| Outbreak prediction |
[97] |
A big data platform is proposed to estimate the outbreak possibility using the huge data sets from Italian Civil Protection sources. |
| The first trial is implemented in Wuhan to predict the population infected with COVID-19 for quarantine. |
| [100] |
A big data-based solution is proposed to implement pandemic modeling to interpret the cumulative numbers of infected people, recovered cases in different regions, i.e., Wuhan, Beijing, and Shanghai. |
| This scheme is able to predict the tendency of the COVID-19 outbreak in the areas at high risks of pandemic. |
| [101] |
A framework is introduced using a large dataset from various regions and countries such as Korea, China, to estimate the pandemic based on a logistic model that can adjudge the reliability of the predictions. |
| [105] |
A big data analytic method is investigated in the US with the large-scale datasets collected from American cities. |
| The approach enables to calculate prediction errors to optimize the data modeling model for improving estimation accuracy. |
| Virus spread tracking |
[107] |
A big data-based analytic methodology for tracking the COVID-19 spread is considered using a large dataset collected from China National Health Commission with 854,424 people. |
| The analytic results show a high correlation between the positive infection cases and the population size. |
| [109] |
A big data-based analytic model is built using datasets collected from China, Singapore, South Korea, and Italy for virus spread tracking. |
| This model can estimate the maximum number of infected patients in a certain area. |
| [110] |
A temperature-based model is proposed to evaluate the relationship between the number of infected cases and the average temperature in different countries necessary for coronavirus tracking. |
| [111] |
A big data-based unsupervised model is designed for COVID-19 spread tracking from online data by incorporating a basic news media coverage metric associated with confirmed COVID-19 cases. |
| The work is in progress for coronavirus tracking tasks. |
| Coronavirus diagnosis/treatment |
[113] |
A robust, sensitive, specific and highly quantitative solution based on multiplex polymerase chain reactions is proposed to diagnose the SARS-CoV-2. |
| The proposed scheme has been shown to be an efficient and low-cost method to diagnose Plasmodium falciparum infections. |
| [115] |
A method is proposed using 6381 proteins in human cells that get infected with COVID-19 virus. |
| This aims to analyze the data gathered from the Kyoto Genes storage to serve COVID-19 diagnosis. |
| [118] |
An array of clinical tests have been implemented from the big dataset, from Typical and Atypical CT/X-ray imaging manifestation to hematology examination and detection of pathogens in the respiratory tract. |
| These tests provide a comprehensive guideline with useful tools to serve the diagnosis and treatment of COVID-19. |
| Vaccine/drug discovery |
[120] |
A method is proposed to investigate the spike proteins of SARS CoV, MERS CoV and SARS-CoV- 2 and four other earlier out-breaking human coronavirus strains. |
| It enables critical screening of the spike sequence and structure from SARS CoV-2 for vaccine development. |
| [122] |
A project is built using a huge dataset collected from the National Center of Biotechnology Information for facilitating vaccine production. Different peptides were proposed for developing a new vaccine against COVID-19. |
| [123] |
A solution is proposed based on molecular docking for drug investigations with over 2500 small molecules, which aims prompting drug repositioning against COVID-19. |
