Metapangenomics of wild and cultivated banana microbiome reveals a plethora of host-associated protective functions.

BACKGROUND: Microbiomes are critical to plants, promoting growth, elevating stress tolerance, and expanding the plant's metabolic repertoire with novel defense pathways. However, generally microbiomes within plant tissues, which intimately interact with their hosts, remain poorly characterized. These endospheres have become a focus in banana (Musa spp.)-an important plant for study of microbiome-based disease protection. Banana is important to global food security, while also being critically threatened by pandemic diseases. Domestication and clonal propagation are thought to have depleted protective microbiomes, whereas wild relatives may hold promise for new microbiome-based biological controls. The goal was to compare metapangenomes enriched from 7 Musa genotypes, including wild and cultivated varieties grown in sympatry, to assess the host associations with root and leaf endosphere functional profiles. RESULTS: Density gradients successfully generated culture-free microbial enrichment, dominated by bacteria, with all together 24,325 species or strains distinguished, and 1.7 million metagenomic scaffolds harboring 559,108 predicted gene clusters. About 20% of sequence reads did not match any taxon databases and ~ 62% of gene clusters could not be annotated to function. Most taxa and gene clusters were unshared between Musa genotypes. Root and corm tissues had significantly richer endosphere communities that were significantly different from leaf communities. Agrobacterium and Rhizobium were the most abundant in all samples while Chitinophagia and Actinomycetia were more abundant in roots and Flavobacteria in leaves. At the bacterial strain level, there were > 2000 taxa unique to each of M. acuminata (AAA genotype) and M. balbisiana (B-genotype), with the latter 'wild' relatives having richer taxa and functions. Gene ontology functional enrichment showed core beneficial functions aligned with those of other plants but also many specialized prospective beneficial functions not reported previously. Some gene clusters with plant-protective functions showed signatures of phylosymbiosis, suggesting long-standing associations or heritable microbiomes in Musa. CONCLUSIONS: Metapangenomics revealed key taxa and protective functions that appeared to be driven by genotype, perhaps contributing to host resistance differences. The recovery of rich novel taxa and gene clusters provides a baseline dataset for future experiments in planta or in vivo bacterization or engineering of wild host endophytes.

Does paid sick leave encourage staying at home? Evidence from the United States during a pandemic.

We study the impact of a temporary U.S. paid sick leave mandate that became effective April 1st, 2020 on self-quarantining, proxied by physical mobility behaviors gleaned from cellular devices. We study this policy using generalized difference-in-differences methods, leveraging pre-policy county-level heterogeneity in the share of workers likely eligible for paid sick leave benefits. We find that the policy leads to increased self-quarantining as proxied by staying home. We also find that COVID-19 confirmed cases decline post-policy.

Effects of hydrometeorological and other factors on SARS-CoV-2 reproduction number in three contiguous countries of tropical Andean South America: a spatiotemporally disaggregated time series analysis.

Background: The COVID-19 pandemic has caused societal disruption globally, and South America has been hit harder than other lower-income regions. This study modeled the effects of six weather variables on district-level SARS-CoV-2 reproduction numbers (Rt ) in three contiguous countries of tropical Andean South America (Colombia, Ecuador, and Peru), adjusting for environmental, policy, healthcare infrastructural and other factors. Methods: Daily time-series data on SARS-CoV-2 infections were sourced from the health authorities of the three countries at the smallest available administrative level. Rt values were calculated and merged by date and unit ID with variables from a unified COVID-19 dataset and other publicly available sources for May-December, 2020. Generalized additive models were fitted. Findings: Relative humidity and solar radiation were inversely associated with SARS-CoV-2 Rt . Days with radiation above 1000 kJ/m2 saw a 1.3% reduction in Rt , and those with humidity above 50% recorded a 0.9% reduction in Rt . Transmission was highest in densely populated districts, and lowest in districts with poor healthcare access and on days with lowest population mobility. Wind speed, temperature, region, aggregate government policy response, and population age structure had little impact. The fully adjusted model explained 4.3% of Rt variance. Interpretation: Dry atmospheric conditions of low humidity increase district-level SARS-CoV-2 reproduction numbers, while higher levels of solar radiation decrease district-level SARS-CoV-2 reproduction numbers - effects that are comparable in magnitude to population factors like lockdown compliance. Weather monitoring could be incorporated into disease surveillance and early warning systems in conjunction with more established risk indicators and surveillance measures. Funding: NASA's Group on Earth Observations Work Programme (16-GEO16-0047).

Mutations in SARS-CoV-2: Insights on structure, variants, vaccines, and biomedical interventions.

COVID-19 is a worldwide pandemic caused by SARS-coronavirus-2 (SARS-CoV-2). Less than a year after the emergence of the Covid-19 pandemic, many vaccines have arrived on the market with innovative technologies in the field of vaccinology. Based on the use of messenger RNA (mRNA) encoding the Spike SARS-Cov-2 protein or on the use of recombinant adenovirus vectors enabling the gene encoding the Spike protein to be introduced into our cells, these strategies make it possible to envisage the vaccination in a new light with tools that are more scalable than the vaccine strategies used so far. Faced with the appearance of new variants, which will gradually take precedence over the strain at the origin of the pandemic, these new strategies will allow a much faster update of vaccines to fight against these new variants, some of which may escape neutralization by vaccine antibodies. However, only a vaccination policy based on rapid and massive vaccination of the population but requiring a supply of sufficient doses could make it possible to combat the emergence of these variants. Indeed, the greater the number of infected individuals, the faster the virus multiplies, with an increased risk of the emergence of variants in these RNA viruses. This review will discuss SARS-CoV-2 pathophysiology and evolution approaches in altered transmission platforms and emphasize the different mutations and how they influence the virus characteristics. Also, this article summarizes the common vaccines and the implication of the mutations and genetic variety of SARS-CoV-2 on the COVID-19 biomedical arbitrations.

In silico epitope-based vaccine design against influenza a neuraminidase protein: Computational analysis established on B- and T-cell epitope predictions.

Objective: Influenza A virus belongs to the most studied virus and its mutant initiates epidemic and pandemics outbreaks. Inoculation is the significant foundation to diminish the risk of infection. To prevent an incidence of influenza from the transmission, various practical approaches require more advancement and progress. More efforts and research must take in front to enhance vaccine efficacy. Methods: The present research emphasizes the development and expansion of a universal vaccine for the influenza virus. Research focuses on vaccine design with high efficacy. In this study, numerous computational approaches were used, covering a wide range of elements and ideas in bioinformatics methodology. Various B and T-cell epitopic peptides derived from the Neuraminidase protein N1 are recognized by these approaches. With the implementation of numerous obtained databases and bioinformatics tools, the different immune framework methods of the conserved sequences of N1 neuraminidase were analyzed. NCBI databases were employed to retrieve amino acid sequences. The antigenic nature of the neuraminidase sequence was achieved by the VaxiJen server and Kolaskar and Tongaonkar method. After screening of various B and T cell epitopes, one efficient peptide each from B cell epitope and T cell epitopes was assessed for their antigenic determinant vaccine efficacy. Identical two B cell epitopes were recognized from the N1 protein when analyzed using B-cell epitope prediction servers. The detailed examination of amino acid sequences for interpretation of B and T cell epitopes was achieved with the help of the ABCPred and Immune Epitope Database. Results: Computational immunology via immunoinformatic study exhibited RPNDKTG as having its high conservancy efficiency and demonstrated as a good antigenic, accessible surface hydrophilic B-cell epitope. Among T cell epitope analysis, YVNISNTNF was selected for being a conserved epitope. T cell epitope was also analyzed for its allergenicity and cytotoxicity evaluation. YVNISNTNF epitope was found to be a non-allergen and not toxic for cells as well. This T-cell epitope with maximum world populace coverages was scrutinized for its association with the HLA-DRB1*0401 molecule. Results from docking simulation analyses showed YVNISNTNF having lower binding energy, the radius of gyration (Rg), RMSD values, and RMSE values which make the protein structure more stable and increase its ability to become an epitopic peptide for influenza virus vaccination. Conclusions: We propose that this epitope analysis may be successfully used as a measurement tool for the robustness of an antigen-antibody reaction between mutant strains in the annual design of the influenza vaccine.

A Chilean Experience of Telestroke in a COVID-19 Pandemic Year.

BACKGROUND AND PURPOSE: Telemedicine for stroke patients' care (telestroke [TS]) has grown notably in recent decades and may offer advantages during health crisis. Hospital admissions related to stroke have decreased globally during the COVID-19 pandemic, but scarce information is available regarding the effect of COVID-19 in TS. Using a population-based TS registry, we investigated the impact of the first year of the COVID-19 pandemic throughout our TS network in Santiago, Chile. METHODS: Stroke codes evaluated after the onset of COVID-19 restrictions in Chile (defined as March 15, 2020) were compared with those evaluated in 2019. We analyzed differences between number of stroke codes, thrombolysis rate, stroke severity, and time from the stroke onset to hospital admission. RESULTS: We observed that the number of stroke codes and the number of patients undergoing reperfusion therapy did not change significantly (p = 0.669 and 0.415, respectively). No differences were found with respect to the median time from the stroke onset to admission (p = 0.581) or in National Institutes of Health Stroke Scale (NIHSS) scores (p = 0.055). The decision-making-to-needle time was significantly shorter in the COVID-19 period (median 5 min [IQR 3-8], p < 0.016), but no significant changes were found at the other times. CONCLUSIONS: This study demonstrates the potential of adapting TS to extreme situations such as the COVID-19 pandemic, as well as the importance of establishing networks that facilitate patient access to quality treatments.

Repositioning of Fungal-based Peptides as Modulators of Angiotensin-converting Enzyme-related Carboxypeptidase, SARS-coronavirus HR2 Domain, and Coronavirus Disease 2019 Main Protease.

BACKGROUND AND OBJECTIVES: Angiotensin-converting enzyme-related carboxypeptidase, SARS-Coronavirus HR2 Domain, and COVID-19 main protease are essential for the cellular entry and replication of coronavirus in the host. This study investigated the putative inhibitory action of peptides form medicinal mushrooms, namely Pseudoplectania nigrella, Russula paludosa, and Clitocybe sinopica, towards selected proteins through computational studies. MATERIALS AND METHODS: The respective physicochemical properties of selected peptides were predicted using ProtParam tool, while the binding modes and binding free energy of selected peptides toward proteins were computed through HawkDock server. The structural flexibility and stability of docked protein-peptide complexes were assessed through iMODS server. RESULTS: The peptides showed an optimum binding afinity with the molecular targets; plectasin from P. nigrella showed the highest binding free energy compared to peptides from R. paludosa and C. sinopica. Besides, molecular dynamic simulations showed all fungal-based peptides could influence the flexibility and stability of selected proteins. CONCLUSION: The study revealed fungal-based peptides could be explored as functional modulators of essential proteins that are involved in the cellular entry of coronavirus.

Controlling of pandemic COVID-19 using optimal control theory.

In 2019, a new infectious disease called pandemic COVID-19 began to spread from Wuhan, China. In spite of the efforts to stop the disease, being out of the control of the governments it spread rapidly all over the world. From then on, much research has been done in the world with the aim of controlling this contagious disease. A mathematical model for modeling the spread of COVID-19 and also controlling the spread of the disease has been presented in this paper. We find the disease-free equilibrium points as trivial equilibrium (TE), virus absenteeism equilibrium (VAE) and virus incidence equilibrium (VIE) for the proposed model; and at the trivial equilibrium point for the presented dynamic system we obtain the Jacobian matrix so as to be used in finding the largest eigenvalue. Radius spectral method has been used for finding the reproductive number. In the following, by adding a controller to the model and also using the theory of optimal control, we can improve the performance of the model. We must have a correct understanding of the system i.e. how it works, the various variables affecting the system, and the interaction of the variables on each other. To search for the optimal values, we need to use an appropriate optimization method. Given the limitations and needs of the problem, the aim of the optimization is to find the best solutions, to find conditions that result in the maximum of susceptiblity, the minimum of infection, and optimal quarantination.

Potential Drugs for the Treatment of COVID-19: Synthesis, Brief History and Application.

Coronaviruses (CoVs) belong to the Betacoronavirus group, an unusually large RNA genome characterized by club-like spikes that project from their surface. An outbreak of a novel coronavirus 2019 (nCOVID-19) already showed a unique replication strategy and infection that has posed significant threat to international health and the economy around the globe. Scientists around the world are investigating few previously used clinical drugs for the treatment of COVID-19. This review provides synthesis and mode of action of recently investigated drugs like Chloroquine, Hydroxychloroquine, Ivermectin, Selamectin, Remdesivir, Baricitinib, Darunavir, Favipiravir, Lopinavir/ ritonavir and Mefloquine hydrochloride that constitute an option for COVID-19 treatment.

Retrospective and prospective application of robots and artificial intelligence in global pandemic and epidemic diseases.

About 4.25% of people have lost their lives due to COVID-19 disease, among SARS-CoV-2 infected patients. In an unforeseen situation, approximately 25,000 frontline healthcare workers have also been infected by this disease while providing treatment to the infected patients. In this devastating scenario, without any drug or vaccine available for the treatment, frontline healthcare workers are highly prone to viral infection. However, some countries are drastically facing a shortage of healthcare workers in hospitals. METHODS: The literature search was conducted in ScienceDirect and ResearchGate, using words "Medical Robots", and "AI in Covid-19" as descriptors. To identify and evaluate the articles that create the impact of robots and artificial intelligence in pandemic diseases. Eligible articles were included publications and laboratory studies before and after covid-19 and also the prospective and retrospective of application of Robots and AI. CONCLUSION: In this pandemic situation, robots were employed in some countries during the COVID-19 outbreak, which are medical robots, UV-disinfectant robots, social robots, drones, and COBOTS. Implementation of these robots was found effective in successful disease management, treatment, most importantly ensures the safety of healthcare workers. Mainly, the Disposal of deceased bodies and the location and transportation of infected patients to hospitals and hospitals were tough tasks and risk of infection. These tasks will be performed by employing mobile robots and automated guided robots respectively. Therefore, in the future, advanced automated robots would be a promising choice in hospitals and healthcare centers to minimize the risk of frontline healthcare workers.

Cerca de un 4,25% de personas han perdido la vida a causa de la COVID-19, entre los pacientes infectados por SARS-CoV-2. En esta situación imprevista, aproximadamente 25.000 trabajadores sanitarios de primera línea se han visto también infectados por esta enfermedad, al proporcionar tratamiento a los pacientes infectados. En este escenario devastador, en el que no se dispone de fármacos o vacunas para el tratamiento, el personal sanitario de primera línea está altamente expuesto a la infección vírica. Sin embargo, algunos países se están enfrentando a un recorte drástico de personal sanitario en sus hospitales. MÉTODOS: Se realizó una búsqueda en la literatura en ScienceDirect y ResearchGate, utilizando los términos «medical robots¼ y «AI in COVID-19¼ como factores descriptivos, para identificar y evaluar los artículos que crean el impacto de los robots y la inteligencia artificial (AI) en las pandemias. Los artículos elegibles incluyeron publicaciones y estudios de laboratorio, antes y después de la COVID-19, y también la aplicación prospectiva y retrospectiva de robots e AI. CONCLUSIÓN: En esta situación de pandemia, algunos países utilizaron robots durante el brote de COVID-19, es decir, robots médicos, robots desinfectantes de rayos UV, robots sociales, drones, y cobots. Se encontró que la implementación de estos robots era eficaz para la gestión y tratamiento de la enfermedad y, más importantemente, la garantía de la seguridad del personal sanitario. En particular, la eliminación de cadáveres y la localización y transporte de pacientes infectados a los hospitales eran tareas duras que suponían un riesgo de infección. Dichas tareas podrán realizarse utilizando robots móviles y robots automatizados, respectivamente. Por tanto, en el futuro, los robots automatizados avanzados constituirán una elección prometedora en hospitales y centros sanitarios, para minimizar el riesgo del personal sanitario de primera línea.

Mutations in SARS-CoV-2; Consequences in structure, function, and pathogenicity of the virus.

The third pandemic of coronavirus infection, called COVID-19 disease, began recently in China. The newly discovered coronavirus, entitled SARS-CoV-2, is the seventh member of the human coronaviruses. The main pathogenesis of SARS-CoV-2 infection is severe pneumonia, RNAaemia, accompanied by glass turbidity, and acute cardiac injury. It possesses a single-stranded positive-sense RNA genome which is 60-140 nm in diameter, and has a size of 26-32 kbp. Viral pathogenesis is accomplished with spike glycoprotein through the employment of a membrane-bound aminopeptidase, called the ACE2, as its primary cell receptor. It has been confirmed that various factors such as different national rules for quarantine and various races or genetic backgrounds might influence the mortality and infection rate of COVID-19 in the geographic areas. In addition to various known and unknown factors and host genetic susceptibility, mutations and genetic variabilities of the virus itself have a critical impact on variable clinical features of COVID-19. Although the SARS-CoV-2 genome is more stable than SARS-CoV or MERS-CoV, it has a relatively high dynamic mutation rate with respect to other RNA viruses. It's noteworthy that, some mutations can be founder mutations and show specific geographic patterns. Undoubtedly, these mutations can drive viral genetic variability, and because of genotype-phenotype correlation, resulting in a virus with more/lower/no decrease in natural pathogenic fitness or on the other scenario, facilitating their rapid antigenic shifting to escape the host immunity and also inventing a drug resistance virus, so converting it to a more infectious or deadly virus. Overall, the detection of all mutations in SARS-CoV-2 and their relations with pathological changes is nearly impossible, mostly due to asymptomatic subjects. In this review paper, the reported mutations of the SARS-CoV-2 and related variations in virus structure and pathogenicity in different geographic areas and genotypes are widely investigated. Many studies need to be repeated in other regions/locations for other people to confirm the findings. Such studies could benefit patient-specific therapy, according to genotyping patterns of SARS-CoV-2 distribution.

Variants in ACE2; potential influences on virus infection and COVID-19 severity.

The third pandemic of coronavirus infection, called COVID-19 disease, was first detected in November 2019th. Various determinants of disease progression such as age, sex, virus mutations, comorbidity, lifestyle, host immune response, and genetic background variation have caused clinical variability of COVID-19. The causative agent of COVID-19 is an enveloped coronavirus named severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that invades host cells using an endocytic pathway. The SARS-CoV-2 spike protein is the main viral protein that contributes to the fusion of the virus particle to the host cell through angiotensin-converting enzyme 2 (ACE2). The highly conserved expression of ACE2 is found in various animals, which indicates its pivotal physiological function. The ACE2 has a crucial role in vascular, renal, and myocardial physiology. Genetic factors contributing to the outcome of SARS-CoV-2 infection are unknown; however, variants in the specific sites of ACE2 gene could be regarded as a main genetic risk factor for COVID-19. Given that ACE2 is the main site for virus landing on host cells, the effect of amino acid sequences of ACE2 on host susceptibility to COVID-19 seems reasonable. It would likely have a substantial role in the occurrence of a wide range of clinical symptoms. Several ACE2 variants can affect the protein stability, influencing the interaction between spike protein and ACE2 through imposing conformational changes while some other variants are known to cause a decrease or an increase in the ligand-receptor affinity. The other variations are located at the proteolytic cleavage site, which can influence virus infection; because soluble ACE2 can act as a decoy receptor for virus and decrease virus intake by cell surface ACE2. Notably, polymorphisms of regulatory and non-coding regions such as promoter in ACE2, can play crucial role in different expression levels of ACE2 among different individuals. Many studies should be performed to investigate the involvement of ACE2 polymorphism with susceptibility to COVID-19. Herein, we discuss some reported associations between variants of ACE2 and COVID-19 in details. In addition, the mode of action of ACE2 and its role in SARS-CoV-2 infection are highlighted which is followed by addressing the effects of several ACE2 variants on its protein stability, viral tropism or ligand-receptor affinity, secondary and tertiary structure or protein conformation, proteolytic cleavage site, and finally inter-individual clinical variability in COVID-19. The polymorphisms of regulatory regions of ACE2 and their effect on expression levels of ACE2 are also provided in this review. Such studies can improve the prediction of the affinity of mutant ACE2 variations with spike protein, and help the biopharmaceutical industry to design effective approaches for recombinant hACE2 therapy and vaccination of COVID-19 disease.

Modeling Ivory Coast COVID-19 cases: Identification of a high-performance model for utilization.

This study modelled the reported daily cumulative confirmed, discharged and death Coronavirus disease 2019 (COVID-19) cases using six econometric models in simple, quadratic, cubic and quartic forms and an autoregressive integrated moving average (ARIMA) model. The models were compared employing R-squared and Root Mean Square Error (RMSE). The best model was used to forecast confirmed, discharged and death COVID-19 cases for October 2020 to February 2021. The predicted number of confirmed and death COVID-19 cases are alarming. Good planning and innovative approaches are required to prevent the forecasted alarming infection and death in Ivory Coast. The applications of findings of this study will ensure that the COVID-19 does not crush the Ivory Coast's health, economic, social and political systems.

In-silico investigation of phytochemicals from Asparagus racemosus as plausible antiviral agent in COVID-19.

COVID-19 has ravaged the world and is the greatest of pandemics in human history, in the absence of treatment or vaccine the mortality and morbidity rates are very high. The present investigation was undertaken to screen and identify the potent leads from the Indian Ayurvedic herb, Asparagus racemosus (Willd.) against SARS-CoV-2 using molecular docking and dynamics studies. The docking analysis was performed on the Glide module of Schrödinger suite on two different proteins from SARS-CoV-2 viz. NSP15 Endoribonuclease and spike receptor-binding domain. Asparoside-C, Asparoside-D and Asparoside -F were found to be most effective against both the proteins as confirmed through their docking score and affinity. Further, the 100 ns molecular dynamics study also confirmed the potential of these compounds from reasonably lower root mean square deviations and better stabilization of Asparoside-C and Asparoside-F in spike receptor-binding domain and NSP15 Endoribonuclease respectively. MM-GBSA based binding free energy calculations also suggest the most favourable binding affinities of Asparoside-C and Asparoside-F with binding energies of -62.61 and -55.19 Kcal/mol respectively with spike receptor-binding domain and NSP15 Endoribonuclease. HighlightsAsparagus racemosus have antiviral potentialPhytochemicals of Shatavari showed promising in-silico docking and MD resultsAsparaoside-C and Asparoside-F has good binding with target proteinsAsparagus racemosus holds promise as SARS-COV-2 (S) and (N) proteins inhibitor Communicated by Ramaswamy H. Sarma.

Pandemic Surveillance and Racialized Subpopulations: Mitigating Vulnerabilities in COVID-19 Apps.

Debates about effective responses to the COVID-19 pandemic have emphasized the paramount importance of digital tracing technology in suppressing the disease. So far, discussions about the ethics of this technology have focused on privacy concerns, efficacy, and uptake. However, important issues regarding power imbalances and vulnerability also warrant attention. As demonstrated in other forms of digital surveillance, vulnerable subpopulations pay a higher price for surveillance measures. There is reason to worry that some types of COVID-19 technology might lead to the employment of disproportionate profiling, policing, and criminalization of marginalized groups. It is, thus, of crucial importance to interrogate vulnerability in COVID-19 apps and ensure that the development, implementation, and data use of this surveillance technology avoids exacerbating vulnerability and the risk of harm to surveilled subpopulations, while maintaining the benefits of data collection across the whole population. This paper outlines the major challenges and a set of values that should be taken into account when implementing disease surveillance technology in the pandemic response.

Review on machine and deep learning models for the detection and prediction of Coronavirus.

The novel Coronavirus disease has increased rapidly in the Wuhan city of China in December 2019. This fatal virus has spread across the whole world like a fire in different stages and affecting millions of population and thousands of deaths worldwide. Therefore, it is essential to classify the infected people, so that they can take the precaution in the earlier stages. Also, due to the increasing cases spread of Coronavirus, there are only limited numbers of polymerase change reaction kits available in the hospitals for testing Coronavirus patients. That why it is extremely important to develop artificial intelligence-based automatic diagnostic tools to classify the Coronavirus outbreak. The objective of this paper is to know the novel disease epidemiology, major prevention from spreading of Coronavirus Severe Acute Respiratory Syndrome, and to assess the machine and deep learning-based architectures performance that is proposed in the present year for classification of Coronavirus images such as, X-Ray and computed tomography. Specifically, advanced deep learning-based algorithms known as the Convolutional neural network, which plays a great effect on extracting highly essential features, mostly in terms of medical images. This technique, with using CT and X-Ray image scans, has been adopted in most of the recently published articles on the Coronavirus with remarkable results. Furthermore, according to this paper, this can be noted and said that deep learning technology has potential clinical applications.

Current perspective on pandemic of COVID-19 in the United States.

Currently, the world is facing its 3rd coronavirus outbreak of the 21st century, which has turned to a pandemic recently. Starting on December 2019, coronavirus disease (COVID-19) was first detected in Wuhan City, Hubei province, China. As of 31st March, 2020, more than 900,000 COVID-19 cases have been reported across the globe involving more than 200 countries. The first case of the United States (US) was confirmed on 20th January, 2020 in a 35-year-old male who had a travel history to Wuhan, China, before returning to the U.S. Since then, the SARS-CoV-2 virus has spread to all the 50 states of US, with more cases being reported every day. New York, New Jersey, Michigan, and California are the worst-hit states. As COVID-19 is growing, every day has been marked by novel developments and updates. We hereby talk about how the U.S. is leading the multiagency effort to fight against this pandemic and the steps that have been taken so far.

COVID-19 created chaos across the globe: Three novel quarantine epidemic models.

The latest version of human coronavirus said to be COVID-19 came out as a sudden pandemic disease within human population and in the absence of vaccination and proper treatment till date, it daunting threats heavily to human lives, infecting more than 12, 11, 214 people and death more than 67, 666 people in 208 countries across the globe as on April 06, 2020, which is highly alarming. When no treatment or vaccine is available till date and to avoid COVID-19 to be transmitted in the community, social distancing is the only way to prevent the disease, which is well taken into account in our novel epidemic models as a special compartment, that is, home isolation. Based on the transmitting behavior of COVID-19 in the human population, we develop three quarantine models of this pandemic taking into account the compartments: susceptible population, immigrant population, home isolation population, infectious population, hospital quarantine population, and recovered population. Local and global asymptotic stability is proved for all the three models. Extensive numerical simulations are performed to establish the analytical results with suitable examples. Our research reveals that home isolation and quarantine to hospitals are the two pivot force-control policies under the present situation when no treatment is available for this pandemic.

The impact of COVID-19 on transport volume and freight capacity dynamics: An empirical analysis in German food retail logistics.

Governmental restrictions aspiring to slow down the spread of epidemic and pandemic outbreaks lead to impairments for economic operations, which impact transportation networks comprising the maritime, rail, air, and trucking industries. Witnessing a substantial increase in the number of infections in Germany, the authorities have imposed drastic restrictions on everyday life. Resulting panic buying and increasing home consumption had versatile impacts on transport volume and freight capacity dynamics in German food retail logistics. Due to the lack of prior research on the effects of COVID-19 on transport volume in retail logistics, as well as resulting implications, this article aspires to shed light on the phenomenon of changing volume and capacity dynamics in road haulage. After analyzing the transport volume of n = 15,715 routes in the timeframe of 23.03.2020 to 30.04.2020, a transport volume growth rate expressing the difference of real and expected transport volume was calculated. This ratio was then examined concerning the number of COVID-19 infections per day. The results of this study prove that the increasing freight volume for dry products in retail logistics does not depend on the duration of the COVID-19 epidemy but on the strength quantified through the total number of new infections per day. This causes a conflict of interest between transportation companies and food retail logistics for non-cooled transport capacity. The contributions of this paper are highly relevant to assess the impact of a possibly occurring second COVID-19 virus infection wave.