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

检测高伤亡流行病对于卫生当局前瞻性地减少疾病传播至关重要 并回顾性地处理流行病之后的健康状况--对于传染病来说， 包括癌症、糖尿病、神经发育障碍、“长COVID”和其他后遗症。不幸的是， 许多低收入和中等收入国家（LMICs）缺乏疫情检测的数据系统，因此， 有能力调动资源，以减少流行病的传播并解决其对健康的影响。的 2019冠状病毒病大流行期间，首次尝试使用卫星图像检测死亡率 埋葬地点。虽然取得了成功，但这些都是小规模的“一次性”努力，因为对卫星的人工分析 图像是非常劳动密集型的。我们建议开发一种算法，用于全自动测量 使用卫星图像的埋葬地点占有率。我们的探索性研究将集中在坦桑尼亚，这是一个典型的 这种算法的高优先级用例，因为它受到过去两次最致命的流行病的严重打击 世纪-艾滋病毒/艾滋病和COVID-19 -自2020年5月以来没有正式报告COVID-19统计数据。 我们的算法将对已经收集的卫星图像采取行动，这些图像可以在任何给定的地区获得。 坦桑尼亚--事实上，全世界--的历史可以追溯到两周前。在目标1中，我们将开发一个区域- 基于卷积神经网络（R-CNN），使用 最新的可用图像。我们将在图像中手动标记埋葬区，用于算法训练和测试， 并将通过实地考察核实标签，以计算埋葬地点的真实占用情况。在目标2中，我们 开发一种新的“点的差异”CNN（SD-CNN），以比较早期与后期图像的占用率。 同一个网站。我们假设SD-CNN将比R-CNN更准确，因为算法 我会有关于一个网站在较早的时间点看起来像什么的信息，可以训练，以注意新的 埋葬地，而忽略了“背景”的变化，如照明和植被。我们再次训练和测试 算法使用标记的图像，并将验证我们的标签与实地考察，我们将观察日期 墓地上的标记最后，在目标3中，我们将测试算法识别死亡率变化的能力 由于流行病。在坦桑尼亚，我们预计，由于艾滋病毒/艾滋病，埋葬地点的死亡率会上升， 艾滋病毒治疗的规模扩大，以及COVID-19导致的突然上升。我们对卫星数据的初步观察 证实坦桑尼亚2020年的埋葬地点占用率与2019年相比显着增加。 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 检测平台回顾过去，我们的研究可以帮助确定受COVID-19影响最严重的地区， 中低收入国家筹集急需的资金，以应对大流行的后果。普罗维登，我们的研究 可以帮助人类更安全地免受未来的流行病，特别是那些在LMIC中出现的流行病， 否则就不被发现或报告，直到为时已晚。

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.