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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Modelling spatial variations of novel coronavirus disease (COVID-19): evidence from a global perspective.

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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Use of Artificial Intelligence on spatio-temporal data to generate insights during COVID-19 pandemic: A Review

Artificial Intelligence: Potential Tool to Subside SARS-CoV-2 Pandemic

Remote sensing is the technique of acquiring data from the earth’s surface from sensors installed on satellites or on manned or unmanned aircrafts. Its use is common in dozens of sectors of science and technology, agriculture, atmosphere, soil, water, land surface, oceans and coasts, snow and ice, and natural disasters, among others. This article focuses on an in-depth literature review of some of the most common and promising disciplines, which are asbestos–cement roof identification, vegetation identification, the oil and gas industry, and geology, with the aim of having clarity on the trends in research on these issues at the international level. The most relevant problems in each sector have been highlighted, evidencing the need for future research in the area in light of technological advances in multi- and hyperspectral sensors and the availability of satellite images with more precise spatial resolution. A bibliometric analysis is proposed for each discipline and the network of related keywords is discussed. Finally, the results suggest that policymakers, urban planners, mine, and oil and gas companies should consider remote sensing as primary tool when planning comprehensive development strategies and in field parameter multitemporal analysis.

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The Widespread Use of Remote Sensing in Asbestos, Vegetation, Oil and Gas, and Geology Applications

This paper explores recent roles of artificial intelligence and extended reality development during the coronavirus pandemic and then predicts their significant roles in the post-COVID-19 era in an interdisciplinary manner. To begin with, we investigate roles of artificial intelligence in tackling coronavirus during the outbreak since 2020 until today. It has been effectively used for many ways, such as forecasting the spread of COVID-19 on multimodal data using data analytics, preliminary diagnosis the virus disease from specific symptoms using machine learning, and analyzing big data from social media platforms to accurately prevent the spread of virus. At the same time, due to rapid advancement in recent immersive technology and extended reality is a very popular research topic in computer science, we discuss roles of extended reality which has been extensively used during the virus outbreak for various purposes, such as supporting for businesses and education and helping the medical and health care workers. For instance, it can used for supporting psychological recovery from medical treatment for virus patients, reducing the face-to-face interactivity of physicians with the symptomatic patients, and helping people with the use of telemedicine. Next, we present a new summary of integrated roles of artificial intelligence and extended reality development in the post-COVID-19 era in an interdisciplinary perspective. Moreover, we suggest possible directions of artificial intelligence in extended reality which can be used to guide the design of the next-generation human-computer interaction applications in the future.

Roles of Artificial Intelligence and Extended Reality Development in the Post-COVID-19 Era

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

Hyperspectral images such as the Earth Observer-1 (EO-1) provides an efficient method of mapping surface mineralogy because it can measures the energy in narrower bands compared with multispectral sensors. The Kirkuk anticline northern Iraq is one of the most petroleum-rich provinces. It is characterized, that is an asymmetrical, cylindrical anticline, with a fold axis trend towards North West- East, South East. The study’s primary goal is to apply satellite processing and techniques on the Eo-1 imagery for identifying lithological and mineral units at a part of Kirkuk anticline northern Iraq. The EO-1 image was corrected at the beginning of atmospheric impacts using the FLAASH module in ENVI software. Processing of (Minimum Noise Fraction- MNF) processing was applied and then it reduced the dimensionality of data, as well as, the processing of (PPI) pixel purity index was applied to spatial reduction. This study tested the potential of (spectral angle mapper supervised classification-SAM) classification for mapping the lithological and mineral units using the Hyperion imagery. three different sources of endmembers or spectra are used for SAM classifications. The one:  is done by Analytical Spectral Devices (ASD) Spectrometer. The second: reference spectra have been taken from the spectral library of USGS. Third: extracting endmembers from the purest pixels of the hyperion image, which was done by applying (MNF and PPI). The endmembers were provided, generated as the training area for SAM classification.  The present results demonstrated the great potential of data used to map the distribution of alteration of minerals and lithological units in a part of Kirkuk anticline. The classified Hyperion image shows that Jarosite and illite are the most dominant altered minerals, as well as, the main lithological units of the upper member of Fatha formation are revealed in the core of the Kirkuk anticline with scattered and small outcrops towards the flanks.

https://mjs.um.edu.my/index.php/MJS/article/download/23304/12910

Mapping lithological and mineralogical units using hyperspectral imagery

Unmanned Aerial Vehicle (UAV) is nowadays a valuable source of data for inspection, surveillance, mapping and 3D modeling issues. UAVs can be considered as a low cost alternative to the classical manned aerial photogrammetry, because they provide users with high spatial resolution images up to a centimeter limit. Following a typical photogrammetric workflow, 3D results like Digital Surface or Terrain Models (DTM/DSM), contours, textured 3D models, vector information, etc. can be produced, even on large areas. The paper reports the state of the art of UAV for Geomatics applications, giving an overview of different UAV platforms, applications and showing also the latest developments of UAV programs. The research presents the sequence of operations should be follow on the images taken with these aircraft for maximum use in remote sensing and air survey. Recent photographs were taken using the DJI Phantom 4 UAV (which was awarded to the Nineveh Department of Antiquities and Heritage by the Italian Mission) for the archaeological Nineveh region in Mosul city and use as a case study.

UAVs/drones for photogrammetry and remote sensing: Nineveh archaeological region as a case study

Analysis of the terrain using three-dimensional models offers a deep insight view of ground surface topography and terrain representation The Chinara anticline is one of the main structures of NW-SE trends for the highly folded zone in northeastern Iraq The objective of this study is to understand the interrelationship between topography and morphotectonic features using three-dimensional models This research employed fourth generates principal raster derivative products from the DEM using ArcGIS To understand the undulating of this anticline with the morphotectonic style, the adaptive equation has been suggested to determine the direction and amount of the main tectonic forces, which can be applied to other undulated anticlines The values of northeastern and southwestern limbs undulating index UI are 117 and 78 respectively that indicates the strong tectonic force towards the northeast Two listric faults have been conducted via the field survey that confirmed by remotely sensed interpretation and DEM products These listric faults had an intensive impact in comparison with concluded strike-slip faults, and then the Chinara anticline would be less structural undulating in a region of vicinity syncline to Perat undulation The morphotectonic landscapes reveal that the listric fault has branched into two parts, the first one extending to form the anticline and the other comprises the structural dilemma

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Rayan Ghazi Thannoun

Papers

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

Mapping lithological and mineralogical units using hyperspectral imagery

UAVs/drones for photogrammetry and remote sensing: Nineveh archaeological region as a case study

Three Dimensional Terrain Modeling for Tectonic Geomorphology of Chinara Anticline, Northern Iraq