Submitted Successfully!
Thank you for your contribution! You can also upload a video entry related to this topic through the link below: https://encyclopedia.pub/user/video_add?id=33665
Check Note
Kamran Munir 2022-11-14 11:38:34
Problem with the Title: Coronavirus Your current title is too generic, please change it to a specific title exactly related to the body of entry. Also, add a conclusion to the entry.
2000/2000
Ver. Summary Created by Modification Content Size Created at Operation
1 -- 2531 2022-11-09 08:05:59 |
2 layout Meta information modification 2531 2022-11-09 08:30:43 |
Coronavirus
Edit
Upload a video

Coronaviruses are indeed a huge family of viruses that are found both in humans and animals. Seven different types have been identified, including the ones that caused COVID-19 and the SARS and MERS illnesses.

COVID-19 variants artificial neural network B.1.1.529 Forecasting
Information
View Times: 47
Entry Collection: COVID-19
Revisions: 2 times (View History)
Update Time: 09 Nov 2022
Table of Contents

    1. Coronavirus

    Coronaviruses are indeed a huge family of viruses that are found both in humans and animals [1]. Seven different types have been identified, including the ones that caused COVID-19 and the SARS and MERS illnesses. According to initial estimations, the retrovirus seemed to be more contagious than the one that caused SARS, although it appeared to be less probable to provoke catastrophic illnesses. There still have a lot to learn about the novel coronavirus (COVID-19) [2].

    2. Symptoms of COVID-19

    COVID-19 has been related to a variety of indications, ranging from simple headaches to life-threatening diseases. Upon being exposed to the illness, symptoms and signs may appear after 2 to 14 days [3]. The severity of the symptoms varies from mild to severe. COVID-19 is a virus that can cause the following symptoms in patients:
    • Temperature or chills
    • Runny nose
    • Coughing
    • Breathing problems
    • Fatigue
    • Aches in the muscles or throughout the body
    • Loss of smell or taste
    • Diarrhea
    • Sore throat
    • Nausea or vomiting
    This is not an extensive list of all symptoms and manifestation. The CDC [3] continues to update the list of possible symptoms whenever new information becomes available from research labs or other academic sources. COVID-19 infection appears to put elderly persons with serious medical conditions, such as diabetes, heart disease, or respiratory problems, at an increased risk of developing more serious conditions.

    3. Types of Coronavirus

    In a new study on COVID-19, UK-based scientists discovered that there are six different varieties of COVID-19 infection, each with its own set of symptoms.
    • Flu-like without a temperature
      Fatigue, muscle aches, absence of smell, sore throat, coughing, shortness of breath, and no temperature are some of the additional symptoms.
    • Flu-like with temperature
      Fatigue, absence of smell, sore throat, coughing, uncontrollable shaking, a decrease in hunger, and a temperature.
    • Gastrointestinal
      Fatigue, absence of smell, sore throat, a decrease in hunger, chest pain, no coughing, and diarrhea.
    • Extreme level one, severe exhaustion
      Fatigue, loss of smell, cough, chest pain, a temperature, and hoarseness.
    • Extreme level two, misconception (uncertainty)
      Fatigue, absence of smell, a decrease in hunger, coughing, sore throat, chest pain, a temperature, hoarseness, muscle pain, and confusion.
    • Extreme level three, abdominal and pulmonary
      Fatigue, absence of smell, a decrease in hunger, coughing, sore throat, chest pain, a temperature, hoarseness, and muscle pain.

    4. Emerging Variants of COVID-19

    New variants are emerging with time. For example, recently, a new mutant (B.1.1.529 also known as Omicron) has emerged, which is fast spreading and can pose a big threat to the effectiveness of COVID-19 vaccinations [4]. Researchers are closely monitoring this novel mutant of COVID-19. This variant contains various changes, which were earlier reported in other mutants, particularly Delta. This new variant has been observed to be expanding rapidly within South Africa. Nowadays, the main goal is to focus on its expansion. The said mutation was identified in Botswana on 11 November 2021 [5] and was identified in a South African traveler who traveled to Hong Kong. Omicron was added to the list of “variants of concern” by the WHO, which also contains Alpha, Beta, Gamma, and Delta. Viruses transform themselves all the time and the majority of mutations are minor. Some of these mutations may be harmful to the virus itself, whereas others can make the infection more aggressive or dangerous. Table 1 illustrates the alterations with the highest risk, which are described as the “variants of concern” and are regularly observed by healthcare practitioners. Regarding vaccinations against COVID-19, the vaccinations from Chinese Sinopharm, Pfizer, and AstraZeneca are very efficacious against the variations after two doses, whereas resistance after one dosage appears to be diminished [6].
    Table 1. Some of the recent variants categorized by WHO.
    There are several variants of SARS-CoV-2, including a brand-new, extremely contagious variant that was detected in the United Kingdom [2]. Another of these new variants is known as VOC202101/02 or P.1 and was reported in visitors from Brazil who traveled to Japan in January 2021. This gene contains the 1–4 nt insertion, three reductions, four identical modifications, and 17 distinct amino acid modifications [7]. Travel restrictions were implemented in an effort to stop the spread of P.1 throughout the nation after it was discovered in the United Kingdom [8]. However, another variety from Brazil (known in the UK as VUI202101/01) was discovered in the UK and comprises a minor recessive mutation. Eight instances of this type, which appeared to be of minimal significance, had been reported as of 14 January 2021. The “expansion and importance of this mutation continues under investigative process”, according to Public Health England (PHE). At same time as the English variant, the South African variant appeared and has since been found in at least 20 countries. According to South African genomic data, the 501Y.V2 mutation swiftly supplanted other circulating progenitors in the country because it appeared to have a greater infection rate and hence is more transmittable. The N501Y and E484K spike protein variants are present in this version, as they are in the English and Brazilian variants.

    5. Variants of Interest (VOI)

    There is significant proof that the differences in the variants have a massive effect on infectivity, disease intensity, and/or resistance, affecting the epidemiologic scenario in the EU/EEA [6]. There is at least reasonable certainty in the findings for these features, which included genetic, epidemiologic, and in vitro investigations. Additionally, all of the prerequisites for the variants of concern and under investigation listed in Table 2 apply. The indications are labeled to show whether they come from the variants themselves (v) or from mutations linked to the variants (m). Evidence with a “low confidence” rating is labeled to highlight that it is inconclusive. Blank fields or null fields indicate that there are no existing evaluations or scientific evidence for the category, whereas “no” means that there has been no change associated with the feature. B.1 is the comparable virus that is presumed to be “wild-type” (with D614G and no other spike protein modifications) [3].
    Table 2. Variants of Interest (VOI) [3].

    6. Variants under Observation

    SARS-CoV-2 variants under observation were discovered as indications through outbreak intelligence, rules-based genomic variant screening, and initial technical data [14]. There is some indication that they are similar to the VOIs in terms of quality; however, the evidence is either inadequate or is still to be examined by the ECDC [3]. One or more outbreaks in communities or proof of the communal spread of the mutation elsewhere in the world must have been established for the mutations mentioned in Table 3.
    Table 3. Variants under observation [3].

    7. Related Work

    Machine learning algorithms often employ data sequences collected over time as the input data to forecast the COVID-19 pandemic situation. The COVID-19 spread has been predicted using a variety of methodologies. The Long Short-Term Memory (LSTM) algorithm is one of the methodologies that has been used. The multi-layer perceptron (MLP), for example, is now being used to forecast the spread of COVID-19. This strategy has made it easier to anticipate the maximum number of COVID-19 victims, the highest proportion of survivors, and the highest number of fatalities per region in a specific time period [20].
    Al-Qanes et al. [21] developed a more advanced form of the adaptive neuro-fuzzy infererence system (ANFIS) to calculate the infected patients in different four countries: United States, Iran, Italy, and Korea. Their approach was founded on the marine predators algorithm, a revolutionary nature-inspired optimization. The ANFIS variables were optimized using this technique, improving prediction accuracy. The model has shown efficient prediction performance for MAE, RMSE, MAPE, and R2 [21]. Other research used an improved ANFIS model by integrating the flower pollination algorithm (FPA) and salp swarm algorithm (SSA). The proposed FPASSA-ANFIS framework was evaluated by employing verified data obtained from the WHO website. Additionally, the proposed model’s performance was evaluated using two different datasets of weekly infected patients [22].
    The Susceptible-Exposed-Infectious-Recovered (SEIR) approach was used by Alsayed et al. [23] to forecast pandemic peaks in Malaysia. Researchers have utilized the ANFIS approach to anticipate the number of infected people in the short term. Additionally, researchers have hypothesized that extending the treatment time may lessen the severity of the pandemic at its height. The MAPE, RMSE, and R2 values for this research were 2.79, 46.87, and 0.9973, respectively [23]. Behnood et al. [24] evaluated the influence of several climate-related elements and the size of the population on the spread of COVID-19 by integrating the viral optimization algorithm (VOA) and ANFIS. They showed that the density of the population had a surprising impact on how well their constructed scenarios operated, highlighting the critical role that social distance plays in reducing the rate as well as the spread of COVID-19. They reported the RMSE as 22.47, MAE as 7.33, and R2 as 0.83 [24].
    Aora et al. [25] employed RNN-related LSTM variations to predict the number of positive patients in India. The LSTM model was chosen for forecasting daily as well as weekly COVID-19 patients with approximated errors of three percent for daily cases and eight percent for weekly cases based on the lowest false alarm rate. Depending on the volume of confirmed patients and everyday progression of the designation of COVID-19 hotspots, they divided Indian states into various zones [25]. A bidirectional LSTM network was used by Fokas et al. [26] to produce a reliable generalization of RNNs. This technique was used to forecast new COVID-19 infected individuals in the United States, Spain, Italy, Germany, France, and Sweden [26].
    The regression model proposed by Yadav et al. [27] for the forecasting of COVID-19 cases was based on six regression analyses including quadratic, third-degree, fourth-degree, fifth-degree, sixth-degree, and exponential polynomials. The sixth-degree polynomial regression method was the best model for the forecasting of short-term new cases [27]. Geographical hierarchies were employed by Kim et al. [28] to develop Hi-COVIDNet in accordance with a neural network of two-level machinery based on information gathered from the continent and at the country level. This approach comprehended the complex connections between far-off nations and connected their unique risks of infection to the targeted community [28].
    Three hybrid techniques for COVID-19 time-series forecasting were developed by Abbasimehr and Paki [29] by combining the Bayesian optimization algorithm with the multi-head attention, LSTM, and CNN deep learning techniques. These findings revealed that deep neural networks outperformed the benchmark model in terms of both the short-term and long-term predictions. In addition, the best deep learning model’s average SMAPE had short-term forecasts of 0.25 and long-term forecasts of 2.59 [29]. Additionally, deep neural networks (DNNs) have been proposed as a technique for prediction. This approach is a significant substitute for estimating a partial differential equation’s solution [30]. Based on the distribution of COVID-19 over three time periods, a recent work employed the K-means approach to group countries into various clusters [30].

    References

    1. Public Health England. SARS-CoV-2 Variants of Concern and Variants under Investigation in England-Technical Briefing 11; Technical report; Public Health England: London, UK, 2021.
    2. Public Health England. SARS-CoV-2 Variants of Concern and Variants under Investigation in England-Technical Briefing 12; Technical report; Public Health England: London, UK, 2021.
    3. ECDC. SARS-CoV-2 Variants of Concern as of 5 August 2021; ECDC: Solna, Sweden, 2021.
    4. Callaway, E. Heavily mutated coronavirus variant puts scientists on alert. Nature 2021, 600, 21.
    5. National Institute for Communicable Diseases. New COVID-19 Variant Detected in South Africa-NICD; National Institute for Communicable Diseases: Johannesburg, South Africa, 2021.
    6. Focosi, D.; Tuccori, M.; Baj, A.; Maggi, F. SARS-CoV-2 Variants: A Synopsis of In Vitro Efficacy Data of Convalescent Plasma, Currently Marketed Vaccines, and Monoclonal Antibodies. Viruses 2021, 13, 1211.
    7. Aarts, M.; Dekker, M.; de Vries, S.; van der Wal, A.; te Riele, H. Generation of a mouse mutant by oligonucleotide-mediated gene modification in ES cells. Nucleic Acids Res. 2006, 34, 147.
    8. Public Health England. Confirmed Cases of COVID-19 Variants Identified in UK-GOV.UK; Public Health England: London, UK, 2021.
    9. Collier, D.A.; De Marco, A.; Ferreira, I.A.; Meng, B.; Datir, R.P.; Walls, A.C.; Kemp, S.A.; Bassi, J.; Pinto, D.; Silacci-Fregni, C.; et al. Sensitivity of SARS-CoV-2 B. 1.1. 7 to mRNA vaccine-elicited antibodies. Nature 2021, 593, 136–141.
    10. Davies, N.G.; Jarvis, C.I.; Edmunds, W.J.; Jewell, N.P.; Diaz-Ordaz, K.; Keogh, R.H. Increased mortality in community-tested cases of SARS-CoV-2 lineage B. 1.1. 7. Nature 2021, 593, 270–274.
    11. Tut, G.; Lancaster, T.; Krutikov, M.; Sylla, P.; Bone, D.; Kaur, N.; Spalkova, E.; Bentley, C.; Amin, U.; Jadir, A.; et al. Profile of Humoral and Cellular Immune Responses to Single BNT162b2 or ChAdOx1 Vaccine in Residents and Staff within Residential Care Homes (VIVALDI): An observational study. Lancet Healthy Longev. 2021, 2, E544–E553.
    12. Lopez Bernal, J.; Andrews, N.; Gower, C.; Gallagher, E.; Simmons, R.; Thelwall, S.; Stowe, J.; Tessier, E.; Groves, N.; Dabrera, G.; et al. Effectiveness of Covid-19 vaccines against the B. 1.617. 2 (delta) variant. N. Engl. J. Med. 2021, 385, 585–594.
    13. Aziz, S.; Jim, M.; Bob, T.; Chris, R. SARS-CoV-2 Delta VOC in Scotland: Demographics, risk of hospital admission, and vaccine effectiveness. Lancet 2021, 397, 2461–2462.
    14. Davies, N.G.; Abbott, S.; Barnard, R.C.; Jarvis, C.I.; Kucharski, A.J.; Munday, J.D.; Pearson, C.A.; Russell, T.W.; Tully, D.C.; Washburne, A.D.; et al. Estimated transmissibility and impact of SARS-CoV-2 lineage B. 1.1. 7 in England. Science 2021, 372, eabg3055.
    15. Tegally, H.; Wilkinson, E.; Giovanetti, M.; Iranzadeh, A.; Fonseca, V.; Giandhari, J.; Doolabh, D.; Pillay, S.; San, E.J.; Msomi, N.; et al. Detection of a SARS-CoV-2 variant of concern in South Africa. Nature 2021, 592, 438–443.
    16. Cele, S.; Gazy, I.; Jackson, L.; Hwa, S.H.; Tegally, H.; Lustig, G.; Giandhari, J.; Pillay, S.; Wilkinson, E.; Naidoo, Y.; et al. Escape of SARS-CoV-2 501Y. V2 from neutralization by convalescent plasma. Nature 2021, 593, 142–146.
    17. Madhi, S.A.; Baillie, V.; Cutland, C.L.; Voysey, M.; Koen, A.L.; Fairlie, L.; Padayachee, S.D.; Dheda, K.; Barnabas, S.L.; Bhorat, Q.E.; et al. Efficacy of the ChAdOx1 nCoV-19 Covid-19 vaccine against the B. 1.351 variant. N. Engl. J. Med. 2021, 384, 1885–1898.
    18. Carl AB Pearson; Russell, T.W.; Davies, N.G.; Kucharski, A.J.; CMMID COVID-19 working group; Edmunds, W.J.; Eggo, R.M. Estimates of Severity and Transmissibility of Novel South Africa SARS-CoV-2 Variant 501Y.V2. Working Paper. Available online: https://cmmid.github.io/topics/covid19/sa-novel-variant.html (accessed on 19 March 2021).
    19. Funk, T.; Pharris, A.; Spiteri, G.; Bundle, N.; Melidou, A.; Carr, M.; Gonzalez, G.; Garcia-Leon, A.; Crispie, F.; O’Connor, L.; et al. Characteristics of SARS-CoV-2 variants of concern B. 1.1. 7, B. 1.351 or P. 1: Data from seven EU/EEA countries, weeks 38/2020 to 10/2021. Eurosurveillance 2021, 26, 2100348.
    20. Khan, R.U.; Haq, A.U.; Hussain, S.M.; Ullah, S.; Almakdi, S.; Kumar, R.; Shah, H.H.; Li, J. Analyzing and Battling The Emerging Variants Of Covid-19 Using Artificial Neural Network And Blockchain. In Proceedings of the 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, China, 16–18 December 2022; pp. 101–105.
    21. Al-Qaness, M.A.; Ewees, A.A.; Fan, H.; Abualigah, L.; Elaziz, M.A. Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea. Int. J. Environ. Res. Public Health 2020, 17, 3520.
    22. Al-Qaness, M.A.A.; Ewees, A.A.; Fan, H.; Abd El Aziz, M. Optimization method for forecasting confirmed cases of COVID-19 in China. J. Clin. Med. 2020, 9, 674.
    23. Alsayed, A.; Sadir, H.; Kamil, R.; Sari, H. Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020. Int. J. Environ. Res. Public Health 2020, 17, 4076.
    24. Behnood, A.; Mohammadi Golafshani, E.; Hosseini, S.M. Determinants of the infection rate of the COVID-19 in the U.S. using ANFIS and virus optimization algorithm (VOA). Chaos Solitons Fractals 2020, 139, 110051.
    25. Arora, P.; Kumar, H.; Panigrahi, B.K. Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India. Chaos Solitons Fractals 2020, 139, 110017.
    26. Fokas, A.S.; Dikaios, N.; Kastis, G.A. Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2. J. R. Soc. Interface 2020, 17, 1–12.
    27. Yadav, R.S. Data analysis of COVID-2019 epidemic using machine learning methods: A case study of India. Int. J. Inf. Technol. 2020, 12, 1321–1330.
    28. Kim, M.; Kang, J.; Kim, D.; Song, H.; Min, H.; Nam, Y.; Park, D.; Lee, J.G. Hi-COVIDNet: Deep Learning Approach to Predict Inbound COVID-19 Patients and Case Study in South Korea. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, Virtual, 6–10 July 2020; pp. 3466–3473.
    29. Abbasimehr, H.; Paki, R. Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization. Chaos Solitons Fractals 2021, 142, 110511.
    30. Silva, R.F.; Xavier, F.; Saraiva, A.M.; Cugnasca, C.E. Unsupervised machine learning and pandemics spread: The case of COVID-19. In Proceedings of the 20th Brazilian Symposium on Computing Applied to Healthcare (SBCAS2020), SBC, Virtual, 18–22 October 2020; pp. 506–511.
    More
    Information
    Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : , , , , , , , , ,
    View Times: 47
    Entry Collection: COVID-19
    Revisions: 2 times (View History)
    Update Time: 09 Nov 2022
    Table of Contents
      1000/1000

      Confirm

      Are you sure to Delete?

      Video Upload Options

      Do you have a full video?
      Cite
      If you have any further questions, please contact Encyclopedia Editorial Office.
      Khan, R.U.; Almakdi, S.; Alshehri, M.; Kumar, R.; Ali, I.; Hussain, S.M.; Haq, A.U.; Khan, I.; Ullah, A.; Uddin, M.I. Coronavirus. Encyclopedia. Available online: https://encyclopedia.pub/entry/33665 (accessed on 27 November 2022).
      Khan RU, Almakdi S, Alshehri M, Kumar R, Ali I, Hussain SM, et al. Coronavirus. Encyclopedia. Available at: https://encyclopedia.pub/entry/33665. Accessed November 27, 2022.
      Khan, Riaz Ullah, Sultan Almakdi, Mohammed Alshehri, Rajesh Kumar, Ikram Ali, Sardar Muhammad Hussain, Amin Ul Haq, Inayat Khan, Aman Ullah, Muhammad Irfan Uddin. "Coronavirus," Encyclopedia, https://encyclopedia.pub/entry/33665 (accessed November 27, 2022).
      Khan, R.U., Almakdi, S., Alshehri, M., Kumar, R., Ali, I., Hussain, S.M., Haq, A.U., Khan, I., Ullah, A., & Uddin, M.I. (2022, November 09). Coronavirus. In Encyclopedia. https://encyclopedia.pub/entry/33665
      Khan, Riaz Ullah, et al. ''Coronavirus.'' Encyclopedia. Web. 09 November, 2022.
      Top
      Feedback