According to a new study, online search can accurately predict regional increases and decreases in COVID-19 cases. Certain types of searches reveal the activities that people will be involved in. Stay-at-home order and Stay-at-home order search volume Predict the number of COVID-19 diagnoses after 1014 days.
Although some of the behaviors leading to SARS-CoV-2 infection are clear, the new wave of COVID-19 cases does not always follow the predicted pattern.
But now, a study by researchers at New York University’s Institute of Mathematics and Science in Coolant explains through analysis of online searches possible ways to detect an outbreak of infection before it occurs.
Researchers found a correlation between a surge in searches related to activities other than home activities that may be at risk for SARS-CoV-2 infection and an increase in COVID-19 cases after 1014 days. I found. Infections decreased when the number of searches related to stay-at-home orders increased.
Anasse Bari, a clinical assistant professor at the Courant Institute, the author of the study, said experts are already using data mining in finance to study satellite images of cars in parking lots to predict business profits. It states that it has succeeded in generating investment in.
Megan Coffee, senior author of the Department of Infectious Diseases and Immunology at New York University (NYU) Grossman School, said our study is fighting pandemics by proactively discovering potential locations for outbreaks. Shows that the same technique can be applied to. Of medicine.
Stay up-to-date on current COVID-19 outbreaks and visit the Coronavirus Hub for preventative and therapeutic advice.
By more accurately identifying the behavior that causes the outbreak of infection, epidemics and policy makers can more effectively formulate public policies such as closures and blockades.
The system described in the research treatise avoids privacy issues by including only large clusters of anonymized data.
This study has been published in Social Network Analysis and Mining.
The researcher’s first step was to develop categories based on traceable search phrases or keywords.
The two main categories they tracked were called Mobility Index and Separation Index.
The team has assigned specific searches to the Mobility Index track, including nearby theaters, airline tickets, and other inquiries about activities, including leaving home and being in physical proximity to others.
As Bari says, someone searching for closing times at a local bar or finding directions to a local gym can give you insight into what the risks might be in the future.
For quarantine index tracks, researchers collected search queries that indicate their intention to stay quarantined at home, such as yoga at home or food delivery.
Researchers categorized keywords based on a survey of Democracy Fund + UCLA Nationscape. In this survey, respondents listed what to do if restrictions were lifted by public health officials’ advice on activities.
According to the survey, the top three activities people missed were attending stadiums / concerts, attending movies, and attending sporting events.
According to Bari, this is the first step in building a tool to help predict COVID-19 outbreaks by capturing higher-risk activities and intended mobility, in the gym and face-to-face. It becomes clear by searching for meals in.
Researchers collected search data from all 50 states in the United States from March to June 2020. They used Google Trends to track data trends and enable them to develop mobility and isolation indexes.
Researchers have also created a net movement index to show the relationship between the two indexes. The increase in net movements shows a shift to mobility search queries and a shift from isolated search.
The authors theoretically say that a sudden decrease in net movement (ie, an increase in the number of people at home) corresponds to a decrease in the spread of COVID-19 with a delay corresponding to the incubation period of COVID-19. I’m expecting it.
In 42 of the 50 states, each increase in net movement accurately predicted an increase in COVID-19 infection after 1014 days.
The authors of the study investigated the relationship between mobility index and infection rate after the curfew was removed in five states: Arizona, California, Florida, New York, and Texas.
After the implementation of each lockdown, the mobility index was significantly reduced, closely reflected in the reduction in infections. However, the relaxation of stay-at-home orders in Arizona, California, Florida, and Texas preceded the surge in mobility-type searches, leading to a surge in the number of infections reported in June 2020.
Another author of this study, Aashish Khubchandani, an undergraduate student at New York University, concludes:
From this work, we want to build a knowledge base about changes in human behavior from alternative data during the pandemic life cycle so that machine learning can predict future epidemic behavior.
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