Coronavirus: Comparison
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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

1. Coronavirus

Coronaviruses are indeed a huge family of viruses that are found both in humans and animals [25][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. WThere still have a lot to learn about the novel coronavirus (COVID-19) [26][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 [27][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
[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.
Scientific Name Name Given by the WHO Spike Protein Substitutions Attributes
  • Flu-like with temperature
][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) [27][3].
Table 2. Variants of Interest (VOI) [27].
Variants of Interest (VOI) [3].
Labeled by the WHO Additional Variations in the Lineage Country First Discovered Spike Changes of Interest Date of First Detection Influence on Transmissibility Possibility of a Negative Effect on Immunity Transmission in Europe
  • Fatigue, absence of smell, sore throat, coughing, uncontrollable shaking, a decrease in hunger, and a temperature.
    70del, A570D, 1. 50% higher spread capability
Eta E484K Nigeria Q677H December 2020 – Neutralization (m) [33
February 2021 Yes (m) [34][][9] 10] Neutralization (m) [26,33][2][9] – Not found B..1.1.7 Alpha 69del, 2. Possible enhanced severity based
E484Q     (S494P), on hospital admissions and case
L452R   Epsilon  B.1.429,(E484K), United States D614G September 2020 Ambiguous [26][2] Neutralization (v) [26][2] Inconsistent/Travels
 
P681R B.1.427 L452R
 
K417N,
was drastically lowered; however,
 
Theta P.3 Philippine D614G January 2021 Yes (m) [34][10] Neutralization (m) [33][9] Inconsistent/Travels   241del there are other EUA monoclonal
    242del antibody treatments available
    243del 3. Condensed neutralization by
      convalescent and post-vaccination sera
    D138Y, D614G, 1. Susceptibility to the combination
P.1 Gamma E484K, H655Y, of bamlanivimab and etesevimab
    K417T, L18F, monoclonal antibody treatment was
    N501Y, P26S drastically lowered; however, there
    R190S, T20N, are other EUA monoclonal antibody
Communities     T1027I treatments available
      2. Condensed neutralization by convalescent
 
 
D614G mortality rates
    P681H, 144del, 3. Treatment with EUA monoclonal
    N501Y, D614G, antibodies has no effect on
    T716I, D1118H, susceptibility
E484K    
P681HS982A
  • Breathing problems
  • Gastrointestinal
  • Fatigue, absence of smell, sore throat, a decrease in hunger, chest pain, no coughing, and diarrhea.
  • Extreme level one, severe exhaustion
  • Fatigue
  • Aches in the muscles or throughout the body
  • Fatigue, loss of smell, cough, chest pain, a temperature, and hoarseness.
  • Loss of smell or taste
  • Diarrhea
  • Sore throat
  • Nausea or vomiting
This is not an extensive list of all symptoms and manifestation. The CDC [27]
  • 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 [28][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 [29][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 [30][6].
Table 1.
Some of the recent variants categorized by WHO.
4. Minimal effect on recovery and
 
 
(K1191N)
post-vaccination serum
N501Y       neutralizing
    A701V, D215G, 1. 50% higher spread capability
B.1.351 Beta D614G, D80A, 2. Susceptibility to a combination
    E484K, of bamlanivimab and etesevimab
    N501Y, monoclonal antibody treatment
  B.1.616 France D614G February 2021 Recognition (c) [25][1] – One-Time Occurrence
G669S
H655Y
V483A
Kappa B.1.617.1
  B.1.214.2 not clear (b) D614G December 2020 – – – found (a)
ins214TDR
N450K
Q414K
  A.23.1+E484K UK E484K December 2020 – Neutralization (m) [33][9] – found (a)
Q613H
V367F
  A.27 not clear (b) A653V December 2020 Yes (m) [34][10] Neutralization (m) [26][2] – found (a) India
N501Y D614G December 2020 Yes (v) [35][11] Neutralization (v) [25,36][1][12] Multiple Occurrences    and post-vaccination sera
    T95I, G142D, 1. Higher spread capability
B.1.617.2 Delta T19R, (V70F), 2. Possible decrease in neutralization
    R158G, (A222V),  E156-, F157-,
B.1.525 by some EUA monoclonal antibody
treatments
    D614G, D950N, 3. Possible decrease in neutralization
    (W258L), (K417N) by post-vaccination sera
    P681R, L452R,  
    T478K
There are several variants of SARS-CoV-2, including a brand-new, extremely contagious variant that was detected in the United Kingdom [26][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 [31][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 [32][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 [30
L452R
E484Q
H655Y
L452R
 
A.28 not clear (b) E484K October 2020 – Neutralization (m) [33][9] – found (a)
P681R
H655Y   B.1.620 Not clear D614G February 2021   Neutralization (m)
N501T[33,37][9][13] Multiple Occurrences
E484K
  C.16 not clear (b) L452R December 2020 – Neutralization (m) [33][9] – found (a) P681H
D614G S477N
Labmda C.37 Peru D614G December 2020 – – – found (a)   B.1.621 Colombia D614G January 2021 Yes (m) [34][10] Neutralization (m) [33][9] Inconsistent/Travels
F490S E484K
L452Q P681H
N501Y
R346K

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 [38][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 [27][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 [27].
Variants under observation [3].
Labeled by the WHO Additional Variations in the Lineage Country First Discovered Spike Changes of Interests Date of First Detection Influence on Transmitability Possibility of a Negative Effect on Immunity Proof of Link to Intensity Transmission in Europe
  B.1.617.3 India D614G
 
B.1.351+P384L
South Africa
A701V
December 2020
Yes (v)
[
39
]
[
15
]
Escape (v)
[
40
,41][16][17] not clear [42][18] found (a)
D614G
E484K
K417N
N501Y
P384L
  B.1.351+E516Q not clear (b) A701V January 2021 Yes (v) [39][15] Escape (v) [40,41][16][17] not clear [42][18] found (a)
D614G
E484K
E516Q
K417N
N501Y
  B.1.1.7+L452R UK D614G January 2021 Yes (v) [34][10] Neutralization (m) [26][2] Yes (v) [43][19] found (a)
L452R
P681H
N501Y
  B.1.1.7+S494P UK D614G January 2021 Yes (v) [34][10] Neutralization (m) [36][12] Yes (v) [43][19] found (a)
N501Y
P681H
S494P
  C.36+L452R Egypt D614G December 2020 – Neutralization (m) [26][2] – found (a)
L452R
Q677H
  AT.1 Russia D614G January 2021 – Neutralization (m) [33][9] – found (a)
E484K
ins679GIAL
N679K
Iota B.1.526 US A701V December 2020 – Neutralization (m) [33][9] – found (a)
D614G
E484K
  B.1.526.1 US D614G October 2020 – Neutralization (m) [26][2] – found (a)
L452R
  B.1.526.2 US D614G December 2020 – – – found (a)
S477N
  B.1.1.318 not clear (b) D614G January 2021 – Neutralization (m) [33][9] – found (a)
E484K
P681H
Zeta P.2 Brazil D614G January 2021 – Neutralization (m) [33][9] – found (a)
E484K
  B.1.1.519 Mexico D614G November 2020 – Neutralization (m) [26][2] – found (a)
T478K
  AV.1 UK D614G March 2021 – Neutralization (m) [33][9] – found (a)
E484K
P681H
N439K
  P.1+P681H Italy D614G February 2021 – not clear – –
H655Y
E484K
N501Y
P681H
K417T

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 [44][20].
Al-Qanes et al. [45][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 [45][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 [20][22].
The Susceptible-Exposed-Infectious-Recovered (SEIR) approach was used by Alsayed et al. [46][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 restudyearch were 2.79, 46.87, and 0.9973, respectively [46][23]. Behnood et al. [47][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 [47][24].
Aora et al. [48][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 [48][25]. A bidirectional LSTM network was used by Fokas et al. [49][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 [49][26].
The regression model proposed by Yadav et al. [50][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 [50][27]. Geographical hierarchies were employed by Kim et al. [51][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 [51][28].
Three hybrid techniques for COVID-19 time-series forecasting were developed by Abbasimehr and Paki [52][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 [52][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 [11][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 [11][30].

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