No gene communication of HA gene between the human H3N2 and H1N1 pandemic 2009 influenza A viruses

Shen, Xuejuan; Pu, Zhiqing; Irwin, David M.; Shen, Yongyi

doi:10.1016/j.jinf.2019.04.014

Cited by 1 publication

(1 citation statement)

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“…The influenza virus A has 8 genetic fragments that can be used for protein translation and the protein surface contain 18 HA and 11 neuraminidase (NA) subtypes [7]. Different combinations of these HA and NA translation classify different subtypes that affect different hosts such as H1N1, H1N2, and H3N2 of the influenza virus [9] that infect human. The combinations of H5N1, H7N9, and H9N2 infect both animals and birds.…”

Section: Introductionmentioning

confidence: 99%

Forecast the Influenza Pandemic Using Machine Learning

Khan¹,

Abidi²,

Ghamdi³

et al. 2020

Computers, Materials &Amp; Continua

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Forecasting future outbreaks can help in minimizing their spread. Influenza is a disease primarily found in animals but transferred to humans through pigs. In 1918, influenza became a pandemic and spread rapidly all over the world becoming the cause behind killing one-third of the human population and killing one-fourth of the pig population. Afterwards, that influenza became a pandemic several times on a local and global levels. In 2009, influenza 'A' subtype H1N1 again took many human lives. The disease spread like in a pandemic quickly. This paper proposes a forecasting modeling system for the influenza pandemic using a feed-forward propagation neural network (MSDII-FFNN). This model helps us predict the outbreak, and determines which type of influenza becomes a pandemic, as well as which geographical area is infected. Data collection for the model is done by using IoT devices. This model is divided into 2 phases: The training phase and the validation phase, both being connected through the cloud. In the training phase, the model is trained using FFNN and is updated on the cloud. In the validation phase, whenever the input is submitted through the IoT devices, the system model is updated through the cloud and predicts the pandemic alert. In our dataset, the data is divided into an 85% training ratio and a 15% validation ratio. By applying the proposed model to our dataset, the predicted output precision is 90%.

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Section: Introductionmentioning

confidence: 99%