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Open accessJournal ArticleDOI: 10.1007/S12518-021-00365-4

COVID-19 prediction analysis using artificial intelligence procedures and GIS spatial analyst: a case study for Iraq

04 Mar 2021-Applied Geomatics (Springer Berlin Heidelberg)-Vol. 13, Iss: 3, pp 1-11
Abstract: The prediction of diseases caused by viral infections is a complex medical task where many real data that consists of different variables must be employed. As known, COVID-19 is the most dangerous disease worldwide; nowhere, an effective drug has been found yet. To limit its spread, it is essential to find a rational method that shows the spread of this virus by relying on many infected people’s data. A model consisting of three artificial neural networks’ (ANN) functions was developed to predict COVID-19 separation in Iraq based on real infection data supplied by the public health department at the Iraqi Ministry of Health. The performance efficiency of this model was evaluated, where its performance efficiency reached 81.6% when employed four statistical error criteria as mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination (R2), and Nash-Sutcliffe coefficient (NC). The severity of the virus’s spread across Iraq was assessed in a short term (in the next 6 months), where the results show that the spread severity will intensify in this short term by 17.1%, and the average death cases will increase by 8.3%. These results clarified by creating spatial distribution maps for virus spread are simulated by employing a Geographic Information System (GIS) environment to be used as a useful database for developing plans for combating viruses in Iraq.

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8 results found


Open accessPosted ContentDOI: 10.1101/2020.11.22.20232959
Gihan Jayatilaka1, Hassan J1, Umar Marikkar1, Rumali Perera1  +12 moreInstitutions (4)
24 Nov 2020-medRxiv
Abstract: The COVID-19 pandemic, within a short time span, has had a significant impact on every aspect of life in almost every country on the planet. As it evolved from a local epidemic isolated to certain regions of China, to the deadliest pandemic since the influenza outbreak of 1918, scientists all over the world have only amplified their efforts to combat it. In that battle, Artificial Intelligence, or AI, with its wide ranging capabilities and versatility, has played a vital role and thus has had a sizable impact. In this review, we present a comprehensive analysis of the use of AI techniques for spatio-temporal modeling and forecasting and impact modeling on diverse populations as it relates to COVID-19. Furthermore, we catalogue the articles in these areas based on spatio-temporal modeling, intrinsic parameters, extrinsic parameters, dynamic parameters and multivariate inputs (to ascertain the penetration of AI usage in each sub area). The manner in which AI is used and the associated techniques utilized vary for each body of work. Majority of articles use deep learning models, compartment models, stochastic methods and numerous statistical methods. We conclude by listing potential paths of research for which AI based techniques can be used for greater impact in tackling the pandemic.

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6 Citations


Open accessJournal ArticleDOI: 10.1007/S10708-021-10427-0
24 Apr 2021-GeoJournal
Abstract: In late December 2019, strange pneumonia was detected in a seafood market in Wuhan, China which was later termed COVID-19 by the World Health Organization. At present, the virus has spread across 232 countries worldwide killing 2,409,011 as of 17 February 2021 (9:37 CET). Motivated by a recent dataset, knowledge gaps, surge in global cases, and the need to combat the virus spread, this study examined the relationship between COVID-19 confirmed cases and attributable deaths at the global and regional levels. We used a panel of 232 countries (further disaggregated into Africa-49, Americas-54, Eastern Mediterranean-23, Europe-61, Southeast Asia-10, and Western Pacific-35) from 03 January 2020 to 28 November 2020, and the instrumental variable generalized method of moment's model (IV-GMM) for analysing the datasets. The results showed that COVID-19 confirmed cases at both the global and regional levels have a strong positive effect on deaths. Thus, the confirmed cases significantly increase attributable deaths at the global and regional levels. At the global level, a 1% increase in confirmed cases increases attributable deaths by 0.78%. Regionally, a 1% increase in confirmed cases increases attributable deaths by 0.65% in Africa, 0.90% in the Americas, 0.67% in the Eastern Mediterranean, 0.72% in Europe, 0.88% in Southeast Asia, and 0.52% in the Western Pacific. This study expands the understanding of the relationship between COVID-19 cases and deaths by using a global dataset and the instrumental variable generalized method of moment's model (IV-GMM) for the analysis that addresses endogeneity and omitted variable issues.

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2 Citations


Open accessJournal ArticleDOI: 10.1016/J.PROCBIO.2021.08.001
Nishanth Gopinath1Institutions (1)
Abstract: Artificial intelligence (AI), a method of simulating the human brain in order to complete tasks in a more effective manner, has had numerous implementations in fields from manufacturing sectors to digital electronics. Despite the potential of AI, it may be obstinate to assume that the person administered society would rely solely on AI; with an example being the healthcare field. With the ever-expanding discoveries made on a regular basis regarding the growth of various diseases and its preservations, utilizing brain power may be deemed essential, but that doesn't leave AI as a redundant asset. With the years of accumulated data regarding patterns and the analysis of various medical circumstances, algorithms can be formed, which could further assist in situations such as diagnosis support and population health management. This matter becomes even more relevant in today's society with the currently ongoing COVID-19 pandemic by SARS-CoV-2. With the uncertainty of this pandemic from strain variants to the rolling speeds of vaccines, AI could be utilized to our advantage in order to assist us with the fight against COVID-19. This review briefly discusses the application of AI in the COVID-19 situation for various health benefits.

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1 Citations


Journal ArticleDOI: 10.1016/J.MATPR.2021.05.349
Abstract: In this theoretical analysis, the B3LYP-DFT approach was combined with the Stuttgart Dresden Triple Zeta ECPs (SDD) basis sets to looked at the electronic structures of oxaliplatinum and some suggested oxaliplatinum metal complexes are Bipyridine-oxalate-platinum O1, Bipyridine-diol-oxalate-platinum O2, and anthracene-diamine-oxalate-platinum O3. We demonstrated that all of the new suggested complexes have a lower energy gap than oxaliplatinum, and that they are also more energetic than oxaliplatin because of their high molecular polarizability. The indicated oxaliplatinum complexes (O1, O2, and O3) have a lower global hardness than oxaliplatinum, meaning that they are more soft complexes in this paper and therefore active complexes to interact with other molecules or species, according to the quantum chemical parameters calculations. The potential of a complex to interact with an enzyme increases as the EHOMO of the complex increases and the ELUMO of the complex decreases.

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1 Citations


Journal ArticleDOI: 10.1016/J.MATPR.2021.05.348
Abstract: This paper introduces the design as well as modelling of a multiband planar antenna based on fractal shapes. Sierpinski carpet as well as Minkowski fractal geometries are applied to a square shaped patch antenna. The result from this combination is multiband, small size, high gain as well as low return loss antenna. The proposed antenna cover multi-frequency bands such as, 3.4, 4.03, 5.8, 6.48, 6.78, 7.32, 8.26, 9.21, 10.09 GHz. Antenna parameters as well as performance have been calculated as well as tested by using FEKO software, where it is full wave electromagnetic software based on advanced numerical methods.

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Topics: Fractal antenna (73%), Patch antenna (64%), Antenna (radio) (63%) ... read more

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38 results found


Open accessJournal ArticleDOI: 10.7326/M20-0504
Stephen A. Lauer1, Kyra H. Grantz1, Qifang Bi1, Forrest K. Jones1  +5 moreInstitutions (2)
Abstract: Using news reports and press releases from provinces, regions, and countries outside Wuhan, Hubei province, China, this analysis estimates the length of the incubation period of COVID-19 and its pu...

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3,940 Citations


Open accessJournal ArticleDOI: 10.1016/J.IJANTIMICAG.2020.105924
Chih-Cheng Lai, Tzu Ping Shih, Wen Chien Ko1, Hung-Jen Tang  +1 moreInstitutions (2)
Abstract: The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; previously provisionally named 2019 novel coronavirus or 2019-nCoV) disease (COVID-19) in China at the end of 2019 has caused a large global outbreak and is a major public health issue. As of 11 February 2020, data from the World Health Organization (WHO) have shown that more than 43 000 confirmed cases have been identified in 28 countries/regions, with >99% of cases being detected in China. On 30 January 2020, the WHO declared COVID-19 as the sixth public health emergency of international concern. SARS-CoV-2 is closely related to two bat-derived severe acute respiratory syndrome-like coronaviruses, bat-SL-CoVZC45 and bat-SL-CoVZXC21. It is spread by human-to-human transmission via droplets or direct contact, and infection has been estimated to have mean incubation period of 6.4 days and a basic reproduction number of 2.24-3.58. Among patients with pneumonia caused by SARS-CoV-2 (novel coronavirus pneumonia or Wuhan pneumonia), fever was the most common symptom, followed by cough. Bilateral lung involvement with ground-glass opacity was the most common finding from computed tomography images of the chest. The one case of SARS-CoV-2 pneumonia in the USA is responding well to remdesivir, which is now undergoing a clinical trial in China. Currently, controlling infection to prevent the spread of SARS-CoV-2 is the primary intervention being used. However, public health authorities should keep monitoring the situation closely, as the more we can learn about this novel virus and its associated outbreak, the better we can respond.

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Topics: Pneumonia (59%), Outbreak (50%)

3,083 Citations


Open accessJournal ArticleDOI: 10.1016/J.IJSU.2020.02.034
Catrin Sohrabi1, Zaid Alsafi2, Niamh O'Neill1, M.N.I. Khan2  +4 moreInstitutions (4)
Abstract: An unprecedented outbreak of pneumonia of unknown aetiology in Wuhan City, Hubei province in China emerged in December 2019. A novel coronavirus was identified as the causative agent and was subsequently termed COVID-19 by the World Health Organization (WHO). Considered a relative of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), COVID-19 is caused by a betacoronavirus named SARS-CoV-2 that affects the lower respiratory tract and manifests as pneumonia in humans. Despite rigorous global containment and quarantine efforts, the incidence of COVID-19 continues to rise, with 90,870 laboratory-confirmed cases and over 3,000 deaths worldwide. In response to this global outbreak, we summarise the current state of knowledge surrounding COVID-19.

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2,691 Citations


Open accessJournal ArticleDOI: 10.1148/RADIOL.2020200490
Zi Yue Zu1, Meng Di Jiang, Peng Peng Xu, Wen Chen  +3 moreInstitutions (1)
21 Feb 2020-Radiology
Abstract: In December 2019, an outbreak of severe acute respiratory syndrome coronavirus 2 infection occurred in Wuhan, Hubei Province, China, and spread across China and beyond. On February 12, 2020, the World Health Organization officially named the disease caused by the novel coronavirus as coronavirus disease 2019 (COVID-19). Because most patients infected with COVID-19 had pneumonia and characteristic CT imaging patterns, radiologic examinations have become vital in early diagnosis and the assessment of disease course. To date, CT findings have been recommended as major evidence for clinical diagnosis of COVID-19 in Hubei, China. This review focuses on the etiology, epidemiology, and clinical symptoms of COVID-19 while highlighting the role of chest CT in prevention and disease control.

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Topics: Coronavirus (56%), Outbreak (51%), Pneumonia (51%)

1,062 Citations


Open accessJournal ArticleDOI: 10.21037/JTD.2020.02.64
Zifeng Yang1, Zifeng Yang2, Zhiqi Zeng2, Ke Wang  +25 moreInstitutions (4)
Abstract: Background: The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policies included large-scale quarantine, strict controls on travel and extensive monitoring of suspected cases. However, it is unknown whether these policies have had an impact on the epidemic. We sought to show how these control measures impacted the containment of the epidemic. Methods: We integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic. Results: We found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction. Conclusions: Our dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size.

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771 Citations


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20217
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