169 citations · 320 across the 15 of their papers we have counts for
16 papers
Estimating the Uncertainty of Neural Network Forecasts for Influenza Prevalence Using Web Search Activity
Michael Morris, Peter Hayes, Ingemar J. Cox +1
Influenza is an infectious disease with the potential to become a pandemic, and hence, forecasting its prevalence is an important undertaking for planning an effective response. Re…
Providing early indication of regional anomalies in COVID19 case counts in England using search engine queries
Elad Yom-Tov, Vasileios Lampos, Ingemar J. Cox +1
COVID19 was first reported in England at the end of January 2020, and by mid-June over 150,000 cases were reported. We assume that, similarly to influenza-like illnesses, people wh…
Go local: The key to controlling the COVID-19 pandemic in the post lockdown era
Isabel Bennett, Jobie Budd, Erin M. Manning +7
The UK government announced its first wave of lockdown easing on 10 May 2020, two months after the non-pharmaceutical measures to reduce the spread of COVID-19 were first introduce…
Tracking COVID-19 using online search
Vasileios Lampos, Maimuna S. Majumder, Elad Yom-Tov +6
Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search que…
Predicting antimicrobial drug consumption using web search data
Niels Dalum Hansen, Kåre Mølbak, Ingemar Cox +1
Consumption of antimicrobial drugs, such as antibiotics, is linked with antimicrobial resistance. Surveillance of antimicrobial drug consumption is therefore an important element i…
Seasonal Web Search Query Selection for Influenza-Like Illness (ILI) Estimation
Niels Dalum Hansen, Kåre Mølbak, Ingemar J. Cox +1
Influenza-like illness (ILI) estimation from web search data is an important web analytics task. The basic idea is to use the frequencies of queries in web search logs that are cor…