded remarkable outcomes in curtailing a less transmissible yet more virulent strain; however, they may prove inadequate against highly transmissible and less virulent variants. Additionally, our model excels in its ability to trace back to the initial infected case (patient zero) through early epidemic patterns. Ultimately, our model extends the frontiers of traditional epidemiological simulation methodologies and offers an alternative approach to epidemic modeling.

Temporal dynamic characteristics of human monkeypox epidemic in 2022 around the world under the COVID-19 pandemic background

Background

The reemergence of the monkeypox epidemic has aroused great concern internationally. Concurrently, the COVID-19 epidemic is still ongoing. It is essential to understand the temporal dynamics of the monkeypox epidemic in 2022 and its relationship with the dynamics of the COVID-19 epidemic. In this study, we aimed to explore the temporal dynamic characteristics of the human monkeypox epidemic in 2022 and its relationship with those of the COVID-19 epidemic.

Methods

We used publicly available data of cumulative monkeypox cases and COVID-19 in 2022 and COVID-19 at the beginning of 2020 for model validation and further analyses. The time series data were fitted with a descriptive model using the sigmoid function. Two important indices (logistic growth rate and semi-saturation period) could be obtained from the model to evaluate the temporal characteristics of the epidemic.

Results

As for the monkeypox epidemic, the growth rate of infection and semi-saturation period showed a negative correlation (r = 0.47, p = 0.034). The growth rate also showed a significant relationship with the locations of the country in which it occurs [latitude (r = -0.45, p = 0.038)]. The development of the monkeypox epidemic did not show significant correlation compared with the that of COVID-19 in 2020 and 2022. When comparing the COVID-19 epidemic with that of monkeypox, a significantly longer semi-saturation period was observed for monkeypox, while a significant larger growth rate was found in COVID-19 in 2020.

Conclusions

This novel study investigates the temporal dynamics of the human monkeypox epidemic and its relationship with the ongoing COVID-19 epidemic, which could provide more appropriate guidance for local governments to plan and implement further fit-for-purpose epidemic prevention policies.

Quantifying COVID-19 recovery process from a human mobility perspective: An intra-city study in Wuhan

The COVID-19 pandemic has brought huge challenges to sustainable urban and community development. Although some recovery signals and patterns have been uncovered, the intra-city recovery process remains underexploited. This study proposes a comprehensive approach to quantify COVID-19 recovery leveraging fine-grained human mobility records. Taking Wuhan, a typical COVID-19 affected megacity in China, as the study area, we identify accurate recovery phases and select appropriate recovery functions in a data-driven manner. We observe that recovery characteristics regarding duration, amplitude, and velocity exhibit notable differences among urban blocks. We also notice that the recovery process under a one-wave outbreak lasts at least 84 days and has an S-shaped form best fitted with four-parameter Logistic functions. More than half of the recovery variance can be well explained and estimated by common variables from auxiliary data, including population, economic level, and built environments. Our study serves as a valuable reference that supports data-driven recovery quantification for COVID-19 and other crises.

Knowledge, attitudes, and practices (KAP) towards COVID-19 pandemic among pregnant women in a tertiary hospital in Karachi, Pakistan

BACKGROUND

The aim of this study was to evaluate the knowledge, attitude, and practices (KAP) of the pregnant population during the COVID-19 pandemic in a tertiary care hospital.

METHODS

This cross-sectional study was conducted at Aga Khan University Hospital, Karachi, Pakistan. KAP towards COVID-19 was assessed using 21-item questionnaires. A score for each category was calculated and points were summed. The outcome variables of KAP were compared with demographic characteristics. Data were analyzed by using SPSS 19.

RESULTS

A total of 377 patients participated in the study. The majority of the patients were multiparous (36.8%) in the age group of 30-40years (42.4%). More than 90% of patients were aware of COVID-19 symptoms and mode of transmission. They were aware of no cure for disease and optimum social distance. Although < 50% of patients truly answered the questions regarding the impact of COVID-19 on the risk of congenital malformation, vertical transmission, and the effect of infection on the mode of delivery. Regarding attitude and practices,> 90% of patients were anxious about fetal and personal safety, they are using a facemask, sanitizing their hands regularly, and avoiding social gatherings. Univariate and multivariable linear regression analysis showed statistically significant results among demographic variables (age, parity, family members, occupational status, and source of information).

CONCLUSION

Pregnant patients demonstrated inadequate knowledge regarding the impact of COVID-19 on pregnancy. However positive attitude and practices on preventive measures were good. This highlights the need for health education for pregnant women for COVID-19 to improve knowledge on a constant basis.

Oscillatory properties of class C notifiable infectious diseases in China from 2009 to 2021

Background

Epidemics of infectious diseases have a great negative impact on people's daily life. How it changes over time and what kind of laws it obeys are important questions that researchers are always interested in. Among the characteristics of infectious diseases, the phenomenon of recrudescence is undoubtedly of great concern. Understanding the mechanisms of the outbreak cycle of infectious diseases could be conducive for public health policies to the government.

Method

In this study, we collected time-series data for nine class C notifiable infectious diseases from 2009 to 2021 using public datasets from the National Health Commission of China. Oscillatory power of each infectious disease was captured using the method of the power spectrum analysis.

Results

We found that all the nine class C diseases have strong oscillations, which could be divided into three categories according to their oscillatory frequencies each year. Then, we calculated the oscillation power and the average number of infected cases of all nine diseases in the first 6 years (2009-2015) and the next 6 years (2015-2021) since the update of the surveillance system. The change of oscillation power is positively correlated to the change in the number of infected cases. Moreover, the diseases that break out in summer are more selective than those in winter.

Conclusion

Our results enable us to better understand the oscillation characteristics of class C infectious diseases and provide guidance and suggestions for the government's prevention and control policies.

Changes in temporal properties for epidemics of notifiable infectious diseases in China during the COVID-19 epidemic: population-based surveillance study

BACKGROUND

The COVID-19 was first reported in 2019, and the Chinese government immediately carried out stringent and effective control measures in response to the epidemics.

OBJECTIVE

These non-pharmaceutical interventions may have impacted incidences of other infectious diseases as well. Potential explanations underlying this reduction, however, are not clear. Hence, in this study, we aimed to study the influence of the COVID-19 prevention policies on other infectious diseases (mainly class B infectious diseases) in China.

METHODS

The time-series datasets between 2017 and 2021 for 23 notifiable infectious diseases were extracted from public datasets from the National Health Commission of China. Several indices (peak and trough amplitude, infection selectivity, preferred time to outbreak, oscillatory strength) of each infectious disease were calculated before and after the COVID-19 outbreak.

RESULTS

We found that the prevention and control policies for COVID-19 had a strong significant reduction effect on outbreaks of other infectious diseases. A clear event-related trough (ERT) was observed after the outbreak of COVID-19 under the strict control policies, and its decreasing amplitude is related to the infection selectivity and preferred outbreak time of the disease before the COVID-19. We also calculated the oscillatory strength before and after the COVID-19 outbreak and found that it is significantly stronger before the COVID-19 outbreak, and does not correlate with the trough amplitude.

CONCLUSIONS

Our results directly demonstrate that prevention policies for the COVID-19 have immediate additional benefits for controlling most class B infectious diseases, and several factors (infection selectivity, preferred outbreak time) may have contributed to the reduction of outbreaks. This study may guide the implementation of non-pharmaceutical interventions to control a wider range of infectious diseases.

CLINICALTRIAL

Research on Monetary Policy Implementation and Industrial Structure Transformation Under COVID-19—Evidence From Eight Economic Zones in Mainland China

The outbreak of COVID-19 has brought a serious impact on the economies of various countries, monetary policy needs to play a role in stimulating economic recovery when the economy encounters a serious negative impact. Since the recurrent outbreak of COVID-19 has caused great obstacles to the normal economic exchanges between countries, it has become particularly important to build the domestic market and optimize the industrial allocation at this time. This paper focuses on studying the dynamic impact of China's monetary policy implementation on the industrial structure during the pandemic. Based on the data of the eight major economic zones in Mainland China and the dataset containing many of China's macroeconomic variables, a SV-TVP-FAVAR model is established. The manuscript compares the time-varying effects of monetary policy tools on the industries at different stages before and after the epidemic. The study supported some interesting conclusions. (1) Either the quantitative or price-based monetary policy shocks have significant time-varying impacts on the industries in different economic zones. The impacts of monetary policy on the primary, secondary, and tertiary industries in each economic zone are uneven. (2) The developed Northern, Eastern, and Southern coastal economic zones in Mainland China are more sensitive to the changes in monetary policy. (3) COVID-19 has brought a tremendous negative shock on the economy, which has destroyed the original steady-state of the economic system and added more uncertainty to the regulatory effect of monetary policy. Compared with other periods in China's economic history that severely negatively impacted (the Southeast Asian financial crisis and the global economic crisis), industries in most economic zones under the COVID-19 epidemic have been affected by monetary policy for a longer lag time. Therefore, for the implementation of monetary policy, at the moment of COVID-19 epidemic, we should pay more attention to the dual-pillar role of macro-prudential regulation, further improve the process of China's interest rate reform, enrich the monetary toolbox, and implement differentiated monetary policies in line with the economic zone's position, to optimize the regional industrial structure, and promote long-term economic growth.

Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model

An unprecedented outbreak of the novel coronavirus (COVID-19) in the form of peculiar pneumonia has spread globally since its first case in Wuhan province, China, in December 2019. Soon after, the infected cases and mortality increased rapidly. The future of the pandemic's progress was uncertain, and thus, predicting it became crucial for public health researchers. These predictions help the effective allocation of health-care resources, stockpiling, and help in strategic planning for clinicians, government authorities, and public health policymakers after understanding the extent of the effect. The main objective of this paper is to develop a hybrid forecasting model that can generate real-time out-of-sample forecasts of COVID-19 outbreaks for five profoundly affected countries, namely the USA, Brazil, India, the UK, and Canada. A novel hybrid approach based on the Theta method and autoregressive neural network (ARNN) model, named Theta-ARNN (TARNN) model, is developed. Daily new cases of COVID-19 are nonlinear, non-stationary, and volatile; thus, a single specific model cannot be ideal for future prediction of the pandemic. However, the newly introduced hybrid forecasting model with an acceptable prediction error rate can help healthcare and government for effective planning and resource allocation. The proposed method outperforms traditional univariate and hybrid forecasting models for the test datasets on an average.

COVID-19, a double-edged sword for the environment: a review on the impacts of COVID-19 on the environment

This review paper discusses the most relevant impacts of the COVID-19 pandemic on the environment. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) originated in Wuhan, China, in December 2019. The disease has infected 70 million people and caused the death of 1.58 million people since the US Food and Drug Administration issued an Emergency Use Authorization to develop a vaccine to prevent COVID-19 on December 11, 2020. COVID-19 is a global crisis that has impacted everything directly connected with human beings, including the environment. This review discusses the impacts of COVID-19 on the environment during the pandemic and post-COVID-19 era. During the first months of the COVID pandemic, global coal, oil, gas, and electricity demands declined by 8%, 5%, 2%, and 20%, respectively, relative to 2019. Stay-at-home orders in countries increased the concentrations of particles in indoor environments while decreasing the concentrations of PM2.5 and NOX in outdoor environments. Remotely working in response to the COVID-19 pandemic increased the carbon, water, and land footprints of Internet usage. Microplastics are released into our environment from the mishandling and mismanagement of personal protective equipment that endanger our water, soils, and sediments. Since the COVID-19 vaccine cannot be stored for a long time and spoils rapidly, more awareness of the massive waste of unused doses is needed. So COVID-19 is a double-edged sword for the environment.

The Interplay Between Policy and COVID-19 Outbreaks in South Asia: Longitudinal Trend Analysis of Surveillance Data

BACKGROUND

COVID-19 transmission rates in South Asia initially were under control when governments implemented health policies aimed at controlling the pandemic such as quarantines, travel bans, and border, business, and school closures. Governments have since relaxed public health restrictions, which resulted in significant outbreaks, shifting the global epicenter of COVID-19 to India. Ongoing systematic public health surveillance of the COVID-19 pandemic is needed to inform disease prevention policy to re-establish control over the pandemic within South Asia.

OBJECTIVE

This study aimed to inform public health leaders about the state of the COVID-19 pandemic, how South Asia displays differences within and among countries and other global regions, and where immediate action is needed to control the outbreaks.

METHODS

We extracted COVID-19 data spanning 62 days from public health registries and calculated traditional and enhanced surveillance metrics. We use an empirical difference equation to measure the daily number of cases in South Asia as a function of the prior number of cases, the level of testing, and weekly shifts in variables with a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R.

RESULTS

Traditional surveillance metrics indicate that South Asian countries have an alarming outbreak, with India leading the region with 310,310 new daily cases in accordance with the 7-day moving average. Enhanced surveillance indicates that while Pakistan and Bangladesh still have a high daily number of new COVID-19 cases (n=4819 and n=3878, respectively), their speed of new infections declined from April 12-25, 2021, from 2.28 to 2.18 and 3.15 to 2.35 daily new infections per 100,000 population, respectively, which suggests that their outbreaks are decreasing and that these countries are headed in the right direction. In contrast, India's speed of new infections per 100,000 population increased by 52% during the same period from 14.79 to 22.49 new cases per day per 100,000 population, which constitutes an increased outbreak.

CONCLUSIONS

Relaxation of public health restrictions and the spread of novel variants fueled the second wave of the COVID-19 pandemic in South Asia. Public health surveillance indicates that shifts in policy and the spread of new variants correlate with a drastic expansion in the pandemic, requiring immediate action to mitigate the spread of COVID-19. Surveillance is needed to inform leaders whether policies help control the pandemic.

Enlightenment on oscillatory properties of 23 class B notifiable infectious diseases in the mainland of China from 2004 to 2020

A variety of infectious diseases occur in mainland China every year. Cyclic oscillation is a widespread attribute of most viral human infections. Understanding the outbreak cycle of infectious diseases can be conducive for public health management and disease surveillance. In this study, we collected time-series data for 23 class B notifiable infectious diseases from 2004 to 2020 using public datasets from the National Health Commission of China. Oscillatory properties were explored using power spectrum analysis. We found that the 23 class B diseases from the dataset have obvious oscillatory patterns (seasonal or sporadic), which could be divided into three categories according to their oscillatory power in different frequencies each year. These diseases were found to have different preferred outbreak months and infection selectivity. Diseases that break out in autumn and winter are more selective. Furthermore, we calculated the oscillation power and the average number of infected cases of all 23 diseases in the first eight years (2004 to 2012) and the next eight years (2012 to 2020) since the update of the surveillance system. A strong positive correlation was found between the change of oscillation power and the change in the number of infected cases, which was consistent with the simulation results using a conceptual hybrid model. The establishment of reliable and effective analytical methods contributes to a better understanding of infectious diseases’ oscillation cycle characteristics. Our research has certain guiding significance for the effective prevention and control of class B infectious diseases.

The effect of human settlement temperature and humidity on the growth rules of infected and recovered cases of COVID-19

This study investigated the impact of humidity and temperature on the spread of COVID-19 (SARS-CoV-2) by statistically comparing modelled pandemic dynamics (daily infection and recovery cases) with daily temperature and humidity of three climate zones (Mainland China, South America and Africa) from January to August 2020. We modelled the pandemic growth using a simple logistic function to derive information of the viral infection and describe the growth of infected and recovered cases. The results indicate that the infected and recovered cases of the first wave were controlled in China and managed in both South America and Africa. There is a negative correlation between both humidity (r = - 0.21; p = 0.27) and temperature (r = -0.22; p = 0.24) with spread of the virus. Though this study did not fully encompass socio-cultural factors, we recognise that local government responses, general health policies, population density and transportation could also affect the spread of the virus. The pandemic can be managed better in the second wave if stricter safety protocols are implemented. We urge various units to collaborate strongly and call on countries to adhere to stronger safety protocols in the second wave.

Synchronized nonpharmaceutical interventions for the control of COVID-19

The world is experiencing an ongoing pandemic of coronavirus disease-2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In attempts to control the pandemic, a range of nonpharmaceutical interventions (NPIs) has been implemented worldwide. However, the effect of synchronized NPIs for the control of COVID-19 at temporal and spatial scales has not been well studied. Therefore, a meta-population model that incorporates essential nonlinear processes was constructed to uncover the transmission characteristics of SARS-CoV-2 and then assess the effectiveness of synchronized NPIs on COVID-19 dynamics in China. Regional synchronization of NPIs was observed in China, and it was found that a combination of synchronized NPIs (the travel restrictions, the social distancing and the infection isolation) prevented 93.7% of SARS-CoV-2 infections. The use of synchronized NPIs at the time of the Wuhan lockdown may have prevented as much as 38% of SARS-CoV-2 infections, compared with the unsynchronized scenario. The interconnectivity of the epicenter, the implementation time of synchronized NPIs, and the number of regions considered all affected the performance of synchronized NPIs. The results highlight the importance of using synchronized NPIs in high-risk regions for the control of COVID-19 and shed light on effective strategies for future pandemic responses.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11071-021-06505-0.