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10 pages, 236 KiB

Open AccessEditorial

Computation to Fight SARS-CoV-2 (COVID-19)

by Simone Brogi and Vincenzo Calderone

Computation 2023, 11(9), 185; https://doi.org/10.3390/computation11090185 - 18 Sep 2023

Viewed by 1797

Abstract

In April 2020, during the last pandemic health emergency, we launched a Special Issue hosted by Computation—section Computational Biology, entitled “Computation to Fight SARS-CoV-2 (COVID-19)” [...] Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

Research

Jump to: Editorial

13 pages, 453 KiB

Open AccessArticle

Impact of Social Media on Knowledge of the COVID-19 Pandemic on Bangladeshi University Students

by Shanjida Chowdhury, Mahfujur Rahman, Indrajit Ajit Doddanavar, Nurul Mohammad Zayed, Vitalii Nitsenko, Olena Melnykovych and Oksana Holik

Computation 2023, 11(2), 38; https://doi.org/10.3390/computation11020038 - 16 Feb 2023

Cited by 4 | Viewed by 3574

Abstract

This study aimed to examine the role and impact of social media on the knowledge of the COVID-19 pandemic in Bangladesh through disseminating actual changes in health safety, trust and belief of social media’s coverage statistics, isolation, and psychological numbness among students. This [...] Read more.

This study aimed to examine the role and impact of social media on the knowledge of the COVID-19 pandemic in Bangladesh through disseminating actual changes in health safety, trust and belief of social media’s coverage statistics, isolation, and psychological numbness among students. This study used a cross-sectional design in which a quantitative approach was adopted. Data from an online survey were collected in a short period of time during the early stages of COVID-19 to determine the relationship between social media activity and knowledge of the COVID-19 pandemic with accuracy. A total of 189 respondents were interviewed using structured questionnaires during the onset of the COVID-19 outbreak in Bangladeshi university students. Exploratory factor analysis (EFA) and path analysis were performed. Out of 189 respondents, about 80% were aged between 16 and 25 years, of which nearly 60.33% were students. This study explored four factors—knowledge and health safety, trust in social media news, social distancing or quarantine, and psychological effect—using factor analysis. These four factors are also found to be positively associated in path analysis. Validation of the model was assessed, revealing that the path diagram with four latent exogenous variables fit well. Each factor coefficient was treated as a factor loading (β = 0.564 to 0.973). The results suggested that the measurement models using four elements were appropriate. The coefficient of determination was 0.98, indicating that the model provided an adequate explanation. Social media is transforming the dynamics of health issues, providing information and warnings about the adverse effects of COVID-19, having a positive impact on lockdown or quarantine, and promoting psychological wellness. This comprehensive study suggested that social media plays a positive role in enhancing knowledge about COVID-19 and other pandemic circumstances. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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26 pages, 3023 KiB

Open AccessArticle

Mathematical Modeling of SARS-CoV-2 Omicron Wave under Vaccination Effects

by Gilberto González-Parra and Abraham J. Arenas

Computation 2023, 11(2), 36; https://doi.org/10.3390/computation11020036 - 15 Feb 2023

Cited by 11 | Viewed by 2728

Abstract

Over the course of the COVID-19 pandemic millions of deaths and hospitalizations have been reported. Different SARS-CoV-2 variants of concern have been recognized during this pandemic and some of these variants of concern have caused uncertainty and changes in the dynamics. The Omicron [...] Read more.

Over the course of the COVID-19 pandemic millions of deaths and hospitalizations have been reported. Different SARS-CoV-2 variants of concern have been recognized during this pandemic and some of these variants of concern have caused uncertainty and changes in the dynamics. The Omicron variant has caused a large amount of infected cases in the US and worldwide. The average number of deaths during the Omicron wave toll increased in comparison with previous SARS-CoV-2 waves. We studied the Omicron wave by using a highly nonlinear mathematical model for the COVID-19 pandemic. The novel model includes individuals who are vaccinated and asymptomatic, which influences the dynamics of SARS-CoV-2. Moreover, the model considers the waning of the immunity and efficacy of the vaccine against the Omicron strain. This study uses the facts that the Omicron strain has a higher transmissibility than the previous circulating SARS-CoV-2 strain but is less deadly. Preliminary studies have found that Omicron has a lower case fatality rate compared to previous circulating SARS-CoV-2 strains. The simulation results show that even if the Omicron strain is less deadly it might cause more deaths, hospitalizations and infections. We provide a variety of scenarios that help to obtain insight about the Omicron wave and its consequences. The proposed mathematical model, in conjunction with the simulations, provides an explanation for a large Omicron wave under various conditions related to vaccines and transmissibility. These results provide an awareness that new SARS-CoV-2 variants can cause more deaths even if their fatality rate is lower. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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18 pages, 7783 KiB

Open AccessArticle

Coarse-Grained Modeling of the SARS-CoV-2 Spike Glycoprotein by Physics-Informed Machine Learning

by David Liang, Ziji Zhang, Miriam Rafailovich, Marcia Simon, Yuefan Deng and Peng Zhang

Computation 2023, 11(2), 24; https://doi.org/10.3390/computation11020024 - 2 Feb 2023

Cited by 2 | Viewed by 2596

Abstract

Coarse-grained (CG) modeling has defined a well-established approach to accessing greater space and time scales inaccessible to the computationally expensive all-atomic (AA) molecular dynamics (MD) simulations. Popular methods of CG follow a bottom-up architecture to match properties of fine-grained or experimental data whose [...] Read more.

Coarse-grained (CG) modeling has defined a well-established approach to accessing greater space and time scales inaccessible to the computationally expensive all-atomic (AA) molecular dynamics (MD) simulations. Popular methods of CG follow a bottom-up architecture to match properties of fine-grained or experimental data whose development is a daunting challenge for requiring the derivation of a new set of parameters in potential calculation. We proposed a novel physics-informed machine learning (PIML) framework for a CG model and applied it, as a verification, for modeling the SARS-CoV-2 spike glycoprotein. The PIML in the proposed framework employs a force-matching scheme with which we determined the force-field parameters. Our PIML framework defines its trainable parameters as the CG force-field parameters and predicts the instantaneous forces on each CG bead, learning the force field parameters to best match the predicted forces with the reference forces. Using the learned interaction parameters, CGMD validation simulations reach the microsecond time scale with stability, at a simulation speed 40,000 times faster than the conventional AAMD. Compared with the traditional iterative approach, our framework matches the AA reference structure with better accuracy. The improved efficiency enhances the timeliness of research and development in producing long-term simulations of SARS-CoV-2 and opens avenues to help illuminate protein mechanisms and predict its environmental changes. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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19 pages, 3385 KiB

Open AccessArticle

Forecasting the Cumulative COVID-19 Cases in Indonesia Using Flower Pollination Algorithm

by Afiahayati, Yap Bee Wah, Sri Hartati, Yunita Sari, I Nyoman Prayana Trisna, Diyah Utami Kusumaning Putri, Aina Musdholifah and Retantyo Wardoyo

Computation 2022, 10(12), 214; https://doi.org/10.3390/computation10120214 - 7 Dec 2022

Cited by 1 | Viewed by 1936

Abstract

Coronavirus disease 2019 (COVID-19) was declared as a global pandemic by the World Health Organization (WHO) on 12 March 2020. Indonesia is reported to have the highest number of cases in Southeast Asia. Accurate prediction of the number of COVID-19 cases in the [...] Read more.

Coronavirus disease 2019 (COVID-19) was declared as a global pandemic by the World Health Organization (WHO) on 12 March 2020. Indonesia is reported to have the highest number of cases in Southeast Asia. Accurate prediction of the number of COVID-19 cases in the upcoming few days is required as one of the considerations in making decisions to provide appropriate recommendations in the process of mitigating global pandemic infectious diseases. In this research, a metaheuristics optimization algorithm, the flower pollination algorithm, is used to forecast the cumulative confirmed COVID-19 cases in Indonesia. The flower pollination algorithm is a robust and adaptive method to perform optimization for curve fitting of COVID-19 cases. The performance of the flower pollination algorithm was evaluated and compared with a machine learning method which is popular for forecasting, the recurrent neural network. A comprehensive experiment was carried out to determine the optimal hyperparameters for the flower pollination algorithm and recurrent neural network. There were 24 and 72 combinations of hyperparameters for the flower pollination algorithm and recurrent neural network, respectively. The best hyperparameters were used to develop the COVID-19 forecasting model. Experimental results showed that the flower pollination algorithm performed better than the recurrent neural network in long-term (two weeks) and short-term (one week) forecasting of COVID-19 cases. The mean absolute percentage error (MAPE) for the flower pollination algorithm model (0.38%) was much lower than that of the recurrent neural network model (5.31%) in the last iteration for long-term forecasting. Meanwhile, the MAPE for the flower pollination algorithm model (0.74%) is also lower than the recurrent neural network model (4.8%) in the last iteration for short-term forecasting of the cumulative COVID-19 cases in Indonesia. This research provides state-of-the-art results to help the process of mitigating the global pandemic of COVID-19 in Indonesia. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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15 pages, 8331 KiB

Open AccessArticle

Dendrograms for Clustering in Multivariate Analysis: Applications for COVID-19 Vaccination Infodemic Data in Brazil

by Maria da Penha Harb, Lena Silva, Thalita Ayass, Nandamudi Vijaykumar, Marcelino Silva and Carlos Renato Francês

Computation 2022, 10(9), 166; https://doi.org/10.3390/computation10090166 - 19 Sep 2022

Cited by 3 | Viewed by 2480

Abstract

Since December 2019, with the discovery of a new coronavirus, humanity has been exposed to a large amount of information from different media. Information is not always true and official. Known as an infodemic, false information can increase the negative effects of the [...] Read more.

Since December 2019, with the discovery of a new coronavirus, humanity has been exposed to a large amount of information from different media. Information is not always true and official. Known as an infodemic, false information can increase the negative effects of the pandemic by impairing data readability and disease control. The paper aims to find similar patterns of behavior of the Brazilian population during 2021 in two analyses: with vaccination data of all age groups and using the age group of 64 years or more, representing 13% of the population, using the multivariate analysis technique. Infodemic vaccination information and pandemic numbers were also used. Dendrograms were used as a cluster visualization technique. The result of the generated clusters was verified by two algorithms: the cophenetic correlation coefficient, which obtained satisfactory results above 0.7, and the elbow method, which corroborated the number of clusters found. In the result of the analysis with all age groups, more homogeneous divisions were perceived among Brazilian states, while the second analysis resulted in more heterogeneous clusters, recalling that at the start of vaccinations they could have had fear, doubts, and significant belief in the infodemic. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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15 pages, 2348 KiB

Open AccessArticle

Capturing the Complexity of COVID-19 Research: Trend Analysis in the First Two Years of the Pandemic Using a Bayesian Probabilistic Model and Machine Learning Tools

by Javier De La Hoz-M, Susana Mendes, María José Fernández-Gómez and Yolanda González Silva

Computation 2022, 10(9), 156; https://doi.org/10.3390/computation10090156 - 8 Sep 2022

Cited by 2 | Viewed by 2253

Abstract

Publications about COVID-19 have occurred practically since the first outbreak. Therefore, studying the evolution of the scientific publications on COVID-19 can provide us with information on current research trends and can help researchers and policymakers to form a structured view of the existing [...] Read more.

Publications about COVID-19 have occurred practically since the first outbreak. Therefore, studying the evolution of the scientific publications on COVID-19 can provide us with information on current research trends and can help researchers and policymakers to form a structured view of the existing evidence base of COVID-19 and provide new research directions. This growth rate was so impressive that the need for updated information and research tools become essential to mitigate the spread of the virus. Therefore, traditional bibliographic research procedures, such as systematic reviews and meta-analyses, become time-consuming and limited in focus. This study aims to study the scientific literature on COVID-19 that has been published since its inception and to map the evolution of research in the time range between February 2020 and January 2022. The search was carried out in PubMed extracting topics using text mining and latent Dirichlet allocation modeling and a trend analysis was performed to analyze the temporal variations in research for each topic. We also study the distribution of these topics between countries and journals. 126,334 peer-reviewed articles and 16 research topics were identified. The countries with the highest number of scientific publications were the United States of America, China, Italy, United Kingdom, and India, respectively. Regarding the distribution of the number of publications by journal, we found that of the 7040 sources Int. J. Environ. Res. Public Health, PLoS ONE, and Sci. Rep., were the ones that led the publications on COVID-19. We discovered a growing tendency for eight topics (Prevention, Telemedicine, Vaccine immunity, Machine learning, Academic parameters, Risk factors and morbidity and mortality, Information synthesis methods, and Mental health), a falling trend for five of them (Epidemiology, COVID-19 pathology complications, Diagnostic test, Etiopathogenesis, and Political and health factors), and the rest varied throughout time with no discernible patterns (Therapeutics, Pharmacological and therapeutic target, and Repercussion health services). Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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17 pages, 1911 KiB

Open AccessArticle

Evaluation of the Effectiveness of Community Activities Restriction in Containing the Spread of COVID-19 in West Java, Indonesia Using Time-Series Clustering

by Dhika Surya Pangestu, Sukono and Nursanti Anggriani

Computation 2022, 10(9), 153; https://doi.org/10.3390/computation10090153 - 4 Sep 2022

Cited by 1 | Viewed by 1886

Abstract

The purpose of this research is to classify time-series data on the number of daily COVID-19 cases based on the dynamics. This research aims to evaluate the effectiveness of community activity restrictions in suppressing the number of new cases of COVID-19 in cities [...] Read more.

The purpose of this research is to classify time-series data on the number of daily COVID-19 cases based on the dynamics. This research aims to evaluate the effectiveness of community activity restrictions in suppressing the number of new cases of COVID-19 in cities and regencies in West Java. We performed time-series clustering on daily positive case data for COVID-19 in 27 cities and regencies in West Java Province, Indonesia for this study. The k-medoids clustering algorithm was used for clustering, with shape-based lock step measures, specifically, the cross correlation-based distance. We used daily new infected cases data for COVID-19 in 27 cities and regencies in West Java Province during the worst situation. We used data from 1 July 2021 to 31 September 2021 and from 1 January 2022 to 31 May 2022, during the Emergency Community Activity Restriction period (PPKM). According to our findings, the optimal number of clusters that could be formed from the data we had was 4 clusters for the first period and 2 clusters for the second period, with silhouette value of 0.2633 and 0.6363, respectively. For the first period, we discovered that PPKM was successful in clusters 1 and 2, namely in 25 cities/districts in West Java, except for Bogor and Depok, while for the second period, we found PPKM to be effective in reducing the number of COVID-19 cases throughout cities and regencies in West Java. This shows there is an improvement from the implementation of PPKM in the first period. We also found that the cluster that was formed was not only influenced by the effectiveness of the PPKM, but also by geography. The closer a city is to a hotspot region for the spread of COVID-19, the earlier the increase in the number of new COVID-19 cases will occur. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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29 pages, 850 KiB

Open AccessArticle

COVID-19 Vaccines Related User’s Response Categorization Using Machine Learning Techniques

by Ahmed Shahzad, Bushra Zafar, Nouman Ali, Uzma Jamil, Abdulaziz Jarallah Alghadhban, Muhammad Assam, Nivin A. Ghamry and Elsayed Tag Eldin

Computation 2022, 10(8), 141; https://doi.org/10.3390/computation10080141 - 18 Aug 2022

Cited by 15 | Viewed by 3602

Abstract

Respiratory viruses known as coronaviruses infect people and cause death. The multiple crown-like spikes on the virus’s surface give them the name “corona”. The pandemic has resulted in a global health crisis and it is expected that every year we will have to [...] Read more.

Respiratory viruses known as coronaviruses infect people and cause death. The multiple crown-like spikes on the virus’s surface give them the name “corona”. The pandemic has resulted in a global health crisis and it is expected that every year we will have to fight against different COVID-19 variants. In this critical situation, the existence of COVID-19 vaccinations provides hope for mankind. Despite severe vaccination campaigns and recommendations from health experts and the government, people have perceptions regarding vaccination risks and share their views and experiences on social media platforms. Social attitudes to these types of vaccinations are influenced by their positive and negative effects. The analysis of such opinions can help to determine social trends and formulate policies to increase vaccination acceptance. This study presents a methodology for sentiment analysis of the global perceptions and perspectives related to COVID-19 vaccinations. The research is performed on five vaccinations that include Sinopharm, Pfizer, Moderna, AstraZeneca, and Sinovac on the Twitter platform extracted using Twitter crawling. To effectively perform this research, tweets datasets are categorized into three groups, i.e., positive, negative and natural. For sentiment classification, different machine learning classifiers are used such as Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), and Support Vector Machine (SVM). It should be noted that the Decision tree classifier achieves the highest classification performance in all datasets as compared to the other machine learning algorithms. For COVID-19 Vaccine Tweets with Sentiment Annotation (CVSA), the highest accuracy obtained is 93.0%, for the AstraZeneca vaccine dataset 90.94%, for the Pfizer vaccine dataset 91.07%, 88.01% accuracy for the Moderna vaccine dataset, for the Sinovac vaccine dataset 92.8% accuracy, and 93.87% accuracy for the Sinopharm vaccine dataset, respectively. The quantitative comparisons demonstrate that the proposed research achieves better accuracy as compared to state-of-the-art research. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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18 pages, 1890 KiB

Open AccessArticle

Assessing Traffic Congestion Hazard Period due to Commuters’ Home-to-Shopping Center Departures after COVID-19 Curfew Timings

by Majed Alinizzi, Husnain Haider and Mohammad Alresheedi

Computation 2022, 10(8), 132; https://doi.org/10.3390/computation10080132 - 2 Aug 2022

Cited by 9 | Viewed by 2982

Abstract

In addition to a wide range of socio-economic impacts, traffic congestion during the era of the COVID-19 pandemic has been identified as a critical issue to be addressed. In urban neighborhoods, the timespan of traffic congestion hazard (H_TC) after the curfew [...] Read more.

In addition to a wide range of socio-economic impacts, traffic congestion during the era of the COVID-19 pandemic has been identified as a critical issue to be addressed. In urban neighborhoods, the timespan of traffic congestion hazard (H_TC) after the curfew lift is subjected to the commuters’ decisions about home-to-shopping center departures. The decision for departing early or late for shopping depends on both the internal (commuter related) and external (shopping center related) factors. The present study developed a practical methodology to assess the H_TC period after the curfew timings. An online questionnaire survey was conducted to appraise the commuters’ perception about departure time and to assess the impact of eight internal (family size, involvement in other activities, nature of job, education level, age, number of vehicles, number of children, and availability of personal driver) and three external (availability of shopping center of choice in near vicinity, distance to shopping center, and size of the city) factors on their decision. With an acceptable 20% response rate, Chi-square and Cramer’s V tests ascertained family size and involvement in other activities as the most significant internal factors and availability of shopping center of choice as the primary external factor. Age, number of children, and size of the city influenced to some extent the commuters’ decisions about early or delayed departure. Large associations were found for most of the factors, except education level and availability of drivers in a household. Fuzzy synthetic evaluation (FSE) first segregated the commuters’ responses over a four level-rating system: no delay (0), short delay (1), moderate delay (3), and long delay (5). Subsequently, the hierarchical bottom-up aggregation effectively determined the period of highest traffic congestion. Logical study findings revealed that most (about 65%) of the commuters depart for shopping within 15 min after the curfew lift, so H_TC in the early part (the first one hour) of the no curfew period needs attention. The traffic regulatory agencies can use the proposed approach with basic socio-demographic data of an urban neighborhood’s residents to identify the H_TC period and implement effective traffic management strategies accordingly. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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32 pages, 10458 KiB

Open AccessArticle

Bioinformatics, Computational Informatics, and Modeling Approaches to the Design of mRNA COVID-19 Vaccine Candidates

by Olugbenga Oluseun Oluwagbemi, Elijah K. Oladipo, Olatunji M. Kolawole, Julius K. Oloke, Temitope I. Adelusi, Boluwatife A. Irewolede, Emmanuel O. Dairo, Ayodele E. Ayeni, Kehinde T. Kolapo, Olawumi E. Akindiya, Jerry A. Oluwasegun, Bamigboye F. Oluwadara and Segun Fatumo

Computation 2022, 10(7), 117; https://doi.org/10.3390/computation10070117 - 8 Jul 2022

Cited by 19 | Viewed by 5667 | Correction

Abstract

This article is devoted to applying bioinformatics and immunoinformatics approaches for the development of a multi-epitope mRNA vaccine against the spike glycoproteins of circulating SARS-CoV-2 variants in selected African countries. The study’s relevance is dictated by the fact that severe acute respiratory syndrome [...] Read more.

This article is devoted to applying bioinformatics and immunoinformatics approaches for the development of a multi-epitope mRNA vaccine against the spike glycoproteins of circulating SARS-CoV-2 variants in selected African countries. The study’s relevance is dictated by the fact that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began its global threat at the end of 2019 and since then has had a devastating impact on the whole world. Measures to reduce threats from the pandemic include social restrictions, restrictions on international travel, and vaccine development. In most cases, vaccine development depends on the spike glycoprotein, which serves as a medium for its entry into host cells. Although several variants of SARS-CoV-2 have emerged from mutations crossing continental boundaries, about 6000 delta variants have been reported along the coast of more than 20 countries in Africa, with South Africa accounting for the highest percentage. This also applies to the omicron variant of the SARS-CoV-2 virus in South Africa. The authors suggest that bioinformatics and immunoinformatics approaches be used to develop a multi-epitope mRNA vaccine against the spike glycoproteins of circulating SARS-CoV-2 variants in selected African countries. Various immunoinformatics tools have been used to predict T- and B-lymphocyte epitopes. The epitopes were further subjected to multiple evaluations to select epitopes that could elicit a sustained immunological response. The candidate vaccine consisted of seven epitopes, a highly immunogenic adjuvant, an MHC I-targeting domain (MITD), a signal peptide, and linkers. The molecular weight (MW) was predicted to be 223.1 kDa, well above the acceptable threshold of 110 kDa on an excellent vaccine candidate. In addition, the results showed that the candidate vaccine was antigenic, non-allergenic, non-toxic, thermostable, and hydrophilic. The vaccine candidate has good population coverage, with the highest range in East Africa (80.44%) followed by South Africa (77.23%). West Africa and North Africa have 76.65% and 76.13%, respectively, while Central Africa (75.64%) has minimal coverage. Among seven epitopes, no mutations were observed in 100 randomly selected SARS-CoV-2 spike glycoproteins in the study area. Evaluation of the secondary structure of the vaccine constructs revealed a stabilized structure showing 36.44% alpha-helices, 20.45% drawn filaments, and 33.38% random helices. Molecular docking of the TLR4 vaccine showed that the simulated vaccine has a high binding affinity for TLR-4, reflecting its ability to stimulate the innate and adaptive immune response. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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18 pages, 6531 KiB

Open AccessArticle

Effect of Key Phytochemicals from Andrographis paniculata, Tinospora cordifolia, and Ocimum sanctum on PLpro-ISG15 De-Conjugation Machinery—A Computational Approach

by Prachi Singh, Shruthi S. Bhat, Ardra Punnapuzha, Amrutha Bhagavatula, Babu U. Venkanna, Rafiq Mohamed and Raghavendra P. Rao

Computation 2022, 10(7), 109; https://doi.org/10.3390/computation10070109 - 30 Jun 2022

Cited by 3 | Viewed by 2427

Abstract

ISGylation is an important process through which interferon-stimulated genes (ISGs) elicit an antiviral response in the host cells. Several viruses, including the SARS-CoV-2, suppress the host immune response by reversing the ISGylation through a process known as de-ISGylation. The PLpro of SARS-CoV-2 interacts [...] Read more.

ISGylation is an important process through which interferon-stimulated genes (ISGs) elicit an antiviral response in the host cells. Several viruses, including the SARS-CoV-2, suppress the host immune response by reversing the ISGylation through a process known as de-ISGylation. The PLpro of SARS-CoV-2 interacts with the host ISG15 and brings about de-ISGylation. Hence, inhibiting the de-ISGylation to restore the activity of ISGs can be an attractive strategy to augment the host immune response against SARS-CoV-2. In the present study, we evaluated several phytochemicals from well-known immunomodulatory herbs, viz. Andrographispaniculata (AG), Tinospora cordifolia (GU), and Ocimum sanctum (TU) for their effect on deISGylation that was mediated by the PLpro of SARS-CoV2. For this purpose, we considered the complex 6XA9, which represents the interaction between SARS-CoV-2 PLpro and ISG15 proteins. The phytochemicals from these herbs were first evaluated for their ability to bind to the interface region between PLpro and ISG15. Molecular docking studies indicated that 14-deoxy-15-isopropylidene-11,12-didehydroandrographolide (AG1), Isocolumbin (GU1), and Orientin (TU1) from AG, GU, and TU, respectively possess better binding energy. The molecular dynamic parameters and MMPBSA calculations indicated that AG1, GU1, and TU1 could favorably bind to the interface and engaged key residues between (PLpro-ISG15)-complex. Protein–protein MMPBSA calculations indicated that GU1 and TU1 could disrupt the interactions between ISG15 and PLpro. Our studies provide a novel molecular basis for the immunomodulatory action of these phytochemicals and open up new strategies to evaluate drug molecules for their effect on de-ISGylation to overcome the virus-mediated immune suppression. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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22 pages, 1498 KiB

Open AccessArticle

A Prospective Method for Generating COVID-19 Dynamics

by Kamal Khairudin Sukandar, Andy Leonardo Louismono, Metra Volisa, Rudy Kusdiantara, Muhammad Fakhruddin, Nuning Nuraini and Edy Soewono

Computation 2022, 10(7), 107; https://doi.org/10.3390/computation10070107 - 24 Jun 2022

Cited by 5 | Viewed by 1978

Abstract

Generating dynamic operators are constructed here from the cumulative case function to recover all state dynamics of a Susceptible–Exposed–Infectious–Recovered (SEIR) model for COVID-19 transmission. In this study, recorded and unrecorded EIRs and a time-dependent infection rate are taken into account to accommodate immeasurable [...] Read more.

Generating dynamic operators are constructed here from the cumulative case function to recover all state dynamics of a Susceptible–Exposed–Infectious–Recovered (SEIR) model for COVID-19 transmission. In this study, recorded and unrecorded EIRs and a time-dependent infection rate are taken into account to accommodate immeasurable control and intervention processes. Generating dynamic operators are built and implemented on the cumulative cases. All infection processes, which are hidden in this cumulative function, can be recovered entirely by implementing the generating operators. Direct implementation of the operators on the cumulative function gives all recorded state dynamics. Further, the unrecorded daily infection rate is estimated from the ratio between IFR and CFR. The remaining dynamics of unrecorded states are directly obtained from the generating operators. The simulations are conducted using infection data provided by Worldometers from ten selected countries. It is shown that the higher number of daily PCR tests contributed directly to reducing the effective reproduction ratio. The simulations of all state dynamics, infection rates, and effective reproduction ratios for several countries in the first and second waves of transmissions are presented. This method directly measures daily transmission indicators, which can be effectively used for the day-to-day control of the epidemic. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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16 pages, 5824 KiB

Open AccessArticle

In Silico Analysis of Peptide-Based Derivatives Containing Bifunctional Warheads Engaging Prime and Non-Prime Subsites to Covalent Binding SARS-CoV-2 Main Protease (M^pro)

by Simone Brogi, Sara Rossi, Roberta Ibba, Stefania Butini, Vincenzo Calderone, Giuseppe Campiani and Sandra Gemma

Computation 2022, 10(5), 69; https://doi.org/10.3390/computation10050069 - 1 May 2022

Cited by 5 | Viewed by 3323

Abstract

Despite the progress of therapeutic approaches for treating COVID-19 infection, the interest in developing effective antiviral agents is still high, due to the possibility of the insurgence of viable SARS-CoV-2-resistant strains. Accordingly, in this article, we describe a computational protocol for identifying possible [...] Read more.

Despite the progress of therapeutic approaches for treating COVID-19 infection, the interest in developing effective antiviral agents is still high, due to the possibility of the insurgence of viable SARS-CoV-2-resistant strains. Accordingly, in this article, we describe a computational protocol for identifying possible SARS-CoV-2 M^pro covalent inhibitors. Combining several in silico techniques, we evaluated the potential of the peptide-based scaffold with different warheads as a significant alternative to nitriles and aldehyde electrophilic groups. We rationally designed four potential inhibitors containing difluorstatone and a Michael acceptor as warheads. In silico analysis, based on molecular docking, covalent docking, molecular dynamics simulation, and FEP, indicated that the conceived compounds could act as covalent inhibitors of M^pro and that the investigated warheads can be used for designing covalent inhibitors against serine or cysteine proteases such as SARS-CoV-2 M^pro. Our work enriches the knowledge on SARS-CoV-2 M^pro, providing a novel potential strategy for its inhibition, paving the way for the development of effective antivirals. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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25 pages, 2545 KiB

Open AccessArticle

In Silico Analysis of the Multi-Targeted Mode of Action of Ivermectin and Related Compounds

by Maral Aminpour, Marco Cannariato, Jordane Preto, M. Ehsan Safaeeardebili, Alexia Moracchiato, Domiziano Doria, Francesca Donato, Eric Adriano Zizzi, Marco Agostino Deriu, David E. Scheim, Alessandro D. Santin and Jack Adam Tuszynski

Computation 2022, 10(4), 51; https://doi.org/10.3390/computation10040051 - 25 Mar 2022

Cited by 11 | Viewed by 7874

Abstract

Some clinical studies have indicated activity of ivermectin, a macrocyclic lactone, against COVID-19, but a biological mechanism initially proposed for this anti-viral effect is not applicable at physiological concentrations. This in silico investigation explores potential modes of action of ivermectin and 14 related [...] Read more.

Some clinical studies have indicated activity of ivermectin, a macrocyclic lactone, against COVID-19, but a biological mechanism initially proposed for this anti-viral effect is not applicable at physiological concentrations. This in silico investigation explores potential modes of action of ivermectin and 14 related compounds, by which the infectivity and morbidity of the SARS-CoV-2 virus may be limited. Binding affinity computations were performed for these agents on several docking sites each for models of (1) the spike glycoprotein of the virus, (2) the CD147 receptor, which has been identified as a secondary attachment point for the virus, and (3) the alpha-7 nicotinic acetylcholine receptor (α7nAChr), an indicated point of viral penetration of neuronal tissue as well as an activation site for the cholinergic anti-inflammatory pathway controlled by the vagus nerve. Binding affinities were calculated for these multiple docking sites and binding modes of each compound. Our results indicate the high affinity of ivermectin, and even higher affinities for some of the other compounds evaluated, for all three of these molecular targets. These results suggest biological mechanisms by which ivermectin may limit the infectivity and morbidity of the SARS-CoV-2 virus and stimulate an α7nAChr-mediated anti-inflammatory pathway that could limit cytokine production by immune cells. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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19 pages, 5391 KiB

Open AccessArticle

Explainable Artificial Intelligence Approach for the Early Prediction of Ventilator Support and Mortality in COVID-19 Patients

by Nida Aslam

Computation 2022, 10(3), 36; https://doi.org/10.3390/computation10030036 - 28 Feb 2022

Cited by 11 | Viewed by 3333

Abstract

Early prediction of mortality and risk of deterioration in COVID-19 patients can reduce mortality and increase the opportunity for better and more timely treatment. In the current study, the DL model and explainable artificial intelligence (EAI) were combined to identify the impact of [...] Read more.

Early prediction of mortality and risk of deterioration in COVID-19 patients can reduce mortality and increase the opportunity for better and more timely treatment. In the current study, the DL model and explainable artificial intelligence (EAI) were combined to identify the impact of certain attributes on the prediction of mortality and ventilatory support in COVID-19 patients. Nevertheless, the DL model does not suffer from the curse of dimensionality, but in order to identify significant attributes, the EAI feature importance method was used. The DL model produced significant results; however, it lacks interpretability. The study was performed using COVID-19-hospitalized patients in King Abdulaziz Medical City, Riyadh. The dataset contains the patients’ demographic information, laboratory investigations, and chest X-ray (CXR) findings. The dataset used suffers from an imbalance; therefore, balanced accuracy, sensitivity, specificity, Youden index, and AUC measures were used to investigate the effectiveness of the proposed model. Furthermore, the experiments were conducted using original and SMOTE (over and under sampled) datasets. The proposed model outperforms the baseline study, with a balanced accuracy of 0.98 and an AUC of 0.998 for predicting mortality using the full-feature set. Meanwhile, for predicting ventilator support a highest balanced accuracy of 0.979 and an AUC of 0.981 was achieved. The proposed explainable prediction model will assist doctors in the early prediction of COVID-19 patients that are at risk of mortality or ventilatory support and improve the management of hospital resources. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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16 pages, 3372 KiB

Open AccessArticle

Virtual Combinatorial Library Screening of Quinadoline B Derivatives against SARS-CoV-2 RNA-Dependent RNA Polymerase

by Simone Brogi, Mark Tristan Quimque, Kin Israel Notarte, Jeremiah Gabriel Africa, Jenina Beatriz Hernandez, Sophia Morgan Tan, Vincenzo Calderone and Allan Patrick Macabeo

Computation 2022, 10(1), 7; https://doi.org/10.3390/computation10010007 - 12 Jan 2022

Cited by 20 | Viewed by 3880

Abstract

The unprecedented global health threat of SARS-CoV-2 has sparked a continued interest in discovering novel anti-COVID-19 agents. To this end, we present here a computer-based protocol for identifying potential compounds targeting RNA-dependent RNA polymerase (RdRp). Starting from our previous study wherein, using a [...] Read more.

The unprecedented global health threat of SARS-CoV-2 has sparked a continued interest in discovering novel anti-COVID-19 agents. To this end, we present here a computer-based protocol for identifying potential compounds targeting RNA-dependent RNA polymerase (RdRp). Starting from our previous study wherein, using a virtual screening campaign, we identified a fumiquinazolinone alkaloid quinadoline B (Q3), an antiviral fungal metabolite with significant activity against SARS-CoV-2 RdRp, we applied in silico combinatorial methodologies for generating and screening a library of anti-SARS-CoV-2 candidates with strong in silico affinity for RdRp. For this study, the quinadoline pharmacophore was subjected to structural iteration, obtaining a Q3-focused library of over 900,000 unique structures. This chemical library was explored to identify binders of RdRp with greater affinity with respect to the starting compound Q3. Coupling this approach with the evaluation of physchem profile, we found 26 compounds with significant affinities for the RdRp binding site. Moreover, top-ranked compounds were submitted to molecular dynamics to evaluate the stability of the systems during a selected time, and to deeply investigate the binding mode of the most promising derivatives. Among the generated structures, five compounds, obtained by inserting nucleotide-like scaffolds (1, 2, and 5), heterocyclic thiazolyl benzamide moiety (compound 3), and a peptide residue (compound 4), exhibited enhanced binding affinity for SARS-CoV-2 RdRp, deserving further investigation as possible antiviral agents. Remarkably, the presented in silico procedure provides a useful computational procedure for hit-to-lead optimization, having implications in anti-SARS-CoV-2 drug discovery and in general in the drug optimization process. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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23 pages, 5209 KiB

Open AccessArticle

Mass Media as a Mirror of the COVID-19 Pandemic

by Kirill Yakunin, Ravil I. Mukhamediev, Elena Zaitseva, Vitaly Levashenko, Marina Yelis, Adilkhan Symagulov, Yan Kuchin, Elena Muhamedijeva, Margulan Aubakirov and Viktors Gopejenko

Computation 2021, 9(12), 140; https://doi.org/10.3390/computation9120140 - 13 Dec 2021

Cited by 12 | Viewed by 4896

Abstract

The media plays an important role in disseminating facts and knowledge to the public at critical times, and the COVID-19 pandemic is a good example of such a period. This research is devoted to performing a comparative analysis of the representation of topics [...] Read more.

The media plays an important role in disseminating facts and knowledge to the public at critical times, and the COVID-19 pandemic is a good example of such a period. This research is devoted to performing a comparative analysis of the representation of topics connected with the pandemic in the internet media of Kazakhstan and the Russian Federation. The main goal of the research is to propose a method that would make it possible to analyze the correlation between mass media dynamic indicators and the World Health Organization COVID-19 data. In order to solve the task, three approaches related to the representation of mass media dynamics in numerical form—automatically obtained topics, average sentiment, and dynamic indicators—were proposed and applied according to a manually selected list of search queries. The results of the analysis indicate similarities and differences in the ways in which the epidemiological situation is reflected in publications in Russia and in Kazakhstan. In particular, the publication activity in both countries correlates with the absolute indicators, such as the daily number of new infections, and the daily number of deaths. However, mass media tend to ignore the positive rate of confirmed cases and the virus reproduction rate. If we consider strictness of quarantine measures, mass media in Russia show a rather high correlation, while in Kazakhstan, the correlation is much lower. Analysis of search queries revealed that in Kazakhstan the problem of fake news and disinformation is more acute during periods of deterioration of the epidemiological situation, when the level of crime and poverty increase. The novelty of this work is the proposal and implementation of a method that allows the performing of a comparative analysis of objective COVID-19 statistics and several mass media indicators. In addition, it is the first time that such a comparative analysis, between different countries, has been performed on a corpus in a language other than English. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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31 pages, 7762 KiB

Open AccessArticle

Estimation of Daily Reproduction Numbers during the COVID-19 Outbreak

by Jacques Demongeot, Kayode Oshinubi, Mustapha Rachdi, Hervé Seligmann, Florence Thuderoz and Jules Waku

Computation 2021, 9(10), 109; https://doi.org/10.3390/computation9100109 - 18 Oct 2021

Cited by 17 | Viewed by 3320

Abstract

(1) Background: The estimation of daily reproduction numbers throughout the contagiousness period is rarely considered, and only their sum R₀ is calculated to quantify the contagiousness level of an infectious disease. (2) Methods: We provide the equation of the discrete dynamics of [...] Read more.

(1) Background: The estimation of daily reproduction numbers throughout the contagiousness period is rarely considered, and only their sum R₀ is calculated to quantify the contagiousness level of an infectious disease. (2) Methods: We provide the equation of the discrete dynamics of the epidemic’s growth and obtain an estimation of the daily reproduction numbers by using a deconvolution technique on a series of new COVID-19 cases. (3) Results: We provide both simulation results and estimations for several countries and waves of the COVID-19 outbreak. (4) Discussion: We discuss the role of noise on the stability of the epidemic’s dynamics. (5) Conclusions: We consider the possibility of improving the estimation of the distribution of daily reproduction numbers during the contagiousness period by taking into account the heterogeneity due to several host age classes. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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22 pages, 4438 KiB

Open AccessArticle

Least-Squares Finite Element Method for a Meso-Scale Model of the Spread of COVID-19

by Fleurianne Bertrand and Emilie Pirch

Computation 2021, 9(2), 18; https://doi.org/10.3390/computation9020018 - 5 Feb 2021

Cited by 9 | Viewed by 3304

Abstract

This paper investigates numerical properties of a flux-based finite element method for the discretization of a SEIQRD (susceptible-exposed-infected-quarantined-recovered-deceased) model for the spread of COVID-19. The model is largely based on the SEIRD (susceptible-exposed-infected-recovered-deceased) models developed in recent works, with additional extension by a [...] Read more.

This paper investigates numerical properties of a flux-based finite element method for the discretization of a SEIQRD (susceptible-exposed-infected-quarantined-recovered-deceased) model for the spread of COVID-19. The model is largely based on the SEIRD (susceptible-exposed-infected-recovered-deceased) models developed in recent works, with additional extension by a quarantined compartment of the living population and the resulting first-order system of coupled PDEs is solved by a Least-Squares meso-scale method. We incorporate several data on political measures for the containment of the spread gathered during the course of the year 2020 and develop an indicator that influences the predictions calculated by the method. The numerical experiments conducted show a promising accuracy of predictions of the space-time behavior of the virus compared to the real disease spreading data. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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15 pages, 942 KiB

Open AccessArticle

On the Application of Advanced Machine Learning Methods to Analyze Enhanced, Multimodal Data from Persons Infected with COVID-19

by Wenhuan Zeng, Anupam Gautam and Daniel H. Huson

Computation 2021, 9(1), 4; https://doi.org/10.3390/computation9010004 - 7 Jan 2021

Cited by 11 | Viewed by 7262

Abstract

The current COVID-19 pandemic, caused by the rapid worldwide spread of the SARS-CoV-2 virus, is having severe consequences for human health and the world economy. The virus affects different individuals differently, with many infected patients showing only mild symptoms, and others showing critical [...] Read more.

The current COVID-19 pandemic, caused by the rapid worldwide spread of the SARS-CoV-2 virus, is having severe consequences for human health and the world economy. The virus affects different individuals differently, with many infected patients showing only mild symptoms, and others showing critical illness. To lessen the impact of the epidemic, one problem is to determine which factors play an important role in a patient’s progression of the disease. Here, we construct an enhanced COVID-19 structured dataset from more than one source, using natural language processing to add local weather conditions and country-specific research sentiment. The enhanced structured dataset contains 301,363 samples and 43 features, and we applied both machine learning algorithms and deep learning algorithms on it so as to forecast patient’s survival probability. In addition, we import alignment sequence data to improve the performance of the model. Application of Extreme Gradient Boosting (XGBoost) on the enhanced structured dataset achieves 97% accuracy in predicting patient’s survival; with climatic factors, and then age, showing the most importance. Similarly, the application of a Multi-Layer Perceptron (MLP) achieves 98% accuracy. This work suggests that enhancing the available data, mostly basic information on patients, so as to include additional, potentially important features, such as weather conditions, is useful. The explored models suggest that textual weather descriptions can improve outcome forecast. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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20 pages, 1247 KiB

Open AccessArticle

An Accuracy vs. Complexity Comparison of Deep Learning Architectures for the Detection of COVID-19 Disease

by Sima Sarv Ahrabi, Michele Scarpiniti, Enzo Baccarelli and Alireza Momenzadeh

Computation 2021, 9(1), 3; https://doi.org/10.3390/computation9010003 - 6 Jan 2021

Cited by 22 | Viewed by 6819

Abstract

In parallel with the vast medical research on clinical treatment of COVID-19, an important action to have the disease completely under control is to carefully monitor the patients. What the detection of COVID-19 relies on most is the viral tests, however, the study [...] Read more.

In parallel with the vast medical research on clinical treatment of COVID-19, an important action to have the disease completely under control is to carefully monitor the patients. What the detection of COVID-19 relies on most is the viral tests, however, the study of X-rays is helpful due to the ease of availability. There are various studies that employ Deep Learning (DL) paradigms, aiming at reinforcing the radiography-based recognition of lung infection by COVID-19. In this regard, we make a comparison of the noteworthy approaches devoted to the binary classification of infected images by using DL techniques, then we also propose a variant of a convolutional neural network (CNN) with optimized parameters, which performs very well on a recent dataset of COVID-19. The proposed model’s effectiveness is demonstrated to be of considerable importance due to its uncomplicated design, in contrast to other presented models. In our approach, we randomly put several images of the utilized dataset aside as a hold out set; the model detects most of the COVID-19 X-rays correctly, with an excellent overall accuracy of 99.8%. In addition, the significance of the results obtained by testing different datasets of diverse characteristics (which, more specifically, are not used in the training process) demonstrates the effectiveness of the proposed approach in terms of an accuracy up to 93%. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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14 pages, 641 KiB

Open AccessArticle

Causal Modeling of Twitter Activity during COVID-19

by Oguzhan Gencoglu and Mathias Gruber

Computation 2020, 8(4), 85; https://doi.org/10.3390/computation8040085 - 29 Sep 2020

Cited by 30 | Viewed by 7113

Abstract

Understanding the characteristics of public attention and sentiment is an essential prerequisite for appropriate crisis management during adverse health events. This is even more crucial during a pandemic such as COVID-19, as primary responsibility of risk management is not centralized to a single [...] Read more.

Understanding the characteristics of public attention and sentiment is an essential prerequisite for appropriate crisis management during adverse health events. This is even more crucial during a pandemic such as COVID-19, as primary responsibility of risk management is not centralized to a single institution, but distributed across society. While numerous studies utilize Twitter data in descriptive or predictive context during COVID-19 pandemic, causal modeling of public attention has not been investigated. In this study, we propose a causal inference approach to discover and quantify causal relationships between pandemic characteristics (e.g., number of infections and deaths) and Twitter activity as well as public sentiment. Our results show that the proposed method can successfully capture the epidemiological domain knowledge and identify variables that affect public attention and sentiment. We believe our work contributes to the field of infodemiology by distinguishing events that correlate with public attention from events that cause public attention. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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14 pages, 3125 KiB

Open AccessArticle

A Computational Study to Identify Potential Inhibitors of SARS-CoV-2 Main Protease (Mpro) from Eucalyptus Active Compounds

by Ibrahim Ahmad Muhammad, Kanikar Muangchoo, Auwal Muhammad, Ya’u Sabo Ajingi, Ibrahim Yahaya Muhammad, Ibrahim Dauda Umar and Abubakar Bakoji Muhammad

Computation 2020, 8(3), 79; https://doi.org/10.3390/computation8030079 - 9 Sep 2020

Cited by 12 | Viewed by 4388

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was found to be a severe threat to global public health in late 2019. Nevertheless, no approved medicines have been found to inhibit the virus effectively. Anti-malarial and antiviral medicines have been reported to target the [...] Read more.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was found to be a severe threat to global public health in late 2019. Nevertheless, no approved medicines have been found to inhibit the virus effectively. Anti-malarial and antiviral medicines have been reported to target the SARS-CoV-2 virus. This paper chose eight natural eucalyptus compounds to study their binding interactions with the SARS-CoV-2 main protease (Mpro) to assess their potential for becoming herbal drugs for the new SARS-CoV-2 infection virus. In-silico methods such as molecular docking, molecular dynamics (MD) simulations, and Molecular Mechanics Poisson Boltzmann Surface Area (MM/PBSA) analysis were used to examine interactions at the atomistic level. The results of molecular docking indicate that Mpro has good binding energy for all compounds studied. Three docked compounds, α-gurjunene, aromadendrene, and allo-aromadendrene, with highest binding energies of −7.34 kcal/mol (−30.75 kJ/mol), −7.23 kcal/mol (−30.25 kJ/mol), and −7.17 kcal/mol (−29.99 kJ/mol) respectively, were simulated with GROningen MAchine for Chemical Simulations (GROMACS) to measure the molecular interactions between Mpro and inhibitors in detail. Our MD simulation results show that α-gurjunene has the strongest binding energy of −20.37 kcal/mol (−85.21 kJ/mol), followed by aromadendrene with −18.99 kcal/mol (−79.45 kJ/mol), and finally allo-aromadendrene with −17.91 kcal/mol (−74.95 kJ/mol). The findings indicate that eucalyptus may be used to inhibit the Mpro enzyme as a drug candidate. This is the first computational analysis that gives an insight into the potential role of structural flexibility during interactions with eucalyptus compounds. It also sheds light on the structural design of new herbal medicinal products against Mpro. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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17 pages, 6304 KiB

Open AccessArticle

Exploring the SARS-CoV-2 Proteome in the Search of Potential Inhibitors via Structure-Based Pharmacophore Modeling/Docking Approach

by Giulia Culletta, Maria Rita Gulotta, Ugo Perricone, Maria Zappalà, Anna Maria Almerico and Marco Tutone

Computation 2020, 8(3), 77; https://doi.org/10.3390/computation8030077 - 8 Sep 2020

Cited by 34 | Viewed by 5200

Abstract

To date, SARS-CoV-2 infectious disease, named COVID-19 by the World Health Organization (WHO) in February 2020, has caused millions of infections and hundreds of thousands of deaths. Despite the scientific community efforts, there are currently no approved therapies for treating this coronavirus infection. [...] Read more.

To date, SARS-CoV-2 infectious disease, named COVID-19 by the World Health Organization (WHO) in February 2020, has caused millions of infections and hundreds of thousands of deaths. Despite the scientific community efforts, there are currently no approved therapies for treating this coronavirus infection. The process of new drug development is expensive and time-consuming, so that drug repurposing may be the ideal solution to fight the pandemic. In this paper, we selected the proteins encoded by SARS-CoV-2 and using homology modeling we identified the high-quality model of proteins. A structure-based pharmacophore modeling study was performed to identify the pharmacophore features for each target. The pharmacophore models were then used to perform a virtual screening against the DrugBank library (investigational, approved and experimental drugs). Potential inhibitors were identified for each target using XP docking and induced fit docking. MM-GBSA was also performed to better prioritize potential inhibitors. This study will provide new important comprehension of the crucial binding hot spots usable for further studies on COVID-19. Our results can be used to guide supervised virtual screening of large commercially available libraries. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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25 pages, 5101 KiB

Open AccessArticle

Is a COVID-19 Second Wave Possible in Emilia-Romagna (Italy)? Forecasting a Future Outbreak with Particulate Pollution and Machine Learning

by Silvia Mirri, Giovanni Delnevo and Marco Roccetti

Computation 2020, 8(3), 74; https://doi.org/10.3390/computation8030074 - 24 Aug 2020

Cited by 44 | Viewed by 5293

Abstract

The Nobel laureate Niels Bohr once said that: “Predictions are very difficult, especially if they are about the future”. Nonetheless, models that can forecast future COVID-19 outbreaks are receiving special attention by policymakers and health authorities, with the aim of putting in place [...] Read more.

The Nobel laureate Niels Bohr once said that: “Predictions are very difficult, especially if they are about the future”. Nonetheless, models that can forecast future COVID-19 outbreaks are receiving special attention by policymakers and health authorities, with the aim of putting in place control measures before the infections begin to increase. Nonetheless, two main problems emerge. First, there is no a general agreement on which kind of data should be registered for judging on the resurgence of the virus (e.g., infections, deaths, percentage of hospitalizations, reports from clinicians, signals from social media). Not only this, but all these data also suffer from common defects, linked to their reporting delays and to the uncertainties in the collection process. Second, the complex nature of COVID-19 outbreaks makes it difficult to understand if traditional epidemiological models, such as susceptible, infectious, or recovered (SIR), are more effective for a timely prediction of an outbreak than alternative computational models. Well aware of the complexity of this forecasting problem, we propose here an innovative metric for predicting COVID-19 diffusion based on the hypothesis that a relation exists between the spread of the virus and the presence in the air of particulate pollutants, such as PM_2.5, PM₁₀, and NO₂. Drawing on the recent assumption of 239 experts who claimed that this virus can be airborne, and further considering that particulate matter may favor this airborne route, we developed a machine learning (ML) model that has been instructed with: (i) all the COVID-19 infections that occurred in the Italian region of Emilia-Romagna, one of the most polluted areas in Europe, in the period of February–July 2020, (ii) the daily values of all the particulates taken in the same period and in the same region, and finally (iii) the chronology according to which restrictions were imposed by the Italian Government to human activities. Our ML model was then subjected to a classic ten-fold cross-validation procedure that returned a promising 90% accuracy value. Finally, the model was used to predict a possible resurgence of the virus in all the nine provinces of Emilia-Romagna, in the period of September–December 2020. To make those predictions, input to our ML model were the daily measurements of the aforementioned pollutants registered in the periods of September–December 2017/2018/2019, along with the hypothesis that the mild containment measures taken in Italy in the so-called Phase 3 are obeyed. At the time we write this article, we cannot have a confirmation of the precision of our predictions. Nevertheless, we are projecting a scenario based on an original hypothesis that makes our COVID-19 prediction model unique in the world. Its accuracy will be soon judged by history—and this, too, is science at the service of society. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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16 pages, 4601 KiB

Open AccessArticle

Particulate Matter and COVID-19 Disease Diffusion in Emilia-Romagna (Italy). Already a Cold Case?

by Giovanni Delnevo, Silvia Mirri and Marco Roccetti

Computation 2020, 8(2), 59; https://doi.org/10.3390/computation8020059 - 23 Jun 2020

Cited by 25 | Viewed by 4439

Abstract

As we prepare to emerge from an extensive and unprecedented lockdown period, due to the COVID-19 virus infection that hit the Northern regions of Italy with the Europe’s highest death toll, it becomes clear that what has gone wrong rests upon a combination [...] Read more.

As we prepare to emerge from an extensive and unprecedented lockdown period, due to the COVID-19 virus infection that hit the Northern regions of Italy with the Europe’s highest death toll, it becomes clear that what has gone wrong rests upon a combination of demographic, healthcare, political, business, organizational, and climatic factors that are out of our scientific scope. Nonetheless, looking at this problem from a patient’s perspective, it is indisputable that risk factors, considered as associated with the development of the virus disease, include older age, history of smoking, hypertension and heart disease. While several studies have already shown that many of these diseases can also be favored by a protracted exposure to air pollution, there has been recently an insurgence of negative commentary against authors who have correlated the fatal consequences of COVID-19 (also) to the exposition of specific air pollutants. Well aware that understanding the real connection between the spread of this fatal virus and air pollutants would require many other investigations at a level appropriate to the scale of this phenomenon (e.g., biological, chemical, and physical), we propose the results of a study, where a series of the measures of the daily values of PM_2.5, PM₁₀, and NO₂ were considered over time, while the Granger causality statistical hypothesis test was used for determining the presence of a possible correlation with the series of the new daily COVID19 infections, in the period February–April 2020, in Emilia-Romagna. Results taken both before and after the governmental lockdown decisions show a clear correlation, although strictly seen from a Granger causality perspective. Moving beyond the relevance of our results towards the real extent of such a correlation, our scientific efforts aim at reinvigorating the debate on a relevant case, that should not remain unsolved or no longer investigated. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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9 pages, 1604 KiB

Open AccessArticle

Computational View toward the Inhibition of SARS-CoV-2 Spike Glycoprotein and the 3CL Protease

by Zhen Qiao, Hongtao Zhang, Hai-Feng Ji and Qian Chen

Computation 2020, 8(2), 53; https://doi.org/10.3390/computation8020053 - 31 May 2020

Cited by 27 | Viewed by 4833

Abstract

Since the outbreak of the 2019 novel coronavirus disease (COVID-19), the medical research community is vigorously seeking a treatment to control the infection and save the lives of severely infected patients. The main potential candidates for the control of viruses are virally targeted [...] Read more.

Since the outbreak of the 2019 novel coronavirus disease (COVID-19), the medical research community is vigorously seeking a treatment to control the infection and save the lives of severely infected patients. The main potential candidates for the control of viruses are virally targeted agents. In this short letter, we report our calculations on the inhibitors for the SARS-CoV-2 3CL protease and the spike protein for the potential treatment of COVID-19. The results show that the most potent inhibitors of the SARS-CoV-2 3CL protease include saquinavir, tadalafil, rivaroxaban, sildenafil, dasatinib, etc. Ergotamine, amphotericin b, and vancomycin are most promising to block the interaction of the SARS-CoV-2 S-protein with human ACE-2. Full article

(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))

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