Tech Enabled Global Health Security

Erscheinungsjahr 2025
ISBN: 978-3-031-86996-9
Verlag: Springer

Buch, Englisch, 368 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 795 g

Reihe: Advanced Sciences and Technologies for Security Applications

This book explores innovative applications of artificial intelligence, machine learning, and modeling to enhance public and global health security. It advocates for a shift from reactive to proactive management of health crises, emphasizing systems-based futures thinking and anticipatory scenarios. Highlighting the lessons from COVID-19, the book underscores the importance of tech-enabled solutions like large-scale simulations and advanced analytics for early detection and response to biological threats. It integrates insights from ecology, climate change, and multi-hazard events, aiming to balance disease control with societal well-being. Essential for public health researchers, policymakers, and national security experts, the book offers recommendations and roadmaps for future health crisis management.

Zielgruppe

Graduate

Autoren/Hrsg.

Jacob, Benjamin

Michael, Edwin

Masys, Anthony J.

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Public Health, Gesundheitsmanagement, Gesundheitsökonomie, Gesundheitspolitik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Technische Wissenschaften Bauingenieurwesen Gebäudesicherheit

Inhaltsverzeichnis

Overview of Artificial Intelligence and Machine Learning for Public Health applications.- Vector borne disease: Seek and Destroy: Malaria eradication in East Africa using AI/ML/GIS.- Building a Public Health/ Global Health Intelligence capability.- Pandemic Planning: Digital Twins to support COVID-19 management.- Decision Support for Public/Global Health.- Mapping community vulnerability to disease outbreaks: hot spot and cold spot mapping.- Tech literacy for Public Health Professionals: insights into tech enabling the public health workforce.- Applying artificial intelligence to solve humanitarian crisis dilemmas.- Implementation Science: from design to impact- bringing AI/ML solutions.- Tech informed Global Health: considering and including the cultural and social implications.- FemTech: the intersection of technology and womans health.

Über Autor(innen)

Dr Benjamin Jacob has a background in spatial statistics with specific training and expertise in real-time geo-spatial artificial intelligence (geo-AI)] and machine learning. Also, he has a background in applied regression (e.g., Poisson, negative binomial etc.) and non-spatial statistics. His research includes real-time, seasonal, predictive, vulnerability mapping environmental landscape changes associated with various vector arthropods (e.g.,  Similium damnosum s.l. black flies for onchocerciasis, Anopheles gambiae s.l. malaria mosquitoes Aedes aegypti mosquitoes for Zika and dengue etc). Dr. Jacob recieved his PHD in Spatial Mathematics at Tulane. He served as a research assistant professor at the School of Medicine at the University of Birmingham at Alabama.  His literature contributions employ eigen-spatial filtering approaches which focus on non-parametrically removing residual zero autocorrelation and other non-Gaussiansim (i.e., heteroscedascitic and or multicollinear covariates in Markovian, semi-parametric, eigen-Bayesian, eigenvector eigen-space, etc.) in public health, epidemiological, time series models  for constructing (a) spatially lagged autoregressive models and (b) simultaneous signature autoregressive models constructed from diagnostic, clinical, field and remote/unmanned aerial vehicle(UAV) or drone sampled, georeferenced,  sentinel site, time sensitive, epidemiological signature covariates. Dr. Jacob’s real time UAV-iOS applications[apps]are based on a infused region-based convolutional neural network (R-CNN) embedded in an interactive intelligent app. He has successfully merged a Cascade region proposal network (RPN) and Fast R-CNN [i.e., a machine learning classifier] within a dashboard web-configurable app  to build capture point, seasonal, aquatic, vector larval habitat, sentinel site, signatures by classifying potential endemic,  landscape capture points (e.g., edges of riverine tributary, agro-pastureland ecosystems,) fDr. Jacob has created two real time integrated vector management programs for geo-spatiotemporally targeting seasonal hyperendemic, sentinel site, capture points using interactive real time dashboards (i.e., Slash and Clear [S & C] for onchocerciasis and Seek and Destroy [S&D for malaria].

Dr Anthony J Masys is an Associate Professor in the College of Public Health, University of South Florida and a senior fellow at the USF Global and National Security Institute (GNSI). A former senior military officer and defence scientist with the Department of National Defence, Dr. Masys is an experienced academic, innovator, facilitator and thought leader working at the intersection of wicked problems, social innovation and applied systems thinking, futures thinking and design thinking. Dr. Masys has conducted research, lectures and workshops across 5 continents working with various government stakeholders in support of disaster risk reduction and design of resilience strategies for security and public safety. Dr. Masys has a BSc in Physics and MSc in Underwater Acoustics and Oceanography from the Royal Military College of Canada and a PhD from the University of Leicester. He is Editor-in-Chief of Springer Book Series: 'Advanced Sciences and Technologies for Security Applications'. He has published extensively in the domains of physics and the social sciences and has published over 23 books pertaining to the application of systems thinking on topics ranging from Global Health Security, human security, disaster risk reduction and resilience.