ArticleNPJ digital medicine2020
Lymelight: forecasting Lyme disease risk using web search data.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review.Current environmental health reports · 2025Review
- Estimating the household secondary attack rate and serial interval of COVID-19 using social media.NPJ digital medicine · 2024Article
- Article
- Leveraging machine learning approaches for predicting potential Lyme disease cases and incidence rates in the United States using Twitter.BMC medical informatics and decision making · 2023Article
- Tick-Bite "Meteo"-Prevention: An Evaluation of Public Responsiveness to Tick Activity Forecasts Available Online.Life (Basel, Switzerland) · 2023Article
- Internet searches and heat-related emergency department visits in the United States.Scientific reports · 2022Article
- An evaluation of Internet searches as a marker of trends in population mental health in the US.Scientific reports · 2022Article
- Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health.Annual review of public health · 2022Review
- Inclusion of environmentally themed search terms improves Elastic net regression nowcasts of regional Lyme disease rates.PloS one · 2022Article
- Estimating disease vector population size from citizen science data.Journal of the Royal Society, Interface · 2021Article
- Internet Interest in Colon Cancer Following the Death of Chadwick Boseman: Infoveillance Study.Journal of medical Internet research · 2021Article
- Determination of the Popularity of Dietary Supplements Using Google Search Rankings.Nutrients · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Lyme disease is the most common tick-borne disease in the Northern Hemisphere. Existing estimates of Lyme disease spread are delayed a year or more. We introduce Lymelight-a new method for monitoring the incidence of Lyme disease in real-time. We use a machine-learned classifier of web search sessions to estimate the number of individuals who search for possible Lyme disease symptoms in a given geographical area for two years, 2014 and 2015. We evaluate Lymelight using the official case count data from CDC and find a 92% correlation (
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.