Research press release



レストラン、フィットネスセンター、カフェ、ホテルなどの施設の営業再開は、SARS-CoV-2の伝播リスクが最も高いことが、米国のデータに基づくモデル研究によって明らかになった。このモデルによれば、これらの施設で利用者の密度を低下させれば、予想感染者数を大幅に減らせる可能性があるとされる。また、今回の研究は、SARS-CoV-2の感染リスクには社会経済的状況による格差が存在することを明確に示している。このような研究結果を報告する論文が、Nature に掲載される。

今回、Jure Leskovecたちの研究チームは、人々の移動の変化がSARS-CoV-2の感染拡大をどのように変化させるかを評価するため、米国の携帯電話データ(2020年3月1日から5月2日までに収集されたもの)を用いて、数百万人の人々がそれぞれの近所からどのように移動したのかを示すマップを作成した。Leskovecたちは、このデータと1つのSARS-CoV-2伝播モデルを組み合わせて、感染リスクが高いと考えられる施設と感染リスクのある集団を特定した。このモデルによるシミュレーションでは、米国の10大都市圏(シカゴ、ニューヨーク市、サンフランシスコなど)の1日の確認症例数が正確に予測された。



Reopening places such as restaurants, fitness centers, cafes, and hotels carries the highest risk for transmitting SARS-CoV-2, according to a modelling study based on data from the United States published in Nature. Reducing occupancy in these venues may result in a large reduction in predicted infections, the model suggests. The study also highlights disparities in infection risk according to socioeconomic status.

To assess how changes in movement might alter the spread of SARS-CoV-2, Jure Leskovec and colleagues use US mobile phone data (collected between 1 March and 2 May 2020) to map the movements of millions of people from different local neighbourhoods. They combine these data with a model of SARS-CoV-2 transmission, which allows them to identify potential high-risk venues and at-risk populations. The simulations from their model accurately predict confirmed daily case counts in ten of the largest metropolitan areas (such as Chicago, New York City and San Francisco).

The level of detail of the mobility data allowed the researchers to model the number of infections occurring, by the hour, at nearly 553,000 distinct locations grouped into 20 categories — termed “points of interest” — that people tended to visit regularly. Their model predicts that a small number of these locations, such as full-service restaurants, account for a large majority of infections. For example, in the Chicago metropolitan area, 10% of the points of interest accounted for 85% of the predicted infections at points of interest. The model predicts that compared with higher-income groups, lower-income populations are more likely to become infected because they have not been able to reduce their mobility as substantially and because the places they visit tend to be smaller and more crowded, which increases the risk of infection. For example, grocery stores visited by lower-income individuals tended to have 59% more people per square foot than those visited by higher-income individuals, and their visitors stayed 17% longer on average.

By modelling who is likely to be infected at which locations, the authors were also able to estimate the effects of different reopening strategies, and they suggest that their model can inform reopening policies. For example, capping the occupancy of a venue at 20% of its maximum capacity is predicted to reduce new infections by over 80%, but would only reduce the overall number of visits by 42%.

doi: 10.1038/s41586-020-2923-3

「Nature 関連誌注目のハイライト」は、ネイチャー広報部門が報道関係者向けに作成したリリースを翻訳したものです。より正確かつ詳細な情報が必要な場合には、必ず原著論文をご覧ください。

メールマガジンリストの「Nature 関連誌今週のハイライト」にチェックをいれていただきますと、毎週最新のNature 関連誌のハイライトを皆様にお届けいたします。