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

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.