Swine Flu Predication Using Machine Learning

Bhatt, Dvijesh; Vyas, Daiwat; Kumhar, Malaram; Patel, Ajay

doi:10.1007/978-981-13-1747-7_60

Cited by 4 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, when in the time of an ongoing epidemics and pandemics, researchers tried predicted the future implications of the outbreak. This includes Influenza [41][3], Ebola [29], Swine Flu [7] etc. Numerous prediction studies have also been carried out for the currently occurring COVID-19 pandemic.…”

Section: Mathematical Prediction Modelmentioning

confidence: 99%

The Past, Present, and Future of COVID-19: A Data-Driven Perspective

Akil,

Eliza,

Hisham

et al. 2020

Preprint

View full text Add to dashboard Cite

Epidemics and pandemics have ravaged human life since time. To combat these, novel ideas have always been created and deployed by humanity, with varying degrees of success. At this very moment, the COVID-19 pandemic is the singular global health crisis. Now, perhaps for the first time in human history, almost the whole of humanity is experiencing some form of hardship as a result of one invisible pathogen. This once again entails novel ideas for quick eradication, healing and recovery, whether it is healthcare, banking, travel, education or any other. For efficient policy-making, clear trends of past, present and future are vital for policy-makers. With the global impacts of COVID-19 so severe, equally important is the analysis of correlations between disease spread and various socio-economic and environmental factors. Furthermore, all of these need to be presented in an integrated manner in real-time to facilitate efficient policy making. To address these issues, in this study, we report results on our development and deployment of a web-based integrated real-time operational dashboard as an important decision support system for COVID-19. In our study, we conducted data-driven analysis based on available data from diverse authenticated sources to predict upcoming consequences of the pandemic through rigorous modeling and statistical analyses. We also explored correlations between pandemic spread and important socio-economic and environmental factors. Furthermore, we also present how outcomes of our work can facilitate efficient policy making in this critical hour.CCS Concepts: • Applied computing → Operations research.

show abstract

Section: Mathematical Prediction Modelmentioning

confidence: 99%

The Past, Present, and Future of COVID-19: A Data-Driven Perspective

Akil,

Eliza,

Hisham

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…This type of regression method enables the detection of complex relations between input variables, as well as the automatic detection of interactions between input variables and the output [ 18 ]. Such an approach has shown to be successful in the modeling of previous epidemics, including but not limited to SARS [ 19 ], H1N1 [ 20 , 21 ], avian flu [ 22 , 23 ], AIDS [ 24 , 25 ], and Ebola [ 26 , 27 ]. Thus, it is evident that the AI-based approach may greatly help with the modeling of COVID-19 epidemiological curves.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review

Musulin

Šegota

Štifanić

et al. 2021

IJERPH

View full text Add to dashboard Cite

COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in the daily lives of billions of people worldwide. Therefore, many efforts have been made by researchers across the globe in the attempt of determining the models of COVID-19 spread. The objectives of this review are to analyze some of the open-access datasets mostly used in research in the field of COVID-19 regression modeling as well as present current literature based on Artificial Intelligence (AI) methods for regression tasks, like disease spread. Moreover, we discuss the applicability of Machine Learning (ML) and Evolutionary Computing (EC) methods that have focused on regressing epidemiology curves of COVID-19, and provide an overview of the usefulness of existing models in specific areas. An electronic literature search of the various databases was conducted to develop a comprehensive review of the latest AI-based approaches for modeling the spread of COVID-19. Finally, a conclusion is drawn from the observation of reviewed papers that AI-based algorithms have a clear application in COVID-19 epidemiological spread modeling and may be a crucial tool in the combat against coming pandemics.

show abstract