Early Phylogenetic Estimate Of The Effective Reproduction Number Of 2019-nCoV

To rec onstr uc t the evoluti onary dynamic s of the 2019 novel c oronavirus , 52 2019-nCOV genomes availabl e on 04 February 2020 at G I SAI D were anal ysed. The two models used t o estimate the r eproduc tion number (c oalescent -based exponenti al growth and a birth-death skyline method) indic at ed an estimated mean evoluti onary rate of 7.8 x 10 -4 subs/si te/year (range 1.1x10 -4 –15x 10 -4 ). The estimated R value was 2.6 ( range 2.1-5.1), and inc rea sed from 0.8 t o 2. 4 in December 2019. The es timat ed mean doubl ing time of the epi demic was between 3.6 and 4.1 days. Thi s study prov es the usefulnes s of phy logeny in supporti ng the surveil l anc e of emerging new inf ec tions even as the epidemic i s growing. . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 23, 2020. ; https://doi.org/10.1101/2020.02.19.20024851doi: medRxiv preprint INTR O D UC TION On 30 Janua ry 2020, the World Health Organi sation ( WH O) dec lar ed that t he outbreak of an infec tion due to a novel coronavi rus (2019-nCoV) was a “Publi c Health Emergency of Interna tional Concern” (ht tps: //www.who.i nt/news-room/d etail /30-01-2020-s tatement- on- the- sec ond- meeti ng-of- the - international- heal th-regul ations-(2005)-emergency-commi ttee -regarding- the-outbreak-of -novel - c oronavirus -(2019-nCoV)) . Emergi ng as a human pa thogen i n the Chines e c i ty of W uhan, 2019 -nC oV (ht tps: //www.who.i nt/docs /defaul t -sourc e/coronaviruse/ si tua ti on-report s/20200121- sitrep-1-2019- nc ov.pdf? sfvr sn=20a99c10_4) has c aused a widespread outbr eak of febril e re spi ratory il lnes s and, as of 13 February 2020, there wer e 60, 349 confi rmed c ase s (inc luding 527 outside mainland China) and a total of 1,360 fat aliti es (ht tps: //gi sa nddata .ma ps. a rc gis .com/ apps /opsda shboa rd/index .html# /bda7594740fd40299423467b48e9e cf6 ). Bel onging to the β -coronavirus ge n us o f t he Coronaviri dae fa m ily , 2 0 19 -nC oV is c lo se ly r ela te d to S AR S-C oV as there i s >70% nucleoti de simila ri ty in their approximatel y 30 kb long geno mes . 1 A r e c e n t s t u d y h a s support ed the view that , like other β-cor onavir use s c ausing huma n inf ec ti ons suc h a s S ARS -CoV and MERS - CoV, 2019-nCoV originated from bat s, and repor ted 96% genomi c identi ty with a previously detected SARS - l i k e b a t c o r o n a v i r u s . 2,3 H owever, i t r emai ns unclear whether the spil lover also involved a di ff erent intermedi ary ani mal hos t. In the case of suc h an epidemi c, i t i s i mportant to mak e an as r eliabl e as possible estimate of the basic reproduc tiv e number ( R 0 , the number of ca se s g enera te d f rom a sing le i nf ec te d per son) a nd the dynami c s of t ransmi ssi on. The aim of t hi s s tudy was to inves ti gat e the temporal origin, rate of vi ral evolution and popul ati on dynamics of 2019-nCoV using 52 full genomes of viral strai ns s ampled in dif ferent c ountri es on known sampli ng dat es available at the moment when study was per formed. . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 23, 2020. ; https://doi.org/10.1101/2020.02.19.20024851doi: medRxiv preprint

AND METH O DS Se quen ce data s e t The analysi s was ba sed on 52 2019-nCOV sequenc es publi cly availabl e at GI SAID (Globa l Ini tia ti ve o n Sharing All Influenza Data) on 4 February 2020 (https://www. gisaid.org/) . The acc ession I Ds, sampling dates and locati ons are summarized in Table S1. The sequenc es were ali gned using the Clustal W Multipl e Alignment program i nc luded in the ac ces sory applicati on of Bioedit s of tware, manua lly controll ed, and cropped to a final l ength of 29,774 bp using BioEdit v. 7.2.6.1 (http:// www. mbio.ncsu. edu/ bioedi t/ bioedit . html ). Phy lodynamic analy s is The simple st evolutionary model bes t fi tting the s equence data wa s selected using s oftware JmodelTes t v. 2.1.7 s of tware, 4 and proved to be the Has egawa-Ki shino-Yano (HKY) model . The virus' phylogeny, evolutiona ry rates, times of t he most rec ent common ance stor ( tMRCA) and demographic growth were c o-estimate d in a Bay esi an framework using a Markov Chai n Monte Ca rlo (MCMC) method i mplemented in v.1. 84 of the BEA ST pac kage. 5 Dif ferent coalescent priors and molecular clock model s (constant popul ation size, exponenti al growth, and a Bayesian skyli ne pl ot, BSP) were t es ted usi ng strict and r el axed mol ecular clock model s. G i ven the large c r e d i b i l i t y i n t e r v a l a n d h i g h l e v e l o f u n c e r t a i n t y d u e t o v e r y c l o s e s a m p l i n g d a t e s , a l l t h e e s t i m a t e s w e r e made using days as the uni t of time and a normal prior with substitution rates obtained from our preliminary estimat es (mean rate 2.2 x 10 -6 subs/sit e/day, with a standard deviati on of 1.1 x 10 -6 ). The MCMC analy si s was run until c onve rgence with sampling every 100,000 generations . Convergence was a s s e s s e d b y e s t i m a t i n g t h e e f f e c t i v e s a m p l i n g s i z e ( E S S ) a f t e r 1 0 % b u r n - i n u s i n g T r a c e r v . 1 . 7 s o f t w a r e (ht tp: // tre e.bi o.ed. ac. uk/softwar e/ trac er /), and accepting ESS val ues of 300 or more. The unc ertainty of the es timat es i s indica ted by 95% highe st margi nal lik elihoods estimated 6 by pa th sampling / st eppi ng stone methods. 7 The final t rees were summaris ed by sel ecting the tr ee wit h the maximum produc t of post erior probabi lit ies (pp) (maximum cl ade credibility or MCC) a fter a 10% burn-i n usi ng Tr ee Annotator v.1.84 (inc luded i n the BEAST package), and were vi sual i sed usi ng Fi gTree v.1.4.2 (http://t ree.bio. ed.ac. uk/sof tware/figtr ee/) . . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 23, 2020. ; https://doi.org/10.1101/2020.02.19.20024851doi: medRxiv preprint The basic r eproduc tive number (R 0 ) was ca lcu la te d on t he ba sis of th e e xp on en tia l gr o wt h r at e (r ) usin g t he equati on R 0 =rD +1 , wh ere D is t he average du r at i o n o f infectio usn ess est im ated as d escrib ed b elo w . 8 T h e doubl ing time of the epidemic was dir ectl y estimated set ting the t ree prior to t he coalescen t exponential growth analy sis with doubling time parameteri zati on. Birth -D ea t h Skylin e est im a t es of the eff ect iv e reproduc tive number (R e ) The birth-death skyline model i mplemented in Bea st 2. 48 was used to inf er changes in the effective reproduc tiv e number (R e ), and other epi demiol ogical parameter s such as the death/ recovery rate (δ), the transmi ssion rate (λ), the origin of the epidemi c, and the sampl ing proporti on (ρ). 9 Given that the sampl es were coll ect ed duri ng a short period of ti me, a “birth-death cont emporary” model was us ed. The analyses wer e based on the previously sel ec ted H KY subs tituti on model and the substi tu tion rate was s e t t o t h e v a l u e o f 8 . 0 x 1 0 -4 sub s/ si te/ year, which c orr esponds to the mean s ubstitution r ate estimated using a relaxed cl ock under the exponent ial coal escent model as transf ormed into units per year . For the birth-deat h anal ysi s, one and t wo i ntervals and a log-normal prior f or R e , w i t h a m e a n ( M ) o f 0 . 0 a n d a v a r i a n c e ( S ) o f 1 . 0 w e r e c h o s e n , w h i c h a l l o w s t h e R e values to change between 5. A normal pri or wi th M=48.7 and S =15 (cor responding to a 95% i nterv al fr om 24.0 to 73.4) was us ed for the rate of bec oming uni nfec tious. Thes e val ues a re expr es sed a s unit s per year and refl ec t the inver se of the t i m e o f i n f e c t i o u s n e s s ( 5 . 3 - 1 9 d a y s , m e a n 7 . 5 ) a c c o r d i n g t o t h e s e r i a l i n t e r v a l e s t i m a t e d b y Q u n L i et a l . 10 Sampling probability (ρ) was estimated as suming a prior Bet a (alpha =1.0 and beta=999), corr esponding to a minority of the sampled cases (between 10 -5 t o 1 0 -3 ). The origin of the epide mic was esti mated using a normal prior wit h M=0.1 and S=0.05 i n units per yea r. The MCMC anal yse s wer e run f or 30 mi llion generations and sampl ed every 3,000 st eps. Convergenc e was a ss ess ed on the basi s of ESS val ues (ESS >200). Uncert ainty in t he estimates wa s i ndicated by 95% highest posterior density (95%HPD) interval s. The mean growth ra te was calcul at ed on the basi s of the bir th and rec overy rat es (r=λ -δ), and the doubling time was es ti mated by the equation: doubling time=ln(2) /r. 11 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 23, 2020. ; https://doi.org/10.1101/2020.02.19.20024851doi: medRxiv preprint RES U L TS The sequenc e analyses under a relaxed (uncorrel ated log- normal ) or stric t molec ul ar cloc k showed tha t the former per formed bet ter as a ss es sed by using BF with path s ampling (PS) and st epping stone sampl ing (SS ) ( s tri c t vs. r elaxed mol ecular clock BF (P S)=-8.66 and BF(SS) =-10.7 f or r elaxed clock). Compari son of the diff er ent demogr aphic model s showed t hat the BS P model best f itt ed the data ( BS P v s . e x p o n e n t i a l g r o w t h BF(PS)= 7.3 and BF(SS) = 8.78 for BSP; BS P vs . constant popul ation size BF( PS)= 7. 3and BF(SS)= 8.78 for BS P). The estimat ed mean evolutionary rat e was 2.15 x 10 -6 subs/ si te/day (95% H P D : 3.22 x 10 -7 –4.18 x 10 -6 ), corresponding to 7.8 x 10 -4 subs/sit e/year (95% HPD: 1.1 x 10 -4 –15 x 10 -4 ). The estima te d mean tMRCA c orr e spondi ng to the root of the t ree da te d 73 day s be fore the end of Ja nuary 2020 (95%HPD: 32.5–142.3), corre spondi ng to 18 November 2019 (95%HPD: 10 September 2019-28 Dec ember 2019 ). The Bayesi an t ree showed three main significant clades. The largest cl ade ( pp=0.84) encompa ssed 10 sequences and con sis ted of two signifi cant sub-clades (pp=0.9 and pp=1). O v erall, thi s cluster included f e w e r r e c e n t i s o l a t e s t h a n t h e o t h e r t w o c l u s t e r s , a n d d a t e d b a c k t o 4 7 . 5 d a y s a g o ( 9 5 % H P D : 2 5 . 5 - 7 6 . 6 ) , c orresponding to 13 Dec ember 2019. The sec ond (pp=0.99) and third si gnific ant c lus ters (pp=0.95) dated bac k to 29.2 (95% HPD: 0.7-47.45) and 21.9 (95% HPD 3.6-54 .7) days ago, c or respondi ng to 01-08 January 2020. The Baye sian skyline plot s howed a r apid increase in the number of inf ections in a peri od betwee n about 45 and 30 days befor e the end of January 2020 (Fig.1, part A) . The IDs and avail abl e data of the sequences involved in the clades are shown in Table S1. The estima ted growth rate under the exponenti al growth model was 0.218 day s -1 , c orrespondi ng to an R 0 es timati on of 2.6 (credi bility interval: 2. 1-5.1). Th e di rect es ti mation of the doubling time of t he epi demic gave a mean 3.6 days (varying from 1.0 t o 7.7) . Figure 1 (part B) shows the Bayesi an bi rth-death skyline plot of the R e estim ates w ith 95 % H P D, a n d in d ic at es th at R e in crease d fro m < 1 (m e an 0 .8 , 95 % H P D : 0 .3-1.3 ) to a mean val ue of 2.4 (95%HPD: 1.5-3. 5) in December 2019, and ha s sinc e remained at thi s value. Table 1 show s the parameter s es ti mat ed using the birth-death skyline plot . The epidemic origina ted an es timat ed mean 3.7 months (c redibil i ty i nterval 3-4) before t he pres ent (BP) , c orresponding t o O c tober - . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 23, 2020. ; https://doi.org/10.1101/2020.02.19.20024851doi: medRxiv preprint November 2019, bef ore the root tree (3. 6 months BP). The es timat ed rec overy r ate (t he time to becoming non-infec tious) was 7. 3 days (CI 4.7-16. 5 days), whe reas t he t ransmi ssi on rate (λ) i ncr eased from 40.5 to 1 1 2.4 in u nits per year in D ecem ber 2 01 9 . O n the basis of t hes e valu es, the gro wt h rate in t he seco nd period i s r=0 .17 (0.16-0. 19), correspondi ng to a mean doubling time of 4.1 days (r ange 3.9-4.3) . . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 23, 2020. ; https://doi.org/10.1101/2020.02.19.20024851doi: medRxiv preprint DISC USSIO N The 2019-nCoV epidemic i s unique i n the history of human i nf ectious di s eases not only becaus e it i s cause d by a novel virus, but al so bec ause of the immediate avail abi lity of epidemi ol ogic al and genomic data ( the fir st enti re genome was publi shed on 24 December 2019) . The pr ompt avail abili ty of researc h data on internet plat forms s uch as the G IS AI D has allowed us and other r esearch groups to make a phylogeneti c reconst ruction of the origin of 2019-nCoV and to share these findings with other scienti sts. The temporal recons truction of the 2019-nCoV phylogeny obt ained in the pres ent study i s in li ne with previ ous estima te s and s uggest s that t he epidemic originated between Oc tober and November 2019, several weeks before the fi rst cases were desc ribed. This was confi rmed by means of c oalescent analysi s and the bir th-death method of estimati ng the origin of the epi demic. The esti mated evolutionary rate i s a l s o i n l i n e w i t h t h a t o f S A R S a n d M E R S v i r u s e s , 12,13 and t he rec ent estimat es c oncer ning 2019-nCoV (ht tp: //virol ogical.org/t /phylodynamic - anal ysi s-67-genomes -08- feb-2020/356). One of the mos t impor tant epidemiologi cal par amet er s when moni tori ng an epidemic is R 0 (i.e . th e n um b er of s econdary cases induced by a single i nfect ed individual in a totally susceptibl e populati on) because it i s fundamental to as ses s the pot enti al spread of a mi cro-organi sm. Its value c hanges duri ng an epi demic bei ng called the effec tive reproduc ti on number (R e ). R 0 is us ually estim ated on the bas i s of the gr owt h r a t e of the number of c ase s. The availabl e epidemiologic al esti mates of 2019-nCoV R 0 range fr om 2.2 to 2.9, al though they changed f rom 1.4 to >7 during the fi r st phas es of the epi demic. 10,14 Rec ently developed evolu tionary model s have made it possible to estimate epi demiological parameter s o n the ba si s of phyl ogenesi s, 9,15 a n d a co a les ce nt a nd a birth -d e at h m eth o ds we re use d to est ima te R 0 and the c hanges i n the R e of t he 2019-nCoV epidemic during a shor t period of time. Thi s has allowed us to make a preliminary es ti mat e that mean R 0 from the begi nning of the epidemic to the fi rs t days of February 2020 was 2.2 (range 3.6-5.8), and the bir th-death skyli ne analysi s showed an i ncr ease i n R e f rom <1 to 2.4 ( CI 1. 5- 3.5) during December 2019. This agrees with the BS P analy si s showi ng an inc rease in the number of infecti ons in the same period of time. On the same ba si s, the estimat ed epidemic doubli ng time was 3.6 days with a c redibility interval b etween 1 t o 7 d a y s . W e a l s o t r i e d t o c a l c u l a t e i t o n t h e b a s i s o f t h e t r a n s m i s s i o n ( λ ) a n d r e c o v e r y r a t e ( δ ) e s t i m a t e d . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 23, 2020. ; https://doi.org/10.1101/2020.02.19.20024851doi: medRxiv preprint using the bi rth-death model, which l ea d to an estimated mean doubling time of 4.1 days, wi th the mos t probabl e values fall ing betwe en 3.9 and 4. 3 days. Previous studi es have suggest ed that the doubling time during the early phases of the epidemi c was about 7.4 days . 10 The di f fer ence in t he estimate here obt ained, may be due to the inc rea sed epidemic growth rate observed during t he las t days of January, or the ini tial del ay in recogni sing and reporting new cas es. Thi s preliminary s tudy ha s some li mi tati ons. The R values and doubli ng time s wer e estimated phylogentically using all of the whol e genomes avai lable in a publi c databas e at the time the study was carried out (https: / /www.gisaid.org/ ). Given the small number of sequences and the r elatively s hor t sampling period, the credibi lity int erval s are wi de and limit the pr eci sion of the es timat es. Moreover, the analysi s incl uded i solates col l ect ed outsi de mainland China a s i t i s a ssumed that t hey all belong to the same epi demic originating in Wuhan. Seri al int erval s were used to estimate t he durat ion of infec tiousness, although we do not y et have any information c oncerni ng the possibl e exi stence and duration of a la tent (pre-inf ec ti ous) period that would contribu te to the se rial int erval. More det ailed a nd acc ura te analy ses c an be made when a l arger number of genomes and more prec i se data on the infec tious period become availabl e. However, al though the R 0 calcu late d o n t h e bas is of t he direc t observation of the number of infec t ed individual s may be af fec ted by omissi ons or delayed notific ations of ca s es, 16 a phylogene ti c es timate of the same parameter may be more reli able. Thi s bec ame particula rly evident rec ently ( on February 12, 2020) when the chang e in diagnosi s clas si fication led to a sudden i ncreas e in the reported cases by H ubei, China (h ttps ://m y e m a il.co nsta ntco nta ct.co m /CO VID-1 9 -Up d ates--- Feb - 12. html ? soid=1107826135286& a id=Kdg8a0rBTAk) . In conclusi on, t hese r esul ts al l owed us to make a phylogenetic estima te of the R 0 of 2019- CoV infection that is similar to that obtained us ing conventional epidemiol ogical methods 17 (https :/ /www.who.int /news - room/detail /23-01-2020- st atement -on-t he-meeting-of-the-i nternati onal -heal th-r egulations -(2005) - emergency -commi ttee-regarding- the-outbreak-of -novel -c oronavi rus- (2019-nc ov ) , and a possibly short er es timat ed doubling time of the number of subjec t s i nvolved at l east duri ng the early phases of the . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 23, 2020. ; https://doi.org/10.1101/2020.02.19.20024851doi: medRxiv preprint epi demic . They al so support the use fulness of phylodynamic as an i mportant complement to cl assic approaches to the surveil l ance and monitoring of an emerging infec ti on, even during the c ourse of an epi demic . . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 23, 2020. ; https://doi.org/10.1101/2020.02.19.20024851doi: medRxiv preprint FI GU RE L EG E N D Fi g. 1. Pa rt A: Bayesi an Skyl ine plot of the 2019-nCoV outbreak . The Y axis indicates Ne and X axis show s the time in year unit s (0=January 30; 0.05=18.2 days; 0.1 =36.5 day s; 0.15=54.7 days and 0.2=73 days before). The thi ck solid li ne re pre sent s the medi an value of the es timat es, and the grey area the 95% HPD. Part B: Birth-death skyline plot of the 2019-nCoV outbreak al lowing two Re i nterval s . The curve and the orange area show the mean Re value s and thei r 95% c onfidenc e i nterval s. The Y and X axes r espectively r epres ent R values and ti me in year s. ACKNOWLED GEMENTS In memory of L i W enliang, Carl o Ur bani and of al l the doctor s and heal th worker s who endangered thei r lives in the fight agai nst epi demics. We acknowledge t he author s, originating and submi tting laborat ories of the s equences from G IS A I D . CON F LIC TS O F IN T E REST The authors declare no conflict of in terest. . 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(which was not certified by peer review) The copyright holder for this preprint this version posted February 23, 2020. ; https://doi.org/10.1101/2020.02.19.20024851doi: medRxiv preprint Table 1. Epidemiological parameters estimated by Birth-death skyline analysis. Re 1 0.8 0.29 1.3 Re 2 2.4 1.5 3.5 o r igin 0.304 0.24 0.36 be come uni nfe cti ous 49.8 22.1 0.36 bi rt1 40.46 7.9 73.8 bi rth2 112.4 82.3 142.9 rh o 0.0044 0.00087 0.0086 tre e - root tMRC A 0.296 0.24 0.35 Pa ra meter Mea n E sti ma te 9 5 %HPD L ow9 5 %HPD Up . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 23, 2020. ; https://doi.org/10.1101/2020.02.19.20024851doi: medRxiv preprint . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 23, 2020. ; https://doi.org/10.1101/2020.02.19.20024851doi: medRxiv preprint