Forecast the Influenza Pandemic Using Machine Learning

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%.

Innovative applications of artificial intelligence during the COVID-19 pandemic

The COVID-19 pandemic has created unprecedented challenges worldwide.Artificial intelligence(AI)technolo-gies hold tremendous potential for tackling key aspects of pandemic management and response.In the present review,we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic.First,we outline the multiple impacts of the current pandemic on public health,the econ-omy,and society.Next,we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction,detection,control,and drug discovery for treatment.Specifically,AI-based predictive analytics models can use clinical,epidemiological,and omics data to forecast disease spread and patient out-comes.Additionally,deep neural networks enable rapid diagnosis through medical imaging.Intelligent systems can support risk assessment,decision-making,and social sensing,thereby improving epidemic control and public health policies.Furthermore,high-throughput virtual screening enables AI to accelerate the identification of ther-apeutic drug candidates and opportunities for drug repurposing.Finally,we discuss future research directions for AI technology in combating COVID-19,emphasizing the importance of interdisciplinary collaboration.Though promising,barriers related to model generalization,data quality,infrastructure readiness,and ethical risks must be addressed to fully translate these innovations into real-world impacts.Multidisciplinary collaboration engag-ing diverse expertise and stakeholders is imperative for developing robust,responsible,and human-centered AI solutions against COVID-19 and future public health emergencies.