{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T18:04:01Z","timestamp":1762625041147,"version":"3.41.2"},"reference-count":57,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"23","content-domain":{"domain":["www.science.org"],"crossmark-restriction":true},"short-container-title":["Sci. Adv."],"published-print":{"date-parts":[[2023,6,9]]},"abstract":"<jats:p>Not all COVID-19 deaths are officially reported, and particularly in low-income and humanitarian settings, the magnitude of reporting gaps remains sparsely characterized. Alternative data sources, including burial site worker reports, satellite imagery of cemeteries, and social media\u2013conducted surveys of infection may offer solutions. By merging these data with independently conducted, representative serological studies within a mathematical modeling framework, we aim to better understand the range of underreporting using examples from three major cities: Addis Ababa (Ethiopia), Aden (Yemen), and Khartoum (Sudan) during 2020. We estimate that 69 to 100%, 0.8 to 8.0%, and 3.0 to 6.0% of COVID-19 deaths were reported in each setting, respectively. In future epidemics, and in settings where vital registration systems are limited, using multiple alternative data sources could provide critically needed, improved estimates of epidemic impact. However, ultimately, these systems are needed to ensure that, in contrast to COVID-19, the impact of future pandemics or other drivers of mortality is reported and understood worldwide.<\/jats:p>","DOI":"10.1126\/sciadv.adg7676","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T17:58:28Z","timestamp":1686333508000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":6,"title":["Alternative epidemic indicators for COVID-19 in three settings with incomplete death registration systems"],"prefix":"10.1126","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6368-9103","authenticated-orcid":true,"given":"Ruth","family":"McCabe","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Oxford, Oxford, UK."},{"name":"NIHR Health Research Protection Unit in Emerging and Zoonotic Infections, Liverpool, UK."},{"name":"MRC Centre for Global Infectious Disease Analysis, 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