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A new study published in Nature modeled the spread of Covid-19 using geolocation data from 98 million people.
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[EN VIDÉO] In video and UV light: this is how the coronavirus spreads in a restaurant A video produced jointly by a group of researchers and the Japanese media NHK News sheds light on the spread of the coronavirus in a restaurant. Using a UV-A lamp, they reveal how the virus (illustrated by a fluorescent lotion) passes from a single individual to an entire group during a meal.
Answer the following question “Qhat are the places where the SARS-CoV-2 spread more? ” It is of crucial importance both from a health point of view to increase precautionary measures in these places, and from an economic point of view to allow the various less risky activities to reopen more quickly. A recent study published in the journal Nature indicates that high-risk places are restaurants, hotels, gyms and religious institutes.
98 million geolocation data used
The researchers reconstructed the movements of part of the American population using 98 million anonymous geolocation data collected between March and May 2020. They coupled this reconstructed network of interactions to a compartmental epidemiological model called Seir (for Susceptible-Exposed-Infectious-Removed o Susceptible-exposed-infectious-eliminated) in order to evaluate the dynamics ofepidemic inside collection places. The results suggest this restaurants, sports halls, cafes, hotels and religious institutes are the places that contribute most strongly to the dynamics of the epidemic.
These spaces should therefore be subjected to increased surveillance when the second imprisonment it will eventually avoid a resurgence of the epidemic. Ideally, their maximum occupancy capacity should be significantly reduced.
The limits of the study
Scientists point out that their model has limitations, particularly because it doesn’t take into account all types of populations or all places that you might think are at risk. Also, as well, this type of epidemiological model does not perfectly reflect the complex reality of disease transmission. However, it is based on the rate of reproduction of the virus. From that moment on, it is able to indicate the places that most contribute to its growth.
Thanks to additional demographics, it was also seen that people with a lower socioeconomic status have a higher infection rate. Their frequentation of the places at risk was greater and their movements less limited, certainly because they could not to work from home. The authors urge policy makers to use their findings to guide them in managing deconfinement.
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