Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 346
Filter
Add filters

Year range
1.
Science ; 372(6537)2021 04 02.
Article in English | MEDLINE | ID: covidwho-1166346

ABSTRACT

Multivalent display of receptor-engaging antibodies or ligands can enhance their activity. Instead of achieving multivalency by attachment to preexisting scaffolds, here we unite form and function by the computational design of nanocages in which one structural component is an antibody or Fc-ligand fusion and the second is a designed antibody-binding homo-oligomer that drives nanocage assembly. Structures of eight nanocages determined by electron microscopy spanning dihedral, tetrahedral, octahedral, and icosahedral architectures with 2, 6, 12, and 30 antibodies per nanocage, respectively, closely match the corresponding computational models. Antibody nanocages targeting cell surface receptors enhance signaling compared with free antibodies or Fc-fusions in death receptor 5 (DR5)-mediated apoptosis, angiopoietin-1 receptor (Tie2)-mediated angiogenesis, CD40 activation, and T cell proliferation. Nanocage assembly also increases severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pseudovirus neutralization by α-SARS-CoV-2 monoclonal antibodies and Fc-angiotensin-converting enzyme 2 (ACE2) fusion proteins.


Subject(s)
Antibodies/chemistry , Antibodies/immunology , Nanostructures , Protein Engineering , Signal Transduction , Angiopoietins/chemistry , Angiopoietins/immunology , Angiopoietins/metabolism , Antibodies, Monoclonal/chemistry , Antibodies, Monoclonal/immunology , Antibodies, Neutralizing/chemistry , Antibodies, Neutralizing/immunology , Antibodies, Viral/chemistry , Antibodies, Viral/immunology , B-Lymphocytes/immunology , CD40 Antigens/chemistry , CD40 Antigens/immunology , CD40 Antigens/metabolism , Cell Line, Tumor , Cell Proliferation , Computer Simulation , Genes, Synthetic , Humans , Immunoglobulin Fc Fragments/chemistry , Lymphocyte Activation , Models, Molecular , Protein Binding , Receptor, TIE-2/metabolism , Receptors, TNF-Related Apoptosis-Inducing Ligand/immunology , Receptors, TNF-Related Apoptosis-Inducing Ligand/metabolism , T-Lymphocytes/immunology , T-Lymphocytes/physiology
2.
Food Chem Toxicol ; 148: 111966, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-1163769

ABSTRACT

BACKGROUND: COVID-19, the presently prevailing global public health emergency has culminated in international instability in economy. This unprecedented pandemic outbreak pressingly necessitated the trans-disciplinary approach in developing novel/new anti-COVID-19 drugs especially, small molecule inhibitors targeting the seminal proteins of viral etiological agent, SARS-CoV-2. METHODS: Based on the traditional medicinal knowledge, we made an attempt through molecular docking analysis to explore the phytochemical constituents of three most commonly used Indian herbs in 'steam inhalation therapy' against well recognized viral receptor proteins. RESULTS: A total of 57 phytochemicals were scrutinized virtually against four structural protein targets of SARS-CoV-2 viz. 3CLpro, ACE-2, spike glycoprotein and RdRp. Providentially, two bioactives from each of the three plants i.e. apigenin-o-7-glucuronide and ellagic acid from Eucalyptus globulus; eudesmol and viridiflorene from Vitex negundo and; vasicolinone and anisotine from Justicia adhatoda were identified to be the best hit lead molecules based on interaction energies, conventional hydrogen bonding numbers and other non-covalent interactions. On comparison with the known SARS-CoV-2 protease inhibitor -lopinavir and RdRp inhibitor -remdesivir, apigenin-o-7-glucuronide was found to be a phenomenal inhibitor of both protease and polymerase, as it strongly interacts with their active sites and exhibited remarkably high binding affinity. Furthermore, in silico drug-likeness and ADMET prediction analyses clearly evidenced the usability of the identified bioactives to develop as drug against COVID-19. CONCLUSION: Overall, the data of the present study exemplifies that the phytochemicals from selected traditional herbs having significance in steam inhalation therapy would be promising in combating COVID-19.


Subject(s)
/therapy , Phytochemicals/administration & dosage , Administration, Inhalation , Computer Simulation , Humans , Molecular Docking Simulation , Phytochemicals/pharmacology , Steam
3.
J R Soc Interface ; 18(176): 20200916, 2021 03.
Article in English | MEDLINE | ID: covidwho-1161023

ABSTRACT

Epidemiological data about SARS-CoV-2 spread indicate that the virus is not transmitted uniformly in the population. The transmission tends to be more effective in select settings that involve exposure to relatively high viral dose, such as in crowded indoor settings, assisted living facilities, prisons or food processing plants. To explore the effect on infection dynamics, we describe a new mathematical model where transmission can occur (i) in the community at large, characterized by low-dose exposure and mostly mild disease, and (ii) in so-called transmission hot zones, characterized by high-dose exposure that can be associated with more severe disease. The model yields different types of epidemiological dynamics, depending on the relative importance of hot zone and community transmission. Interesting dynamics occur if the rate of virus release/deposition from severely infected people is larger than that of mildly infected individuals. Under this assumption, we find that successful infection spread can hinge upon high-dose hot zone transmission, yet the majority of infections are predicted to occur in the community at large with mild disease. In this regime, residual hot zone transmission can account for continued virus spread during community lockdowns, and the suppression of hot zones after community interventions are relaxed can cause a prolonged lack of infection resurgence following the reopening of society. This gives rise to the notion that targeted interventions specifically reducing virus transmission in the hot zones have the potential to suppress overall infection spread, including in the community at large. Epidemiological trends in the USA and Europe are interpreted in light of this model.


Subject(s)
/epidemiology , Models, Biological , Pandemics , Basic Reproduction Number/statistics & numerical data , Computer Simulation , Humans , Mathematical Concepts , Pandemics/prevention & control , Pandemics/statistics & numerical data , Quarantine , Viral Load/statistics & numerical data
4.
JAMA Netw Open ; 4(3): e213793, 2021 03 01.
Article in English | MEDLINE | ID: covidwho-1160315

ABSTRACT

Importance: Resurgent COVID-19 cases have resulted in the reinstitution of nonpharmaceutical interventions, including school closures, which can have adverse effects on families. Understanding the associations of school closures with the number of incident and cumulative COVID-19 cases is critical for decision-making. Objective: To estimate the association of schools being open or closed with the number of COVID-19 cases compared with community-based nonpharmaceutical interventions. Design, Setting, and Participants: This decision analytical modelling study developed an agent-based transmission model using a synthetic population of 1 000 000 individuals based on the characteristics of the population of Ontario, Canada. Members of the synthetic population were clustered into households, neighborhoods, or rural districts, cities or rural regions, day care facilities, classrooms (ie, primary, elementary, or high school), colleges or universities, and workplaces. Data were analyzed between May 5, 2020, and October 20, 2020. Exposures: School reopening on September 15, 2020, vs schools remaining closed under different scenarios for nonpharmaceutical interventions. Main Outcomes and Measures: Incident and cumulative COVID-19 cases between September 1, 2020, and October 31, 2020. Results: Among 1 000 000 simulated individuals, the percentage of infections among students and teachers acquired within schools was less than 5% across modeled scenarios. Incident COVID-19 case numbers on October 31, 2020, were 4414 (95% credible interval [CrI], 3491-5382) cases in the scenario with schools remaining closed and 4740 (95% CrI, 3863-5691) cases in the scenario for schools reopening, with no other community-based nonpharmaceutical intervention. In scenarios with community-based nonpharmaceutical interventions implemented, the incident case numbers on October 31 were 714 (95% CrI, 568-908) cases for schools remaining closed and 780 (95% CrI, 580-993) cases for schools reopening. When scenarios applied the case numbers observed in early October in Ontario, the cumulative case numbers were 777 (95% CrI, 621-993) cases for schools remaining closed and 803 (95% CrI, 617-990) cases for schools reopening. In scenarios with implementation of community-based interventions vs no community-based interventions, there was a mean difference of 39 355 cumulative COVID-19 cases by October 31, 2020, while keeping schools closed vs reopening them yielded a mean difference of 2040 cases. Conclusions and Relevance: This decision analytical modeling study of a synthetic population of individuals in Ontario, Canada, found that most COVID-19 cases in schools were due to acquisition in the community rather than transmission within schools and that the changes in COVID-19 case numbers associated with school reopenings were relatively small compared with the changes associated with community-based nonpharmaceutical interventions.


Subject(s)
/prevention & control , Pandemics , Residence Characteristics , Schools , /transmission , Computer Simulation , Humans , Models, Biological , Ontario , School Teachers , Students , Universities , Workplace
5.
Molecules ; 26(7)2021 Mar 29.
Article in English | MEDLINE | ID: covidwho-1159212

ABSTRACT

The COVID-19 pandemic has reached over 100 million worldwide. Due to the multi-targeted nature of the virus, it is clear that drugs providing anti-COVID-19 effects need to be developed at an accelerated rate, and a combinatorial approach may stand to be more successful than a single drug therapy. Among several targets and pathways that are under investigation, the renin-angiotensin system (RAS) and specifically angiotensin-converting enzyme (ACE), and Ca2+-mediated SARS-CoV-2 cellular entry and replication are noteworthy. A combination of ACE inhibitors and calcium channel blockers (CCBs), a critical line of therapy for pulmonary hypertension, has shown therapeutic relevance in COVID-19 when investigated independently. To that end, we conducted in silico modeling using BIOiSIM, an AI-integrated mechanistic modeling platform by utilizing known preclinical in vitro and in vivo datasets to accurately simulate systemic therapy disposition and site-of-action penetration of the CCBs and ACEi compounds to tissues implicated in COVID-19 pathogenesis.


Subject(s)
Antiviral Agents/pharmacokinetics , Drug Repositioning/methods , Hypertension, Pulmonary/drug therapy , Angiotensin-Converting Enzyme Inhibitors/pharmacokinetics , Antiviral Agents/blood , Biosimilar Pharmaceuticals , Calcium Channel Blockers/pharmacokinetics , Computer Simulation , Databases, Pharmaceutical , Drug Development/methods , Humans , Hypertension, Pulmonary/virology , Tissue Distribution
6.
Molecules ; 26(6)2021 Mar 23.
Article in English | MEDLINE | ID: covidwho-1154456

ABSTRACT

Bats are unique in their potential to serve as reservoir hosts for intracellular pathogens. Recently, the impact of COVID-19 has relegated bats from biomedical darkness to the frontline of public health as bats are the natural reservoir of many viruses, including SARS-Cov-2. Many bat genomes have been sequenced recently, and sequences coding for antimicrobial peptides are available in the public databases. Here we provide a structural analysis of genome-predicted bat cathelicidins as components of their innate immunity. A total of 32 unique protein sequences were retrieved from the NCBI database. Interestingly, some bat species contained more than one cathelicidin. We examined the conserved cysteines within the cathelin-like domain and the peptide portion of each sequence and revealed phylogenetic relationships and structural dissimilarities. The antibacterial, antifungal, and antiviral activity of peptides was examined using bioinformatic tools. The peptides were modeled and subjected to docking analysis with the region binding domain (RBD) region of the SARS-CoV-2 Spike protein. The appearance of multiple forms of cathelicidins verifies the complex microbial challenges encountered by these species. Learning more about antiviral defenses of bats and how they drive virus evolution will help scientists to investigate the function of antimicrobial peptides in these species.


Subject(s)
Cathelicidins/chemistry , Cathelicidins/pharmacology , Chiroptera/genetics , Spike Glycoprotein, Coronavirus/metabolism , Animals , Anti-Bacterial Agents/chemistry , Anti-Bacterial Agents/pharmacology , Antifungal Agents/chemistry , Antifungal Agents/pharmacology , Antimicrobial Cationic Peptides/chemistry , Antimicrobial Cationic Peptides/pharmacology , Antiviral Agents/chemistry , Antiviral Agents/pharmacology , Binding Sites , Cathelicidins/genetics , Cathelicidins/metabolism , Computational Biology/methods , Computer Simulation , Genome , Molecular Docking Simulation , Phylogeny
7.
PLoS Comput Biol ; 17(2): e1008728, 2021 02.
Article in English | MEDLINE | ID: covidwho-1154072

ABSTRACT

Large-scale serological testing in the population is essential to determine the true extent of the current SARS-CoV-2 pandemic. Serological tests measure antibody responses against pathogens and use predefined cutoff levels that dichotomize the quantitative test measures into sero-positives and negatives and use this as a proxy for past infection. With the imperfect assays that are currently available to test for past SARS-CoV-2 infection, the fraction of seropositive individuals in serosurveys is a biased estimator of the cumulative incidence and is usually corrected to account for the sensitivity and specificity. Here we use an inference method-referred to as mixture-model approach-for the estimation of the cumulative incidence that does not require to define cutoffs by integrating the quantitative test measures directly into the statistical inference procedure. We confirm that the mixture model outperforms the methods based on cutoffs, leading to less bias and error in estimates of the cumulative incidence. We illustrate how the mixture model can be used to optimize the design of serosurveys with imperfect serological tests. We also provide guidance on the number of control and case sera that are required to quantify the test's ambiguity sufficiently to enable the reliable estimation of the cumulative incidence. Lastly, we show how this approach can be used to estimate the cumulative incidence of classes of infections with an unknown distribution of quantitative test measures. This is a very promising application of the mixture-model approach that could identify the elusive fraction of asymptomatic SARS-CoV-2 infections. An R-package implementing the inference methods used in this paper is provided. Our study advocates using serological tests without cutoffs, especially if they are used to determine parameters characterizing populations rather than individuals. This approach circumvents some of the shortcomings of cutoff-based methods at exactly the low cumulative incidence levels and test accuracies that we are currently facing in SARS-CoV-2 serosurveys.


Subject(s)
/methods , /epidemiology , Models, Statistical , Pandemics , Antibodies, Viral/blood , Asymptomatic Infections/epidemiology , /statistics & numerical data , Computational Biology , Computer Simulation , Confidence Intervals , False Negative Reactions , False Positive Reactions , Humans , Incidence , Likelihood Functions , Pandemics/statistics & numerical data , ROC Curve , Reproducibility of Results , Sensitivity and Specificity
8.
BMC Bioinformatics ; 22(1): 163, 2021 Mar 26.
Article in English | MEDLINE | ID: covidwho-1153986

ABSTRACT

BACKGROUND: Coronavirus Disease 2019 (COVID-19) is a viral pandemic disease that may induce severe pneumonia in humans. In this paper, we investigated the putative implication of 12 vaccines, including BCG, OPV and MMR in the protection against COVID-19. Sequences of the main antigenic proteins in the investigated vaccines and SARS-CoV-2 proteins were compared to identify similar patterns. The immunogenic effect of identified segments was, then, assessed using a combination of structural and antigenicity prediction tools. RESULTS: A total of 14 highly similar segments were identified in the investigated vaccines. Structural and antigenicity prediction analysis showed that, among the identified patterns, three segments in Hepatitis B, Tetanus, and Measles proteins presented antigenic properties that can induce putative protective effect against COVID-19. CONCLUSIONS: Our results suggest a possible protective effect of HBV, Tetanus and Measles vaccines against COVID-19, which may explain the variation of the disease severity among regions.


Subject(s)
Antigens, Viral/immunology , Viral Proteins/immunology , Viral Vaccines/immunology , BCG Vaccine , Computer Simulation , Cross Protection , Humans , Protein Conformation
9.
Sci Rep ; 11(1): 6927, 2021 03 25.
Article in English | MEDLINE | ID: covidwho-1152880

ABSTRACT

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a newly-discovered coronavirus and responsible for the spread of coronavirus disease 2019 (COVID-19). SARS-CoV-2 infected millions of people in the world and immediately became a pandemic in March 2020. SARS-CoV-2 belongs to the beta-coronavirus genus of the large family of Coronaviridae. It is now known that its surface spike glycoprotein binds to the angiotensin-converting enzyme-2 (ACE2), which is expressed on the lung epithelial cells, mediates the fusion of the cellular and viral membranes, and facilitates the entry of viral genome to the host cell. Therefore, blocking the virus-cell interaction could be a potential target for the prevention of viral infection. The binding of SARS-CoV-2 to ACE2 is a protein-protein interaction, and so, analyzing the structure of the spike glycoprotein of SARS-CoV-2 and its underlying mechanism to bind the host cell receptor would be useful for the management and treatment of COVID-19. In this study, we performed comparative in silico studies to deeply understand the structural and functional details of the interaction between the spike glycoprotein of SARS-CoV-2 and its cognate cellular receptor ACE2. According to our results, the affinity of the ACE2 receptor for SARS-CoV-2 was higher than SARS-CoV. According to the free energy decomposition of the spike glycoprotein-ACE2 complex, we found critical points in three areas which are responsible for the increased binding affinity of SARS-CoV-2 compared with SARS-CoV. These mutations occurred at the receptor-binding domain of the spike glycoprotein that play an essential role in the increasing the affinity of coronavirus to ACE2. For instance, mutations Pro462Ala and Leu472Phe resulted in the altered binding energy from - 2 kcal mol-1 in SARS-COV to - 6 kcal mol-1 in SARS-COV-2. The results demonstrated that some mutations in the receptor-binding motif could be considered as a hot-point for designing potential drugs to inhibit the interaction between the spike glycoprotein and ACE2.


Subject(s)
/metabolism , Spike Glycoprotein, Coronavirus/metabolism , Antiviral Agents/pharmacology , Computer Simulation , Drug Design , Humans , Protein Binding
10.
Sci Rep ; 11(1): 6652, 2021 03 23.
Article in English | MEDLINE | ID: covidwho-1147846

ABSTRACT

How human respiratory physiology and the transport phenomena associated with inhaled airflow in the upper airway proceed to impact transmission of SARS-CoV-2, leading to the initial infection, stays an open question. An answer can help determine the susceptibility of an individual on exposure to a COVID-2019 carrier and can also provide a preliminary projection of the still-unknown infectious dose for the disease. Computational fluid mechanics enabled tracking of respiratory transport in medical imaging-based anatomic domains shows that the regional deposition of virus-laden inhaled droplets at the initial nasopharyngeal infection site peaks for the droplet size range of approximately 2.5-19 [Formula: see text]. Through integrating the numerical findings on inhaled transmission with sputum assessment data from hospitalized COVID-19 patients and earlier measurements of ejecta size distribution generated during regular speech, this study further reveals that the number of virions that may go on to establish the SARS-CoV-2 infection in a subject could merely be in the order of hundreds.


Subject(s)
/transmission , Nasopharynx/virology , /physiology , /pathology , Computer Simulation , Humans , Sputum/virology , Viral Load
11.
FASEB J ; 35(4): e21360, 2021 04.
Article in English | MEDLINE | ID: covidwho-1145195

ABSTRACT

The novel coronavirus disease, COVID-19, has grown into a global pandemic and a major public health threat since its breakout in December 2019. To date, no specific therapeutic drug or vaccine for treating COVID-19 and SARS has been FDA approved. Previous studies suggest that berberine, an isoquinoline alkaloid, has shown various biological activities that may help against COVID-19 and SARS, including antiviral, anti-allergy and inflammation, hepatoprotection against drug- and infection-induced liver injury, as well as reducing oxidative stress. In particular, berberine has a wide range of antiviral activities such as anti-influenza, anti-hepatitis C, anti-cytomegalovirus, and anti-alphavirus. As an ingredient recommended in guidelines issued by the China National Health Commission for COVID-19 to be combined with other therapy, berberine is a promising orally administered therapeutic candidate against SARS-CoV and SARS-CoV-2. The current study comprehensively evaluates the potential therapeutic mechanisms of berberine in preventing and treating COVID-19 and SARS using computational modeling, including target mining, gene ontology enrichment, pathway analyses, protein-protein interaction analysis, and in silico molecular docking. An orally available immunotherapeutic-berberine nanomedicine, named NIT-X, has been developed by our group and has shown significantly increased oral bioavailability of berberine, increased IFN-γ production by CD8+ T cells, and inhibition of mast cell histamine release in vivo, suggesting a protective immune response. We further validated the inhibition of replication of SARS-CoV-2 in lung epithelial cells line in vitro (Calu3 cells) by berberine. Moreover, the expression of targets including ACE2, TMPRSS2, IL-1α, IL-8, IL-6, and CCL-2 in SARS-CoV-2 infected Calu3 cells were significantly suppressed by NIT-X. By supporting protective immunity while inhibiting pro-inflammatory cytokines; inhibiting viral infection and replication; inducing apoptosis; and protecting against tissue damage, berberine is a promising candidate in preventing and treating COVID-19 and SARS. Given the high oral bioavailability and safety of berberine nanomedicine, the current study may lead to the development of berberine as an orally, active therapeutic against COVID-19 and SARS.


Subject(s)
Antiviral Agents/pharmacology , Berberine/pharmacology , Gene Expression Regulation/drug effects , Models, Biological , SARS Virus/metabolism , Severe Acute Respiratory Syndrome , Administration, Oral , /metabolism , Cell Line , Computer Simulation , Humans , Pandemics , Severe Acute Respiratory Syndrome/drug therapy , Severe Acute Respiratory Syndrome/metabolism
12.
Math Biosci Eng ; 18(2): 1833-1844, 2021 02 22.
Article in English | MEDLINE | ID: covidwho-1145636

ABSTRACT

In this paper, we present an SEIIaHR epidemic model to study the influence of recessive infection and isolation in the spread of COVID-19. We first prove that the infection-free equilibrium is globally asymptotically stable with condition R0<1 and the positive equilibrium is uniformly persistent when the condition R0>1. By using the COVID-19 data in India, we then give numerical simulations to illustrate our results and carry out some sensitivity analysis. We know that asymptomatic infections will affect the spread of the disease when the quarantine rate is within the range of [0.3519, 0.5411]. Furthermore, isolating people with symptoms is important to control and eliminate the disease.


Subject(s)
/epidemiology , Epidemics , Models, Biological , Asymptomatic Infections/epidemiology , Basic Reproduction Number/statistics & numerical data , /transmission , Computer Simulation , Epidemics/prevention & control , Epidemics/statistics & numerical data , Humans , India/epidemiology , Markov Chains , Mathematical Concepts , Monte Carlo Method , Pandemics/prevention & control , Pandemics/statistics & numerical data , Quarantine/statistics & numerical data
14.
Proc Natl Acad Sci U S A ; 118(14)2021 04 06.
Article in English | MEDLINE | ID: covidwho-1142535

ABSTRACT

Increasing evidence indicates that superspreading plays a dominant role in COVID-19 transmission. Recent estimates suggest that the dispersion parameter k for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is on the order of 0.1, which corresponds to about 10% of cases being the source of 80% of infections. To investigate how overdispersion might affect the outcome of various mitigation strategies, we developed an agent-based model with a social network that allows transmission through contact in three sectors: "close" (a small, unchanging group of mutual contacts as might be found in a household), "regular" (a larger, unchanging group as might be found in a workplace or school), and "random" (drawn from the entire model population and not repeated regularly). We assigned individual infectivity from a gamma distribution with dispersion parameter k We found that when k was low (i.e., greater heterogeneity, more superspreading events), reducing random sector contacts had a far greater impact on the epidemic trajectory than did reducing regular contacts; when k was high (i.e., less heterogeneity, no superspreading events), that difference disappeared. These results suggest that overdispersion of COVID-19 transmission gives the virus an Achilles' heel: Reducing contacts between people who do not regularly meet would substantially reduce the pandemic, while reducing repeated contacts in defined social groups would be less effective.


Subject(s)
/epidemiology , Contact Tracing/statistics & numerical data , Models, Statistical , Pandemics , Age Factors , /virology , Computer Simulation , Humans , Quarantine/statistics & numerical data , /physiology , Social Networking
15.
Sci Rep ; 11(1): 6397, 2021 03 18.
Article in English | MEDLINE | ID: covidwho-1142453

ABSTRACT

A new and more aggressive strain of coronavirus, known as SARS-CoV-2, which is highly contagious, has rapidly spread across the planet within a short period of time. Due to its high transmission rate and the significant time-space between infection and manifestation of symptoms, the WHO recently declared this a pandemic. Because of the exponentially growing number of new cases of both infections and deaths, development of new therapeutic options to help fight this pandemic is urgently needed. The target molecules of this study were the nitro derivatives of quinoline and quinoline N-oxide. Computational design at the DFT level, docking studies, and molecular dynamics methods as a well-reasoned strategy will aid in elucidating the fundamental physicochemical properties and molecular functions of a diversity of compounds, directly accelerating the process of discovering new drugs. In this study, we discovered isomers based on the nitro derivatives of quinoline and quinoline N-oxide, which are biologically active compounds and may be low-cost alternatives for the treatment of infections induced by SARS-CoV-2.


Subject(s)
Quinolines/chemistry , /chemistry , /drug therapy , Computer Simulation , Density Functional Theory , Drug Evaluation, Preclinical , Molecular Docking Simulation , Molecular Dynamics Simulation , Quinolines/therapeutic use
16.
Sci Rep ; 11(1): 6251, 2021 03 18.
Article in English | MEDLINE | ID: covidwho-1142452

ABSTRACT

We established an individual-based computer model to simulate the occurrence, infection, discovery, quarantine, and quarantine release (recovery) of asymptomatic SARS-CoV-2 infected individuals or patients within the community. The model was used to explore the effects of control measures, such as active tracing, laboratory testing, active treatment, and home quarantine on the epidemic. Considering the condition that R0 = 1.2, when a case of an imported asymptomatic infected individual (AII) was reported in the community, the implementation of control measures reduced the number of AIIs and patients by 62.2% and 62.4%, respectively. The number of undetected AIIs and patients peaked at 302 days of the epidemic, reaching 53 and 20 individuals, respectively. The implementation of sustained active tracing, laboratory testing, active treatment, and home quarantine can significantly reduce the probability of disease outbreaks and block the spread of the COVID-19 epidemic caused by AIIs in the community.


Subject(s)
/transmission , Disease Outbreaks/prevention & control , Models, Biological , Asymptomatic Infections/epidemiology , /prevention & control , Computer Simulation , Humans , Incidence
17.
PLoS One ; 16(3): e0248708, 2021.
Article in English | MEDLINE | ID: covidwho-1140536

ABSTRACT

COVID-19 pandemic is an immediate major public health concern. The search for the understanding of the disease spreading made scientists around the world turn their attention to epidemiological studies. An interesting approach in epidemiological modeling nowadays is to use agent-based models, which allow to consider a heterogeneous population and to evaluate the role of superspreaders in this population. In this work, we implemented an agent-based model using probabilistic cellular automata to simulate SIR (Susceptible-Infected-Recovered) dynamics using COVID-19 infection parameters. Differently to the usual studies, we did not define the superspreaders individuals a priori, we only left the agents to execute a random walk along the sites. When two or more agents share the same site, there is a probability to spread the infection if one of them is infected. To evaluate the spreading, we built the transmission network and measured the degree distribution, betweenness, and closeness centrality. The results displayed for different levels of mobility restriction show that the degree reduces as the mobility reduces, but there is an increase of betweenness and closeness for some network nodes. We identified the superspreaders at the end of the simulation, showing the emerging behavior of the model since these individuals were not initially defined. Simulations also showed that the superspreaders are responsible for most of the infection propagation and the impact of personal protective equipment in the spreading of the infection. We believe that this study can bring important insights for the analysis of the disease dynamics and the role of superspreaders, contributing to the understanding of how to manage mobility during a highly infectious pandemic as COVID-19.


Subject(s)
/epidemiology , Systems Analysis , /prevention & control , Communicable Disease Control , Computer Simulation , Humans , Pandemics , Personal Protective Equipment , Probability , /isolation & purification
18.
Sci Rep ; 11(1): 6264, 2021 03 17.
Article in English | MEDLINE | ID: covidwho-1139754

ABSTRACT

Many educational institutions have partially or fully closed all operations to cope with the challenges of the ongoing COVID-19 pandemic. In this paper, we explore strategies that such institutions can adopt to conduct safe reopening and resume operations during the pandemic. The research is motivated by the University of Illinois at Urbana-Champaign's (UIUC's) SHIELD program, which is a set of policies and strategies, including rapid saliva-based COVID-19 screening, for ensuring safety of students, faculty and staff to conduct in-person operations, at least partially. Specifically, we study how rapid bulk testing, contact tracing and preventative measures such as mask wearing, sanitization, and enforcement of social distancing can allow institutions to manage the epidemic spread. This work combines the power of analytical epidemic modeling, data analysis and agent-based simulations to derive policy insights. We develop an analytical model that takes into account the asymptomatic transmission of COVID-19, the effect of isolation via testing (both in bulk and through contact tracing) and the rate of contacts among people within and outside the institution. Next, we use data from the UIUC SHIELD program and 85 other universities to estimate parameters that describe the analytical model. Using the estimated parameters, we finally conduct agent-based simulations with various model parameters to evaluate testing and reopening strategies. The parameter estimates from UIUC and other universities show similar trends. For example, infection rates at various institutions grow rapidly in certain months and this growth correlates positively with infection rates in counties where the universities are located. Infection rates are also shown to be negatively correlated with testing rates at the institutions. Through agent-based simulations, we demonstrate that the key to designing an effective reopening strategy is a combination of rapid bulk testing and effective preventative measures such as mask wearing and social distancing. Multiple other factors help to reduce infection load, such as efficient contact tracing, reduced delay between testing and result revelation, tests with less false negatives and targeted testing of high-risk class among others. This paper contributes to the nascent literature on combating the COVID-19 pandemic and is especially relevant for educational institutions and similarly large organizations. We contribute by providing an analytical model that can be used to estimate key parameters from data, which in turn can be used to simulate the effect of different strategies for reopening. We quantify the relative effect of different strategies such as bulk testing, contact tracing, reduced infectivity and contact rates in the context of educational institutions. Specifically, we show that for the estimated average base infectivity of 0.025 ([Formula: see text]), a daily number of tests to population ratio T/N of 0.2, i.e., once a week testing for all individuals, is a good indicative threshold. However, this test to population ratio is sensitive to external infectivities, internal and external mobilities, delay in getting results after testing, and measures related to mask wearing and sanitization, which affect the base infection rate.


Subject(s)
/prevention & control , Pandemics/prevention & control , Schools/standards , Universities/standards , Asymptomatic Diseases , Computer Simulation , Contact Tracing/methods , Humans , Saliva/virology
19.
J Transl Med ; 19(1): 109, 2021 03 16.
Article in English | MEDLINE | ID: covidwho-1136231

ABSTRACT

BACKGROUND: No versatile web app exists that allows epidemiologists and managers around the world to comprehensively analyze the impacts of COVID-19 mitigation. The http://covid-webapp.numerusinc.com/ web app presented here fills this gap. METHODS: Our web app uses a model that explicitly identifies susceptible, contact, latent, asymptomatic, symptomatic and recovered classes of individuals, and a parallel set of response classes, subject to lower pathogen-contact rates. The user inputs a CSV file of incidence and, if of interest, mortality rate data. A default set of parameters is available that can be overwritten through input or online entry, and a user-selected subset of these can be fitted to the model using maximum-likelihood estimation (MLE). Model fitting and forecasting intervals are specifiable and changes to parameters allow counterfactual and forecasting scenarios. Confidence or credible intervals can be generated using stochastic simulations, based on MLE values, or on an inputted CSV file containing Markov chain Monte Carlo (MCMC) estimates of one or more parameters. RESULTS: We illustrate the use of our web app in extracting social distancing, social relaxation, surveillance or virulence switching functions (i.e., time varying drivers) from the incidence and mortality rates of COVID-19 epidemics in Israel, South Africa, and England. The Israeli outbreak exhibits four distinct phases: initial outbreak, social distancing, social relaxation, and a second wave mitigation phase. An MCMC projection of this latter phase suggests the Israeli epidemic will continue to produce into late November an average of around 1500 new case per day, unless the population practices social-relaxation measures at least 5-fold below the level in August, which itself is 4-fold below the level at the start of July. Our analysis of the relatively late South African outbreak that became the world's fifth largest COVID-19 epidemic in July revealed that the decline through late July and early August was characterised by a social distancing driver operating at more than twice the per-capita applicable-disease-class (pc-adc) rate of the social relaxation driver. Our analysis of the relatively early English outbreak, identified a more than 2-fold improvement in surveillance over the course of the epidemic. It also identified a pc-adc social distancing rate in early August that, though nearly four times the pc-adc social relaxation rate, appeared to barely contain a second wave that would break out if social distancing was further relaxed. CONCLUSION: Our web app provides policy makers and health officers who have no epidemiological modelling or computer coding expertise with an invaluable tool for assessing the impacts of different outbreak mitigation policies and measures. This includes an ability to generate an epidemic-suppression or curve-flattening index that measures the intensity with which behavioural responses suppress or flatten the epidemic curve in the region under consideration.


Subject(s)
/epidemiology , Infection Control , Internet , Mobile Applications , /etiology , Computer Simulation , Effect Modifier, Epidemiologic , England/epidemiology , Epidemics , Forecasting/methods , Humans , Infection Control/methods , Infection Control/organization & administration , Infection Control/standards , Israel/epidemiology , Markov Chains , Population Surveillance/methods , Risk Factors , South Africa/epidemiology
20.
Nat Commun ; 12(1): 1655, 2021 03 12.
Article in English | MEDLINE | ID: covidwho-1132070

ABSTRACT

Digital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15-20 minutes and closer than 2-3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide.


Subject(s)
/prevention & control , Contact Tracing/methods , Pandemics , Basic Reproduction Number/prevention & control , Basic Reproduction Number/statistics & numerical data , /transmission , Computer Simulation , Contact Tracing/statistics & numerical data , Humans , Models, Statistical , Pandemics/prevention & control , Pandemics/statistics & numerical data , Privacy , Quarantine/methods , Quarantine/statistics & numerical data , Risk Factors
SELECTION OF CITATIONS
SEARCH DETAIL