– Okay, so let's.
Shall we start? Yeah.
So good afternoon, myname is Qianying Lin, I'm gonna present this topic but first of all there's a few things that I should tell all of you guys.
So during the presentingeveryone except me will have their microphoneor camera turned off and if you have questions orcomments on my presentation please enter it into the Q and A section at the bottom of the screen and I will answer the questions once I'm done with the slides and we get into the Q and A section.
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The information about this webinar including what to do ifthere is a technical issue, links to publications et cetera can be found on the MIDAS website.
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Yeah so if everything, like everybody's okay so I will just start.
Hi everybody, good afternoon, welcome to this webinar.
It's my first time doing this so yeah.
This topic is Coronavirus 19 outbreak in Wuhan, China because I'm from China, in retrospect and in prospect.
So first of all I will give a little bit background introduction of myself.
Right now I'm a Data Science Fellow of Michigan Institute for Data Science in University of Michigan, Ann Arbor and before I joined this position I gained my Ph.
D degreein Applied Mathematics at Hong Kong PolytechnicUniversity last year.
My research interests focus on mathematical epidemiology including various kind of infectious diseases including influenza, MERS-Coronavirus, HIV et cetera and also I study some human factors, the impacts on epidemics and the trends.
So here is the outlineof this presentation.
First of all I will give an introduction on the Coronavirus infectious disease and we'll build on the epidemics in Wuhan, China and also the government's actionsand then I will give a basic concept in epidemiologyfor better understanding of this study or our mathematical models.
Then I will talk about two of my previous publications on reporting rates and R naughts that will explain a little bit later and then also the impactof governmental actions and individual which is closely related to the situation rightnow in United States.
Last is the Q and A section which may be like 10 or 15 minutes.
So here is the simpleintroduction on the virus for all of you to understand.
I have some knowledge on the viruses.
It's first confirmed inDecember 2019 in Wuhan, China.
It is the novel Coronavirus which means at the very beginning noone knows things about this, everything is unknown.
It's genetically close to the other two, the previous Coronavirus, which are SARS in Hong Kong in 2003 and the Middle East RespiratorySyndrome Coronavirus basically in Saudi Arabia in 2015.
This virus would cause pneumonia and the symptoms of infection is non specific.
It could be cough, fever, shortness of breath, diarrhea, swollen eyes et cetera.
So it could be transmittedthrough respiratory droplets through contact with other people especially with infected individuals and until now, the intermediatehost is still unknown.
As of today there are over 81, 000 confirmations and with over 3000 deaths in China.
So there are ongoingmajor outbreaks in Italy, Iran and South Korea.
The outbreaks in United States right now are at the very early stage, although as of today, justlike three minutes ago, there are 1990 confirmations with 12 confirmations in Michigan with 41 deaths in United States.
So before we go deep into the research I will give a brief review of the events and actionstaken by the government in Wuhan throughoutthis outbreak in China.
So the first hospital admission in Wuhan was on December 16th although the firstconfirmation can be dated back to December 1st, 2019 which was not relatedto the seafood market that is reported to be the so called source of the outbreaks.
Then the supermarkets shut down on January 1st, 2020 and then the genome sequences of the novel Coronavirusreleased on January 12th.
There's a first householdhuman to human transmission confirmed in Guangdong Province in China.
Before that it was reportedthat the transmission between humans were limited.
On January 23rd the Wuhan government shut down the publictransportation and then they shut down the whole province.
The Hubai Province publicationand also the government banned all cars in downtown.
On February 11th WHO officially named the disease caused bythe novel coronavirus as Coronavirus Infectious Disease 2019.
So the reason why I go through this review of the events and actions because one of my focus in the studies was to model the trends of the outbreaks incorporating some human factors including individual response to the outbreaks and also governmental actions.
So here are some basicconcepts in epidemiology in case someone may beunfamiliar with them.
These are very crucial concepts.
Let's go to the primary case first.
The whole duration of infections, which is from the start of the infection and so called the end of the infection can be divided into different periods.
So the first category is we divide into latent period which is from the start of the infection to the start of infectiousness which means this patient has the abilityto spread the disease to infect other people after that.
Then from the start of the infectiousness to the end of infectiousnessthat is so called the infectious period.
So during this period, this primary case can infect other people which can be the secondary case.
By the end of the infectiousness then it's the end of infection.
The second way to divide thiswhole duration of infection is from the start of infection to the start of onset of the symptoms which is called the incubation period.
So by comparing the latent period and the incubation period we can see that these individuals can spread the disease even before the symptoms appear.
So from the onset of the symptoms we have the end of the symptoms and then the end of the infection.
So there are two conceptsdefined by these periods.
One is called the generation time which is the time intervalsbetween the primary infection and the secondary infection.
And another one, which is equal to the generation time in value is called thecritical onset serial interval which you can find everywhere.
It is defined as the onset of the symptoms in the primary case tothe onset of the symptoms in the secondary case.
So these two concepts, these two time intervals are equal in value but obviously the serialinterval could be much more observable and thenfrom previous literature the median incubationperiod of Coronavirus 2019 is about 5.
Reported by the news itcould be as long as 14 days.
That's why people willsuggest 14 days self isolation when you contacted with infected individuals.
So another important or maybe well known conceptis called R naught or R zero, like the basic reproduction number.
So it has the expectednumber of secondary cases infected by one primary infection in the pure susceptible population.
What is a pure susceptible population? Which means that these people are healthy and immune not protected by vaccine or maybe by other antibodies.
So when these properties, when R naught is less than one the disease will die out in the long run.
Otherwise it will probablyspread across the population.
So this is a very critical property to evaluate the contagiousnessand the severity, contagiousness of the infectious diseases and the severity of the epidemics.
So here I share some information on the severe intervals andR naughts of other diseases.
So, for example, measlesis high contagious and very severe disease which has severe intervals of 10 to 13 days and R naught of 12 to 18.
MERS in human, I mean inhumans, has severe intervals of six to 7.
8 days and R naught of 0.
3 to 0.
I emphasize humans because it's reported that there's very limitedtransmission between humans.
The outbreak was due to the zoonotics, that is the camel to human transmissions.
So one thing that I should emphasize is the influenza, likethe Spanish pandemic in 1918 has severe intervals of two to four days and R naught two to three.
So please remember these two quantities.
I will compare it with the coronaviruses because they are both pandemics right now.
My previous publication, my first publication mainly focused on estimating R naughts, the basic reproductionnumber of Coronavirus Disease 2019 in Wuhan.
So I highlight the key components that we use to estimate the R naught which isfirst the growth rate.
We assume that the casesincrease exponentially when one individual could infect more than twothe cases grows exponentially and then that is the severeinterval that I mentioned before because it differs from people to people so we assume that itfollows the distribution and the third one is the reporting rate which is a pretty crucial factor here both in modeling the epidemicsor estimating the R naught.
That is a ratio of reported confirmations over the actual infections.
So when reporting rateis 100%, which means it's perfectly correctin reported new cases, but when it's lower than 100%there's some under reported cases that is like potential cases that can spread the disease or over reported which means that it is larger than 100%.
It can be due to the previousunder reported cases.
So we use this formula to calculate, to estimate the R naught.
So here are some results.
So I showed this figure in order to mention that the reporting rate is pretty crucial in estimating the basic reproduction number.
So actually we assume different reporting ratios increments and these two are the extreme cases here.
So first of all from the first row we assume 100% reporting rate, all the time there is no under reporting, no over reporting, every cases are correctly reported.
So here is the figure in the middleis the daily new cases.
Blue dot is the so called adjusted cases.
Green circle is the reported cases.
They match perfectlyand we can use the curve and the formula I mentioned before to estimate the basic reproduction number as 5.
71 with 95% confidence interval as 4.
24 to 7.
Then in the second row weassume an eight fold increment which means that at thebeginning of the outbreaks there is only 12.
5% reporting rate and then it increase gradually to the end to peak at100% on January 21st.
So from the middle figures we can see that daily new cases, the first one is about 41 cases.
It's under reported, the actual number here is eight times of the reported cases which is over 300 here.
Then this one is zero cases and from here we increase the reporting rate and then on January 21st it's 100%.
So here is the figure of cumulative cases thenusing this increment of reporting rate.
So we have these blue dots here.
We use these so calledadjusted actual infections to estimate the basic reproduction number so we can estimate the R naught at 2.
24 with a 95% confidence interval 1.
96 to 2.
So these two examples show that the reporting rate is very, very important in estimating the basicreproduction number which is critical property on evaluating thecontagiousness and severity of the epidemics.
So as we all know that at the beginning, because nobody isfamiliar with this virus, everybody knew nothing about the virus, due to various factors, for example lack of the toolkits to testing results or like people are notaware of these diseases so there will be manyunder reporting here.
But when the public aware of this disease the reporting rate will increase.
So the reporting rate is not a constant.
It varies through time.
So here is the main conclusion here.
The changing reporting rate over time had a great impact on estimation of the basic reproduction number, R naught as I showed before.
So the under reported couldmake an over estimation of the basic reproduction number.
Also the estimation of R naught dropped from 5.
7 to 2.
24 when weadopt different ranges of increment in reporting rates.
So one of our publicationsactually estimates the reporting rate can be as low as 5% at the beginning and with that result we estimate the R naught at 2.
56 with a 95% confidence interval of 2.
49 to 2.
So next we should talk about the relations between theCoronavirus Disease 2019 and the 1928 influenza pandemic.
So they share a lot of similarities here.
For example, they havea very similar R naught which is two to three, suggested by the WHO actually.
They have a relativelyshort mean serial interval.
For example, Coronavirushave around four to five days and influenza have 3.
So here I highlightedthe four to five days.
If you guys remember I mentioned that the mean incubation period of the Coronavirus is about 5.
1 days, so a relatively short serial intervalwith a relatively long incubation period could actually imply asymptomatic transmission, that is like a patient can infectother people even before the symptoms appears which is a pretty crucialproperty of these viruses here.
They also have sharedrelatively low fatality rates, it's around 2% and also they have a significant proportion of death due to pneumonia after infection.
So with all these similarities between these two pandemics we can refer some properties of the1918 Spanish pandemic to model the dynamics right now in Wuhan, China or maybe around the world.
So first I will introducethe epidemiological compartmental models.
So basically in here, basically this model is likedivide the whole population into different classes which is different stages of infection and then we assume the rates of transition between these classes orinteractions between these classes and we modeled these transitions, the size of these classes in ordinary differential equations.
So by these definitions it seems that it's very, very flexiblebecause we can divide the classes as detailed as we want as long as we have the rates of transmission between them.
So it could be very complicated and very realistic to the data.
And then let me introducethe conceptual model for the Coronavirus 2019 outbreak in Wuhan that we used in our paper just published.
There's a few assumptionswe make for the models for better fitting, or not better fitting, for more realistic and also much easier to understand.
So first we have the zoonotic, that is the animal to human transmissionat the first month because we have about 41 cases at the very beginning.
So then we assume these first 41 cases are due to animal to human infections and after that all infections were caused by humanto human transmission.
And then because the outbreak is during the period of Chinese New Year, so everybody is rushing to go back to their hometown, so there would be aroundfive million people left Wuhan from December 31st 2019 to January 22nd 2020.
So the whole population in Wuhan City is 14 million which means that the outbreak after the city was locked down, there areonly nine million people stayed at the city.
So also we assume that10% of the population are not susceptible to the Coronavirus because from previous reports the infected rate among children, especially among childrenunder 15 is pretty low so we assume that children is not that susceptible to the virus than adults or than elderly people.
So here two componentsare not included here due to limited knowledge.
For example, we did not includeasymptomatic transmission because right now we do not know how large is the proportion of the population could havethe asymptomatic transmission and also we did notinclude the temperature because the impact of the temperature on the outbreak was still unknown.
Here is a detailedexplanation on the model.
I know this is a little bit advanced and too much informationbut it's very crucial so please pay a littlebit attention to this.
So as I previously mentionedthe compartmental models divided the whole population into different classesor different stages.
For example in this modelit's a very basic and classic SEIR model framework.
For example this population was divided into susceptible, S, E is the exposed orlatent which is infected but not infectious, and I is infectious.
That portion of populationcan have the ability to spread the disease, and R is for recovered or removed.
Sometimes just eliminatedfrom the population by isolated, quarantined et cetera.
So F here, as I mentioned, we included zoonotic introductionthe previous 41 cases and then D here representsthe daily number of severe cases.
It also represents the perception of risk regarding the severity of the epidemic which play a crucial role in human response, humanbehavior or response towards the diseases.
Because when you find out the number is pretty high, for example the number of deaths is prettyhigh, you could be very cautious about your daily life.
For example, frequent hand washing and maybe keep some distancewhen contacting with people and C here is the number of all infections including the reported or unreported.
That's how we include the changing under reporting ratios here.
For beta 0 is the initial transmission between S and I, like beforethe government's action, before everybody know the virus and had some response to it and beta (t) is the timevarying transmission rate.
It's changing due tothe governmental action, due to the individual response.
I will explain that in detail in the next slide how we model thischange in transmission rate.
So other parameters it's like the inverse sigma is the latent period in days and inverse gamma actually is the infectious period in days and also because fivemillion people left Wuhan before the lockdown of the city so we have the emigration rate here and we have the rate of severe cases and decay of perception which means that people is really easy to forgot about the severity of the diseases, so just lose their cautions I guess.
So in this slide I will explain how we modeled the government action and individual reaction and incorporate them into thetime varying transmission.
N is the total population in Wuhan.
It starts from 14 million andthen dropped to nine million after the lockdown of the city and then D, as I explained previously, is the daily number of severe cases, also represent the perception of risk on the virus.
Alpha here, we call it thestrength of governmental action and kappa here represent the intensity of individual reaction.
So these three components here build up the whole timevarying transmission.
So one minus alpha, whichincludes the reduction of transmission ratesby governmental action, for example governmental action including transportation shut down and quarantine and also hospitalizations, isolation et cetera.
These personal, individual reaction includes hand washing, decreasing contacts with people for example also represent a reduction of transmission rate by personal reaction to the proportion of the severe cases.
So right now we go intothe finding of this paper.
So from figure (a) we havethe daily new infections with a reporting delay which we assumed there's 14 days of reporting delay.
Here please pay attentionto the Y axis here as in lock 10 so the axis is a little bit not realistic but I will explain why.
So there we have the gray dotted line here represents the reported cases.
So we have assumed three scenarios here.
One is called naive which means the government did nothingto control the diseases as well as the individuals did nothing to prevent infections.
So you see that if everybody did nothing it will go up as a peak over 100, 000 cases, one day.
That is the daily new infections.
So the second scenariohere is we only include the individual reaction, which means only depends on individualsto wash their hands, cancel any gathering, decreasecontacts with other people.
So you become about 10, 000 a day here and like keep for a long time.
Once we include both factors which is the individual reactionand also the governmental action here, which is the green line, you can see it's veryeffective control here, drop vastly and by the end of April the epidemic will be totally controlled.
So all the parameters are referenced from a previous paper on1918 influenza epidemic and other official news releases.
So in figure (b) we also calculated the reporting rate here.
So reporting rate means the green line, the number in gray dotted line over the number in green line here.
So at the very beginning, early January here, the reporting rate is very low and then it jump a little bit to about 50% in early February.
So it shows that the reporting rate is exactly time changing and some of you may ask how about like this over 200% reporting cases here? Because it's due to theunder reported cases in the early stage so the government just assigned examinationof the previous cases so that's why it had the overestimated condition here.
We also did some sensitivities on the governmentalactions which is alpha, the trends of the governmental action and also the individual reactions controlled by kappa.
From figure (a) alsois daily new infections with a reporting delay in lock 10.
We use different strength here from weak to strong.
You may see that is avery, very effective way to control or maybe end the outbreaks with a strong governmental action, for example shut down the transportation, shut down the school et cetera.
Then in the second figure here, figure (b) we also use a weak intensity of individual response toa strong intensity here and we find now that it helps to control and you haveto accelerate the control, the end of the outbreaks under a very strong governmental actions.
So the second part conclusion here is the outbreak in Wuhan would be completely controlled by the end of April under current policies and restrictions.
Second, the reportingrate is time-varying.
At the beginning, in early January, it was below 10% and then increased to around 50% in early February.
Individual caution in daily life helps to reduce the transmission and accelerates the end of the outbreaks.
Governmental actions, forexample holiday extension, travel restriction, quarantine et cetera are very effective means tocontrol and end the outbreaks.
So last part right now some discussion here.
So since United States is actually at very, very beginning of the outbreak United States is actually here, at the very beginning of the outbreak by comparing to stages of other countries.
For example China is at the end, almost end the outbreaks here.
South Korea, Japan andSingapore and United States is only at the beginning stages.
So should the government ofUnited States do the same thing? What kind of factors we should consider to make political decisions? In my opinion there aresome factors including population density, age structure because I mentioned that young people or maybe young children are less susceptible to the disease than older people.
So also the transportation patterns because the outbreaks in Wuhan, Wuhan is actually the centralof the high rate train in China so hundreds of thousandsof travelers, passengers would go to Wuhan and transferto other cities every day.
Also the individual reaction here.
For example in SouthKorea, once the government announced the outbreaks everybody just like on the streets, nobody on the street and everybody was wearing masks and doing veryfrequent hand washing.
So these factors we should consider but I should emphasizethat governmental action would be very, very effective tool, effective means to controland end the outbreaks.
So here is the references that I mentioned or that I used for the parameters.
Okay thank you for you guys so here we can have the Q and A section or if you want furtherquestions in detailed discussion you can just email meafter the presentation.
Thank you guys for your coming.
– [James] All right, and this is James Walsh here, I'm administrative support here at MIDAS.
I'm gonna be helping with the Q and A.
So we're just gonna take a second to read through some of the questions that have come throughduring the presentation and then I will read those out loud and try to answer as many as we can in the time that we have left.
So we'll start with thefirst one that came through.
So first question we had today was, why would we not allowyoung, healthy individuals to interact with oneanother in the community in order to allow fortransmission of Covid-19 within this low risk population which would then generate herd immunity, therefore protecting community at large? – Okay so the question here is we do not reallyunderstand the mechanics of the disease so it would be very risky to just allow so called young individualsbecause sometimes young individuals canhave very severe symptoms after infected.
So I would say it's very risky.
It would be very riskyhere to just allow them to contact, yeah.
– [James] More thathave come through here.
– Yeah this one.
– This one? All right, so next question.
Is the mortality rate of Covid-19 likely substantiallydifferent than statistic being published by theWorld Health Organization and the Center for Disease Control? For example data from South Korea where population based testingis being conducted appear quite different from data in places where just symptomaticindividuals are being tested.
– Yeah I would say of course because for a country who only testedsymptomatic infections, which means in my opinion there is a lot of underreported rates here.
You do not know these asymptomatic and actually for theasymptomatic transmission we do not know whether these individuals are asymptomatic or presymptomatic which means that the symptoms does not appear yet and maybe appear later.
So the fatality ratewould be a huge difference and also, you know, South Korea government dida really, really great job in controlling the outbreaks.
So it's a different story in South Korea because the outbreak in South Korea is due to a super infectious event.
A patient that belongs to a religious group they just wandering around and also the groupmembers wandering around and there is major outbreaksin one of the cities with a relatively dense population.
After that the South Koreagovernment immediately locked down the religious facilities and also have a very strict testing or examination or quarantine to everybody and the public in SouthKorea very cautious about the outbreak so they did a very good controlling measuresto stop or to end or maybe to stop the disease.
Actually South Korea is almostat the end of the outbreak.
– [James] Answering some of the questions that we can (laughs) handle in here so we'll look through some ofthe next ones coming through.
– No, no so skip that.
(James mumbles) – [James] There for you.
– Take this.
So what is the risk ofreemergence of epidemic in Wuhan after a completecontrol has been achieved by the end of April? – So I would say thereemergence actually is like most of the cases in China or maybe in Wuhan right now are actually imported cases from other countries, for example from Italy or maybe from Iran.
So the risk of reemergencewould totally depend on the testing or thequarantine at the airport.
So right now I would say the risk is very low because Chinese governmentdid a pretty great job on surveillance or testing theincoming travelers right now.
(James laughs) Answer, yes.
– [James] So, from Aaron King.
You effectively used thedata on the time course of the epidemic in Wuhanto infer the effectiveness of the government and popular response.
With more data on thespread of the disease to other localities within China can you test these inferences? – Yes, I mean with more data including, I would say including the transportation datalike how many passengers go and leave Wuhan every dayto other locations in China and the population densities and also because the government actionvaries in different location we could test it and usestatistical inference methods to test this data and also compare the strands or compare intensities of the strands of government actions and also the intensitieson individual response in different locations within China or even like in other countries.
Okay so yes.
– [James] Good, so from Paul Franco.
We have many thanks forthis important discussion.
Would you say that the serial interval and the R naught are verysimilar to the flu of 1918? Is the main differencethe higher fatality rate of Covid-19? – Yes I would say that but another major difference between pandemic 1918 and the 2019 is the Covid has substantial or maybe some proportion of asymptomatic transmission which we are not clear about that part and also in accompanying the incubation period couldbe as long as 14 days.
So that's why, becausedue to this asymptomatic transmission here that's why I would say the prompt and also in time measure taken by the governmentwould be very crucial and critical to decrease the number ofinfection or even stop or even end these outbreaks.
The fatality rate actually depends on many other factors because as far as I know the fatality rate in South Korea is very, very low.
It's under 1% but in China is about 2% and also in United States it's about 3% because the major outbreak in Seattle is around elderly people.
I don't know actually (laughs).
– [James] Think it looks like our Q and A has slowed down just a little bit here.
If we weren't able toget to your questions either they're just kinda out of the scope of what we're working with or there may just nothave been enough time to prepare an answer.
So I think at this point we will wrap things up.
– Thank you all for coming to this webinar Thank you so much.