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Donald R. Olson

Researcher at New York City Department of Health and Mental Hygiene

Publications -  43
Citations -  2598

Donald R. Olson is an academic researcher from New York City Department of Health and Mental Hygiene. The author has contributed to research in topics: Pandemic & Disease surveillance. The author has an hindex of 18, co-authored 41 publications receiving 2153 citations. Previous affiliations of Donald R. Olson include National Institutes of Health & Columbia University.

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Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales.

TL;DR: GFT data may not provide reliable surveillance for seasonal or pandemic influenza and should be interpreted with caution until the algorithm can be improved and evaluated, and current internet search query data are no substitute for timely local clinical and laboratory surveillance, or national surveillance based on local data collection.
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Epidemiological evidence of an early wave of the 1918 influenza pandemic in New York City

TL;DR: Using routinely collected monthly age-stratified mortality data, it is shown that an unmistakable shift in the age distribution of epidemic deaths occurred during the 1917/1918 influenza season in New York City.
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Preliminary Estimates of Mortality and Years of Life Lost Associated with the 2009 A/H1N1 Pandemic in the US and Comparison with Past Influenza Seasons.

TL;DR: It is concluded that the 2009 A/H1N1 pandemic virus had a substantial health burden in the US over the first few months of circulation in terms of years of life lost, justifying the efforts to protect the population with vaccination programs.
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Enhancing disease surveillance with novel data streams: challenges and opportunities

TL;DR: This paper outlines a conceptual framework for integrating NDS into current public health surveillance and presents the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical.