Abstract:Corona pandemic has affected the whole world, and it is a highly researched area in biological sciences. As the current pandemic has affected countries socially and economically, the purpose of this bibliometric analysis is to provide a holistic review of the corona pandemic in the field of social sciences. This study aims to highlight significant, influential aspects, research streams, and themes. We have reviewed 395 journal articles related to coronavirus in the field of social sciences from 2003 to 2020. W… Show more
“…For instance, the MAE with the exponential weight provides is around 44215 which is 51% less than the MAE obtained with the piecewise constant weight function. In terms of R 2 , that reflects the goodness of fit, we can notice that the exponential function of the weights provide the highest value 0.94, which is very close to 1, compared to the R 2 achieved by the piecewise constant weight function (i.e., R 2 = 0.86). This proves the efficiency of the fitting of the exponential weights.…”
Section: A Training Phase and Weights Impact: The Case Of Russiamentioning
confidence: 77%
“…, N . Hence and for notation purposes only, we use this discrete notation n instead of the continuous one t used in (2). Finally, we propose to employ a weighted Non-Linear Least Squares (NLLS) method where the objective function that calculates the Mean Squared Error (MSE) between the predicted and the real data values given the estimated values of the fitting variables is expressed as follows:…”
Since early 2020, the world has been afflicted with an unprecedented global pandemic. The SARS-CoV-19 (COVID-19) has levied massive economic and public health costs across many countries. Due to its virulence, the pathogen is rapidly propagating throughout the world in such a way that makes it incredibly challenging for officials to contain its spread. Therefore, there is a pressing need for national and local authorities to have tools that aid in their ability to assess and extrapolate the future trends of the spread of COVID-19, so they may make rational and informed decisions that minimize public harm. Mechanistic models are prominent mathematical tools that are used to characterize epidemics. In this paper, we propose a generalized mechanistic model with eight states characterizing the COVID-19 pandemic evolution from a susceptible state to discharged states while passing by quarantined and hospitalized states. The parameters of the model are determined by solving a fitting optimization problem with three observed inputs: the number of infected, deceased, and reported cases. The model's objective function is weighted over the training days so as to guide the fitting algorithm towards the latest pandemic period and lead to more accurate trend predictions for a stronger forecast. We solve the fitting problem with the Levenberg-Marquardt algorithm; we compare the performance of the model generated from this algorithm to the one of another state-of-the-art fitting algorithm as well as to the one of another compartmental model widely used in literature. We test the model on the COVID-19 data from four highly afflicted countries. The fitting algorithm has been validated graphically and through numerical metrics, and results show significantly accurate results for most of the countries. Once the model's parameters are estimated, forecasting results are derived and uncertainty regions of the expected scenarios are provided. INDEX TERMS COVID-19, fitting optimization, mathematical modeling, mechanistic model, nonlinear differential equations, pandemic.
“…For instance, the MAE with the exponential weight provides is around 44215 which is 51% less than the MAE obtained with the piecewise constant weight function. In terms of R 2 , that reflects the goodness of fit, we can notice that the exponential function of the weights provide the highest value 0.94, which is very close to 1, compared to the R 2 achieved by the piecewise constant weight function (i.e., R 2 = 0.86). This proves the efficiency of the fitting of the exponential weights.…”
Section: A Training Phase and Weights Impact: The Case Of Russiamentioning
confidence: 77%
“…, N . Hence and for notation purposes only, we use this discrete notation n instead of the continuous one t used in (2). Finally, we propose to employ a weighted Non-Linear Least Squares (NLLS) method where the objective function that calculates the Mean Squared Error (MSE) between the predicted and the real data values given the estimated values of the fitting variables is expressed as follows:…”
Since early 2020, the world has been afflicted with an unprecedented global pandemic. The SARS-CoV-19 (COVID-19) has levied massive economic and public health costs across many countries. Due to its virulence, the pathogen is rapidly propagating throughout the world in such a way that makes it incredibly challenging for officials to contain its spread. Therefore, there is a pressing need for national and local authorities to have tools that aid in their ability to assess and extrapolate the future trends of the spread of COVID-19, so they may make rational and informed decisions that minimize public harm. Mechanistic models are prominent mathematical tools that are used to characterize epidemics. In this paper, we propose a generalized mechanistic model with eight states characterizing the COVID-19 pandemic evolution from a susceptible state to discharged states while passing by quarantined and hospitalized states. The parameters of the model are determined by solving a fitting optimization problem with three observed inputs: the number of infected, deceased, and reported cases. The model's objective function is weighted over the training days so as to guide the fitting algorithm towards the latest pandemic period and lead to more accurate trend predictions for a stronger forecast. We solve the fitting problem with the Levenberg-Marquardt algorithm; we compare the performance of the model generated from this algorithm to the one of another state-of-the-art fitting algorithm as well as to the one of another compartmental model widely used in literature. We test the model on the COVID-19 data from four highly afflicted countries. The fitting algorithm has been validated graphically and through numerical metrics, and results show significantly accurate results for most of the countries. Once the model's parameters are estimated, forecasting results are derived and uncertainty regions of the expected scenarios are provided. INDEX TERMS COVID-19, fitting optimization, mathematical modeling, mechanistic model, nonlinear differential equations, pandemic.
“…The biblioshiny is the web interface of bibliometrix (R package). The detailed bibliometric analysis and comprehensive bibliography can be found in [1] .…”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
“… Paper Total Citations TC per Year Policies and Technical Guidelines for Urban Planning of High-Density Cities–Air Ventilation Assessment (AVA) of Hong Kong [2] 266 22.1667 Responding to Global Infectious Disease Outbreaks: Lessons from Sars on the Role of Risk Perception, Communication and Management [3] 248 16.5333 Perceived Travel Risks Regarding Terrorism and Disease: The Case of Thailand [4] 177 14.75 Distinguishing Knowledge-Sharing, Knowledge-Construction, and Knowledge-Creation Discourses [5] 149 12.4167 The Impact of Crisis Events and Macroeconomic Activity on Taiwan's International Inbound Tourism Demand [6] 120 10 Representations of SARS in the British Newspapers [7] 107 6.2941 Within the Boundaries of Politics: News Framing of Sars in China and the United States [8] 100 6.25 The Airborne Transmission of Infection between Flats in High-Rise Residential Buildings: Particle Simulation [9] 99 7.6154 Assessing Impacts of Sars and Avian Flu on International Tourism Demand to Asia [10] 93 7.1538 Pandemics, tourism and global change: A rapid assessment of COVID-19 [11] 88 88 Fig. 1 The proposed method for analysis adapted from [1] . …”
The year 2020 has changed the living style of people all around the world. Corona pandemic has affected the people in all fields of life economically, physically, and mentally. This dataset is a collection of published articles discussing the effect of COVID and SARS on the social sciences from 2003 to 2020. This dataset collection and analysis highlight the significance and influential aspects, research streams, and themes in this domain. The analysis provides top journals, highly cited articles, mostly used keywords, top affiliation institutes, leading countries based on the citation, potential research streams, a thematic map, and future directions in this area of research. In the future, this dataset will be helpful for every researcher and policymakers to proceed as a starting point to identify the relevant research based on the analysis of 18 years of research in this domain.
“…We can provide an in-depth view of the data and suggest a future research agenda after completing these analysis steps. [8] used a similar pattern to analyze the influential and conceptual aspects of the corona pandemic in social science.…”
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