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13 Apr 2024: The Betty Behrens Seminar on Classics of Historiography. Wednesday 22 May 2024, 2pm-4pm. Michael Nylan. Distinguished Professor, University of California, Berkeley. Documents [Shū] (ca. 221 BC) by Fu Sheng. Two chapters (ch. 1, pp. 1-3; ch. 14, pp. 1-4) from the text in English follow. (out of a total of 30 that were in circulation in the Han). Ch. 1 is also in Chinese to see how much emendation Professor Nylan brings to the text, based on the extant sources. 1. Canon of Yao <a>1A.1 encomium in verse<a> Yao, Examiner of Old Heroes, named "Propagator of Merit," was impressive and clear-sighted. He knew how to put all things in order and at ease. Truly attentive to his duties, he was capable of relinquishing his powers and privileges to worthy men. His bright influence shone wide to earth's four corners, reaching above and below. He was able to make his instructive example shine forth, to draw the Nine Clans close to him. Once the Nine Clans were settled, then Yao distinguished by insignia the Hundred Families. And once the Hundred Families themselves became shining examples, that induced harmony in the myriad polities, at which point the masses flourished, and the era came to be at peace. <a>1A.2 account of Xi and He and

5 Apr 2024: Small Data, Big Time—A retrospect of the first weeks of COVID-19. Qingyuan Zhao. [To be read before The Royal Statistical Society at the Society’s 2021 annual conference held inManchester on Wednesday, September 8th, 2021, the President, Professor Sylvia Richardson, in theChair]. Abstract. This article reviews some early investigations and research studies in the first weeks of the coron-avirus disease 2019 (COVID-19) pandemic from a statistician’s perspective. These investigations werebased on very small datasets but were momentous in the initial global reactions to the pandemic. Thearticle discusses the initial evidence of high infectiousness of COVID-19 and why that conclusion wasnot reached faster than in reality. Further reanalyses of some published COVID-19 studies show thatthe epidemic growth was dramatically underestimated by compartmental models, and the lack of fitcould have been clearly identified by simple data visualization. Finally, some lessons for statisticiansare discussed.Keywords: Infectious disease modeling; Selection bias; COVID-19; Model diagnostics. 1 Introduction. Starting from a regional disease outbreak in Wuhan, China, the coronavirus disease 2019 (COVID-19)rapidly grew into a once-in-a-lifetime