Deep Data-Driven Neural Network for Malaria Vaccination

Deep Data-Driven Neural Network for Malaria Vaccination | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Deep Data-Driven Neural Network for Malaria Vaccination Esi Abaka-Quansah, Stephen E. Moore This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5789145/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Malaria still remains a significant global health challenge that requires innovative strategies for its control and eventual eradication. In this article, we present a malaria vaccination model to assess and predict the effects of vaccination interventions. The model parameters are learned via feedforward Neural Network. We employed Residual Neural Network and Recurrent Neural Networks(GRU, LSTM, and BiLSTM) to predict and forecast daily and sequential malaria cases using generated data from the Ghana vaccination population. Artificial Intelligence and Machine Learning Health Economics & Outcomes Research Applied Mathematics Computational Mathematics Malaria Vaccination deep learning ResNet RNN k-fold cross validation Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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