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自分が読んで興味深く感じた英文記事を中心に取り上げる予定です

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Neil Ferguson率いるチームによるインペリアル・カレッジの報告書が出てから1ヶ月経ったのですね。

16 March 2020 Imperial College COVID-19 Response Team

Neil Ferguson博士はこれまでの感染症でも数理モデルを作成していた経験豊富な方だったようです。彼の業績がコンパクトにまとまっている記事から。

15-04-2020

This is not Ferguson’s first time in the limelight. In 2002, at the age of 34, the Oxford alumnus was appointed Order of the British Empire (OBE) for his work in modelling the UK’s foot-and-mouth disease outbreak, which killed over six million cows and sheep and hit Ferguson’s birthplace of Cumbria especially hard.
 
Since then, Ferguson has been involved in data modelling for three different viral outbreaks: swine flu in 2009, Middle East Respiratory Syndrome (MERS) in 2012, and Ebola in 2016. During the former, in an article for Lancet Infectious Diseases, he published an article named “Closure of schools during an influenza pandemic”, which looked at – among other things – the economic effects such a closure would have, estimating that a 12-week closure could cost up to 1% of GDP.

あの報告書が英政府の方向性を変えたと言われていますが、それまでに各方面からの提言もあったようですので、単純化しないようにご本人は語っています。そうは言っても英政府の当初の方向性による感染者の推定を見ると政策判断の重みを感じます。

Ferguson added: “It looked… like that was the paper that informed policymaking, but in fact a much wider range of scientific advice and modelling advice had gone into government in the previous two weeks, all of which pointed in that direction.” Indeed, the UK government didn’t just have China to look at as a model: it had Italy. By 1 March, the coronavirus death toll in Italy had increased nearly six-fold in the past week alone, from six to 34. One of Ferguson's colleagues, Helen Ward, has stated in the Guardian that the UK government's failure on 12 March to follow Imperial's guidelines – on that day, a government announcement shifted away from contact tracing and social distancing – meant that between 12 and 23 March, hundreds of thousands of people would have been infected. With a 1% mortality rate, 100,000 infections translates to 1,000 deaths.

じゃ今後どうするのか10日前の記事では、ロックダウン解除後に再度ロックダウンするのも難しいので、テストを増やして現状把握して対応していくしかないのではと語っていました。

APRIL 7 2020 By: Jemima Kelly

数理モデルにフォーカスを当てた記事がNatureに載っていました。数理モデルを作成すれば簡単に推計できる訳ではなく、十分な生データも必要ですし、またデータが集まれば適宜アップデートしているそうで、色々考慮すべき点があるのがわかるものでした。

NEWS FEATURE  02 APRIL 2020  CORRECTION 03 APRIL 2020 
How epidemiologists rushed to model the coronavirus pandemic.
David Adam

ここでもテストの必要性を最後に触れていました。

Testing needed
As researchers discover more about the virus, they are updating many other key variables. In the 26 March report5 on the global impact of COVID-19, the Imperial team revised its 16 March estimate of R0 upwards to between 2.4 and 3.3; in a 30 March report9 on the spread of the virus in 11 European countries, the researchers put it somewhere in the range of 3 to 4.7.

But some crucial information remains hidden from the modellers. A reliable test to see who has been infected without showing symptoms — and so could be moved to the recovered group — would be a game changer for modellers, and might significantly alter the predicted path of the pandemic, says Edmunds.

To stress the need for such a test, a team at the University of Oxford, UK, led by theoretical epidemiologist Sunetra Gupta, has suggested that the pattern of recorded deaths in the United Kingdom might fit a range of SIR models, including one that assumes millions of people have already been infected but haven’t shown any symptoms10. Only tests that reveal such past infections can show what’s going on in reality.

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