First two months of the 2019 Coronavirus Disease (COVID-19) epidemic in China: real-time surveillance and evaluation with a second derivative model

Chaos of COVID-19 Superspreading Events: An Analysis Via a Data-driven Approach

Superspreading has become a key mechanism of COVID-19 transmission which creates chaos. The classical approach of compartmental models may not sufficiently reflect the epidemiological situation amid superspreading events (SSEs). We perform a data-driven approach and recognise the deterministic chaos of confirmed cases. The first derivative ( ≈difference of total confirmed cases) and the second derivative ( ≈difference of the first derivative) are used upon SSEs to showcase the chaos. Varying solution trajectories, sensitivity and numerical unpredictability are the chaotic characteristics discussed here.

Mathematical analysis of the impact of the media coverage in mitigating the outbreak of COVID-19

In this paper, a mathematical model with a standard incidence rate is proposed to assess the role of media such as facebook, television, radio and tweeter in the mitigation of the outbreak of COVID-19. The basic reproduction numberR 0 which is the threshold dynamics parameter between the disappearance and the persistence of the disease has been calculated. And, it is obvious to see that it varies directly to the number of hospitalized people, asymptomatic, symptomatic carriers and the impact of media coverage. The local and the global stabilities of the model have also been investigated by using the Routh-Hurwitz criterion and the Lyapunov's functional technique, respectively. Furthermore, we have performed a local sensitivity analysis to assess the impact of any variation in each one of the model parameter on the thresholdR 0 and the course of the disease accordingly. We have also computed the approximative rate at which herd immunity will occur when any control measure is implemented. To finish, we have presented some numerical simulation results by using some available data from the literature to corroborate our theoretical findings.

[Knowledge of COVID-19 and hand washing]

BACKGROUND

The best way to prevent counting the COVID-19 is hand washing. However, there is little dissemination about the importance that it has and unfortunately it has not been given the importance it deserves.

OBJECTIVE

To determine the knowledge and practices that the relatives of the 5 semester nursing students have about COVID-19 and hand washing.

METHOD

Descriptive research, with an intentional sampling of 83 people to respond to a pretest and posts with a preventive and informative training as a mediating tool for the use of presaberes, was carried out between March and May 2020.

RESULTS

The informants had basic and adequate prior knowledge of what is involved in the practice of hand washing. A reflective interpretation was made with the design of an info graphic that visualized the state of opinion from a comparison between the instruments. The results and discussion suggested that the practice of hand washing is more than just a simple act.

CONCLUSIONS

There was prior knowledge that was strengthened by training and it was interpreted that health is a heritage that must be safeguarded with responsibility and biosecurity.

Adoption of measures to mitigate the impact of COVID-19: In search of a Hofstedian explanation for patterns among individual countries and country clusters

We consider multiple safety measures in relation to the COVID-19 virus and look at their adoption levels for a variety of 15 individual countries, based on data from Yougov.co.uk. Subsequently, we establish correlation coefficients between measure-specific uptake levels and Hofstede dimension scores for all countries considered. We notably find that Power Distance Index (PDI) and Individualism (IDV) have a considerable explanatory power. In addition, we carried out a Principal Components Analysis (PCA) and a cluster analysis to see whether the behavioural patterns across countries can be grouped, and which Hofstede dimensions correlate strongest with the two main components that follow from the PCA. The PCA provides further confirmation of PDI and IDV being the most important explanatory factors for the uptake of measures across countries. The cluster analysis, in turn, reveals four broad groups, which only partly coincide with the way that the mental image clustering scheme by Wursten (2019) allots countries into its respective clusters. Hence, this provides a basis to suggest that data-driven exercises like the ones from our paper can serve to adjust Wursten's intuitive scheme.

Discrete models for analyzing the behavior of COVID-19 pandemic in the State of Mexico, Mexico

In this paper we analyze the behavior of the COVID-19 pandemic during a certain period of the year 2020 in the state of Mexico, Mexico. For this, we will use the discrete models obtained by the first, third and fourth authors of this work. The first is a one-dimensional model, and the second is two-dimensional, both non-linear. It is assumed that the population of the state of Mexico is constant and that the parameters used are the infection capacity, which we will initially assume to be constant, and the recovery and mortality parameters in that state. We will show that even when the statistical data obtained are disperse, and the process could be stabilized, this has been slow due to chaotic mitigation, creating situations of economic, social, health and political deterioration in that region of the country. We note that the observed results of the behavior of the epidemic during that period for the first variants of the virus have continued to be observed for the later variants, which has not allowed the eradication of the pandemic.

Identification and structural studies of natural inhibitors against SARS-CoV-2 viral RNA methyltransferase (NSP16)

Pathogenic RNA viruses are emerging as one of the major threats and posing challenges to human community. RNA viruses have an exceptionally shorter generation time and easy to adapt in host cells. The recent emergence of SARS-CoV-2, a long RNA virus, has shown us how difficult it is to overcome this kind of pandemic without understanding the viral infection and replication mechanisms. It is essential to comprehend replications of the viral genome, including RNA polymerization and the final capping process. The mRNAs of SARS-CoV-2 coronaviruses are protected at their 5'-ends by cap structure. The cap-like system plays a significant role in viral translational process, viral RNA stability, and scatting in detecting innate immune recognition in host cells. Two coronavirus enzymes, Nsp14 and Nsp16, critically help in the formation of capping and are considered as potential drug targets for antiviral therapy. Natural and herbal medicines have a past record of treating various acute respiratory diseases. In this work, we have exploited 56000 natural compounds to screen potential inhibitors against NSP16. In silico virtual screening, docking and Molecular Dynamics (MD) simulation studies were performed to understand how these potential inhibitors are bound to NSP16. We observed that the most highly screened compound binds to protein molecules with a high dock score, primarily through hydrophobic interactions and hydrogen bonding, as previously reported for NSP16. Compound-13 (2-hydroxy-N-({1-[2-hydroxy-1-(hydroxymethyl)ethyl]piperidin-3-yl}methyl)-5-methylbenzamide) and compound-51 (N-(2-isobutoxybenzyl)-N,2-dimethyl-2,8-diazaspiro[4.5]decane-3-carboxamide) occupied in active site along with good pharmokinetices properties. In conclusion, the selected compounds could be used as a novel therapeutic against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Impacts of COVID-19 Restrictions on Regional and Local Air Quality Across Selected West African Cities

The emergence of COVID-19 brought with it panic and a sense of urgency causing governments to impose strict restrictions on human activities and vehicular movements. With anthropogenic emissions, especially waste management (domestic and municipal), traffic, and industrial activities, said to be a significant contributor to ambient air pollution, this study assessed the impacts of the imposed restrictions on the concentrations and size distribution of atmospheric aerosols and concentration of gaseous pollutants over West African subregion and seven major COVID-19 epicenters in the subregion. Satellite retrievals and reanalysis data sets were used to study the impact of the restrictions on Aerosol Optical Depth (AOD) and atmospheric concentrations NO2, SO2, CO, and O3. The anomalies were computed for 2020 relative to 2017-2019 (the reference years). In 2020 relative to the reference years, for area-averaged AOD levels, there was a consequential mean percentage change between -6.7% ± 21.0% and 19.2% ± 27.9% in the epicenters and -10.1% ± 15.4% over the subregion. The levels of NO2 and SO2 also reduced substantially at the epicenters, especially during the periods when the restrictions were highly enforced. However, the atmospheric levels of CO and ozone increased slightly in 2020 compared to the reference years. This study shows that "a one cap fits all" policy cannot reduced the level of air pollutants and that traffic and industrial processes are not the predominant sources of CO in major cities in the subregion.

SQEIR: An epidemic virus spread analysis and prediction model

In 2019, a new strain of coronavirus pneumonia spread quickly worldwide. Viral propagation may be simulated using the Susceptible Infectious Removed (SIR) model. However, the SIR model fails to consider that separation of patients in the COVID-19 incubation stage entails difficulty and that these patients have high transmission potential. The model also ignores the positive effect of quarantine measures on the spread of the epidemic. To address the two flaws in the SIR model, this study proposes a new infectious disease model referred to as the Susceptible Quarantined Exposed Infective Removed (SQEIR) model. The proposed model uses the weighted least squares for the optimal estimation of important parameters in the infectious disease model. Based on these parameters, new differential equations were developed to describe the spread of the epidemic. The experimental results show that this model exhibits an accuracy 6.7% higher than that of traditional infectious disease models.

The association between peritraumatic distress, perceived stress, depression in pregnancy, and NR3C1 DNA methylation among Chinese pregnant women who experienced COVID-19 lockdown

Prenatal stress can affect pregnant women in an epigenetic way during the critical period of conception of their offspring. The study aims to investigate the relationship between peritraumatic distress, prenatal perceived stress, depression, and glucocorticoid receptor (NR3C1) DNA methylation among pregnant women who experienced COVID-19 lockdown in China. Study data were collected from 30 pregnant women in Wuhan and Huanggang, China. The Peritraumatic Distress Inventory was used to measure peritraumatic distress, the Edinburgh Postnatal Depression Scale was used to measure depressive symptoms, and the Perceived Stress Scale was used to measure perceived stress. DNA methylation in the exon 1F promoter region of NR3C1 gene from the venous blood mononuclear cell genome was characterized by bisulfite sequencing. Correlation and linear regression were used for data analysis. The mean level of peritraumatic distress, perceived stress, and depression was 6.30 (SD = 5.09), 6.50 (SD = 5.41), and 6.60 (SD = 4.85), respectively, with 23.33% of pregnant women being depressed. The mean NR3C1 methylation was 0.65 (SD = 0.22). Prenatal depression was positively correlated with the degree of methylation in venous blood from the mother (r = 0.59, p = 0.001), and depression predicted methylation of NR3C1 gene at the CpG 8 site (β = 0.05, p = 0.03). No association was found between peritraumatic distress as well as perceived stress and methylation of NR3C1. NR3C1 gene was susceptible to epigenetic modification of DNA methylation in the context of prenatal stress, and maternal depression was associated with increased NR3C1 methylation among women who experienced COVID-19 lockdown.

Predicting COVID-19 Infections in Eswatini Using the Maximum Likelihood Estimation Method

COVID-19 country spikes have been reported at varying temporal scales as a result of differences in the disease-driving factors. Factors affecting case load and mortality rates have varied between countries and regions. We investigated the association between socio-economic, weather, demographic and health variables with the reported cases of COVID-19 in Eswatini using the maximum likelihood estimation method for count data. A generalized Poisson regression (GPR) model was fitted with the data comprising 15 covariates to predict COVID-19 risk in the whole of Eswatini. The results show that the variables that were key determinants in the spread of the disease were those that included the proportion of elderly above 55 years at 98% (95% CI: 97–99%) and the proportion of youth below the age of 35 years at 8% (95% CI: 1.7–38%) with a pseudo R-square of 0.72. However, in the early phase of the virus when cases were fewer, results from the Poisson regression showed that household size, household density and poverty index were associated with reported COVID-19 cases in the country. We then produced a disease-risk map of predicted COVID-19 in Eswatini using variables that were selected by the regression model at a 5% significance level. The map could be used by the country to plan and prioritize health interventions against COVID-19. The identified areas of high risk may be further investigated to find out the risk amplifiers and assess what could be done to prevent them.

Queueing Theory and COVID-19 Prevention: Model Proposal to Maximize Safety and Performance of Vaccination Sites

Introduction

COVID-19 (Coronavirus Disease 19) has rapidly spread all around the world. Vaccination represents one of the most promising counter-pandemic measures. There is still little specific evidence in literature on how to safely and effectively program access and flow through specific healthcare settings to avoid overcrowding in order to prevent SARS-CoV-2 transmission. Literature regarding appointment scheduling in healthcare is vast. Unpunctuality however, especially when targeting healthcare workers during working hours, is always possible. Therefore, when determining how many subjects to book, using a linear method assuming perfect adhesion to scheduled time could lead to organizational problems.

Methods

This study proposes a "Queuing theory" based approach. A COVID-19 vaccination site targeting healthcare workers based in a teaching hospital in Rome was studied to determine real-life arrival rate variability. Three simulations using Queueing theory were performed.

Results

Queueing theory application reduced subjects queueing over maximum safety requirements by 112 in a real-life based vaccination setting, by 483 in a double-sized setting and by 750 in a mass vaccination model compared with a linear approach. In the 3 settings, respectively, the percentage of station's time utilization was 98.6, 99.4 and 99.8%, while the average waiting time was 27.2, 33.84, and 33.84 min.

Conclusions

Queueing theory has already been applied in healthcare. This study, in line with recent literature developments, proposes the adoption of a Queueing theory base approach to vaccination sites modeling, during the COVID-19 pandemic, as this tool enables to quantify ahead of time the outcome of organizational choices on both safety and performance of vaccination sites.

Modelling the spreading of the SARS-CoV-2 in presence of the lockdown and quarantine measures by a kinetic-type reactions approach

We propose a realistic model for the evolution of the COVID-19 pandemic subject to the lockdown and quarantine measures, which takes into account the timedelay for recovery or death processes. The dynamic equations for the entire process are derived by adopting a kinetic-type reactions approach. More specifically, the lockdown and the quarantine measures are modelled by some kind of inhibitor reactions where susceptible and infected individuals can be trapped into inactive states. The dynamics for the recovered people is obtained by accounting people who are only traced back to hospitalized infected people. To get the evolution equation we take inspiration from the Michaelis Menten's enzyme-substrate reaction model (the so-called MM reaction) where the enzyme is associated to the available hospital beds, the substrate to the infected people, and the product to the recovered people, respectively. In other words, everything happens as if the hospitals beds act as a catalyzer in the hospital recovery process. Of course, in our case, the reverse MM reaction has no sense in our case and, consequently, the kinetic constant is equal to zero. Finally, the ordinary differential equations (ODEs) for people tested positive to COVID-19 is simply modelled by the following kinetic scheme $S+I\Rightarrow 2I$ with $I\Rightarrow R$ or $I\Rightarrow D$, with $S$, $I$, $R$ and $D$ denoting the compartments susceptible, infected, recovered and deceased people, respectively. The resulting kinetic-type equations provide the ODEs, for elementary reaction steps, describing the number of the infected people, the total number of the recovered people previously hospitalized, subject to the lockdown and the quarantine measure and the total number of deaths. The model foresees also the second wave of infection by coronavirus. The tests carried out on real data for Belgium, France and Germany confirmed the correctness of our model.

Factors associated with COVID-19 vaccination behaviour in Latvian population: cross-sectional study

Vaccination is the most effective way of limiting the spread of COVID-19. However, despite the proven effectiveness and safety of vaccines, there is resistance in society and the course of vaccination is slow. The aim of this study was to identify the factors associated with COVID-19 vaccination behaviour.

Methods: The data originate from a representative sample of Latvian residents (N  = 1017) taken in September 2021. The data were analysed using Chi-squared test, Mann–Whitney test, Spearman's rank correlation coefficient, Kruskal Wallis test, and Binary Logistic regression analysis.

Results: The results of the study reveal several factors that are associated with COVID-19 vaccination behaviour. These factors are a higher level of education, motivation for protecting oneself against illness, for promoting collective immunity, protecting relatives and other people from infection, as well as motivation for vaccination in order to keep a job or continue studies, and institutional trust. On the other hand, perceived social support from relatives is negatively associated with vaccination behaviour.

Conclusion: A higher level of education and confidence in evidence-based information on COVID-19, provided by official sources of information, is the key factor in deciding whether to protect oneself from serious illness or to make a choice to promote collective immunity and protect other people. The need for vaccination in order to continue working and/or learning is also an essential motive for vaccination. On the other hand, the belief that, if necessary, it is possible to receive support from relatives may be a delaying factor in the behaviour of vaccination.

Comparative Content, Biological and Anticancer Activities of Heracleum humile Extracts Obtained by Ultrasound-Assisted Extraction Method

As the safety and effectiveness of synthetic drugs remain in doubt, researchers are trying to develop natural medicines from medicinal plants. Herein, ethyl acetate, methanol and water extracts from the Heracleum humile plant were obtained by an ultrasonic-assisted extraction process and the aim was to evaluate some biological effects of the extracts due to the limited data on the pharmacological properties of Heracleum humile in the literature. Weak antibacterial activity was observed on tested bacterial species. The minimum inhibitory concentration and the minimum bactericidal concentration values ranged from 250 to 500 μg/mL. In addition, cytotoxic activity was determined using the MTT test. The strongest findings were determined for ethyl acetate extract on the MDA-MB-231 cell lines at the 48^th  hour (IC₅₀ :97.94 μg/mL), followed by the MCF-7 cell lines at the 24^th  hour (IC₅₀ :103.9 μg/mL). All extracts of Heracleum humile contained mainly flavonoids, phenolic acids and their derivatives, i. e., well-known compounds that possess numerous biological activities such as antioxidant, anti-inflammatory, anticancer, antimicrobial etc. The study results could provide important information that Heracleum humile could be a potential candidate as a natural enzyme inhibitor. It can be concluded that these extracts could be useful in the elementary step of improving novel plant-derived multifunctional pharmaceuticals.

Analysis and prediction of COVID-19 epidemic in South Africa

The coronavirus disease-2019 (COVID-19) has been spreading rapidly in South Africa (SA) since its first case on 5 March 2020. In total, 674,339 confirmed cases and 16,734 mortality cases were reported by 30 September 2020, and this pandemic has made severe impacts on economy and life. In this paper, analysis and long-term prediction of the epidemic dynamics of SA are made, which could assist the government and public in assessing the past Infection Prevention and Control Measures and designing the future ones to contain the epidemic more effectively. A Susceptible-Infectious-Recovered model is adopted to analyse epidemic dynamics. The model parameters are estimated over different phases with the SA data. They indicate variations in the transmissibility of COVID-19 under different phases and thus reveal weakness of the past Infection Prevention and Control Measures in SA. The model also shows that transient behaviours of the daily growth rate and the cumulative removal rate exhibit periodic oscillations. Such dynamics indicates that the underlying signals are not stationary and conventional linear and nonlinear models would fail for long-term prediction. Therefore, a large class of mappings with rich functions and operations is chosen as the model class and the evolutionary algorithm is utilized to obtain the optimal model for long term prediction. The resulting models on the daily growth rate, the cumulative removal rate and the cumulative mortality rate predict that the peak and inflection point will occur on November 4, 2020 and October 15, 2020, respectively; the virus shall cease spreading on April 28, 2021; and the ultimate numbers of the COVID-19 cases and mortality cases will be 785,529 and 17,072, respectively. The approach is also benchmarked against other methods and shows better accuracy of long-term prediction.

Biology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the humoral immunoresponse: a systematic review of evidence to support global policy-level actions and research

BACKGROUND

Both population-level epidemiological data and individual-level biological data are needed to control the coronavirus disease 2019 (COVID-19) pandemic. Population-level data are widely available and efforts to combat COVID-19 have generated proliferate data on the biology and immunoresponse to the causative pathogen, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there remains a paucity of systemized data on this subject.

OBJECTIVE

In this review, we attempt to extract systemized data on the biology and immuno-response to SARS-CoV-2 from the most up-to-date peer-reviewed studies. We will focus on the biology of the virus and immunological variations that are key for determining long-term immunity, transmission potential, and prognosis.

DATA SOURCES AND METHODS

Peer-reviewed articles were sourced from the PubMed database and by snowballing search of selected publications. Search terms included: "Novel Coronavirus" OR "COVID-19" OR "SARS-CoV-2" OR "2019-nCoV" AND "Immunity" OR "Immune Response" OR "Antibody Response" OR "Immunologic Response". Studies published from December 31, 2019 to December 31, 2020 were included. To ensure validity, papers in pre-print were excluded.

RESULTS

Of 2 889 identified papers, 36 were included. Evidence from these studies suggests early seroconversion in patients infected with SARS-CoV-2. Antibody titers appear to markedly increase two weeks after infection, followed by a plateau. A more robust immune response is seen in patients with severe COVID-19 as opposed to mild or asymptomatic presentations. This trend persists with regard to the length of antibody maintenance. However, overall immunity appears to wane within two to three months post-infection.

CONCLUSION

Findings of this study indicate that immune responses to SARS-CoV-2 follow the general pattern of viral infection. Immunity generated through natural infection appears to be short, suggesting a need for long-term efforts to control the pandemic. Antibody testing will be essential to gauge the epidemic and inform decision-making on effective strategies for treatment and prevention. Further research is needed to illustrate immunoglobulin-specific roles and neutralizing antibody activity.

Cancer Patients’ Challenges During COVID-19 Pandemic: An Approach to Decision-Making in Management and Policy-Making

Context: Cancer patients are more susceptible to novel coronavirus infection due to their immune system deficiency and anticancer treatments. During the COVID-19 outbreak, cancer patients have faced many challenges. The present study aimed to review the literature on cancer patients’ challenges during the COVID-19 pandemic to offer an approach to decision-making in management and policy-making. Evidence Acquisition: In this review study, national and international databases were searched. Inclusion Criteria were the risk of COVID-19 in cancer patients, medical services, surgery, and cancer screening during the COVID-19 pandemic, cancer patients’ challenges during the COVID-19 pandemic, and management and policy-making in this pandemic. Results: Cancer patients’ challenges during the COVID 19 pandemic were classified as follows: (1) risk of COVID-19 for cancer patients; (2) access to medical services and screening during the COVID-19 pandemic; and (3) psychological disorders during the COVID-19 pandemic. Studies have revealed that individuals with cancer experience a higher risk of the COVID-19 infection and mortality compared to healthy individuals. Most oncology clinics postpone unnecessary appointments; however, patients with invasive cancer are treated with no delay. Proper management, disease control, and attention to mental health care can prevent psychological disorders. Conclusions: Managing cancer patients’ challenges during the SARS-CoV-2 is of paramount importance. Cancer clinics need to develop new care and follow-up protocols. Moreover, policy-makers should provide appropriate policies to address the challenges of this disease in the future.

Transmission of COVID-19 from community to healthcare agencies and back to community: a retrospective study of data from Wuhan, China

BACKGROUND

The early spatiotemporal transmission of COVID-19 remains unclear. The community to healthcare agencies and back to community (CHC) model was tested in our study to simulate the early phase of COVID-19 transmission in Wuhan, China.

METHODS

We conducted a retrospective study. COVID-19 case series reported to the Municipal Notifiable Disease Report System of Wuhan from December 2019 to March 2020 from 17 communities were collected. Cases from healthcare workers (HW) and from community members (CM) were distinguished by documented occupations. Overall spatial and temporal relationships between HW and CM COVID-19 cases were visualised. The CHC model was then simulated. The turning point separating phase 1 and phase 2 was determined using a quadratic model. For phases 1 and 2, linear regression was used to quantify the relationship between HW and CM COVID-19 cases.

RESULTS

The spatial and temporal distributions of COVID-19 cases between HWs and CMs were closely correlated. The turning point was 36.85±18.37 (range 15-70). The linear model fitted well for phase 1 (mean R2=0.98) and phase 2 (mean R2=0.93). In phase 1, the estimated [Formula: see text]s were positive (from 18.03 to 94.99), with smaller [Formula: see text]s (from 2.98 to 15.14); in phase 2, the estimated [Formula: see text]s were negative (from -4.22 to -81.87), with larger [Formula: see text]s (from 5.37 to 78.12).

CONCLUSION

Transmission of COVID-19 from the community to healthcare agencies and back to the community was confirmed in Wuhan. Prevention and control measures for COVID-19 in hospitals and among HWs are crucial and warrant further attention.

Resilient and Accelerated Scale-Up of Subcutaneously Administered Depot-Medroxyprogesterone Acetate in Nigeria (RASuDiN): A Mid-Line Study in COVID-19 Era

Background

Injectable contraceptives are fast becoming the method of choice among women in sub-Saharan Africa (SSA). Specifically, the subcutaneously administered depot–medroxyprogesterone acetate (DMPA-SC) is gaining traction as a convenient, private and effective method to address unmet need for family planning (FP). The objective of this study was to determine the trend in DMPA-SC use in Nigeria.

Methods

Data was extracted from the National Health Management Information System (NHMIS) FP register on DMPA-SC uptake in public health facilities and through community-oriented resource providers (CORPS) in 10 Nigerian states. The linear trend model was adopted in data analysis based on lowest measure of dispersion and/or highest adjusted coefficient of determination (R²). The statistical significance was determined at 5%.

Results

There was an upward trend in the use of DMPA-SC among clients who received the service through health providers, CORPS and self-injection in the 10 project states over a period of 12 months (August 2019–July 2020). In addition, the linear trend model showed that for every unit increase in months, the average number of women expected to use DMPA-SC through health providers, CORPS and self-injection will increase by 1308.3 (Yt = 3799.7 +1308.3*t), 756.73 (Yt = −1030.8 +756.73*t) and 77.864 (Yt = −159.7 +77.864*t) respectively. In all models, the adjusted coefficient of determination was 99.9% which showed good model fitness. The results also showed that the number of DMPA-SC clients varied across the project states with Niger (32,988) and Oyo (31,511) states reporting the highest number of clients over the period of 12 months.

Conclusion

There was an increasing use of DMPA-SC and self–injection among clients over time. Health facility and community-based FP programs should be strengthened to ensure improved access to FP services.

Modelling the effect of Covid-19 mortality on the economy of Nigeria

Objectives

This paper is aimed at modelling the effect of COVID-19 mortality per population (CMP), a proxy for COVID-19 on the Gross Domestics Product (GDP) per capita per COVID-19 cases (RGDPC), a proxy for the economic wellbeing of a nation.

Methods

Nine models divided into three groups (Gaussian polynomial, other non-linear, and Gamma generalized polynomial models) were fitted for RGDPC data on CMP, collected from 1st June to 31st December 2020.

Results

The result showed that the gamma cubic model was selected as the best model out of the 9 competing models to predict the economic wellbeing of Nigeria. Predictions were made for the whole day in the year 2021.

Conclusion

It is therefore concluded that there is a non-linear relationship between COVID-19 mortality and the economic wellbeing of Nigerians. Thus, COVID-19 mortality has an adverse effect on the wellbeing of Nigerians. The economic wellbeing of Nigerians can be improved if COVID-19 mortality is stopped.

Trends in non-pharmaceutical intervention (NPI) related community practice for the prevention of COVID-19 in Addis Ababa, Ethiopia

BACKGROUND

The COVID-19 pandemic has affected Ethiopia since March 13, 2020, when the first case was detected in Addis Ababa. Since then, the incidence of cases has continued to increase day by day. As a result, the health sector has recommended universal preventive measures to be practiced by the public. However, studies on adherence to these preventive measures are limited.

OBJECTIVE

To monitor the status of preventive practices of the population related to hand washing, physical distancing, and respiratory hygiene practices at selected sites within the city of Addis Ababa.

METHODS

Weekly cross-sectional non-participatory observations were done during the period of April-June, 2020. Data was collected using the Open Data Kit (ODK) tool in ten public sites involving eight public facilities targeted for individual observations. Ten individuals were randomly observed at each facility over two days a week at peak hours of public services. WHO operational definitions of the preventive behaviors were adopted for this study. Observations were conducted anonymously at gates or entrances of public facilities and places.

RESULTS

A total of 12,056 individual observations with 53% males and 82% in an estimated age range of 18-50 years age group were involved in this study. There was an increase in the practice of respiratory hygiene from 14% in week one to 77% in week 10, while those of hand hygiene and physical distancing changed little over the weeks from their baseline of 24% and 34%, respectively. Overall, respiratory hygiene demonstrated an increased rate of 6% per week, while hand hygiene and physical distancing had less than a 1% change per week, Females and the estimated age group of 18-50 years had practice changes in respiratory hygiene with no difference in hand hygiene and physical distancing practices. Respiratory hygiene took about six weeks to reach a level of 77% from its baseline of 24%, making an increment of about 9% per week.

CONCLUSION

The public practice of respiratory hygiene improved threefold whereas hand hygiene and physical distancing revealed no change. Regularly sustained public mobilization and mass education are required to sustain the achievements gained in respiratory hygiene and further hand hygiene and physical distancing.

Clinical Impact, Costs, and Cost-effectiveness of Expanded Severe Acute Respiratory Syndrome Coronavirus 2 Testing in Massachusetts

BACKGROUND

We projected the clinical and economic impact of alternative testing strategies on coronavirus disease 2019 (COVID-19) incidence and mortality in Massachusetts using a microsimulation model.

METHODS

We compared 4 testing strategies: (1) hospitalized: polymerase chain reaction (PCR) testing only for patients with severe/critical symptoms warranting hospitalization; (2) symptomatic: PCR for any COVID-19-consistent symptoms, with self-isolation if positive; (3) symptomatic + asymptomatic once: symptomatic and 1-time PCR for the entire population; and (4) symptomatic + asymptomatic monthly: symptomatic with monthly retesting for the entire population. We examined effective reproduction numbers (Re = 0.9-2.0) at which policy conclusions would change. We assumed homogeneous mixing among the Massachusetts population (excluding those residing in long-term care facilities). We used published data on disease progression and mortality, transmission, PCR sensitivity/specificity (70%/100%), and costs. Model-projected outcomes included infections, deaths, tests performed, hospital-days, and costs over 180 days, as well as incremental cost-effectiveness ratios (ICERs, $/quality-adjusted life-year [QALY]).

RESULTS

At Re = 0.9, symptomatic + asymptomatic monthly vs hospitalized resulted in a 64% reduction in infections and a 46% reduction in deaths, but required >66-fold more tests/day with 5-fold higher costs. Symptomatic + asymptomatic monthly had an ICER <$100 000/QALY only when Re ≥1.6; when test cost was ≤$3, every 14-day testing was cost-effective at all Re examined.

CONCLUSIONS

Testing people with any COVID-19-consistent symptoms would be cost-saving compared to testing only those whose symptoms warrant hospital care. Expanding PCR testing to asymptomatic people would decrease infections, deaths, and hospitalizations. Despite modest sensitivity, low-cost, repeat screening of the entire population could be cost-effective in all epidemic settings.

Mathematical epidemiologic and simulation modelling of first wave COVID-19 in Malaysia

Since the first coronavirus disease 2019 (COVID-19) outbreak appeared in Wuhan, mainland China on December 31, 2019, the geographical spread of the epidemic was swift. Malaysia is one of the countries that were hit substantially by the outbreak, particularly in the second wave. This study aims to simulate the infectious trend and trajectory of COVID-19 to understand the severity of the disease and determine the approximate number of days required for the trend to decline. The number of confirmed positive infectious cases [as reported by Ministry of Health, Malaysia (MOH)] were used from January 25, 2020 to March 31, 2020. This study simulated the infectious count for the same duration to assess the predictive capability of the Susceptible-Infectious-Recovered (SIR) model. The same model was used to project the simulation trajectory of confirmed positive infectious cases for 80 days from the beginning of the outbreak and extended the trajectory for another 30 days to obtain an overall picture of the severity of the disease in Malaysia. The transmission rate, β also been utilized to predict the cumulative number of infectious individuals. Using the SIR model, the simulated infectious cases count obtained was not far from the actual count. The simulated trend was able to mimic the actual count and capture the actual spikes approximately. The infectious trajectory simulation for 80 days and the extended trajectory for 110 days depicts that the inclining trend has peaked and ended and will decline towards late April 2020. Furthermore, the predicted cumulative number of infectious individuals tallies with the preparations undertaken by the MOH. The simulation indicates the severity of COVID-19 disease in Malaysia, suggesting a peak of infectiousness in mid-March 2020 and a probable decline in late April 2020. Overall, the study findings indicate that outbreak control measures such as the Movement Control Order (MCO), social distancing and increased hygienic awareness is needed to control the transmission of the outbreak in Malaysia.

Funeral Processes During the COVID-19 Pandemic: Perceptions Among Islamic Religious Leaders in Indonesia

Controversies surrounding the handling of corpses have been amplified during the present COVID-19 pandemic. According to Indonesian scholars, certain perspectives driving these controversies inhibit the implementation of health protocols issued by the government. This study comprehensively explores the diverse perceptions and responses of religious leaders regarding COVID-19 funeral management. Participants comprised six scholars from major Islamic religious organizations, two community leaders, and two families representing COVID-19 patients. Furthermore, content analysis was used to analyze the data. The results showed that the religious leaders, all men aged over 50 years, supported the health directives designed to reduce high transmission risk. However, there were substantial disparities in corpse preparation processes, potentially due to organizational beliefs around burial rites. Some religious leaders aligned their protocols with their religious beliefs. Conversely, families of the deceased insisted that the approved protocol for handling corpses went against their religious and cultural values. Therefore, promotion of protocols and coordination among the government, religious leaders, and the community are needed to decrease the misperceptions and misinformation surrounding the new COVID-19 funeral protocols.

Aerosolization risk during endoscopic transnasal surgery: a prospective qualitative and quantitative microscopic analysis of particles spreading in the operating room

OBJECTIVE

The coronavirus disease 2019 (COVID-19) pandemic represents the greatest public health emergency of this century. The primary mode of viral transmission is droplet transmission through direct contact with large droplets generated during breathing, talking, coughing, and sneezing. However, the virus can also demonstrate airborne transmission through smaller droplets (< 5 μm in diameter) generated during various medical procedures, collectively termed aerosol-generating procedures. The aim of this study was to analyze droplet contamination of healthcare workers and splatter patterns in the operating theater that resulted from endoscopic transnasal procedures in noninfected patients.

METHODS

A prospective nonrandomized microscopic evaluation of contaminants generated during 10 endoscopic transnasal procedures performed from May 14 to June 11, 2020, in the same operating theater was carried out. A dilution of monosodium fluorescein, repeatedly instilled through nasal irrigation, was used as a marker of contaminants generated during surgical procedures. Contaminants were collected on detectors worn by healthcare workers and placed in standard points in the operating theater. Analysis of number, dimensions, and characteristics of contaminants was carried out with fluorescence microscopy.

RESULTS

A total of 70 samples collected from 10 surgical procedures were analyzed. Liquid droplets and solid-tissue fragments were identified as contaminants on all detectors analyzed. All healthcare workers appeared to have been exposed to a significant number of contaminants. A significant degree of contamination was observed in every site of the operating room. The mean (range) diameter of liquid droplets was 4.1 (1.0-26.6) μm and that of solid fragments was 23.6 (3.5-263.3) μm.

CONCLUSIONS

Endoscopic endonasal surgery is associated with the generation of large amounts of contaminants, some of which measure less than 5 μm. All healthcare workers in the surgical room are exposed to a significant and similar risk of contamination; therefore, adequate personal protective equipment should be employed when performing endoscopic endonasal surgical procedures.

Levels of economic growth and cross-province spread of the COVID-19 in China

BACKGROUND

After the first COVID-19 case detected on 8 December 2019 in Wuhan, the Provincial Capital of Hubei, the epidemic quickly spread throughout the whole country of China. Low developmental levels are often associated with infectious disease epidemic, this study attempted to test this notion with COVID-19 data.

METHODS

Data by province from 8 December 2019 to 16 February 2020 were analysed using regression method. Outcomes were days from the first COVID-19 case in the origin of Hubei Province to the date when case was first detected in a destination province, and cumulative number of confirmed cases. Provincial gross domestic products (GDPs) were used to predict the outcomes while considering spatial distance and population density.

RESULTS

Of the total 70 548 COVID-19 cases in all 31 provinces, 58 182 (82.5%) were detected in Hubei and 12 366 (17.5%) in other destination provinces. Regression analysis of data from the 30 provinces indicated that GDP was negatively associated with days of virus spreading (β=-0.2950, p<0.10) and positively associated with cumulative cases (β=97.8709, p<0.01) after controlling for spatial distance. The relationships were reversed with β=0.1287 (p<0.01) for days and β=-54.3756 (p<0.01) for cumulative cases after weighing in population density and controlling for spatial distance.

CONCLUSION

Higher levels of developmental is a risk factor for cross-province spread of COVID-19. This study adds new data to literature regarding the role of economic growth in facilitating spatial spreading of infectious diseases, and provides timely data informing antiepidemic strategies and developmental plan to balance economic growth and people's health.

Decoding China’s COVID‐19 ‘virus exceptionalism’: Community‐based digital contact tracing in Wuhan

During the COVID‐19 pandemic, comprehensive, accurate, and timely digital contact tracing serves as a decisive measure in curbing viral transmission. Such a strategy integrates corporate innovation, government decision‐making, citizen participation, and community coordination with big data analytics. This article explores how key stakeholders in an open innovation ecosystem interact within the digital context to overcome challenges to public health and socio‐economic welfare imposed by the pandemic. To enhance the digital contact tracing effectiveness, communities are deployed to moderate the interactions between government, enterprises and citizens. As an example, we study the community‐based digital contact tracing in Wuhan, a representative case of China’s ‘virus exceptionalism’ in COVID‐19 mitigation. We discuss the effectiveness of this strategy and raise critical ethical concerns regarding decision‐making in R&D management.

Multiplicative Holts Winter Model for Trend Analysis and Forecasting of COVID-19 Spread in India

The surge of the novel COVID-19 caused a tremendous effect on the health and life of the people resulting in more than 4.4 million confirmed cases in 213 countries of the world as of May 14, 2020. In India, the number of cases is constantly increasing since the first case reported on January 30, 2020, resulting in a total of 81,997 cases including 2649 deaths as of May 14, 2020. To assist the government and healthcare sector in preventing the transmission of disease, it is necessary to predict the future confirmed cases. To predict the dynamics of COVID-19 cases, in this paper, we project the forecast of COVID-19 for five most affected states of India such as Maharashtra, Tamil Nadu, Delhi, Gujarat, and Andhra Pradesh using the real-time data. Using Holt–Winters method, a forecast of the number of confirmed cases in these states has been generated. Further, the performance of the method has been determined using RMSE, MSE, MAPE, MAE and compared with other standard algorithms. The analysis shows that the proposed Holt–Winters model generates RMSE value of 76.0, 338.4, 141.5, 425.9, 1991.5 for Andhra Pradesh, Maharashtra, Gujarat, Delhi and Tamil Nadu, which results in more accurate predictions over Holt’s Linear, Auto-regression (AR), Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA) model. These estimations may further assist the government in employing strong policies and strategies for enhancing healthcare support all over India.

Mental Health of Nursing Students amid Coronavirus Disease 2019 Pandemic

The coronavirus disease 2019 (COVID-19) pandemic is a global disaster, and recent studies have shown its association with increasing mental health problems such as post-traumatic stress disorder (PTSD), depression, anxiety, and stress. Nursing students, especially nursing interns, may be shunned, harassed, and even blamed as potential COVID-19 spreaders, though they were an important reserve force against COVID-19 and other diseases. Of note, the psychological influences of COVID-19 on nursing students remained unclear. The aim of this study was to evaluate the mental health of nursing students during the COVID-19 pandemic. A cross-sectional online survey was conducted on nursing students in a vocational college from April 12 to 23, 2020. The Impact of Event Scale-Revised, 21-item Depression, Anxiety and Stress Scale, and Pittsburgh Sleep Quality Index were used to assess the degree of symptoms of PTSD, depression, anxiety, stress, and insomnia, respectively. Multivariable logistic regression analysis was performed to determine the potential risk factors for the psychological symptoms. A total of 1,780 college nursing students were asked to participate in this online survey, with 1,532 complete responses. In total, 682 (44.5%) college nursing students reported having PTSD, 358 (22.8%) students reported insomnia, and few students reported depression (n = 45, 2.9%), anxiety (n = 44, 2.9%), and stress (n = 17, 1.1%) symptoms. As compared with junior, female, and rural nursing students, the senior, male, and urban nursing students had higher rates of PTSD, depression, anxiety, stress, respectively, whereas male nursing students had a higher insomnia rate. Multivariable analysis showed that senior nursing students had higher risks of PTSD, depression, anxiety; being male was associated with higher risks of PTSD, depression, anxiety, stress, and insomnia; and urban nursing students had higher risks of PTSD, depression, anxiety, and stress. In summary, a considerable number of nursing students reported mental symptoms of PTSD and insomnia, though few reported mental symptoms of depression, anxiety, and stress. Furthermore, senior, male, and urban nursing students are at risk for developing mental symptoms. Appropriate psychological interventions should be implemented to assure the mental health of nursing students.

Breaking community boundary: Comparing academic and social communication preferences regarding global pandemics

The global spread of COVID-19 has caused pandemics to be widely discussed. This is evident in the large number of scientific articles and the amount of user-generated content on social media. This paper aims to compare academic communication and social communication about the pandemic from the perspective of communication preference differences. It aims to provide information for the ongoing research on global pandemics, thereby eliminating knowledge barriers and information inequalities between the academic and the social communities. First, we collected the full text and the metadata of pandemic-related articles and Twitter data mentioning the articles. Second, we extracted and analyzed the topics and sentiment tendencies of the articles and related tweets. Finally, we conducted pandemic-related differential analysis on the academic community and the social community. We mined the resulting data to generate pandemic communication preferences (e.g., information needs, attitude tendencies) of researchers and the public, respectively. The research results from 50,338 articles and 927,266 corresponding tweets mentioning the articles revealed communication differences about global pandemics between the academic and the social communities regarding the consistency of research recognition and the preferences for particular research topics. The analysis of large-scale pandemic-related tweets also confirmed the communication preference differences between the two communities.

A cross-cultural exploratory analysis of pandemic growth: The case of COVID-19

Although the novel coronavirus that has resulted in more than 3 million deaths and 140 million cases of infection worldwide has wreaked havoc globally, some nations were more successful than others in curbing growth in their number of cases, thereby saving lives. In this research note, we integrate insights from cross-cultural research with inquiry in social psychology and public health literatures to advance a theoretically grounded and culturally derived explanation of cross-national variance in the growth rate of COVID-19. Our multi-level analyses, based on longitudinal time series data from 107 nations, and focused on the first 91 days of this pandemic in different nations, illustrate the direct and interactive effects of culture. Specifically, we find that individualism and uncertainty avoidance have a positive impact, while power distance and masculinity have a negative impact, on the growth rate of COVID-19 cases. Three-way interaction analyses between time, government stringency, and culture indicate that early government stringency attenuated pandemic growth, and this attenuation effect was more significant in collectivistic than in individualistic nations, and in high rather than low power distance nations. Our findings provide evidence that can enable policymakers and organizations to develop strategies that not only conform to science but that also consider the cultural orientation of nations.

Time series data analysis and ARIMA modeling to forecast the short-term trajectory of the acceleration of fatalities in Brazil caused by the corona virus (COVID-19)

OBJECTIVE

This paper incorporates the concept of acceleration to fatalities caused by the coronavirus in Brazil from time series data beginning on 17th March 2020 (the day of the first death) to 3rd February 2021 to explain the trajectory of the fatalities for the next six months using confirmed infections as the explanatory variable.

METHODS

Acceleration of the cases of confirmed infection and fatalities were calculated by using the concept of derivatives. Acceleration of fatality function was then determined from multivariate linear function and calculus chain rule for composite function with confirmed infections as an explanatory variable. Different ARIMA models were fitted for each acceleration of fatality function: the de-seasonalized Auto ARIMA Model, the adjusted lag model, and the auto ARIMA model with seasonality. The ARIMA models were validated. The most realistic models were selected for each function for forecasting. Finally, the short run six-month forecast was conducted on the trajectory of the acceleration of fatalities for all the selected best ARIMA models.

RESULTS

It was found that the best ARIMA model for the acceleration functions were the seasonalized models. All functions suggest a general decrease in fatalities and the pace at which this change occurs will eventually slow down over the next six months.

CONCLUSION

The decreasing fatalities over the next six-month period takes into consideration the direct impact of the confirmed infections. There is an early increase in acceleration for the forecast period, which suggests an increase in daily fatalities. The acceleration eventually reduces over the six-month period which shows that fatalities will eventually decrease. This gives health officials an idea on how the fatalities will be affected in the future as the trajectory of confirmed COVID-19 infections change.

Rhinosurgery during and after the COVID-19 Pandemic: International Consensus Conference Statement on Preliminary Perioperative Safety Measures

BACKGROUND

The coronavirus disease of 2019 (COVID-19) pandemic has widely affected rhinosurgery, given the high risk of contagion and the elective nature of the aesthetic procedure, generating many questions on how to ensure safety. The Science and Research Committee of the Rhinoplasty Society of Europe aimed at preparing consensus recommendations on safe rhinosurgery in general during the COVID-19 pandemic by appointing an international panel of experts also including delegates of The Rhinoplasty Society.

METHODS

A Zoom meeting was performed with a panel of 14 international leading experts in rhinosurgery. During 3.5 hours, four categories of questions on preoperative safety measures in private practice and outpatient clinics, patient assessment before and during surgery, and legal issues were presented by four chairs and discussed by the expert group. Afterward, the panelists were requested to express an online, electronic vote on each category and question. The panel's recommendations were based on current evidence and expert opinions. The resulting report was circulated in an iterative open e-mail process until consensus was obtained.

RESULTS

Consensus was obtained in several important points on how to safely restart performing rhinosurgery in general. Preliminary recommendations with different levels of agreement were prepared and condensed in a bundle of safety measures.

CONCLUSION

The implementation of the panel's recommendations may improve safety of rhinoplasty by avoiding operating on nondetected COVID-19 patients and minimizing severe acute respiratory syndrome coronavirus 2 virus spread in outpatient clinics and operating rooms.

Forecasting the 2020 COVID-19 Epidemic: A Multivariate Quasi-Poisson Regression to Model the Evolution of New Cases in Chile

Objectives: To understand and forecast the evolution of COVID-19 (Coronavirus disease 2019) in Chile, and analyze alternative simulated scenarios to better predict alternative paths, in order to implement policy solutions to stop the spread and minimize damage.

Methods: We have specified a novel multi-parameter generalized logistic growth model, which does not only look at the trend of the data, but also includes explanatory covariates, using a quasi-Poisson regression specification to account for overdispersion of the count data. We fitted our model to data from the onset of the disease (February 28) until September 15. Estimating the parameters from our model, we predicted the growth of the epidemic for the evolution of the disease until the end of October 2020. We also evaluated via simulations different fictional scenarios for the outcome of alternative policies (those analyses are included in the Supplementary Material).

Results and Conclusions: The evolution of the disease has not followed an exponential growth, but rather, stabilized and moved downward after July 2020, starting to increase again after the implementation of the Step-by-Step policy. The lockdown policy implemented in the majority of the country has proven effective in stopping the spread, and the lockdown-relaxation policies, however gradual, appear to have caused an upward break in the trend.

Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model

Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the log transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectories. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. We evaluated the model by training it on progressively longer periods of the pandemic and computing its predictive accuracy over 21-day forecasts. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Colorado, and West Virginia. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course.

Dynamics of the coronavirus disease 2019 (COVID-19) epidemic in Wuhan City, Hubei Province and China: a second derivative analysis of the cumulative daily diagnosed cases during the first 85 days

BACKGROUND

Controlling the coronavirus disease 2019 (COVID-19) epidemic requires information beyond new and cumulative cases. This study aims to conduct an in-depth analysis by geographic strata: Wuhan City (hereafter referred to as Wuhan) only, Hubei Province (hereafter referred to as Hubei) excluding Wuhan, and China excluding Hubei.

METHODS

Daily cumulative confirmed COVID-19 cases between December 8, 2019 (the date of symptom onset based on patients' recall during the investigation), and March 1, 2020, from official sources and published studies were analyzed. The second derivative model was used for information extraction. Data analysis was conducted separately for the three strata.

RESULTS

A total of 80 026 diagnosed COVID-19 cases were reported during the first 85 days of the epidemic, with 49 315 cases from Wuhan, 17 788 from Hubei excluding Wuhan, and 12 923 from China excluding Hubei. Analytical results indicate that the COVID-19 epidemic consists of an Acceleration, a Deceleration, and a Stabilization Phase in all three geographic strata, plus a Silent Attack Phase for Wuhan only. Given the reported incubation period of 14 days, effects of the massive anti-epidemic actions were revealed by both the Acceleration and Deceleration Phases. The Acceleration Phase signaled the effect of the intervention to detect the infected; the Deceleration Phase signaled the declines in new infections after the infected were detected, treated and quarantined.

CONCLUSION

Findings of the study provide new evidence to better monitor the epidemic, evaluate its response to intervention, and predict the trend long. In addition to re-evaluating the control of the COVID-19 epidemic in China, this study provided a model for monitoring outbreaks of COVID-19 in different countries across the world.

Levels of economic developement and the spread of coronavirus disease 2019 (COVID-19) in 50 U.S. states and territories and 28 European countries: an association analysis of aggregated data

Background

The coronavirus disease 2019 (COVID-19) became a global pandemic within several months after it was first reported at the end of December, 2019. Countries in the Northern Hemisphere have been affected the most, including the United States and European countries. Contrary to the common knowledge that infectious diseases are more prevalent in low- and middle-income countries, COVID-19 appears to affect wealthy countries more. This paper attempts to quantify the relationship between COVID-19 infections and levels of economic development with data from the U.S. and Europe.

Methods

Public domain data on the confirmed COVID-19 cases during January 1 and May 31, 2020 by states and territories in the U.S. and by countries in Europe were included. Incidence rate was estimated using the 2019 total population. COVID-19 cases were associated with 2019 gross domestic product (GDP) using regression models after a logarithmic transformation of the data. The U.S. data and European data were analyzed separately, considering significant heterogeneity between the two.

Results

A total of 2 451 691 COVID-19 cases during a 5-month period were analyzed, including 1 787 414 from 50 U.S. states and territories and 664 277 from 28 European countries. The overall incidence rate was 5.393/1000 for the U.S. and 1.411/1 000 for European countries with large variations. Lg (total cases) was significantly associated with lg (GDP) for U.S. states (= 1.2579, P < 0.001) and European countries (= 0.7156, P < 0.001), respectively.

Conclusion

This study demonstrated a positive correlation between COVID-19 case incidence and GDP in the United States and 28 European countries. Study findings suggest a potential role of high-level development in facilitating infectious disease spread, such as more advanced transportation system, large metropolitan cities with high population density, better domestic and international travel for businesses, leisure, and more group activities. These factors must be considered in controlling the COVID-19 epidemic. This study focuses on the impact of economic development, many other factors might also have contributed to the rapid spread of COVID-19 in these countries and states, such as differences in national and statewide anti-epidemic strategies, people’s behavior, and healthcare systems. Besides, low- and middle-income countries may have an artificially low COVID-19 case count just due to lack of diagnostic capabilities. Findings of this study also encourage future research with individual-level data to detect risk factors at the personal level to understand the risk of COVID-19.

A review on COVID-19 forecasting models

The novel coronavirus (COVID-19) has spread to more than 200 countries worldwide, leading to more than 36 million confirmed cases as of October 10, 2020. As such, several machine learning models that can forecast the outbreak globally have been released. This work presents a review and brief analysis of the most important machine learning forecasting models against COVID-19. The work presented in this study possesses two parts. In the first section, a detailed scientometric analysis presents an influential tool for bibliometric analyses, which were performed on COVID-19 data from the Scopus and Web of Science databases. For the above-mentioned analysis, keywords and subject areas are addressed, while the classification of machine learning forecasting models, criteria evaluation, and comparison of solution approaches are discussed in the second section of the work. The conclusion and discussion are provided as the final sections of this study.

The Impact of COVID-19 on Otolaryngology Community Practice in Massachusetts

OBJECTIVES

Coronavirus disease 2019 (COVID-19) significantly affected many health care specialties, including otolaryngology. In response to governmental policy changes, many hospitals and private practices in Massachusetts canceled or postponed nonurgent office visits and elective surgeries. The objective of this study was to quantify the impact of COVID-19 on the provision and practice trends of otolaryngology services for 10 private practices in Massachusetts.

STUDY DESIGN

Retrospective review.

SETTING

Multipractice study for community practices in Massachusetts.

METHODS

Electronic billing records from 10 private otolaryngology practices in Massachusetts were obtained for the first 4 months of 2019 and 2020. Questionnaires from these otolaryngology practices were collected to assess financial and staffing impact of COVID-19.

RESULTS

The local onset of the COVID-19 pandemic had a significant decrease of 63% of visits in comparison to equivalent weeks in 2019. Virtual visits overtook in-person visits over time. A greater decline in operating room (OR) procedures than for office procedures was recorded. Ninety percent of practices reduced working hours, and 80% furloughed personnel. Seventy percent of practices applied for the Paycheck Protection Program (PPP).

CONCLUSION

COVID-19 has had a multifaceted impact on private otolaryngology practices in Massachusetts. A significant decline in provision of otolaryngology services aligned with the Massachusetts government's public health policy changes. The combination of limited personnel and personal protective equipment, as well as suspension of nonessential office visits and surgeries, led to decrease in total office visits and even higher decrease in OR procedures.

Epidemiological characteristics of patients with severe COVID-19 infection in Wuhan, China: evidence from a retrospective observational study

BACKGROUND

The new coronavirus (COVID-19) rapidly resulted in a pandemic. We report the characteristics of patients with severe or critical severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Wuhan city, China, and the risk factors related to infection severity and death.

METHODS

We extracted the demographic and clinical data of 7283 patients with severe COVID-19 infection from designated Wuhan hospitals as of 25 February 2020. Factors associated with COVID-19 critical illness and mortality were analysed using logistic- and Cox-regression analyses.

RESULTS

We studied 6269 patients with severe COVID-19 illness and 1014 critically ill patients. The median (IQR) age was 64 (53-71) years; 51.2% were male, 38.9% were retirees and 7.4% had self-reported histories of chronic disease. Up to the end of the study, 1180 patients (16.2%) recovered and were discharged, 649 (8.9%) died and the remainder were still receiving treatment. The number of daily confirmed critical cases peaked between 23 January and 1 February 2020. Patients with advanced age [odds ratio (OR), 1.03; 95% confidence intervals (CIs), 1.03-1.04], male sex (OR, 1.57; 95% CI, 1.33-1.86) and pre-existing diabetes (OR, 2.11), hypertension (OR, 2.72), cardiovascular disease (OR, 2.15) or respiratory disease (OR, 3.50) were more likely to be critically ill. Compared with those who recovered and were discharged, patients who died were older [hazard ratio (HR), 1.04; 95% CI, 1.03-1.05], more likely to be male (HR, 1.74; 95% CI, 1.44-2.11) and more likely to have hypertension (HR, 5.58), cardiovascular disease (HR, 1.83) or diabetes (HR, 1.67).

CONCLUSION

Advanced age, male sex and a history of chronic disease were associated with COVID-19 critical illness and death. Identifying these risk factors could help in the clinical monitoring of susceptible populations.

In silico virtual screening-based study of nutraceuticals predicts the therapeutic potentials of folic acid and its derivatives against COVID-19

The recent outbreak of the novel coronavirus (SARS-CoV-2) in the Wuhan province of China has taken millions of lives worldwide. In this pandemic situation and absence of known drugs and vaccines against novel coronavirus disease (COVID-19), there is an urgent need for the repurposing of the existing drugs against it. So, here we have examined a safe and cheap alternative against this virus by screening hundreds of nutraceuticals compounds against known therapeutic targets of SARS-COV-2 by molecular docking. The virtual screening results were then analyzed for binding energy and interactive residues and compared with some already known hits in the best binding pose. All these analyses of this study strongly predicted the potential of Folic acid and its derivates like Tetrahydrofolic acid and 5-methyl tetrahydrofolic acid against SARS-COV-2. The strong and stable binding affinity of this water-soluble vitamin and its derivatives against the SARS-COV-2, indicating that they could be valuable drugs against the management of this COVID-19 pandemic. This study could serve as the starting point for further investigation of these molecules through in vitro and in vivo assays.

Changes in Anxiety, Depression, and Stress in 1 Week and 1 Month Later After the Wuhan Shutdown Against the COVID-19 Epidemic

Objective:

The aim of this study was to investigate the changes in Chinese residents’ psychological state and its influencing factors after the Wuhan shutdown on January 23, 2020.

Methods:

Two surveys were conducted on February 1-5 and February 20-24, separately, using an online self-administrated questionnaire among 3145 and 3814 participants, respectively. Subjective indicators of daily-life changes include level of attention, risk of infection, impact of daily life, self-perceived health status, and mental health help-seeking. Individual scores on changes in anxiety, depression, and stress were generated by 6-item, 4-item, and 3-item questions. A multivariate regression model was fitted in each survey, separately and combined.

Results:

A total of 6959 residents participated in the study, with 32.78% male and 67.22% female, people living in Wuhan and other cities in Hubei Province accounted for 25.22% and 18.85%, respectively. One week after the Wuhan shutdown, their anxiety, depression, and stress had all increased. Compared with the first survey, the changes in the scores of anxiety, depression, and stress in the second survey were decreased (β = −1.220, −0.798, and −0.623, all P < 0.001). The level of attention, risk of infection, and self-perceived health status tended to be positively associated with the changes in the scores of anxiety, depression, and stress.

Conclusions:

The study showed that the lives and psychological conditions of residents had undergone negative changes after the Wuhan shutdown, but the measures taken during this period were effective. These results may provide guidance for public health policies in other countries and regions.

COVID-19 epidemic prediction and the impact of public health interventions: A review of COVID-19 epidemic models

The coronavirus disease outbreak of 2019 (COVID-19) has been spreading rapidly to all corners of the word, in a very complex manner. A key research focus is in predicting the development trend of COVID-19 scientifically through mathematical modelling. We conducted a systematic review of epidemic prediction models of COVID-19 and the public health intervention strategies by searching the Web of Science database. 55 studies of the COVID-19 epidemic model were reviewed systematically. It was found that the COVID-19 epidemic models were different in the model type, acquisition method, hypothesis and distribution of key input parameters. Most studies used the gamma distribution to describe the key time period of COVID-19 infection, and some studies used the lognormal distribution, the Erlang distribution, and the Weibull distribution. The setting ranges of the incubation period, serial interval, infectious period and generation time were 4.9-7 days, 4.41-8.4 days, 2.3-10 days and 4.4-7.5 days, respectively, and more than half of the incubation periods were set to 5.1 or 5.2 days. Most models assumed that the latent period was consistent with the incubation period. Some models assumed that asymptomatic infections were infectious or pre-symptomatic transmission was possible, which overestimated the value of R0. For the prediction differences under different public health strategies, the most significant effect was in travel restrictions. There were different studies on the impact of contact tracking and social isolation, but it was considered that improving the quarantine rate and reporting rate, and the use of protective face mask were essential for epidemic prevention and control. The input epidemiological parameters of the prediction models had significant differences in the prediction of the severity of the epidemic spread. Therefore, prevention and control institutions should be cautious when formulating public health strategies by based on the prediction results of mathematical models.

Is spread of COVID-19 a chaotic epidemic?

The COVID-19 epidemic challenges humanity in 2020. It has already taken an enormous number of human lives and had a substantial negative economic impact. Traditional compartmental epidemiological models demonstrated limited ability to predict the scale and dynamics of COVID-19 epidemic in different countries. In order to gain a deeper understanding of its behavior, we turn to chaotic dynamics, which proved fruitful in analyzing previous diseases such as measles. We hypothesize that the unpredictability of the pandemic could be a fundamental property if the disease spread is a chaotic dynamical system. Our mathematical examination of COVID-19 epidemic data in different countries reveals similarity of this dynamic to the chaotic behavior of many dynamics systems, such as logistic maps. We conclude that the data does suggest that the COVID-19 epidemic demonstrates chaotic behavior, which should be taken into account by public policy makers. Furthermore, the scale and behavior of the epidemic may be essentially unpredictable due to the properties of chaotic systems, rather than due to the limited data available for model parameterization.