Comparative infection modeling and control of COVID-19 transmission patterns in China, South Korea, Italy and Iran

Co-infection of SARS-CoV-2 and influenza A/B among patients with COVID-19: a systematic review and meta-analysis

BACKGROUND

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19) is a public health problem and may result in co-infection with other pathogens such as influenza virus. This review investigates the co-infection of SARS-CoV-2 and influenza A/B among patients with COVID-19.

METHODS

This meta- analysis included 38 primary studies investigating co-infection of SARS-CoV-2 with influenza in confirmed cases of COVID-19. The global online databases were used to identify relevant studies published between December 2019 and July 2024. Data analysis was performed using STATA Ver. 17 software, and standard errors of prevalence were calculated using the binomial distribution formula. Heterogeneity of study results was evaluated using the I-square and Q index, and publication bias was examined using the Begg's and Egger's tests, as well as funnel plot. A random effects model was used to determine prevalence rates, and a forest plot diagram was used to present results with 95% confidence intervals. In addition, sensitivity analyses were performed to check the impact of each primary study on the overall estimate.

RESULT

The analysis found that the prevalence of influenza in co-infected patients at 95% confidence interval using a random effect model was 14% (95% CI: 8-20%). Significant heterogeneity was observed in the random-effects model for influenza A, 11% (95% CI: 5-18%) and B, 4% (95% CI: 2-7%) in co-infected patients. The highest prevalence of influenza A/B (21%), influenza A (17%) and influenza B (20%) was shown in Asia and Europe respectively. Subgroup analysis by study year showed that the co-prevalence of COVID-19 and influenza A/B was similar in the pre-2021 and post-2021 time periods, at 14% (95% CI: 5-23%) for pre-2021 and 6-22% for 2021 and post-2021. Also, the overall prevalence of influenza A and B in COVID-19 patients is 11% and 4%, and there was no significant difference between the time periods before and after 2021. Meta-regression with a random-effects model showed that the variables location, year group, and total patients showed only 2.71% of very high heterogeneity (I² = 99.92%), and none of these variables had a significant effect on the co-prevalence of COVID-19 and influenza A/B (p > 0.05). Also, meta-regression results showed that these variables had no significant effect on influenza A and B prevalence (p > 0.05) and showed only a small proportion of the very high heterogeneity (I² = 99.72%), (I² = 68.78%). In our study, Egger's test indicated that there was publication bias or small study effects in this meta-analysis (p = 0.0000).

CONCLUSION

The combination of SARS-CoV-2 with influenza and other respiratory viruses requires the best treatment protocols to reduce the severity of the disease. In this approach, high vaccination coverage against seasonal influenza and SARS-CoV-2 could reduce the risk of co-infection in the recent pandemic.

Research on Korean Translation in the Context of Epidemic Prevention and Control

An emergency like COVID-19 requires a theoretical framework for policy implementation that involves public and private sector collaborations. After policy failures, new institutions have formed that trigger PPP's later, allowing the incumbent administration to continue in office longer. It focuses on novel approaches to dealing with pandemics. The present administration put these rules in place to keep COVID-19 under control. When it comes to Real Time - polymerase chain reaction (RT-PCR) testing, South Korea's government and corporations partnered to swiftly raise the quantity of testing in the country. Models of policy change are shown to be dynamic, cyclical, and recursive. During the COVID-19 outbreak in South Korea, an empirical content research was conducted. Even though South Korea's leader was at risk of losing public support to the point where impeachment was mentioned as a possible option, he dramatically reversed public mood to win general elections by a wide margin in April 2020, while the pandemic scenario persisted. To win reelection, democratic administrations are under more pressure to effectively perform crisis management when faced with a crisis. As a result, they are under even more pressure to immediately mobilize public and private resources. The emergency use authorization (EUA) protocol for test kits is an example of "leapfrogging actors" – up-and-coming innovators – who helped turn a pandemic tragedy into a possibility for sustained leadership and for them. The results based on infected premises culling rate ratio is 82.3%, number of measles cases report is 86.4%, spread and epidemic ratio is 84.2%, important of epidemiology is 89.35%, transmission potential of COVID-19 is 91.24% and illustration of epidemic control is 92.45. The results based on infected premises culling rate ratio is 82.3%, number of measles cases report is 86.4%, spread and epidemic ratio is 84.2%, important of epidemiology is 89.35%, transmission potential of COVID-19 is 91.24% and illustration of epidemic control is 92.45.

Parameter estimation of the COVID-19 transmission model using an improved quantum-behaved particle swarm optimization algorithm

The outbreak of coronavirus disease (COVID-19) and its accompanying pandemic have created an unprecedented challenge worldwide. Parametric modeling and analyses of the COVID-19 play a critical role in providing vital information about the character and relevant guidance for controlling the pandemic. However, the epidemiological utility of the results obtained from the COVID-19 transmission model largely depends on accurately identifying parameters. This paper extends the susceptible-exposed-infectious-recovered (SEIR) model and proposes an improved quantum-behaved particle swarm optimization (QPSO) algorithm to estimate its parameters. A new strategy is developed to update the weighting factor of the mean best position by the reciprocal of multiplying the fitness of each best particle with the average fitness of all best particles, which can enhance the global search capacity. To increase the particle diversity, a probability function is designed to generate new particles in the updating iteration. When compared to the state-of-the-art estimation algorithms on the epidemic datasets of China, Italy and the US, the proposed method achieves good accuracy and convergence at a comparable computational complexity. The developed framework would be beneficial for experts to understand the characteristics of epidemic development and formulate epidemic prevention and control measures.

Understanding the spatial diffusion dynamics of the COVID-19 pandemic in the city system in China

Investigating the spatial epidemic dynamics of COVID-19 is crucial in understanding the routine of spatial diffusion and in surveillance, prediction, identification and prevention of another potential outbreak. However, previous studies attempting to evaluate these spatial diffusion dynamics are limited. Using city as the research unit and spatial association analysis as the primary strategy, this study explored the changing primary risk factors impacting the spatial spread of COVID-19 across Chinese cities under various diffusion assumptions and throughout the epidemic stage. Moreover, this study investigated the characteristics and geographical distributions of high-risk areas in different epidemic stages. The results empirically indicated rapid intercity diffusion at the early stage and primarily intracity diffusion thereafter. Before countermeasures took effect, proximity, GDP per capita, medical resources, outflows from Wuhan and intercity mobility significantly affected early diffusion. With speedily effective countermeasures, outflows from the epicenter, proximity, and intracity outflows played an important role. At the early stage, high-risk areas were mainly cities adjacent to the epicenter, with higher GDP per capita, or a combination of higher GDP per capita and better medical resources, with more outflow from the epicenter, or more intercity mobility. After countermeasures were effected, cities adjacent to the epicenter, or with more outflow from the epicenter or more intracity mobility became high-risk areas. This study provides an insightful understanding of the spatial diffusion of COVID-19 across cities. The findings are informative for effectively handling the potential recurrence of COVID-19 in various settings.

Mathematical modeling of the impact of Omicron variant on the COVID-19 situation in South Korea

The rise of newer coronavirus disease 2019 (COVID-19) variants has brought a challenge to ending the spread of COVID-19. The variants have a different fatality, morbidity, and transmission rates and affect vaccine efficacy differently. Therefore, the impact of each new variant on the spread of COVID-19 is of interest to governments and scientists. Here, we proposed mathematical SEIQRDVP and SEIQRDV3P models to predict the impact of the Omicron variant on the spread of the COVID-19 situation in South Korea. SEIQEDVP considers one vaccine level at a time while SEIQRDV3P considers three vaccination levels (only one dose received, full doses received, and full doses + booster shots received) simultaneously. The omicron variant's effect was contemplated as a weighted sum of the delta and omicron variants' transmission rate and tuned using a hyperparameter k. Our models' performances were compared with common models like SEIR, SEIQR, and SEIQRDVUP using the root mean square error (RMSE). SEIQRDV3P performed better than the SEIQRDVP model. Without consideration of the variant effect, we don't see a rapid rise in COVID-19 cases and high RMSE values. But, with consideration of the omicron variant, we predicted a continuous rapid rise in COVID-19 cases until maybe herd immunity is developed in the population. Also, the RMSE value for the SEIQRDV3P model decreased by 27.4%. Therefore, modeling the impact of any new risen variant is crucial in determining the trajectory of the spread of COVID-19 and determining policies to be implemented.

Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China

The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact; while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 °C and 8.17 °C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental awareness of potential health risks and therefore influence epidemic control strategies.

Application of Mathematical Modeling in Prediction of COVID-19 Transmission Dynamics

The entire world has been affected by the outbreak of COVID-19 since early 2020. Human carriers are largely the spreaders of this new disease, and it spreads much faster compared to previously identified coronaviruses and other flu viruses. Although vaccines have been invented and released, it will still be a challenge to overcome this disease. To save lives, it is important to better understand how the virus is transmitted from one host to another and how future areas of infection can be predicted. Recently, the second wave of infection has hit multiple countries, and governments have implemented necessary measures to tackle the spread of the virus. We investigated the three phases of COVID-19 research through a selected list of mathematical modeling articles. To take the necessary measures, it is important to understand the transmission dynamics of the disease, and mathematical modeling has been considered a proven technique in predicting such dynamics. To this end, this paper summarizes all the available mathematical models that have been used in predicting the transmission of COVID-19. A total of nine mathematical models have been thoroughly reviewed and characterized in this work, so as to understand the intrinsic properties of each model in predicting disease transmission dynamics. The application of these nine models in predicting COVID-19 transmission dynamics is presented with a case study, along with detailed comparisons of these models. Toward the end of the paper, key behavioral properties of each model, relevant challenges and future directions are discussed.

Investigating the Relationship between Human Activity and the Urban Heat Island Effect in Melbourne and Four Other International Cities Impacted by COVID-19

Climate change is one of the biggest challenges of our times, even before the onset of the Coronavirus (COVID-19) pandemic. One of the main contributors to climate change is greenhouse gas (GHG) emissions, which are mostly caused by human activities such as the burning of fossil fuels. As the lockdown due to the pandemic has minimised human activity in major cities, GHG emissions have been reduced. This, in turn, is expected to lead to a reduction in the urban heat island (UHI) effect in the cities. The aim of this paper is to understand the relationship between human activity and the UHI intensity and to provide recommendations towards developing a sustainable approach to minimise the UHI effect and improve urban resilience. In this study, historical records of the monthly mean of daily maximum surface air temperatures collected from official weather stations in Melbourne, New York City, Tokyo, Dublin, and Oslo were used to estimate the UHI intensity in these cities. The results showed that factors such as global climate and geographic features could dominate the overall temperature. However, a direct relationship between COVID-19 lockdown timelines and the UHI intensity was observed, which suggests that a reduction in human activity can diminish the UHI intensity. As lockdowns due to COVID-19 are only temporary events, this study also provides recommendations to urban planners towards long-term measures to mitigate the UHI effect, which can be implemented when human activity returns to normal.

Compliance with safety measures and risk of COVID-19 transmission among healthcare workers

Aim:

This study aimed to determine the compliance of healthcare workers (HCWs) with the hospital safety measures and the prevalence of hospital-acquired COVID-19 infection among them.

Methodology:

HCWs at King Abdullah University Hospital (KAUH) assigned for COVID-19 patients between 18 March and 10 June 2020 were tested for past infection using total anti-SARS-CoV-2 immunoglobulin assay, demographic data and compliance with safety measures were assessed using a questionnaire.

Results:

A total of 340 HCWs participated in the study, 260 were close direct care. Three HCWs tested positive for total anti-SARS-CoV-2 immunoglobulin. Close direct care were more compliant with personal protective guidelines than those providing direct care.

Conclusion:

HCWs compliance with personal protective guidelines might explain the low prevalence of COVID-19 infection in hospital settings.

Hospitals around the globe have implemented safety measures in order to decrease the risk of spreading the virus among healthcare workers (HCWs); our aim in this study is to assess the compliance of HCWs to the safety measures introduced in our hospital and the prevalence of contracting COVID-19 among them using total anti-SARS-CoV-2 immunoglobulin assay. A total of 113 physicians and 227 nurses participated in the study; results showed a high level of compliance among HCWs working in close direct care and a relatively lower level of compliance among those providing direct care. Three of the HCWs tested positive for the total immunoglobulin assay, indicating the importance of adhering to the safety measures to decrease the risk of contracting the virus.

Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic

Background

Currently, the knowledge of associations among newly recovered cases (NR), newly healed cases (NH), newly confirmed cases (NC), and newly dead cases (ND) can help to monitor, evaluate, predict, control, and curb the spreading of coronavirus disease 2019 (COVID-19). This study aimed to explore the panel associations of ND, NH, and NR with NC.

Methods

Data from China Data Lab in Harvard Dataverse with China (January 15, 2020 to January 14, 2021), the United States of America (the USA, January 21, 2020 to April 5, 2021), and the World (January 22, 2020 to March 20, 2021) had been analyzed. The main variables included in the present analysis were ND, NH, NR, and NC. Pooled regression, stacked within-transformed linear regression, quantile regression for panel data, random-effects negative binomial regression, and random-effects Poisson regression were conducted to reflect the associations of ND, NH, and NR with NC. Event study analyses were performed to explore how the key events influenced NC.

Results

Descriptive analyses showed that mean value of ND/NC ratio regarding China was more than those regarding the USA and the World. The results from tentative analysis reported the significant relationships among ND, NH, NR, and NC regarding China, the USA, and the World. Panel regressions confirmed associations of ND, NH, and NR with NC regarding China, the USA, and the World. Panel event study showed that key events influenced NC regarding USA and the World more greatly than that regarding China.

Conclusion

The findings in this study confirmed the panel associations of ND, NH, and NR with NC in the three datasets. The efficiencies of various control strategies of COVID-19 pandemic across the globe were compared by the regression outcomes. Future direction of research work could explore the influencing mechanisms of the panel associations.

Modeling and Analyzing Transmission of Infectious Diseases Using Generalized Stochastic Petri Nets

Some infectious diseases such as COVID-19 have the characteristics of long incubation period, high infectivity during the incubation period, and carriers with mild or no symptoms which are more likely to cause negligence. Global researchers are working to find out more about the transmission of infectious diseases. Modeling plays a crucial role in understanding the transmission of the new virus and helps show the evolution of the epidemic in stages. In this paper, we propose a new general transmission model of infectious diseases based on the generalized stochastic Petri net (GSPN). First, we qualitatively analyze the transmission mode of each stage of infectious diseases such as COVID-19 and explain the factors that affect the spread of the epidemic. Second, the GSPN model is built to simulate the evolution of the epidemic. Based on this model’s isomorphic Markov chain, the equilibrium state of the system and its changing laws under different influencing factors are analyzed. Our paper demonstrates that the proposed GSPN model is a compelling tool for representing and analyzing the transmission of infectious diseases from system-level understanding, and thus contributes to providing decision support for effective surveillance and response to epidemic development.

COVID-19 pandemic spread against countries' non-pharmaceutical interventions responses: a data-mining driven comparative study

Background

The first half of 2020 has been marked as the era of COVID-19 pandemic which affected the world globally in almost every aspect of the daily life from societal to economical. To prevent the spread of COVID-19, countries have implemented diverse policies regarding Non-Pharmaceutical Intervention (NPI) measures. This is because in the first stage countries had limited knowledge about the virus and its contagiousness. Also, there was no effective medication or vaccines. This paper studies the effectiveness of the implemented policies and measures against the deaths attributed to the virus between January and May 2020.

Methods

Data from the European Centre for Disease Prevention and Control regarding the identified cases and deaths of COVID-19 from 48 countries have been used. Additionally, data concerning the NPI measures related policies implemented by the 48 countries and the capacity of their health care systems was collected manually from their national gazettes and official institutes. Data mining, time series analysis, pattern detection, machine learning, clustering methods and visual analytics techniques have been applied to analyze the collected data and discover possible relationships between the implemented NPIs and COVID-19 spread and mortality. Further, we recorded and analyzed the responses of the countries against COVID-19 pandemic, mainly in urban areas which are over-populated and accordingly COVID-19 has the potential to spread easier among humans.

Results

The data mining and clustering analysis of the collected data showed that the implementation of the NPI measures before the first death case seems to be very effective in controlling the spread of the disease. In other words, delaying the implementation of the NPI measures to after the first death case has practically little effect on limiting the spread of the disease. The success of implementing the NPI measures further depends on the way each government monitored their application. Countries with stricter policing of the measures seems to be more effective in controlling the transmission of the disease.

Conclusions

The conducted comparative data mining study provides insights regarding the correlation between the early implementation of the NPI measures and controlling COVID-19 contagiousness and mortality. We reported a number of useful observations that could be very helpful to the decision makers or epidemiologists regarding the rapid implementation and monitoring of the NPI measures in case of a future wave of COVID-19 or to deal with other unknown infectious pandemics. Regardless, after the first wave of COVID-19, most countries have decided to lift the restrictions and return to normal. This has resulted in a severe second wave in some countries, a situation which requires re-evaluating the whole process and inspiring lessons for the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11251-4.

Differential evolution and particle swarm optimization against COVID-19

COVID-19 disease, which highly affected global life in 2020, led to a rapid scientific response. Versatile optimization methods found their application in scientific studies related to COVID-19 pandemic. Differential Evolution (DE) and Particle Swarm Optimization (PSO) are two metaheuristics that for over two decades have been widely researched and used in various fields of science. In this paper a survey of DE and PSO applications for problems related with COVID-19 pandemic that were rapidly published in 2020 is presented from two different points of view: 1. practitioners seeking the appropriate method to solve particular problem, 2. experts in metaheuristics that are interested in methodological details, inter comparisons between different methods, and the ways for improvement. The effectiveness and popularity of DE and PSO is analyzed in the context of other metaheuristics used against COVID-19. It is found that in COVID-19 related studies: 1. DE and PSO are most frequently used for calibration of epidemiological models and image-based classification of patients or symptoms, but applications are versatile, even interconnecting the pandemic and humanities; 2. reporting on DE or PSO methodological details is often scarce, and the choices made are not necessarily appropriate for the particular algorithm or problem; 3. mainly the basic variants of DE and PSO that were proposed in the late XX century are applied, and research performed in recent two decades is rather ignored; 4. the number of citations and the availability of codes in various programming languages seems to be the main factors for choosing metaheuristics that are finally used.

Spatio-temporal distribution characteristics of COVID-19 in China: a city-level modeling study

Background

The coronavirus disease 2019 (COVID-19) has become a pandemic. Few studies have been conducted to investigate the spatio-temporal distribution of COVID-19 on nationwide city-level in China.

Objective

To analyze and visualize the spatiotemporal distribution characteristics and clustering pattern of COVID-19 cases from 362 cities of 31 provinces, municipalities and autonomous regions in mainland China.

Methods

A spatiotemporal statistical analysis of COVID-19 cases was carried out by collecting the confirmed COVID-19 cases in mainland China from January 10, 2020 to October 5, 2020. Methods including statistical charts, hotspot analysis, spatial autocorrelation, and Poisson space–time scan statistic were conducted.

Results

The high incidence stage of China’s COVID-19 epidemic was from January 17 to February 9, 2020 with daily increase rate greater than 7.5%. The hot spot analysis suggested that the cities including Wuhan, Huangshi, Ezhou, Xiaogan, Jingzhou, Huanggang, Xianning, and Xiantao, were the hot spots with statistical significance. Spatial autocorrelation analysis indicated a moderately correlated pattern of spatial clustering of COVID-19 cases across China in the early phase, with Moran’s I statistic reaching maximum value on January 31, at 0.235 (Z = 12.344, P = 0.001), but the spatial correlation gradually decreased later and showed a discrete trend to a random distribution. Considering both space and time, 19 statistically significant clusters were identified. 63.16% of the clusters occurred from January to February. Larger clusters were located in central and southern China. The most likely cluster (RR = 845.01, P < 0.01) included 6 cities in Hubei province with Wuhan as the centre. Overall, the clusters with larger coverage were in the early stage of the epidemic, while it changed to only gather in a specific city in the later period. The pattern and scope of clusters changed and reduced over time in China.

Conclusions

Spatio-temporal cluster detection plays a vital role in the exploration of epidemic evolution and early warning of disease outbreaks and recurrences. This study can provide scientific reference for the allocation of medical resources and monitoring potential rebound of the COVID-19 epidemic in China.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06515-8.

An AHP-based regional COVID-19 vulnerability model and its application in China

Since the COVID-19 outbreak, four cities-Wuhan, Beijing, Urumqi and Dalian-have experienced the process from outbreak to stabilization. According to the China Statistical Yearbook and China Center for Disease Control records, regional, pathological, medical and response attributes were selected as regional vulnerability factors of infectious diseases. Then the Analytic Hierarchy Process (AHP) method was used to build a regional vulnerability index model for the infectious disease. The influence of the COVID-19 outbreak at a certain place was assessed computationally in terms of the number of days of epidemic duration and cumulative number of infections, and then fitted to the city data. The resulting correlation coefficient was 0.999952. The range of the regional vulnerability index for COVID-19 virus was from 0.0513 to 0.9379. The vulnerability indexes of Wuhan, Urumqi, Beijing and Dalian were 0.8733, 0.1951, 0.1566 and 0.1119, respectively. The lack of understanding of the virus became the biggest breakthrough point for the rapid spread of the virus in Wuhan. Due to inadequate prevention and control measures, the city of Urumqi was unable to trace the source of infection and close contacts, resulting in a relatively large impact. Beijing has both high population density and migration rate, which imply that the disease outbreak in this city had a great impact. Dalian has perfect prevention and good regional attributes. In addition, the regional vulnerability index model was used to analyze other Chinese cities. Accordingly, the regional vulnerability index and the prevention and control suggestions for them were discussed.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40808-021-01244-y.

Herbal Medicine, Gut Microbiota, and COVID-19

Coronavirus Disease 19 (COVID-19) is a respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has grown to a worldwide pandemic with substantial mortality. The symptoms of COVID-19 range from mild flu-like symptoms, including cough and fever, to life threatening complications. There are still quite a number of patients with COVID-19 showed enteric symptoms including nausea, vomiting, and diarrhea. The gastrointestinal tract may be one of the target organs of SARS-CoV-2. Angiotensin converting enzyme 2 (ACE2) is the main receptor of SARS-CoV-2 virus, which is significantly expressed in intestinal cells. ACE2 links amino acid malnutrition to microbial ecology and intestinal inflammation. Intestinal flora imbalance and endotoxemia may accelerate the progression of COVID-19. Many herbs have demonstrated properties relevant to the treatment of COVID-19, by supporting organs and systems of the body affected by the virus. Herbs can restore the structure of the intestinal flora, which may further modulate the immune function after SARS-CoV-2 infection. Regulation of intestinal flora by herbal medicine may be helpful for the treatment and recovery of the disease. Understanding the role of herbs that regulate intestinal flora in fighting respiratory virus infections and maintaining intestinal flora balance can provide new ideas for preventing and treating COVID-19.

Willingness of Chinese, Studying in Germany to Fly Back to China Due to Their Risk Perception About COVID-19

Purpose

In this study, we aimed to examine the risk perception of Chinese students studying in Germany, which is the country fifth-most affected by COVID-19 in the world, who wish to return to China.

Patients and Methods

After controlling the COVID-19 situation in the country, China reopened the entire country, including Wuhan, which was the epicenter of the COVID-19 pandemic. A well-structured questionnaire was sent to Chinese students through a WeChat survey, a special feature within this mobile application, similar to Google Docs. The link was sent to 2000 students studying in Germany, and we received 1232 responses.

Results

The study found that the majority of Chinese students are willing to come back to China, considering the current risk of COVID-19 in Germany. A higher mortality rate influences their wish to return to China. Additionally, the special family size of “One Child” in the family also a key driver of Chinese student’s wish to get back home.

Conclusion

This study provides useful information to policymakers to implement proactive measures to manage students who want to return to China, as they may be the cause of the second wave of COVID-19 in China.

Monitoring and Forecasting COVID-19: Heuristic Regression, Susceptible-Infected-Removed Model and, Spatial Stochastic

The COVID-19 pandemic has had worldwide devastating effects on human lives, highlighting the need for tools to predict its development. The dynamics of such public-health threats can often be efficiently analyzed through simple models that help to make quantitative timely policy decisions. We benchmark a minimal version of a Susceptible-Infected-Removed model for infectious diseases (SIR) coupled with a simple least-squares Statistical Heuristic Regression (SHR) based on a lognormal distribution. We derive the three free parameters for both models in several cases and test them against the amount of data needed to bring accuracy in predictions. The SHR model is ≈ ±2% accurate about 20 days past the second inflexion point in the daily curve of cases, while the SIR model reaches a similar accuracy a fortnight before. All the analyzed cases assert the utility of SHR and SIR approximants as a valuable tool to forecast the disease's evolution. Finally, we have studied simulated stochastic individual-based SIR dynamics, which yields a detailed spatial and temporal view of the disease that cannot be given by SIR or SHR methods.

COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning

A multiple objective space-time forecasting approach is presented involving cyclical curve log-regression, and multivariate time series spatial residual correlation analysis. Specifically, the mean quadratic loss function is minimized in the framework of trigonometric regression. While, in our subsequent spatial residual correlation analysis, maximization of the likelihood allows us to compute the posterior mode in a Bayesian multivariate time series soft-data framework. The presented approach is applied to the analysis of COVID-19 mortality in the first wave affecting the Spanish Communities, since March 8, 2020 until May 13, 2020. An empirical comparative study with Machine Learning (ML) regression, based on random k-fold cross-validation, and bootstrapping confidence interval and probability density estimation, is carried out. This empirical analysis also investigates the performance of ML regression models in a hard- and soft-data frameworks. The results could be extrapolated to other counts, countries, and posterior COVID-19 waves.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00477-021-02021-0.

COVID-19 in China: Risk Factors and R0 Revisited

The COVID-19 epidemic spread rapidly through China and subsequently proliferated globally leading to a pandemic situation around the globe. Human-to-human transmission, as well as asymptomatic transmission of the infection, have been confirmed. As of April 03, 2020, public health crisis in China due to COVID-19 was potentially under control. We compiled a daily dataset of case counts, mortality, recovery, temperature, population density, and demographic information for each prefecture during the period of January 11 to April 07, 2020. Understanding the characteristics of spatial clustering of the COVID-19 epidemic and R0 is critical in effectively preventing and controlling the ongoing global pandemic. Considering this, the prefectures were grouped based on several relevant features using unsupervised machine learning techniques. Subsequently, we performed a computational analysis utilizing the reported cases in China to estimate the revised R0 among different regions. Finally, our overall research indicates that the impact of temperature and demographic factors on virus transmission may be characterized using a stochastic transmission model. Such predictions will help in prevention planning in an ongoing global pandemic, prioritizing segments of a given community/region for action and providing a visual aid in designing prevention strategies for a specific geographic region. Furthermore, revised estimation and our methodology will aid in improving the human health consequences of COVID-19 elsewhere.

Impact of control interventions on COVID-19 population dynamics in Malaysia: a mathematical study

Coronavirus disease 2019 (COVID-19) pandemic has posed a serious threat to both the human health and economy of the affected nations. Despite several control efforts invested in breaking the transmission chain of the disease, there is a rise in the number of reported infected and death cases around the world. Hence, there is the need for a mathematical model that can reliably describe the real nature of the transmission behaviour and control of the disease. This study presents an appropriately developed deterministic compartmental model to investigate the effect of different pharmaceutical (treatment therapies) and non-pharmaceutical (particularly, human personal protection and contact tracing and testing on the exposed individuals) control measures on COVID-19 population dynamics in Malaysia. The data from daily reported cases of COVID-19 between 3 March and 31 December 2020 are used to parameterize the model. The basic reproduction number of the model is estimated. Numerical simulations are carried out to demonstrate the effect of various control combination strategies involving the use of personal protection, contact tracing and testing, and treatment control measures on the disease spread. Numerical simulations reveal that the implementation of each strategy analysed can significantly reduce COVID-19 incidence and prevalence in the population. However, the results of effectiveness analysis suggest that a strategy that combines both the pharmaceutical and non-pharmaceutical control measures averts the highest number of infections in the population.

Comparison of spatio-temporal transmission characteristics of COVID-19 and its mitigation strategies in China and the US

Investigating the spatio-temporal transmission features and process of novel coronavirus disease 2019 (COVID-19) mitigation strategies are of great practical significance to understand the development of COVID-19 and establish international cooperation for prevention and control. In this paper, the cumulative number of confirmed cases, number of confirmed cases per day and cumulative number of deaths, were used to compare transmission paths, outbreaks timelines, and coping strategies of COVID-19 in China and the US. The results revealed that: first, the COVID-19 outbreaks in both China and the US exhibited a 6-week initiation stage. In China, the COVID-19 erupted in late January. It lasted only a short period of time and was almost completely contained within 6–8 weeks. But the COVID-19 erupted in early March in the US and was still in the peak or post-peak stage. Second, in China, the COVID-19 emerged in Wuhan and spread to other regions of Hubei Province and then nationwide, exhibiting a cross(“+”)-shaped of spread with Wuhan city as the center. Importantly, the COVID-19 in China had a large concentration and there were no national outbreaks. In contrast, the COVID-19 in the US first spread through New York and the western and eastern coasts but has since emerged throughout the entire country. Third, the lack of emergency response planning in both countries in the early stage (about 6-week) hampered COVID-19 prevention. However, actively high-pressure prevention and control measures were used to basically control COVID-19 in early March in China. And then China has gradually resumed business and production activities. Unfortunately, the US government missed the best opportunity to contain the epidemic. Faced with the choice between economic recovery and coronavirus containment, the US removed the quarantine and restriction measures too early. The COVID-19 is continuing to spread in the country and blossom everywhere, still showing no signs of receding.