Tech Enabled Global Health Security
暫譯: 科技驅動的全球健康安全

本書探討了人工智慧、機器學習和建模的創新應用，以增強公共和全球健康安全。它主張從被動應對轉向主動管理健康危機，強調基於系統的未來思維和預測情境。書中突顯了COVID-19的教訓，強調了技術驅動解決方案的重要性，例如大規模模擬和先進分析，以便及早偵測和應對生物威脅。它整合了生態學、氣候變遷和多重災害事件的見解，旨在平衡疾病控制與社會福祉。本書對公共衛生研究者、政策制定者和國家安全專家至關重要，並提供了未來健康危機管理的建議和路線圖。

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.

本傑明·雅各博士擁有空間統計學的背景，並在即時地理空間人工智慧（geo-AI）和機器學習方面具備專業訓練和專長。此外，他在應用迴歸（例如，泊松迴歸、負二項迴歸等）和非空間統計方面也有背景。他的研究包括與各種媒介節肢動物（例如，導致河盲症的黑蠅 Similium damnosum s.l.、導致瘧疾的 Anopheles gambiae s.l. 蚊子、以及導致茲卡和登革熱的 Aedes aegypti 蚊子等）相關的即時、季節性、預測性、脆弱性地圖環境景觀變化。雅各博士在杜蘭大學獲得空間數學博士學位。他曾擔任阿拉巴馬州伯明翰大學醫學院的研究助理教授。他的文獻貢獻採用了特徵空間過濾方法，專注於非參數性地去除殘餘的零自相關和其他非高斯性（即，馬可夫、半參數、特徵貝葉斯、特徵向量特徵空間等中的異方差性和/或多重共線性協變數）在公共衛生、流行病學、時間序列模型中，用於構建（a）空間滯後自回歸模型和（b）從診斷、臨床、現場和遙控/無人機（UAV）或無人機取樣的地理參考、哨兵站、時間敏感的流行病學特徵協變數構建的同時特徵自回歸模型。雅各博士的即時無人機-iOS應用程式基於嵌入在互動智能應用中的區域基卷積神經網絡（R-CNN）。他成功地將級聯區域提議網絡（RPN）和快速 R-CNN（即，機器學習分類器）合併在一個儀表板網頁配置應用中，以建立捕獲點、季節性、水生、媒介幼蟲棲息地、哨兵站、通過分類潛在地方性、景觀捕獲點（例如，河流支流的邊緣、農業牧場生態系統等）來生成的特徵。雅各博士創建了兩個即時綜合媒介管理程序，針對季節性超地方性、哨兵站、捕獲點進行地理時空定位，使用互動即時儀表板（即，針對河盲症的 Slash and Clear [S & C] 和針對瘧疾的 Seek and Destroy [S&D]）。

安東尼·J·馬西斯博士是南佛羅里達大學公共衛生學院的副教授，也是南佛羅里達大學全球與國家安全研究所（GNSI）的高級研究員。馬西斯博士曾是國防部的高級軍官和國防科學家，是一位經驗豐富的學者、創新者、促進者和思想領袖，專注於棘手問題、社會創新以及應用系統思維、未來思維和設計思維的交集。馬西斯博士在五大洲進行研究、講座和工作坊，與各種政府利益相關者合作，支持災害風險減少和安全及公共安全的韌性策略設計。馬西斯博士擁有加拿大皇家軍事學院的物理學學士學位和水下聲學及海洋學碩士學位，以及萊斯特大學的博士學位。他是施普林格書系列《安全應用的先進科學與技術》的主編。他在物理學和社會科學領域發表了大量文獻，並出版了超過23本與系統思維應用相關的書籍，主題涵蓋全球健康安全、人類安全、災害風險減少和韌性等。