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Working paper
Early in the Epidemic: Impact of Preprints on Global Discourse of 2019-nCoV Transmissibility
In: Early in the Epidemic: Impact of Preprints on Global Discourse about COVID-19 Transmissibility. Lancet Glob Health. 2020 Mar 24. pii: S2214-109X(20)30113-3. doi: 10.1016/S2214-109X(20)30113-3.
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Data Source Concordance for Infectious Disease Epidemiology
BACKGROUND: As highlighted by the COVID-19 pandemic, researchers are eager to make use of a wide variety of data sources, both government-sponsored and alternative, to characterize the epidemiology of infectious diseases. To date, few studies have investigated the strengths and limitations of sources currently being used for such research. These are critical for policy makers to understand when interpreting study findings. METHODS: To fill this gap in the literature, we compared infectious disease reporting for three diseases (measles, mumps, and varicella) across four different data sources: Optum (health insurance billing claims data), HealthMap (online news surveillance data), Morbidity and Mortality Weekly Reports (official government reports), and National Notifiable Disease Surveillance System (government case surveillance data). We reported the yearly number of national- and state-level disease-specific case counts and disease clusters according to each of our sources during a five-year study period (2013–2017). FINDINGS: Our study demonstrated drastic differences in reported infectious disease incidence across data sources. When compared against the other three sources of interest, Optum data showed substantially higher, implausible standardized case counts for all three diseases. Although there was some concordance in identified state-level case counts and disease clusters, all four sources identified variations in state-level reporting. INTERPRETATION: Researchers should consider data source limitations when attempting to characterize the epidemiology of infectious diseases. Some data sources, such as billing claims data, may be unsuitable for epidemiological research within the infectious disease context.
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Estimating a feasible serial interval range for Zika fever
In: Bulletin of the World Health Organization
Designing Efficient Contact Tracing Through Risk-Based Quarantining
In: NBER Working Paper No. w28135
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Evaluating COVID-19 Lockdown and Business-Sector-Specific Reopening Policies for Three US States
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Evaluating Criminal Justice Reform During COVID-19: The Need for a Novel Sentiment Analysis Package
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Evaluating COVID-19 Lockdown Policies For India: A Preliminary Modeling Assessment for Individual States
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Leveraging Data Science for Global Health
This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.
Evaluating Interest in Off-Label Use of Disinfectants for COVID-19 with Google Trends
In: The Lancet Digital Health, Band 2, Heft 11
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Modeling Between-Population Variation in COVID-19 Dynamics in Hubei, Lombardy, and New York City
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