Tech Enabled Global Health Security
Springer International Publishing (Verlag)
978-3-031-86996-9 (ISBN)
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.
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 Edwin Michael is an epidemiologist who studies the spread and control of global infectious diseases. The overriding objective of Dr. Michael's research is to address questions regarding the population ecology, epidemiology, dynamics, and control of tropical vector-borne and zoonotic diseases, including lymphatic filariasis, onchocerciasis, schistosomiasis, leishmaniasis, Chagas disease, dengue and malaria, enteric diseases, and more recently epidemic diseases, such as SARS-Cov-2/Covid-19. A common theme running across these research programs is a primary focus on the development and implementation of novel analytical and computational approaches for providing a deeper understanding of the determinants, pathways and dynamics of disease transmission in endemic communities and using the insights gained for identifying and analyzing sustainable approaches to disease control. Insights are based on the integrated application of emerging tools and approaches particularly in the fields of system dynamics modeling, computational science, community ecology, socio-ecology, spatio-temporal analysis, molecular biology, parasite population genetics, information technology, biostatistics, and health policy. In his laboratory, Dr. Michael also studies the influence of global climate change on vector- and environmentally-mediated infectious disease transmission, as well as the increasingly important public health topic of the epidemiology of chronic and infectious disease co-occurrence and morbidity in developing populations, focusing in particular on the implications of the growing incidence of diabetes mellitus in thes
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.
| Erscheinungsdatum | 09.08.2025 |
|---|---|
| Reihe/Serie | Advanced Sciences and Technologies for Security Applications |
| Zusatzinfo | VIII, 368 p. 92 illus., 80 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Studium ► Querschnittsbereiche ► Prävention / Gesundheitsförderung | |
| Technik ► Bauwesen | |
| Schlagworte | Anticipatory Innovation • Artificial Intelligence • Global Health Security • machine learning • Public Health Innovation |
| ISBN-10 | 3-031-86996-6 / 3031869966 |
| ISBN-13 | 978-3-031-86996-9 / 9783031869969 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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