Where there is no death certificate: Using artificial intelligence to detect high-casualty epidemics from satellite imagery of burial sites - Resubmission - 1

在没有死亡证明的情况下：使用人工智能从埋葬地点的卫星图像中检测高伤亡流行病 - 重新提交 - 1

• 批准号: 10576534
• 负责人: Anna Bershteyn
• 金额: $ 27.68万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-12 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10576534
• 关键词: AIDS/HIV problem; Address; Africa South of the Sahara; Algorithmic Analysis; Algorithms; Architecture; Area; Artificial Intelligence; Back; Bereavement; Burial; COVID-19; COVID-19 pandemic; Cessation of life; Communicable Diseases; Containment; Country; Data; Data Collection; Data Reporting; Data Sources; Death Certificates; Detection; Diabetes Mellitus; Disasters; Disease; Epidemic; Event; Foundations; Funding; Future; Gold; Growth; HIV; Health; Image; Imagery; Information Systems; Infrastructure; Label; Lighting; Location; Long COVID; Malignant Neoplasms; Manuals; Marshal; Measurement; Methods; Nature; Neurodevelopmental Disorder; Patients; Performance; Population; Poverty; Process; Reporting; Research; Resolution; Resources; Savings; Site; Spottings; Tanzania; Testing; Time; Training; Trauma; Validation; Visit; Work; Yemen; algorithm training; artificial intelligence algorithm; authority; base; convolutional neural network; cost; design; detection platform; falls; food insecurity; health care service utilization; health care settings; health disparity; image processing; improved; innovation; long-term sequelae; low and middle-income countries; machine vision; mortality; neural network algorithm; novel; pandemic disease; performance tests; prevent; prospective; scale up; statistics; success; tool; transfer learning

Detecting high-casualty epidemics is essential for health authorities to prospectively reduce disease spread and retrospectively address health conditions in an epidemic’s aftermath – which for infectious diseases can include cancer, diabetes, neurodevelopmental disorders, “long COVID,” and other sequelae. Unfortunately, many low- and middle-income countries (LMICs) lack data systems for epidemic detection, and thus are ill- equipped to mobilize resources to reduce the spread of epidemics and address their health effects. The COVID-19 pandemic saw some of the first efforts to detect mortality during an epidemic using satellite imagery of burial sites. Though successful, these were small “one-off” efforts because manual analysis of satellite imagery is extremely labor-intensive. We propose to develop an algorithm for fully-automated measurement of burial site occupancy using satellite imagery. Our exploratory research will focus on Tanzania, which typifies a high-priority use case for such an algorithm because it was hard-hit by the two deadliest pandemics of the past century – HIV/AIDS and COVID-19 – and has not officially reported COVID-19 statistics since May 2020. Our algorithm will act upon already-collected satellite imagery, which can be obtained for any given area of Tanzania – and, indeed, the world – dating no more than two weeks back. In Aim 1, we will develop a region- based convolutional neural network (R-CNN) to automatically count the occupancy of burial sites using the most current available imagery. We will manually label burial plots in images for algorithm training and testing, and will validate the labeling with field visits to count the true occupancy of burial sites. In Aim 2, we will develop a novel “spot-the-difference” CNN (SD-CNN) to compare occupancy in earlier vs. later imagery of the same site. We hypothesize that the SD-CNN will be more accurate than the R-CNN because the algorithm would have information about what a site looked like at an earlier time-point and can be trained to notice new burial plots while ignoring “background” changes such as lighting and vegetation. We again train and test the algorithm using labeled imagery and will validate our labeling with field visits in which we will observe date markers on burial plots. Finally, in Aim 3, we will test the ability of the algorithm to identify changes in mortality due to epidemics. In Tanzania we expect burial sites to show a rise in mortality due to HIV/AIDS, a fall due to scale-up of HIV treatment, and an abrupt rise due to COVID-19. Our preliminary observations of satellite data confirm marked increases in burial site occupancy in Tanzania over the year 2020 relative to 2019. If successful, our algorithm will enable the world’s first low-cost, scalable, and globally equitable epidemic detection platform. Retrospectively, our research could help to identify areas hardest-hit by COVID-19, helping LMICs to marshal much-needed funding to address the pandemic’s aftermath. Prospectively, our research could help to keep humanity safer from future pandemics, especially those that arise in LMICs and may otherwise go undetected or unreported until it is too late.

检测高度休闲事件对于卫生当局前瞻性减少疾病传播至关重要 回顾性地解决流行病的健康状况 - 对于传染病，可以 包括癌症，糖尿病，神经发育障碍，“长卷”和其他后遗症。很遗憾， 许多低收入和中等收入国家（LMIC）缺乏用于流行病的数据系统，因此 配备了动员资源，以减少流行病的传播并解决其健康影响。这 COVID-19大流行是使用卫星图像在流行病期间检测死亡率的一些首次努力 埋葬地点。尽管成功，但它们是小的“一次性”努力，因为手动分析卫星 图像是实验室密集型的。我们建议开发一种用于完全自动测量的算法 使用卫星图像埋葬现场占用。我们的探索性研究将集中于坦桑尼亚，这是 这种算法的高优先级用例 Century - HIV/AIDS和COVID-19-自2020年5月以来，尚未正式报道Covid-19的统计数据。 我们的算法将采取已收集的卫星图像，可以在任何给定的区域获得 坦桑尼亚 - 确实是世界 - 约会不超过两周。在AIM 1中，我们将开发一个地区 - 基于卷积神经网络（R-CNN），可以自动计算使用埋葬地点的占用 当前最可用的图像。我们将在图像中手动标记算法训练和测试的图像， 并将通过实地访问来验证标签，以计算埋葬地点的真正占用。在AIM 2中，我们将 开发出小说的“斑点差异” CNN（SD-CNN），以比较早期的占用率与后来的图像 同一网站。我们假设SD-CNN比R-CNN更准确，因为该算法 会有关于网站在较早时间点的外观的信息，并且可以接受培训以注意新的 埋葬地块，忽略“背景”变化，例如照明和植被。我们再次训练并测试 使用标签图像的算法，并将通过现场访问验证我们的标签，我们将在其中观察日期 埋葬地上的标记。最后，在AIM 3中，我们将测试算法识别死亡率变化的能力 由于情节。在坦率 艾滋病毒治疗的扩大，以及由于19号而突然上升。我们对卫星数据的初步观察 相对于2019年，坦桑尼亚的墓地入住率明显明显增加。 如果成功，我们的算法将使世界上第一个低成本，可扩展和全球公平的流行病 检测平台。回顾性地，我们的研究可以帮助确定Covid-19的最严重打击的领域，并帮助 LMIC备受谨慎的资金，以解决大流行的后果。前瞻性，我们的研究 可以帮助使人类从未来的大流行中更安全，尤其是那些在LMIC中出现的人，并且可能 否则，直到为时已晚。