Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020

Modeling the dynamics of COVID-19 using fractal-fractional operator with a case study

This research study consists of a newly proposed Atangana-Baleanu derivative for transmission dynamics of the coronavirus (COVID-19) epidemic. Taking the advantage of non-local Atangana-Baleanu fractional-derivative approach, the dynamics of the well-known COVID-19 have been examined and analyzed with the induction of various infection phases and multiple routes of transmissions. For this purpose, an attempt is made to present a novel approach that initially formulates the proposed model using classical integer-order differential equations, followed by application of the fractal fractional derivative for obtaining the fractional COVID-19 model having arbitrary order Ψ and the fractal dimension Ξ . With this motive, some basic properties of the model that include equilibria and reproduction number are presented as well. Then, the stability of the equilibrium points is examined. Furthermore, a novel numerical method is introduced based on Adams-Bashforth fractal-fractional approach for the derivation of an iterative scheme of the fractal-fractional ABC model. This in turns, has helped us to obtained detailed graphical representation for several values of fractional and fractal orders Ψ and Ξ , respectively. In the end, graphical results and numerical simulation are presented for comprehending the impacts of the different model parameters and fractional order on the disease dynamics and the control. The outcomes of this research would provide strong theoretical insights for understanding mechanism of the infectious diseases and help the worldwide practitioners in adopting controlling strategies.

Clinical analysis of severe COVID-19 patients

BACKGROUND:

Patients with unexplained pneumonia appeared in Wuhan, Hubei Province at the end of 2019.

OBJECTIVE:

To analyze the clinical data of patients with severe COVID-19.

METHODS:

Medical records of 28 severe patients admitted to the intensive care unit of Wuhan Xinzhou District People’s Hospital were collected from January 31 to March 17.

RESULTS:

The mortality rate of severe patients in our study was 39.3%. There were statistically significant differences in age, admission systolic blood pressure, lymphocyte count, albumin, total bilirubin, and lactate dehydrogenase between the death group and the survival group (P< 0.05). There were statistically significant differences in APACHE II, CURB-65, SOFA, respiratory frequency, systolic pressure, platelet, procalcitonin, albumin, creatinine, creatine kinase isoenzyme, lactate dehydrogenase, chloride ion, prothrombin time, international normalized ratio, arterial partial pressure of oxygen, and FiO2 at ICU between the death group and the survival group (P< 0.05).

CONCLUSIONS:

Fever and cough are the main symptoms, which is useful for predicting the prognosis to dynamically measure the APACHE II, CURB-65, SOFA, respiratory frequency, lymphocyte count, platelet, lactate dehydrogenase, and coagulation tests. The drugs that protect the liver and heart may improve the survival rate of patients with severe COVID-19.

Using Non-Pharmaceutical Interventions and High Isolation of Asymptomatic Carriers to Contain the Spread of SARS-CoV-2 in Nursing Homes

More than 40% of the deaths recorded in the first wave of the SARS-CoV-2 pandemic were linked to nursing homes. Not only are the residents of long-term care facilities (LTCFs) typically older and more susceptible to endemic infections, the facilities' high degree of connection to wider communities makes them especially vulnerable to local COVID-19 outbreaks. In 2008, in the wake of the SARS-CoV-1 and MERS epidemics and anticipating an influenza pandemic, we created a stochastic compartmental model to evaluate the deployment of non-pharmaceutical interventions (NPIs) in LTCFs during influenza epidemics. In that model, the most effective NPI by far was a staff schedule consisting of 5-day duty periods with onsite residence, followed by an 4-to-5 day off-duty period with a 3-day quarantine period just prior to the return to work. Unlike influenza, COVID-19 appears to have significant rates of pre-symptomatic transmission. In this study, we modified our prior modeling framework to include new parameters and a set of NPIs to identify and control the degree of pre-symptomatic transmission. We found that infections, deaths, hospitalizations, and ICU utilization were projected to be high and largely irreducible, even with rigorous application of all defined NPIs, unless pre-symptomatic carriers can be identified and isolated at high rates. We found that increasingly rigorous application of NPIs is likely to significantly decrease the peak of infections; but even with complete isolation of symptomatic persons, and a 50% reduction in silent transmission, the attack rate is projected to be nearly 95%.

Changes in Oral Health Policies and Guidelines During the COVID-19 Pandemic

The aim of this study was to describe the changes in oral health policies and guidelines in response to the Coronavirus disease 2019 (COVID-19) pandemic in different countries and regions around the world. Information on oral health policies and guidelines from 9 countries (Canada, China including Hong Kong, Egypt, India, Japan, New Zealand, Nigeria, Switzerland, and Thailand) were summarized, and sources of the information were mostly the national or regional health authorities and/or dental council/associations. The changes made to the oral health guidelines depended on the severity of the COVID-19 pandemic. This included suspension of non-emergency dental care services at the peak of the COVID-19 outbreak, and easing the restrictions on non-essential and elective dental care when the pandemic became under control. The COVID-19 risk mitigation strategies include strict adherence to infection control practices (use of hand sanitizers, facemask and maintaining social distancing), reducing the amount of aerosol production in the dental setting, and managing the quality of air in the dental treatment rooms by reducing the use of air conditioners and improving air exchange. The COVID-19 pandemic has shown a major impact on dental practice. Dental professionals are trying to adapt to the new norms, while the medium to long-term impact of COVID-19 on dentistry needs further investigation.

Epidemiological Characteristics and Transmissibility for SARS-CoV-2 of Population Level and Cluster Level in a Chinese City

Background

To date, there is a lack of sufficient evidence on the type of clusters in which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is most likely to spread. Notably, the differences between cluster-level and population-level outbreaks in epidemiological characteristics and transmissibility remain unclear. Identifying the characteristics of these two levels, including epidemiology and transmission dynamics, allows us to develop better surveillance and control strategies following the current removal of suppression measures in China.

Methods

We described the epidemiological characteristics of SARS-CoV-2 and calculated its transmissibility by taking a Chinese city as an example. We used descriptive analysis to characterize epidemiological features for coronavirus disease 2019 (COVID-19) incidence database from 1 Jan 2020 to 2 March 2020 in Chaoyang District, Beijing City, China. The susceptible-exposed-infected-asymptomatic-recovered (SEIAR) model was fitted with the dataset, and the effective reproduction number (Reff ) was calculated as the transmissibility of a single population. Also, the basic reproduction number (R0) was calculated by definition for three clusters, such as household, factory and community, as the transmissibility of subgroups.

Results

The epidemic curve in Chaoyang District was divided into three stages. We included nine clusters (subgroups), which comprised of seven household-level and one factory-level and one community-level cluster, with sizes ranging from 2 to 17 cases. For the nine clusters, the median incubation period was 17.0 days [Interquartile range (IQR): 8.4-24.0 days (d)], and the average interval between date of onset (report date) and diagnosis date was 1.9 d (IQR: 1.7 to 6.4 d). At the population level, the transmissibility of the virus was high in the early stage of the epidemic (Reff = 4.81). The transmissibility was higher in factory-level clusters (R0 = 16) than in community-level clusters (R0 = 3), and household-level clusters (R0 = 1).

Conclusions

In Chaoyang District, the epidemiological features of SARS-CoV-2 showed multi-stage pattern. Many clusters were reported to occur indoors, mostly from households and factories, and few from the community. The risk of transmission varies by setting, with indoor settings being more severe than outdoor settings. Reported household clusters were the predominant type, but the population size of the different types of clusters limited transmission. The transmissibility of SARS-CoV-2 was different between a single population and its subgroups, with cluster-level transmissibility higher than population-level transmissibility.

Remdesivir and dexamethasone as tools to relieve hospital care systems stressed by COVID-19: A modelling study on bed resources and budget impact

Remdesivir and dexamethasone are the only drugs providing reductions in the lengths of hospital stays for COVID-19 patients. We assessed the impacts of remdesivir on hospital-bed resources and budgets affected by the COVID-19 outbreak. A stochastic agent-based model was combined with epidemiological data available on the COVID-19 outbreak in France and data from two randomized control trials. Strategies involving treating with remdesivir only patients with low-flow oxygen and patients with low-flow and high-flow oxygen were examined. Treating all eligible low-flow oxygen patients during the entirety of the second wave would have decreased hospital-bed occupancy in conventional wards by 4% [2%; 7%] and intensive care unit (ICU)-bed occupancy by 9% [6%; 13%]. Extending remdesivir use to high-flow-oxygen patients would have amplified reductions in ICU-bed occupancy by up to 14% [18%; 11%]. A minimum remdesivir uptake of 20% was required to observe decreases in bed occupancy. Dexamethasone had effects of similar amplitude. Depending on the treatment strategy, using remdesivir would, in most cases, generate savings (up to 722€) or at least be cost neutral (an extra cost of 34€). Treating eligible patients could significantly limit the saturation of hospital capacities, particularly in ICUs. The generated savings would exceed the costs of medications.

Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data

Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We quantified interpersonal contact at the population level using mobile device geolocation data. We computed the frequency of contact (within 6 feet) between people in Connecticut during February 2020 to January 2021 and aggregated counts of contact events by area of residence. When incorporated into a SEIR-type model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns. Contact in Connecticut explains the initial wave of infections during March to April, the drop in cases during June to August, local outbreaks during August to September, broad statewide resurgence during September to December, and decline in January 2021. The transmission model fits COVID-19 transmission dynamics better using the contact rate than other mobility metrics. Contact rate data can help guide social distancing and testing resource allocation.

Non-extensive thermodynamic entropy to predict the dynamics behavior of COVID-19

The current world observations in COVID-19 are hardly tractable as a whole, making situations of information to be incompleteness. In pandemic era, mathematical modeling helps epidemiological scientists to take informing decisions about pandemic planning and predict the disease behavior in the future. In this work, we proposed a non-extensive entropy-based model on the thermodynamic approach for predicting the dynamics of COVID-19 disease. To do so, the epidemic details were considered into a single and time-dependent coefficients model. Their four constraints, including the existence of a maximum point were determined analytically. The model was worked out to give a log-normal distribution for the spread rate using the Tsallis entropy. The width of the distribution function was characterized by maximizing the rate of entropy production. The model predicted the number of daily cases and daily deaths with a fairly good agreement with the World Health Organization (WHO) reported data for world-wide, Iran and China over 2019-2020-time span. The proposed model in this work can be further calibrated to fit on different complex distribution COVID-19 data over different range of times.

Does the novel coronavirus use the ocular surface as an entrance into the body or as an infection site?

This study attempts to review whether the coronavirus disease-2019 (COVID-19) is transmitted through the ocular surface and examine the symptoms and signs of ocular disease. Considering that COVID-19 is transmitted by airborne droplets and close contact with infected individuals, we will also review the conditions to which eye clinics and ophthalmologists should pay attention to prevent the transmission of the disease. Although some researchers have argued that COVID-19 transmission cannot occur through the ocular surface, most of them are of the opinion that the ocular surface is a potential pathway of transmission. Until date, ocular signs and symptoms have been rarely reported in the COVID-19 patients. However, there are case reports of conjunctivitis as the first, and rarely, the only clinical symptom of the disease. In addition, low coronavirus RNA positivity can be detected in the ocular surface samples. Further laboratory and clinical investigations are needed to ascertain whether the ocular surface is one of the potential transmission pathways through which severe acute respiratory syndrome-coronavirus 2 can gain entry into the human body.

Lesión renal aguda en COVID-19: puesta al día y revisión de la literatura

Los coronavirus humanos son virus que se asocian a varias patologías respiratorias como el síndrome respiratorio agudo severo y el síndrome respiratorio del Medio Oriente. Esto ha puesto a esta familia de virus en el centro de atención de la comunidad científica debido a la alta patogenicidad en humanos, especialmente ahora con la nueva pandemia por la enfermedad por coronavirus del 2019 (COVID-19). La COVID-19 se manifiesta principalmente como enfermedad respiratoria aguda con compromiso respiratorio bajo, pero puede afectar múltiples órganos como lo es el riñón, lo cual a conlleva a peores desenlaces. En este manuscrito revisaremos el compromiso renal por los diferentes coronavirus, en especial en la COVID-19, al igual que las terapias que juegan algún papel en el tratamiento de esta.

Potential natural products that target the SARS-CoV-2 spike protein identified by structure-based virtual screening, isothermal titration calorimetry and lentivirus particles pseudotyped (Vpp) infection assay

BACKGROUND AND AIM

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters cells through the binding of the viral spike protein with human angiotensin-converting enzyme 2 (ACE2), resulting in the development of coronavirus disease 2019 (COVID-19). To date, few antiviral drugs are available that can effectively block viral infection. This study aimed to identify potential natural products from Taiwan Database of Extracts and Compounds (TDEC) that may prevent the binding of viral spike proteins with human ACE2 proteins.

METHODS

The structure-based virtual screening was performed using the AutoDock Vina program within PyRX software, the binding affinities of compounds were verified using isothermal titration calorimetry (ITC), the inhibitions of SARS-CoV-2 viral infection efficacy were examined by lentivirus particles pseudotyped (Vpp) infection assay, and the cell viability was tested by 293T cell in MTT assay.

RESULTS AND CONCLUSION

We identified 39 natural products targeting the viral receptor-binding domain (RBD) of the SARS-CoV-2 spike protein in silico. In ITC binding assay, dioscin, celastrol, saikosaponin C, epimedin C, torvoside K, and amentoflavone showed dissociation constant (K d) = 0.468 μM, 1.712 μM, 6.650 μM, 2.86 μM, 3.761 μM and 4.27 μM, respectively. In Vpp infection assay, the compounds have significantly and consistently inhibition with the 50-90% inhibition of viral infection efficacy. In cell viability, torvoside K, epimedin, amentoflavone, and saikosaponin C showed IC50 > 100 μM; dioscin and celastrol showed IC50 = 1.5625 μM and 0.9866 μM, respectively. These natural products may bind to the viral spike protein, preventing SARS-CoV-2 from entering cells.

SECTION 1

Natural Products.

TAXONOMY CLASSIFICATION BY EVISE

SARS-CoV-2, Structure-Based Virtual Screening, Isothermal Titration Calorimetry and Lentivirus Particles Pseudotyped (Vpp) Infection Assay, in silico and in vitro study.

COVID-19 pandemic control using restrictions and vaccination

This work deals with the impact of the vaccination in combination with a restriction parameter that represents non-pharmaceutical interventions measures applied to the compartmental SEIR model in order to control the COVID-19 epidemic. This restriction parameter is used as a control parameter, and the univariate autoregressive integrated moving average (ARIMA) is used to forecast the time series of vaccination of all individuals of a specific country. Having in hand the time series of the population fully vaccinated (real data + forecast), the Levenberg-Marquardt algorithm is used to fit an analytic function that models this evolution over time. Here, it is used two time series of real data that refer to a slow vaccination obtained from India and Brazil, and two faster vaccination as observed in Israel and the United States of America. Together with vaccination, two different control approaches are presented in this paper, which enable reduces the infected people successfully: namely, the feedback and nonfeedback control methods. Numerical results predict that vaccination can reduce the peaks of infections and the duration of the pandemic, however, a better result is achieved when the vaccination is combined with any restrictions or prevention policy.

“Quarantined within a quarantine”

When the Australian state and lone isle of Tasmania went into coronavirus disease 2019 (COVID-19) quarantine lockdown in March, within a quarantine-imposed Australian continent, thinking it was being very prudent, unforeseen was the lurking virus. Australia across January had been watching the global northern hemisphere scenario occurring and by February was preparing to quarantine itself, echoing its existing and long-term biosecurity exclusion regime. On a much grander scale, following through on a previously trialed national pandemic training exercise, no one had factored in the Ruby Princess variable and its major consequences that would require unprecedented pandemic response. The concentrated impact of cruise ship virus dissemination and escalation has been palpable across the world, but the Ruby Princess will remain a disaster in Australia's history. For Tasmania, several elderly passengers retraveled from Sydney to Tasmania, and a minor cluster has occurred. This chapter contextualizes what has been transpiring in Australia with the pandemic, with particular attention upon Tasmania, including discussion about the new COVIDSafe.App, and then explains the potential application of a Systems Dynamics Modeling exercise of the COVID-19 spread, in collaboration with a custom-built 2D/3D geographic information system (GIS) Dynamic Scenario Planning Model to spatially visualize potential “what-if” scenarios of COVID-19 spread (and other future pandemics) to identify high-risk areas and vulnerable communities in the northern areas of Tasmania that is aiding real-time pattern mapping and preparation work and to further consider and enable the most effective emergency response and recovery scenarios.

Epidemiological Characteristics of COVID-19 under Government-mandated Control Measures during January-February 2020 in Inner Mongolia, China

The coronavirus disease 2019 (COVID-19) pandemic caused severe health impacts to worldwide. The aim of our study was to provide suggestions for government to manage serious infectious disease outbreaks in remote regions with relatively poor medical resources. The basic reproduction number (R₀), incubation period, time from symptom onset to confirmed and duration of hospitalization were analyzed. We compared the composition of imported and local secondary cases, and cases with mild/common and severe/critical illness according to age, sex, and clinical symptoms. From January 23 to February 19, 2020 (less than 1 month), 75 local COVID-19 cases were confirmed in Inner Mongolia. Among them, the median age was 45.0 years and 33 (44.0%) were imported. More than 80.0% cases were mild/common. The case fatality rate was 1.3% and the R₀ was estimated to be 2.3. The median incubation period was 8.5 days. There was a significant difference in the incubation period between imported and local secondary cases (P<0.001). The early and mandatory control strategies implemented by government were associated with a rapid reduction in COVID-19 incidence in Inner Mongolia.

Recent Advancements on COVID-19: A Comprehensive Review

Over the last few decades, there have been several global outbreaks of severe respiratory infections. The causes of these outbreaks were coronaviruses that had infected birds, mammals and humans. The outbreaks predominantly caused respiratory tract and gastrointestinal tract symptoms and other mild to very severe clinical signs. The current coronavirus disease-2019 (COVID-19) outbreak, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a rapidly spreading illness affecting millions of people worldwide. Among the countries most affected by the disease are the United States of America (USA), India, Brazil, and Russia, with France recording the highest infection, morbidity, and mortality rates. Since early January 2021, thousands of articles have been published on COVID-19. Most of these articles were consistent with the reports on the mode of transmission, spread, duration, and severity of the sickness. Thus, this review comprehensively discusses the most critical aspects of COVID-19, including etiology, epidemiology, pathogenesis, clinical signs, transmission, pathological changes, diagnosis, treatment, prevention and control, and vaccination.

Estimating a time-to-event distribution from right-truncated data in an epidemic: A review of methods

Time-to-event data are right-truncated if only individuals who have experienced the event by a certain time can be included in the sample. For example, we may be interested in estimating the distribution of time from onset of disease symptoms to death and only have data on individuals who have died. This may be the case, for example, at the beginning of an epidemic. Right truncation causes the distribution of times to event in the sample to be biased towards shorter times compared to the population distribution, and appropriate statistical methods should be used to account for this bias. This article is a review of such methods, particularly in the context of an infectious disease epidemic, like COVID-19. We consider methods for estimating the marginal time-to-event distribution, and compare their efficiencies. (Non-)identifiability of the distribution is an important issue with right-truncated data, particularly at the beginning of an epidemic, and this is discussed in detail. We also review methods for estimating the effects of covariates on the time to event. An illustration of the application of many of these methods is provided, using data on individuals who had died with coronavirus disease by 5 April 2020.

Estimating the transmission advantage of the D614G mutant strain of SARS-CoV-2, December 2019 to June 2020

IntroductionThe SARS-CoV-2 lineages carrying the amino acid change D614G have become the dominant variants in the global COVID-19 pandemic. By June 2021, all the emerging variants of concern carried the D614G mutation. The rapid spread of the G614 mutant suggests that it may have a transmission advantage over the D614 wildtype.AimOur objective was to estimate the transmission advantage of D614G by integrating phylogenetic and epidemiological analysis.MethodsWe assume that the mutation D614G was the only site of interest which characterised the two cocirculating virus strains by June 2020, but their differential transmissibility might be attributable to a combination of D614G and other mutations. We define the fitness of G614 as the ratio of the basic reproduction number of the strain with G614 to the strain with D614 and applied an epidemiological framework for fitness inference to analyse SARS-CoV-2 surveillance and sequence data.ResultsUsing this framework, we estimated that the G614 mutant is 31% (95% credible interval: 28-34) more transmissible than the D614 wildtype. Therefore, interventions that were previously effective in containing or mitigating the D614 wildtype (e.g. in China, Vietnam and Thailand) may be less effective against the G614 mutant.ConclusionOur framework can be readily integrated into current SARS-CoV-2 surveillance to monitor the emergence and fitness of mutant strains such that pandemic surveillance, disease control and development of treatment and vaccines can be adjusted dynamically.

The burden of isolation to the individual: a comparison between isolation for COVID-19 and for other influenza-like illnesses in Japan

At present, there is scarce evidence about the burden associated with the isolation of COVID-19 patients. We aimed to assess the differences between COVID-19 and other influenza-like illnesses (ILIs) in disease burden brought by isolation. We conducted an online survey of 302 respondents who had COVID-19 or other ILIs and compared the burden of isolation due to sickness with one-to-one propensity score matching. The primary outcomes are the duration and productivity losses associated with isolation, the secondary outcome is the health-related quality of life (HRQoL) valuation on the day of the survey. Acute symptoms of outpatient COVID-19 and other ILIs lasted 17 (interquartile range (IQR) 9-32) and 7 (IQR 4-10) days, respectively. The length of isolation due to COVID-19 was 18 (IQR 10-33) days and that due to other ILIs was 7 (IQR 4-11) days, respectively. The monetary productivity loss of isolation due to COVID-19 was 1424.3 (IQR 825.6-2545.5) USD and that due to other ILIs was 606.1 (IQR 297.0-1090.9) USD, respectively. HRQoL at the time of the survey was lower in the COVID-19 group than in the 'other ILIs' group (0.89 and 0.96, P = 0.001). COVID-19 infection imposes a substantial disease burden, even in patients with non-severe disease. This burden is larger for COVID-19 than other ILIs, mainly because the required isolation period is longer.

Bibliometric Analysis and Systematic Review of Global Coronavirus Research Trends Before COVID-19: Prospects and Implications for COVID-19 Research

Coronaviruses (CoV) cause respiratory and intestinal infections. We conducted this bibliometric analysis and systematical review to explore the CoV-related research trends from before COVID-19. We systematically searched the Ovid MEDLINE, Ovid Embase, and Web of Science (WOS) databases for published bibliometric analyses of CoV from database inception to January 24, 2021. The WOS Collection was searched from inception to January 31, 2020, to acquire the CoV-related publications before COVID-19. One-Way ANOVA and Bonferroni multiple-comparison tests were used to compare differences. Visualization mapping and keyword cluster graphs were made to illustrate the research topics and hotpots. We included 14,141 CoV-related publications for the bibliometric analysis and 16 (12 articles) CoV-related bibliometric analyses for the systematic review. Both the systematic review and bibliometric analysis showed (1) the number of publications showed two steep upward trajectories in 2003-2004 and in 2012-2014; (2) the research hotpots mainly focused on the mechanism, pathology, epidemiology, clinical diagnosis, and treatment of the coronavirus in MERS-CoV and SARS-Cov; (3) the USA, and China; the University of Hong Kong; and Yuen KY, came from the University of Hong Kong contributed most; (4) the Journal of Virology had the largest number of CoV related studies. More studies should focus on prevention, diagnosis, and treatment in the future.

Management of Dental School During the COVID-19 Pandemic: Application of Intervention Mapping

Background: Coronavirus Diesease-2019 (COVID-19) outbreak has led to the suspension of the activities of dental schools. Therefore, reorganizing clinical settings and supporting services as quickly as possible has received much attention to reopen dental schools. The present study aimed to evaluate the applicability of the Intervention Mapping (IM) approach for designing, implementing, and evaluating an intervention program to prevent and control COVID-19 in dental schools. Methods: Following the IM protocol, six steps were completed in the planning and development of an intervention, targeting, and management of Dental School during the COVID-19 pandemic. Results: The information obtained from the needs assessment revealed that the COVID-19 outbreak prevention was associated with the use of personal protective equipment by all target groups, infection control measures taken in the environment, preparation of the environment and equipment, changes in the treatment plan according to the COVID-19 pandemic, changing the admission process of patients, and reduction of attendance of target groups in the school are linked with. In this study, determinant factors affecting the COVID-19 prevention at the individual level were identified based on the Protection Motivation Theory (PMT). In this program, various methods, such as presentation of information, modeling role, and persuasion measures, were utilized and the practical programs included educational films and group discussions implemented. Conclusions: Our findings indicated that intervention in dental environments on the basis of the IM process can develop a comprehensive and structured program in the dental school and hence can reduce the risk of the COVID-19 infection.

Transmission dynamics and successful control measures of SARS-CoV-2 in the mega-size city of Guangzhou, China

The severe acute respiratory syndrome coronavirus 2 has caused a worldwide pandemic. Control measures differ among countries and have a varying degree of effectiveness, which requires assessment. To evaluate the effectiveness of public health interventions of the coronavirus disease 2019 (COVID-19) in Guangzhou by 3 periods according to interventions: January 7 to 22 (no intervention), January 23 to February 23 (implemented intensive interventions), and February 24 to May 17 (the normalization mode of COVID-19 prevention and control).We collected the information of 745 COVID-19 patients and their close contacts as well as control measures in Guangzhou from January 7 to May 17, 2020. We estimated the epidemiological characteristics, disease spectrum of COVID-19 cases, key time-to-event intervals, and effective reproduction number over the 3 periods. The basic reproduction number of severe acute respiratory syndrome coronavirus 2 was also calculated over period 1.Approximately 45.8%, 49.8%, and 4.4% of cases from close contacts were asymptomatic, symptomatic, and severe, respectively. The median incubation period was 5.3 days (the percentiles of 2.5-97.5, 1.5-18.4 days) and the median serial interval fitted with gamma distribution was 5.1 days (the percentiles of 2.5-97.5, 0.8-15.9 days). The estimated median of onset-to-quarantined time in Period 1 to 3 were 7.5, 3.4, and 2.9 days (the percentiles of 2.5-97.5, 2.1-14.2, 3.9-14.7, and 6.0-20.0 days) respectively and the median of onset-to-confirmation time in period 1 to 3 were 8.9, 4.9 and 2.4 days (the percentiles of 2.5-97.5, 2.6-16.6, 0.9-14.6, and 0.5-11.8 days). In period 1, the reproduction number was 0.9 (95% confidence interval, 0.5-1.4) and fluctuated below 1.0 before January 22 except for January 14. The effective reproduction number gradually decreased in the period 2 with the lowest point of 0.1 on February 20, then increased again since March 27 and reach a spike of 1.8 on April 12. The number decreased to below 1.0 after April 17 and decreased further to <0.2 after May 7 in the period 3.Under prospective dynamic observation, close contacts turned into infected cases could provide a spectrum of COVID-19 cases from real-world settings. The lockdown of Wuhan and closed-loop management of people arriving Guangzhou were effective in halting the spread of the COVID-19 cases to Guangzhou. The spread of COVID-19 was successfully controlled in Guangzhou by social distancing, wearing a face mask, handwashing, disinfection in key places, mass testing, extensive contact tracing, and strict quarantine of close contacts.

Revealing spatiotemporal transmission patterns and stages of COVID-19 in China using individual patients’ trajectory data

Gauging viral transmission through human mobility in order to contain the COVID-19 pandemic has been a hot topic in academic studies and evidence-based policy-making. Although it is widely accepted that there is a strong positive correlation between the transmission of the coronavirus and the mobility of the general public, there are limitations to existing studies on this topic. For example, using digital proxies of mobile devices/apps may only partially reflect the movement of individuals; using the mobility of the general public and not COVID-19 patients in particular, or only using places where patients were diagnosed to study the spread of the virus may not be accurate; existing studies have focused on either the regional or national spread of COVID-19, and not the spread at the city level; and there are no systematic approaches for understanding the stages of transmission to facilitate the policy-making to contain the spread.

To address these issues, we have developed a new methodological framework for COVID-19 transmission analysis based upon individual patients’ trajectory data. By using innovative space–time analytics, this framework reveals the spatiotemporal patterns of patients’ mobility and the transmission stages of COVID-19 from Wuhan to the rest of China at finer spatial and temporal scales. It can improve our understanding of the interaction of mobility and transmission, identifying the risk of spreading in small and medium-sized cities that have been neglected in existing studies. This demonstrates the effectiveness of the proposed framework and its policy implications to contain the COVID-19 pandemic.

A Hybrid Approach Toward COVID-19 Pandemic Modeling in Saudi Arabia Using the Modified Susceptible-Exposed-Infectious-Recovered Model and Open Data Sources

The coronavirus disease 2019 (COVID-19) pandemic has caused the world to operate uncharacteristically for almost the last two years. Governments across the globe have taken different control measures to eradicate it. The Oxford COVID-19 Government Response Tracker (OxCGRT) provides open access data for different countries on 20 control measures, including numerous aggregated indices. This paper employs the modified Susceptible-Exposed-Infectious-Recovered (SEIR) epidemiology model to study the COVID-19 pandemic in Saudi Arabia. The modification has been achieved by including control measures and the infectiousness of exposed compartment. A hybrid approach has been used to estimate and incorporate control measures. Initially, a composite control measure has been derived from OxCGRT data to make an attempt to fit the COVID-19 pattern in Saudi Arabia. The derived model has proven to be satisfactory through statistical tests. Nonetheless, the model patterns do not resemble the reported patterns more closely. Hence, a second heuristic approach has been utilized to devise effective control measures from the reported pattern of COVID-19 from the Saudi government agency. A satisfactory model was derived utilizing this approach with successful validation through statistical tests. Also, the model patterns more closely resemble the reported patterns of COVID-19 cases. This hybrid approach proves more robust and ensures the validity of model parameters better. The R naught (R0) value with the current control measures has varied from 0.515 to 1.892, with a mean value of 1.119, and is presently less than 1. The threshold herd immunity, in the absence of any control measure, is estimated to be 47.12% with an R0 value of 1.89 and would end up infecting 76.32% of the population. The scenario analysis with gradual partial and complete relaxations up to December 31, 2021, shows that the peaks are likely to occur in 2022; therefore, Saudi Arabia must continue to inoculate its population to eradicate COVID-19.

Prevalence and transmission of COVID-19 in community and household levels of Bangladesh: Longini and Koopman epidemic modelling approach

AIM

To estimate the prevalence of COVID-19 pandemic and its transmission rates among people in both community and household levels of Bangladesh.

METHODS

We use the cross-sectional online survey data of 2080 individuals, collected from 442 households during June to September 2020 in Bangladesh. The Longini and Koopman stochastic epidemic modelling approach was adapted for analysing the data. To validate the results, a simulation study was conducted using the Markov Chain Monte Carlo (MCMC) method via the Metropolis-Hastings algorithm in the context of the Bayesian framework.

RESULTS

Overall, the prevalence of COVID-19 pandemic was 15.1% (315 out of 2080) among people in Bangladesh. This proportion was higher in smaller households (size one: 40.0%, two: 35.7% and three: 25.9%) than larger (four: 15.8%, five: 13.3%, six: 14.1%, seven: 12.5% eight: 8.7%, nine: 14.8% and ten or eleven: 5.7%). The transmission rate of COVID-19 in community people was higher (12.0%, 95% CI: 10.0% to 13.0%) than household members (9.0%, 95% CI: 6.0% to 11.0%).

CONCLUSION

The susceptible individuals have a higher risk of community infection than the household and the community transmission is more responsible than the household for COVID-19 pandemic in Bangladesh.

Sleeping habits during COVID-19 induced confinement: A study from Jordan

Sleep can significantly modulate the immune response to infectious agents. In the current study, changes in sleep quality during COVID-19-induced confinement among adults were investigated. This was a cross-sectional survey study of the public using social media. Participants (n = 1846) were recruited in the study, of which >92% reported a variety of confinement procedures such as self-quarantine, physical distancing, banning of public events, school closure, and lockdown. Majority of the participants (53-59%) reported an increase in most of the sleep parameters except a decrease (49.1%) in daytime sleep. Age was associated with changes in sleeping disturbances during COVID-19 confinement (p < 0.001). Young participants were more likely to experience sleeping disturbance than older ones (p < 0.05). In addition, gender (p < 0.001) is an independent predictor of nighttime sleeping. Being a male is associated with a "decrease" and being a female is associated with an "increase" in nighttime sleeping hours (p < 0.05). Moreover, change in daytime sleeping was related to age, gender, and job type (p < 0.05). In conclusion, changes in sleep quality during COVID-19-induced confinement were reported. Intervention programs and strategies are warranted to further improve sleep during the current and future disease-induced confinement.

Data-driven multi-scale mathematical modeling of SARS-CoV-2 infection reveals heterogeneity among COVID-19 patients

Patients with coronavirus disease 2019 (COVID-19) often exhibit diverse disease progressions associated with various infectious ability, symptoms, and clinical treatments. To systematically and thoroughly understand the heterogeneous progression of COVID-19, we developed a multi-scale computational model to quantitatively understand the heterogeneous progression of COVID-19 patients infected with severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2). The model consists of intracellular viral dynamics, multicellular infection process, and immune responses, and was formulated using a combination of differential equations and stochastic modeling. By integrating multi-source clinical data with model analysis, we quantified individual heterogeneity using two indexes, i.e., the ratio of infected cells and incubation period. Specifically, our simulations revealed that increasing the host antiviral state or virus induced type I interferon (IFN) production rate can prolong the incubation period and postpone the transition from asymptomatic to symptomatic outcomes. We further identified the threshold dynamics of T cell exhaustion in the transition between mild-moderate and severe symptoms, and that patients with severe symptoms exhibited a lack of naïve T cells at a late stage. In addition, we quantified the efficacy of treating COVID-19 patients and investigated the effects of various therapeutic strategies. Simulations results suggested that single antiviral therapy is sufficient for moderate patients, while combination therapies and prevention of T cell exhaustion are needed for severe patients. These results highlight the critical roles of IFN and T cell responses in regulating the stage transition during COVID-19 progression. Our study reveals a quantitative relationship underpinning the heterogeneity of transition stage during COVID-19 progression and can provide a potential guidance for personalized therapy in COVID-19 patients.

Coronavirus disease 2019 (COVID-19) is currently destroying both lives and economies. However, patients infected with severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2) usually present heterogeneous and complicated progressions, such as different incubation periods (short and long), symptoms (asymptomatic and symptomatic) and severity (mild-moderate and severe). Currently, various clinical data and experimental data are available from different countries, which has great significance for integrating different types of data to comprehensively understand the diverse disease progression in COVID-19 patients and guide individual treatment strategies. Here, we developed a multi-scale computational model to describe the dynamical process of patients infected with SARS-CoV-2, including intracellular viral dynamics, multicellular infection process, and immune responses. By combining data integration, stochastic simulation and quantitative analysis based on the multi-scale mathematical model, we addressed an important question regarding how IFN response and T cell exhaustion quantitatively affect heterogeneous progression in patients with respect to incubation periods, symptoms and severity. Furthermore, the efficacy of various therapeutic strategies for treating COVID-19 patients with different severity degrees was evaluated and validated. The computational framework in this study can also be extended to explore the dynamical process of other coronavirus infections.

A network-based group testing strategy for colleges

Group testing has recently become a matter of vital importance for efficiently and rapidly identifying the spread of Covid-19. In particular, we focus on college towns due to their density, observability, and significance for school reopenings. We propose a novel group testing strategy which requires only local information about the underlying transmission network. By using cellphone data from over 190,000 agents, we construct a mobility network and run extensive data-driven simulations to evaluate the efficacy of four different testing strategies. Our results demonstrate that our group testing method is more effective than three other baseline strategies for reducing disease spread with fewer tests.

Joint Estimation of Generation Time and Incubation Period for Coronavirus Disease 2019

BACKGROUND

Coronavirus disease 2019 (COVID-19) has caused a heavy disease burden globally. The impact of process and timing of data collection on the accuracy of estimation of key epidemiological distributions are unclear. Because infection times are typically unobserved, there are relatively few estimates of generation time distribution.

METHODS

We developed a statistical framework to jointly estimate generation time and incubation period from human-to-human transmission pairs, accounting for sampling biases. We applied the framework on 80 laboratory-confirmed human-to-human transmission pairs in China. We further inferred the infectiousness profile, serial interval distribution, proportions of presymptomatic transmission, and basic reproduction number (R0) for COVID-19.

RESULTS

The estimated mean incubation period was 4.8 days (95% confidence interval [CI], 4.1-5.6), and mean generation time was 5.7 days (95% CI, 4.8-6.5). The estimated R0 based on the estimated generation time was 2.2 (95% CI, 1.9-2.4). A simulation study suggested that our approach could provide unbiased estimates, insensitive to the width of exposure windows.

CONCLUSIONS

Properly accounting for the timing and process of data collection is critical to have correct estimates of generation time and incubation period. R0 can be biased when it is derived based on serial interval as the proxy of generation time.

How is COVID-19 perceived by medical students? A survey in Aceh province, Indonesia

The coronavirus disease 2019 (COVID-19) has burdened the health system and medical education programmes both locally and globally, requiring medical students to continue their education whilst engaging in prevention programmes to support primary health services. This study aims to describe medical students' knowledge, attitudes in the prevention of COVID-19, and to determine the relationship between the knowledge of COVID-19 preventive behaviour and attitudes towards it. This study used an analytical cross-sectional observational design with a sample of 290 students. Data were collected through a self-assessment method using a validated questionnaire. Analysis of the frequency distribution test for knowledge found that 54.1% of the respondents and 99.0% of the students had a good understanding of the mode of transmission of COVID-19. The majority of medical students (73.1%) also had a good attitude towards COVID-19 and around 84.3% of the students considered that good ethics is needed when coughing or sneezing during this pandemic. As many as 46.6% of the respondents had good preventive behaviour against COVID-19 and always imposed restrictions on using public transportation during a pandemic. The Spearman's correlation test shows a weak but meaningful relationship between knowledge (r = 0.214, p = 0.000) and COVID-19 preventive behaviour, and a significant relationship between attitudes (r = 0.477, p = 0.000) towards COVID-19 preventive behaviour. This study concludes that medical students have good knowledge, preventive behaviour and an understanding of infection risk. An enhanced knowledge and awareness will increase preventive behaviours that will provide support in controlling the disease.

The Impact of Disease Control Measures on the Spread of COVID-19 in the Province of Sindh, Pakistan

The province of Sindh reported the first COVID-19 case in Pakistan on 26th February 2020. The Government of Sindh has employed numerous control measures to limit its spread. However, for low-and middle-income countries such as Pakistan, the management protocols for controlling a pandemic are not always as definitive as they would be in other developed nations. Given the dire socio-economic conditions of Sindh, continuation of province-wise lockdowns may inadvertently cause a potential economic breakdown. By using a data driven SEIR modelling framework, this paper describes the evolution of the epidemic projections because of government control measures. The data from reported COVID-19 prevalence and google mobility is used to parameterize the model at different time points. These time points correspond to the government's call for advice on the prerequisite actions required to curtail the spread of COVID-19 in Sindh. Our model predicted the epidemic peak to occur by 18th June 2020 with approximately 3500 reported cases at that peak, this projection correlated with the actual recorded peak during the first wave of the disease in Sindh. The impact of the governmental control actions and religious ceremonies on the epidemic profile during this first wave of COVID-19 are clearly reflected in the model outcomes through variations in the epidemic peaks. We also report these variations by displaying the trajectory of the epidemics had the control measures been guided differently; the epidemic peak may have occurred as early as the end of May 2020 with approximately 5000 reported cases per day had there been no control measures and as late as August 2020 with only around 2000 cases at the peak had the lockdown continued, nearly flattening the epidemic curve.

An outlook on coronavirus disease 2019 detection methods

Diagnostic testing plays a fundamental role in the mitigation and containment of coronavirus disease 2019 (COVID-19), as it enables immediate quarantine of those who are infected and contagious and is essential for the epidemiological characterization of the virus and estimating the number of infected cases worldwide. Confirmation of viral infections, such as COVID-19, can be achieved through two general approaches: nucleic acid amplification tests (NAATs) or molecular tests, and serological or antibody-based tests. The genetic material of the pathogen is detected in NAAT, and in serological tests, host antibodies produced in response to the pathogen are identified. Other methods of diagnosing COVID-19 include radiological imaging of the lungs and in vitro detection of viral antigens. This review covers different approaches available to diagnosing COVID-19 by outlining their advantages and shortcomings, as well as appropriate indications for more accurate testing.

•

Diagnostic tests to detect coronavirus disease 2019 (COVID-19).

•

Advantages and disadvantages associated with each detection method.

•

Implications for a more accurate and rapid testing of COVID-19 or other similar future emergent viruses.

Comparison of chest CT findings in outpatient and hospitalized COVID-19 RT-PCR positive patients of Shiraz

INTRODUCTION

Chest CT provides valuable information regarding coronavirus disease 2019 (COVID-19) during the treatment process. The present study aimed to assess the distribution of chest CT findings in outpatient (OPD) and hospitalized corona patients.

MATERIAL AND METHOD

This was a retrospective study. Archived corona patient's data on the picture archiving and communication system (PACS) was assessed in terms of demographic data and patients' lungs' radiologic features. The OPD and hospitalized patients referred to University hospitals from February 20 to the study's date were evaluated. Data were analyzed using independent chi-square and t-test.

RESULTS

Five hundred and fifty nine patients, including 187 OPD and 372 hospitalized patients, were analyzed. The frequency of normal chest CT, typical, and possible corona features was 37.4%, 40.8%, and 14.3%. The normal chest CT rate was significantly higher in outpatient versus hospitalized patients (P<0.001). Consolidation and/or ground-glass opacity were seen in 61% of patients, considerably higher in hospitalized patients (P<0.001). 2% and 15% OPD and hospitalized patients had more than 25% lung involvement, respectively. The frequency of other signs such as Crazy Paving, atoll sign, subpleural band/distortion also was significantly higher in hospitalized patients (P<0.001).

CONCLUSION

Most OPD patients had less than 5% lung involvement or normal chest CT. The typical features of lung involvement in COVID-19 were significantly higher in hospitalized patients.

Non-Respiratory Droplet Transmission of COVID-19 in the Isolation Ward of a Secondary Hospital in Oman: A Return to Isolation Basics

Front-line health care workers are among the most vulnerable groups at risk for acquiring COVID-19, yet the exact mechanisms of how infections occur in health care settings are yet to be identified.

OBJECTIVES

To review the epidemiologic and environmental factors surrounding a cluster of nosocomially acquired COVID-19 cases with possible non respiratory droplet transmission indicating the importance of strict adherence with isolation procedures for patients and staff.

METHODS

Contact and environmental investigations were completed to determine the source and possible routes of hospital transmission of COVID-19.

RESULTS

Seven cases of COVID-19 occurred in the COVID-19 isolation ward of a secondary hospital from 12/04/2020 to 01/05/2020. Analysis also included first four COVID-19 patients admitted to this facility. Epidemiologic links included exposure to massive bowel movements of two infected patients in adjoining rooms on the same day. Serious gaps in infection control practices lead to further spread the virus and cross infection between the staff.

CONCLUSION

Despite preparedness to combat pandemics, the infection control precautions taken at this facility were found not sufficient to prevent nosocomial spread of COVID-19. Issues were identified with compliance, enforcement, and failure to update to the most current guidance. Urgent review of ongoing hygiene and isolation practices in hospitals of this type is indicated. Further studies are required to better understand the role of fecal oral transmission and environmental contamination in the transmission of COVID-19.

Current status, advances, challenges and perspectives on biosensors for COVID-19 diagnosis in resource-limited settings

As the COVID-19 pandemic has profoundly impacted human life, prompt diagnostic tests are becoming an essential part of the social activities. However, the expensive and time-consuming laboratory-based traditional methods do not suffice the enormous needs for massive number of tests, especially in resource-limited settings. Therefore, more affordable, rapid, sensitive and specific field-practical diagnostic devices play an important role in the fight against the disease. In this review, we present the current status and advances in the biosensing technologies for diagnosing COVID-19, ranging from commercial achievements to research developments. Starting from a brief introduction to the disease biomarkers, this review summarizes the working principles of the biosensing technologies, followed by a review of the commercial products and research advances in academia. We recapitulate the literatures with a wide scope of bio/marker detections, embracing nucleic acids, viral proteins, human immune responses, and other potential bio/markers. Further, the challenges and perspectives for their employment in future point-of-care applications are discussed, with an extended appraisal on the practical strategies to enlarge the testing capability without high cost. This critical review provides a comprehensive insight into the diagnostic tools for COVID-19 and will encourage the industry and academia in the field of diagnostic biosensing for future evolvement to large-scale point-of-care screening of COVID-19.

SARS-CoV-2 new variants: Characteristic features and impact on the efficacy of different vaccines

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its new variants reported in different countries have posed a serious threat to human health and social fabrics worldwide. In addition, these new variants hindered the efforts of vaccines and other therapeutic developments. In this review article, we explained the emergence of new variants of SARS-CoV-2, their transmission risk, mortality rate, and, more importantly, the impact of each new variant on the efficacy of the developed vaccines reported in different literature and findings. The literature reported that with the emergence of new variants, the efficacy of different vaccines is declined, hospitalization and the risk of reinfection is increased. The reports concluded that the emergence of a variant that entirely evades the immune response triggered by the vaccine is improbable. The emergence of new variants and reports of re-infections are creating a more distressing situation and therefore demands further investigation to formulate an effective therapeutic strategy.

Systems dynamics and the uncertainties of diagnostics, testing and contact tracing for COVID-19

The current COVID-19 pandemic contains an unprecedented amount of uncertainty and variability and thus, there is a critical need for understanding of the variation documented in the biological, policy, sociological, and infrastructure responses during an epidemic to support decisions at all levels. With the significant asymptomatic spread of the virus and without an immediate vaccine and pharmaceuticals available, the best feasible strategies for testing and diagnostics, contact tracing, and quarantine need to be optimized. With potentially high false negative test results, infected people would not be enrolled in contact-trace programs and thus, may not be quarantined. Similarly, without broad testing, asymptomatic people are not identified and quarantined. Interconnected system dynamics models can be used to optimize strategies for mitigations for decision support during a pandemic. We use a systems dynamics epidemiology model along with other interconnected system models within public health including hospitals, intensive care units, masks, contact tracing, social distancing, and a newly developed testing and diagnostics model to investigate the uncertainties with testing and to optimize strategies for detecting and diagnosing infected people. Using an orthogonal array Latin Hypercube experimental design, we ran 54 simulations each for two scenarios of 10% and 30% asymptomatic people, varying important inputs for testing and social distancing. Systems dynamics modeling, coupled with computer experimental design and statistical analysis can provide rapid and quantitative results for decision support. Our results show that widespread testing, contacting tracing and quarantine can curtail the pandemic through identifying asymptomatic people in the population.

Experimental measurements of airflow features and velocity distribution exhaled from sneeze and speech using particle image velocimetry

Airflow exhaled from sneeze and speech is an important source of viruses and droplets in daily life and may cause imperceptible virus propagation. The velocities of sneeze and speech airflow exhaled from 10 healthy young participants repeatedly using high-frequency (2986 Hz) particle image velocimetry are measured. The parameters for describing the dynamic process of sneeze airflow, such as sneeze duration time (SDT), peak velocity time (PVT), maximum velocities, and sneeze spread angle, are analyzed. The sneeze airflow lasts 430 ms (SDT) and reaches the peak velocity in the first 20 ms (PVT). The maximum sneeze airflow velocity is approximately 15.9 m/s. The temporal variation of the sneeze velocity exhibits the gamma distribution. For speech airflow, the maximum instantaneous velocity and maximum time-averaged velocity are reported. The maximum instantaneous velocity is approximately 6.25 m/s, whereas the time-averaged value is only 0.208 m/s owing to the extremely small airflow velocity among syllables. The vertical/horizontal spread angles of the airflow are 15.1°/15.4° for sneeze and 52.9°/42.9° for speech. The difference in airflow features based on gender is generally slight for both sneeze and speech. Subsequently, an ensemble-average operation is conducted to obtain the general and representative velocity distributions. We report each component of the temporal and spatial velocity distributions of the sneeze airflow and the time-averaged velocity distribution of the speech airflow. These detailed distribution data can provide a comprehensive understanding of sneeze and speech airflow movement mechanisms as well as a detailed database for future sneeze and speech computational fluid dynamics simulations.

The COVID-19 Pandemic: Are There Any Impacts on Sustainability?

This paper sets out to explain and describe the potential ways to control COVID-19′s impact on the environment and what controllable strategies and anticipations emerge from rethinking sustainable production. The rapid and devastating spread of this disease has made millions of people throughout the world cover themselves, wear gloves, and use hand sanitizers and other medical applications. However, it means that a huge amount of clinical waste is being dumped into landfills or the oceans, and such activity may simply worsen the infection’s transmission and the sustainability of the environment, the socio-economy, and sustainable productions. This disease has greatly changed the way people live and has caused considerable occupational job losses and misfortunes, sending sustainable businesses and other organizations to the wall. Virtually every country is trying to stop the infection transmission by testing patients and isolating people, but the environmental effects of the pandemic and sustainable business have not previously been analyzed. The study suggests that the current options for sustainable production must be measured and also further researched.

Human metapneumovirus-associated community-acquired pneumonia in adults during the first wave of COVID-19

Objective: The clinical course of human metapneumovirus (hMPV) infection is similar to that of coronavirus 2019 disease (COVID-19). However, community-acquired hMPV infections in adults have not yet been sufficiently investigated. We examined the detection status of hMPV antigens and the clinical features of positive patients during the first wave of COVID-19, which coincided with the epidemic season of hMPV infection in Japan.

Methods: In this cross-sectional, observational, and single-center study, we recruited consecutive individuals who visited the Japan Agricultural Cooperatives Kochi Hospital due to fever, respiratory symptoms, or close contact with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected persons during the period from January to May 2020.

Results: The positive rate of immunochromatography for hMPV antigens from nasopharyngeal swabs was 9.5% (4/42), and four positive cases were community-acquired pneumonia (CAP) (5.3% of all CAP). The positive rate of hMPV antigens in the CAP group (30.8%, 4/13) was higher than that in the non-pneumonia group (0.0%, 0/19) (p < 0.05). The average age of the four adult patients with CAP was 69.8 years (range 35–93). Mean white blood cell counts and C-reactive protein blood levels were 6,250 cells/μL (3,500–12,180) and 4.30 mg/dL (4.05–7.04), respectively. Chest computed tomography images were diverse and two patients showed dense consolidation. No multi-organ disorder was noted during the clinical course in any of the four cases, and their prognoses were good.

Conclusion: hMPV infection may be considered in the differential diagnosis of COVID-19 and CAP in Japan under the preventive measures for SARS-CoV-2 infection, at least during the epidemic season of hMPV infection.

Predictors of Illness Severity in COVID-19 Cases in Saudi Arabia

Purpose

Multiple studies worldwide have reported the clinical and epidemiological features of coronavirus disease 2019 (COVID-19), with limited reports from the Middle East. This study describes the clinical and epidemiological features of COVID-19 cases in the Eastern Province of Saudi Arabia and identified factors associated with the severity of illness.

Patients and Methods

This was an observational study of 341 COVID-19 cases. These cases were reported in the first three months after the first case in the country was identified. Clinical and demographic data were analyzed and described to identify the effects of age, sex, and ethnicity on illness severity. In addition, the duration of viral shedding and cycle threshold (Ct) values of real-time PCR were evaluated as predictors of severity.

Results

The median age was 45 years. Males were twice as likely to be infected than females (p <0.0001). The duration of viral shedding ranged from 9 to 36 days. The most common clinical presentations include fever, shortness of breath, cough, myalgia, sore throat, vomiting, and headache. Critical cases were significantly higher in men (23% vs 8.7%), senior adults (>65 years), individuals of Bengali ethnicity, and in patients with comorbidities including diabetes, hypertension, and dyslipidemia (p =0.001). The case fatality rate was found to be 10%. The fatality was significantly higher in males than females (13.8% vs 2.6%), and in Asians (17.9%) than Arabs (6%) and Africans (0) (p =0.002). No association was found between viral load, represented by the RT-PCR cycle threshold (Ct) values, and severity of illness.

Conclusion

Age, sex, and ethnicity are important predictors of COVID-19 severity. The cycle threshold (Ct) of the SARS-CoV-2 RT-PCR test cannot be used as a predictor of the criticality of illness.

Quality of biosafety guidelines for dental clinical practice in world in early COVID-19 pandemic: A systematic review

OBJECTIVES

To conduct a systematic review of coronavirus disease 2019 (COVID-19)-related biosafety guidelines for dental clinical practice in the early stage of the pandemic, focusing on quality assessment.

METHODS

Electronic (via PubMed, Scopus, Web of Science, Latin American and Caribbean Health Sciences Literature database, Brazilian Library in Dentistry, and Cochrane Library) and gray literature searches were performed for documents published up to May 12, 2020. Guidelines updated until April 17, 2021 were identified. Documents were included as guidelines if they (1) consisted of a set of statements, directions, or principles presenting current or future rules or policy; (2) were developed by government agencies, institutions, organizations, or expert panels; and (3) were related to the general conduct of healthcare activities rather a particular condition. Two researchers, using the Appraisal of Guidelines for Research & Evaluation II, independently extracted the recommendations and evaluated the quality of the guidelines.

RESULTS

Twenty-seven documents from 19 countries were included in the review. These documents presented 122 recommendations related to (1) professional biosafety; (2) patients’/companions’ safety; (3) the organization and biosafety of the physical dental facility environment; and (4) the work process in dental care. Overall, the scientific quality of the guidelines was considered low. Some recommendations presented in these guidelines would require further research to establish their effectiveness.

CONCLUSIONS

We found a wide variety of biosafety guidelines for dental practice regarding COVID-19 in the early months of the pandemic, but their quality was low. Biosafety recommendations should be frequently updated.

COVID-19 Masked by Pyogenic Lumbar Discitis and Bacteremia: A Case Report

CASE

A 65-year-old man experienced backache, and 9 days later, he developed cellulitis in his left foot. On the 20th day, his body temperature was 37°C, and he had intermittent and shallow cough. On the 29th day, he was diagnosed with pyogenic lumbar discitis and bacteremia. Computed tomography examinations showed no evidence of pneumonia, but his cough persisted, and an elevated d-dimer level was observed. Finally, he tested positive for coronavirus disease 2019 (COVID-19).

CONCLUSIONS

This case shows possible associations among COVID-19, venous thrombosis, cellulitis, and bacteremia. Other infections may coexist with COVID-19 and mask it.

Changes in Dietary Patterns and Clinical Health Outcomes in Different Countries during the SARS-CoV-2 Pandemic

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to an excess in community mortality across the globe. We review recent evidence on the clinical pathology of COVID-19, comorbidity factors, immune response to SARS-CoV-2 infection, and factors influencing infection outcomes. The latter specifically includes diet and lifestyle factors during pandemic restrictions. We also cover the possibility of SARS-CoV-2 transmission through food products and the food chain, as well as virus persistence on different surfaces and in different environmental conditions, which were major public concerns during the initial days of the pandemic, but have since waned in public attention. We discuss useful measures to avoid the risk of SARS-CoV-2 spread through food, and approaches that may reduce the risk of contamination with the highly contagious virus. While hygienic protocols are required in food supply sectors, cleaning, disinfection, avoidance of cross-contamination across food categories, and foodstuffs at different stages of the manufacturing process are still particularly relevant because the virus persists at length on inert materials such as food packaging. Moreover, personal hygiene (frequent washing and disinfection), wearing gloves, and proper use of masks, clothes, and footwear dedicated to maintaining hygiene, provide on-site protections for food sector employees as well as supply chain intermediates and consumers. Finally, we emphasize the importance of following a healthy diet and maintaining a lifestyle that promotes physical well-being and supports healthy immune system function, especially when government movement restrictions ("lockdowns") are implemented.

Inhibition Potencies of Phytochemicals Derived from Sesame Against SARS-CoV-2 Main Protease: A Molecular Docking and Simulation Study

The ongoing COVID-19 pandemic, caused by SARS-CoV-2, has now spread across the nations with high mortality rates and multifaceted impact on human life. The proper treatment methods to overcome this contagious disease are still limited. The main protease enzyme (Mpro, also called 3CLpro) is essential for viral replication and has been considered as one of the potent drug targets for treating COVID-19. In this study, virtual screening was performed to find out the molecular interactions between 36 natural compounds derived from sesame and the Mpro of COVID-19. Four natural metabolites, namely, sesamin, sesaminol, sesamolin, and sesamolinol have been ranked as the top interacting molecules to Mpro based on the affinity of molecular docking. Moreover, stability of these four sesame-specific natural compounds has also been evaluated using molecular dynamics (MD) simulations for 200 nanoseconds. The molecular dynamics simulations and free energy calculations revealed that these compounds have stable and favorable energies, causing strong binding with Mpro. These screened natural metabolites also meet the essential conditions for drug likeness such as absorption, distribution, metabolism, and excretion (ADME) properties as well as Lipinski's rule of five. Our finding suggests that these screened natural compounds may be evolved as promising therapeutics against COVID-19.

Reliably quantifying the evolving worldwide dynamic state of the COVID-19 outbreak from death records, clinical parametrization, and demographic data

The dynamic characterization of the COVID-19 outbreak is critical to implement effective actions for its control and eradication but the information available at a global scale is not sufficiently reliable to be used directly. Here, we develop a quantitative approach to reliably quantify its temporal evolution and controllability through the integration of multiple data sources, including death records, clinical parametrization of the disease, and demographic data, and we explicitly apply it to countries worldwide, covering 97.4% of the human population, and to states within the United States (US). The validation of the approach shows that it can accurately reproduce the available prevalence data and that it can precisely infer the timing of nonpharmaceutical interventions. The results of the analysis identified general patterns of recession, stabilization, and resurgence. The diversity of dynamic behaviors of the outbreak across countries is paralleled by those of states and territories in the US, converging to remarkably similar global states in both cases. Our results offer precise insights into the dynamics of the outbreak and an efficient avenue for the estimation of the prevalence rates over time.

Change sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis

We are concerned with the issue of detecting changes and their signs from a data stream. For example, when given time series of COVID-19 cases in a region, we may raise early warning signals of an epidemic by detecting signs of changes in the data. We propose a novel methodology to address this issue. The key idea is to employ a new information-theoretic notion, which we call the differential minimum description length change statistics (D-MDL), for measuring the scores of change sign. We first give a fundamental theory for D-MDL. We then demonstrate its effectiveness using synthetic datasets. We apply it to detecting early warning signals of the COVID-19 epidemic using time series of the cases for individual countries. We empirically demonstrate that D-MDL is able to raise early warning signals of events such as significant increase/decrease of cases. Remarkably, for about [Formula: see text] of the events of significant increase of cases in studied countries, our method can detect warning signals as early as nearly six days on average before the events, buying considerably long time for making responses. We further relate the warning signals to the dynamics of the basic reproduction number R0 and the timing of social distancing. The results show that our method is a promising approach to the epidemic analysis from a data science viewpoint.

African Perspective of Social Distancing in Pandemics: Adoption Challenges

In public health, social distancing is a set of non-pharmaceutical interventions or measures intended to prevent the spread of a contagious disease, by maintaining a physical distance between people. During the COVID-19 pandemic, the World Health Organization suggested the term, ‘physical distancing,’ as opposed to ‘social distancing’, arguing that it is a physical distance which prevents transmission; people can remain socially connected via technology. This paper discusses the concepts of social distance, social distancing, physical distancing, self-quarantine, self-isolation, symptomatic, asymptomatic and parasymptomatic cases as they relate to COVID-19 and African perception of pandemic diseases. Although the idea of social distancing is not novel to the Africans, but a challenge in its implementation is that historically, social distancing is rather applied to non-infectious cases like mental illness, epilepsy, infertility, aging, victims of sexual violence and the like. The paper utilizes health-related theories and pertinent empirical findings to explain African perspective of social distancing and the challenges of adoption in pandemic situations. The theories on health risk perception reviewed include the protection motivation theory, the health belief model, the extended parallel process model and the precaution adoption process model. From consumer psychology background on product adoption, a conceptual model for ‘social distancing’ adoption in pandemics was advanced. These ancient and novel health-related theories and models were applied to explain the erroneous understanding, perception and adoption challenges of social distancing in Africa, leading to possible increase in the spread of the coronavirus.

Addressing hospitalisations with non-error-free data by generalised SEIR modelling of COVID-19 pandemic

Successive generalisations of the basic SEIR model have been proposed to accommodate the different needs of the organisations handling the SARS-CoV-2 epidemic. These generalisations have not been able until today to represent the potential of the epidemic to overwhelm hospital capacity until today. This work builds on previous generalisations, including a new compartment for hospital occupancy that allows accounting for the infected patients that need specialised medical attention. Consequently, a deeper understanding of the hospitalisations rate and probability as well as of the recovery rates for hospitalised and non-hospitalised individuals is achieved, offering new information and predictions of crucial importance for the planning of the health systems and global epidemic response. Additionally, a new methodology to calibrate epidemic flows between compartments is proposed. We conclude that the two-step calibration procedure is able to recalibrate non-error-free data and showed crucial to reconstruct the series in a specific situation characterised by significant errors over the official recovery cases. The performed modelling also allowed us to understand how effective the several interventions (lockdown or other mobility restriction measures) were, offering insight for helping public authorities to set the timing and intensity of the measures in order to avoid the implosion of the health systems.