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Supporting COVID-19 Hospital Planning and State Reopening Using Model Projections

May 22, 2020

Supporting COVID-19 Hospital Planning and State Reopening Using Model Projections

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  • 00:00I would like to now introduce
  • 00:03our next speaker, a Doctor,
  • 00:05Forrest Crawford, now to Crossville,
  • 00:07is an associate professor of high statistics,
  • 00:10associate professor of ecology
  • 00:11and evolutionary biology,
  • 00:12associated professor of Management
  • 00:14and associate professor of
  • 00:16statistics and data science.
  • 00:18Out of Crawford's work focuses on
  • 00:20mathematical and statistical problems
  • 00:22related to discrete structures
  • 00:24in stochastic processes, mapping,
  • 00:26genealogy, public health, bio,
  • 00:28medison and Evolutionary Science.
  • 00:30Doctor Crawford thank you for being here.
  • 00:35Great, thank you very much.
  • 00:37I'm very happy to be here.
  • 00:39Very honored to be among
  • 00:40these amazing presenters.
  • 00:42I would like to present for you
  • 00:442 recent projects and I won't go
  • 00:47into a lot of technical detail.
  • 00:49There's some mathematics and
  • 00:50statistics behind this work,
  • 00:52and I'm not going to talk about any of that.
  • 00:55I'll just try to talk about.
  • 00:58Uh, the need that we were trying
  • 01:00to respond to when we worked on
  • 01:03these projects and what the research
  • 01:05product square and where to find them.
  • 01:08So this is joint work with post
  • 01:11doc solely omarova Richard Lee.
  • 01:13So hey Lexi and PhD students
  • 01:16Margaret Earline's,
  • 01:17daughter Jinhao Son and also the
  • 01:19COVID-19 statistics policy modeling
  • 01:21and Epidemiology collective.
  • 01:23So the first thing that happened,
  • 01:25I think this was in in late
  • 01:28March was that we heard.
  • 01:31That there was an acute needed to Yale.
  • 01:33New Haven health system for
  • 01:35help with capacity planning.
  • 01:37Trying to prepare the hospital
  • 01:39and health system.
  • 01:40For what was then believed to be a
  • 01:44coming onslaught of new patients,
  • 01:46which had the potential to overwhelm
  • 01:49the health system to overwhelm the
  • 01:51supply of ICU beds and Ventilators?
  • 01:54So we we tried to respond to this challenge,
  • 01:58which came,
  • 01:59I think,
  • 02:00from directly from senior hospital
  • 02:03leadership to build a model,
  • 02:05an idealized representation of
  • 02:07the dynamics of patient flow
  • 02:09through the hospital COVID-19
  • 02:11patients who presented to the Ed.
  • 02:14And then we moved to the floor,
  • 02:17possibly released their move,
  • 02:19possibly to the ICU,
  • 02:21and then received care in the hospital.
  • 02:25And we are especially interested in
  • 02:27helping the health system helping Yale,
  • 02:30New Haven and also other health
  • 02:32systems to plan their expansion
  • 02:34in capacity to plan the ability
  • 02:37to accommodate patients who are
  • 02:39coming in every day so that the
  • 02:42systems would not be overwhelmed.
  • 02:43And we ended up in a very short
  • 02:46amount of time writing software for
  • 02:49web application that implemented
  • 02:51in mathematical model whose
  • 02:52structure I I'm not going to show.
  • 02:55I guess beyond beyond this last this diagram.
  • 02:59And the idea here is that if you are
  • 03:01helping to manage the health system,
  • 03:03then you can dial in a lot of the
  • 03:05features of your health system,
  • 03:07the capacity,
  • 03:08the number of beds you have in the floor,
  • 03:10and I see you.
  • 03:12How you expect the patterns of
  • 03:14change of patient presentations
  • 03:15to the D to change overtime,
  • 03:18you can dial in your expected or
  • 03:21planned capacity increases in
  • 03:22terms of beds into the future,
  • 03:24and you can look to see how how
  • 03:27patients will end up flowing
  • 03:29through the hospital.
  • 03:30So I think this was this was useful
  • 03:33in augmenting some of the existing
  • 03:35capacity planning tools and software
  • 03:37that Yale New Haven Health System had,
  • 03:40and we did receive feedback from
  • 03:42health systems throughout the country.
  • 03:44That they were using this and
  • 03:47other tools to help plan for.
  • 03:50For a very rapidly increasing number
  • 03:53of patients presenting to the D,
  • 03:55so this was this is a project
  • 03:57that was done
  • 03:59very quickly in late March in anticipation
  • 04:02of a very fast increase in the number
  • 04:05of cases were very fortunate in
  • 04:08Connecticut that hospital systems were
  • 04:10able to expand capacity quite rapidly
  • 04:13and at the state level at least.
  • 04:15The number of covered patients
  • 04:18did not outpaced the hospital's
  • 04:20ability to accommodate them.
  • 04:22So I think the the need for this particular
  • 04:25application has waned a little bit
  • 04:28since mid April when hospitalization,
  • 04:30census covert hospitalization census
  • 04:32began to decline in Connecticut.
  • 04:34If there is a second wave of
  • 04:36infections in Connecticut,
  • 04:38we anticipate this tool becoming
  • 04:40very useful and relevant again.
  • 04:43But the main thing that I'd like
  • 04:45to talk to you about today is work
  • 04:48in support of the Connecticut
  • 04:50governor's plans to reopen the state.
  • 04:53Governor Lamont convened a panel of experts
  • 04:56that reopened Connecticut advisory panel,
  • 04:58including many people from Yale,
  • 05:00and I was asked to support the work of that
  • 05:03panel by providing modeling projections,
  • 05:06transmission, modeling,
  • 05:07projections of COVID-19 incidents,
  • 05:09hospitalizations,
  • 05:09and deaths under reopening scenarios.
  • 05:11Articulated at the time in a very
  • 05:13general way by the governor to plan
  • 05:16for interventions like testing,
  • 05:18contact tracing and to assess the
  • 05:21risk of a second wave of infections
  • 05:24occurring over the summer or in the fall.
  • 05:28Following reopen and release of
  • 05:30contact that had been suppressed
  • 05:33during the state lockdown.
  • 05:35As you probably know,
  • 05:38Connecticut began its reopening
  • 05:40phases yesterday on May 20th.
  • 05:43And the work of this this committee
  • 05:45to assist in that process may
  • 05:48be coming to a close.
  • 05:50But I think that there is a very
  • 05:52important ongoing need for projections
  • 05:54to inform decision making and
  • 05:57epidemiological study design at
  • 05:58the Department of Public health
  • 06:00and at the state level overall,
  • 06:02as the state considers how to move
  • 06:05forward in its reopening phases,
  • 06:07whether there is a need to revert to
  • 06:10a previous more restrictive phase and
  • 06:12how this process should play out.
  • 06:15In particular,
  • 06:16I think policymakers are very
  • 06:18interested in having an early
  • 06:19warning system that could tell them
  • 06:21if there is a coming but hidden
  • 06:24wave of new infections that will
  • 06:26become hospitalizations and deaths
  • 06:27in the near future.
  • 06:29I think that it is fair to say
  • 06:32that Connecticut policymakers,
  • 06:33along with a lot of decision
  • 06:35makers throughout the world,
  • 06:37have access to very high quality data
  • 06:40streams that describe the current
  • 06:41state of the pandemic in their area.
  • 06:44Here in Connecticut,
  • 06:45the governor has access to various
  • 06:47dashboards and reports daily reports
  • 06:49from the Department of Public Health
  • 06:51on the number of tests administered,
  • 06:53the number of positive tests the
  • 06:56Connecticut Hospital Association
  • 06:57reports daily.
  • 06:57The hospitalization census from
  • 06:59the previous night.
  • 07:00The number of beds that are theoretically
  • 07:02available for kovid patients,
  • 07:04including search capacity and beds that
  • 07:07have been added on a temporary basis.
  • 07:10Decision makers have access to near
  • 07:12real time information about case counts
  • 07:14and deaths and possibly excess deaths
  • 07:16that are occurring in major Health Systems.
  • 07:19An outside.
  • 07:20And this is very good policy.
  • 07:22Makers have access to this
  • 07:24real time information.
  • 07:25But that information alone
  • 07:26may not be enough to tell them
  • 07:29when a second wave is building,
  • 07:31and about two occur, and if that is
  • 07:34going to occur sooner this summer.
  • 07:36The model projections that
  • 07:38my group has been developing.
  • 07:40Have the ability to tell us about
  • 07:43possible futures instead of the
  • 07:44current state of the metrics that
  • 07:46the state has chosen to track.
  • 07:48What we're really interested in is
  • 07:50what might occur in the future.
  • 07:51What are the things that we can't
  • 07:54see today that will become observable
  • 07:56two or three weeks from now?
  • 07:58So in particular,
  • 07:59we want these projections to inform
  • 08:02reopening phases in the state.
  • 08:03The decision about how and even whether
  • 08:06to open schools for for young people,
  • 08:09and also colleges and universities.
  • 08:11How to inform efforts to expand
  • 08:14testing and contact tracing in a way
  • 08:17that is equitable and also targets
  • 08:20the areas that are highest need.
  • 08:23And how to develop continued or
  • 08:25modified distancing guidelines
  • 08:27into the future and possibly
  • 08:29change those guidelines as needed.
  • 08:34And in doing this work we asked ourselves
  • 08:36and probably other people ask themselves,
  • 08:39does the world really need another
  • 08:42COVID-19 transmission model?
  • 08:43and I think you know at the worldwide level,
  • 08:47even at the national level,
  • 08:49the answer is probably no.
  • 08:51But locally at least I
  • 08:52think that Connecticut does.
  • 08:54We saw a very acute need,
  • 08:56especially at the state level right
  • 08:58now to develop a scenario analysis
  • 09:00tool that is specifically responsive
  • 09:03to the needs of Connecticut leadership
  • 09:05as they plan to re inform to inform
  • 09:08reopening strategies to reopen the state
  • 09:10and to design interventions that are
  • 09:12appropriate for Connecticut specifically.
  • 09:14And to do that we have access to a
  • 09:16lot of data streams that essentially
  • 09:19none of the national level.
  • 09:21Transmission modeling efforts have access to.
  • 09:24In particular,
  • 09:25we have a connection to the
  • 09:27Connecticut Hospital Association,
  • 09:29so we know exactly how many
  • 09:32patients are hospitalized.
  • 09:33Throughout the state and what the
  • 09:35bed capacity is as a dynamically
  • 09:38changes overtime.
  • 09:39We can calibrate transmission models
  • 09:41in particularly clinical models,
  • 09:42of what happens to patients after
  • 09:44they enter the health system using
  • 09:46patient trajectory data from Yale.
  • 09:48New Haven health system.
  • 09:50We've accessed at Yale here.
  • 09:53Fortunately to the ale emerging
  • 09:55infections program,
  • 09:56surveillance data from DPH and
  • 09:58close connection to people who are
  • 10:01planning and conducting testing
  • 10:03and seroprevalence surveys to
  • 10:05inform further scientific efforts.
  • 10:08I hope that in the future we will
  • 10:10continue to have access to colleagues
  • 10:12at the Department of Public health
  • 10:14who are actually implementing
  • 10:16the intervention strategies.
  • 10:17Contact tracing and testing,
  • 10:19and encouraging individuals who
  • 10:21test positive to isolate themselves
  • 10:22and we want to be able to help
  • 10:25them design those interventions.
  • 10:26So we built a model.
  • 10:28I'm not going to show the structure.
  • 10:31It is a generalization of the sci,
  • 10:34our class of transmission models
  • 10:35that has been described previously.
  • 10:37Today we fit that model along with
  • 10:40the information that we have about
  • 10:42when the governor closed schools
  • 10:44and when the state lockdown occurred.
  • 10:47To produce projections,
  • 10:48and here I'm showing projections that
  • 10:51begin in early March and we have real data,
  • 10:54actual observed data up to,
  • 10:56I think yesterday overlaid as dots.
  • 10:58So on the left we have hospitalizations.
  • 11:02Reported an projected and we have
  • 11:04cumulative deaths on the right
  • 11:06and the model overall recovers.
  • 11:08Historical dynamics of hospitalizations
  • 11:09and deaths very, very accurately,
  • 11:11and I think this is partly because
  • 11:14we have very specific information
  • 11:16about what the governor did and when.
  • 11:19And how those interventions affected
  • 11:23transmission and these downstream outcomes?
  • 11:27Here are some projections that the
  • 11:29group just finished working on this.
  • 11:31I should have said earlier this is
  • 11:33specifically joint work with oleum,
  • 11:34rozafa and and Richard Lee,
  • 11:36who have worked tirelessly over the
  • 11:38last couple of days to put all of
  • 11:41this together and also to write 2
  • 11:43reports which I'll tell you about
  • 11:45in the moment.
  • 11:46So in the upper left hand corner we have.
  • 11:50A representation of the amount of
  • 11:53interpersonal contact that occurs
  • 11:54in Connecticut,
  • 11:55historically prior to March 20th.
  • 11:58Sorry, May 20th the first drop is
  • 12:01due to the governor's closure of
  • 12:04schools in the second drop is due
  • 12:07to the state stay at home order.
  • 12:10And the changes in that contact
  • 12:13curve that occur after May 20th.
  • 12:15Our guess is this is a scenario
  • 12:18that we developed based on
  • 12:20ideas about a slow reopening,
  • 12:22in which contact between
  • 12:24individuals returns to baseline
  • 12:26or returns to normal very slowly,
  • 12:28and by slowly I mean that 10% of
  • 12:31this latent suppressed contact is
  • 12:34released roughly once per month.
  • 12:36And so the time series of contact
  • 12:39going forward is just the step
  • 12:42function that increases by 10% of
  • 12:44the suppressed amount every month.
  • 12:47So this is what we imagine.
  • 12:49This is not necessarily what
  • 12:50will occur in real life.
  • 12:51It could be better, could be worse,
  • 12:53but this is one scenario that we
  • 12:55want to present to the governor.
  • 12:57Um?
  • 12:57And here we look at the implications
  • 12:59of this scenario in terms of new
  • 13:03infections or daily incidents.
  • 13:04In Connecticut we see a small
  • 13:07spike after reopening,
  • 13:08but daily incidence remains low and
  • 13:10begins to rise only into late August.
  • 13:13In the lower left hand corner.
  • 13:15We see hospitalizations.
  • 13:17The dotted line above is the overalls
  • 13:21hospital bed capacity in Connecticut,
  • 13:24including temporary or search beds.
  • 13:27And you can see that under this
  • 13:29very slow reopening scenario,
  • 13:31hospitalization continues its slow decline,
  • 13:33becomes very flat in July,
  • 13:35and part of August,
  • 13:37and begins to rise very slowly
  • 13:39as we get towards September.
  • 13:41But overall hospitalization remains
  • 13:43well below the census peak which
  • 13:46occurred in mid April and likewise
  • 13:49deaths begin to flatten out and.
  • 13:51And we end up with almost 6000
  • 13:53deaths in our simulations.
  • 13:55In this scenario,
  • 13:57under slow reopening,
  • 13:58I think this is an optimistic scenario.
  • 14:01Here's a more pessimistic scenario in
  • 14:04which contact for returns much more
  • 14:06quickly to the pre lock down baseline.
  • 14:09Here we release 10% of this latent
  • 14:12suppressed contact every two weeks.
  • 14:14This is a much more rapid rise in contact.
  • 14:18Again,
  • 14:18we don't know what exactly will happen when.
  • 14:22People return to work and maybe
  • 14:24children return to summer camps in
  • 14:26day cares and things like that,
  • 14:28but this is perhaps a more pessimistic
  • 14:31scenario in which people experience much
  • 14:33more interpersonal contact than they did,
  • 14:35say,
  • 14:36a week ago.
  • 14:37Here we see a really dramatic rise in
  • 14:40daily incidents into August and September.
  • 14:43Uh,
  • 14:43with very large numbers of individuals
  • 14:46getting infected per day in Connecticut.
  • 14:49Likewise,
  • 14:50hospitalizations rise very dramatically
  • 14:51in August under this scenario,
  • 14:53and we are looking at the possibility
  • 14:56of possibly exceeding hospital capacity.
  • 14:59Even the surge capacity by mid
  • 15:01August or early September,
  • 15:03and this is very bad because people
  • 15:06who need hospitalization but don't
  • 15:09get it are very likely to die much
  • 15:12faster than they would otherwise.
  • 15:14Likewise,
  • 15:15here we see a dramatic increase
  • 15:17in deaths in August,
  • 15:18and it just gets worse into
  • 15:21September under this scenario.
  • 15:22So I think in reality,
  • 15:24what will occur in Connecticut is
  • 15:26probably something between these
  • 15:28two extreme scenarios, but these,
  • 15:30I think might be benchmarks against
  • 15:32which we measure the governments
  • 15:35true response and the response of
  • 15:37the people in terms of their contact.
  • 15:40We're not just interested in looking into
  • 15:43a crystal ball an predicting the future.
  • 15:46We also want to be able to inform
  • 15:50concrete intervention efforts,
  • 15:51including scientific intervention,
  • 15:53with scientific efforts to learn
  • 15:55more about the Epidemiology of
  • 15:57COVID-19 specifically in Connecticut.
  • 15:59In particular,
  • 15:59the design and planning and
  • 16:01implementation of future seroprevalence
  • 16:03studies will require accurate
  • 16:05estimates of cumulative incidence.
  • 16:07That is,
  • 16:08the number of people in Connecticut
  • 16:11who have evidence of prior infection.
  • 16:14And so these are things that
  • 16:16actually will come out of the
  • 16:19model projections if you plan to
  • 16:20run so prevalent study in a month,
  • 16:23we can tell you under different scenarios,
  • 16:26roughly how many people are likely
  • 16:28to have evidence of prior infections
  • 16:30at that moment under the assumptions
  • 16:33articulated in the model.
  • 16:34So we hope that this tool will
  • 16:37be useful prospectively for study
  • 16:39planning and design of testing
  • 16:41and other interventions,
  • 16:43In addition to just predicting the future.
  • 16:46So, uh, so going forward?
  • 16:50We want to be able to share this
  • 16:53information in the form of reports
  • 16:56with policymakers, policymakers,
  • 16:58in the state government,
  • 17:00and decision makers throughout the state.
  • 17:02So we put together a website
  • 17:05along with the code for software
  • 17:08and two reports so far.
  • 17:10One policy report in one technical
  • 17:13report on how the model works,
  • 17:15this website just went live about
  • 17:18an hour ago and now now these?
  • 17:21Reports are posted publicly for
  • 17:23anyone to see as we update these
  • 17:26reports in real time.
  • 17:27We will document the updates and
  • 17:29post new versions on the website.
  • 17:32If we ever change anything,
  • 17:33we will provide a note saying
  • 17:36what has changed so that you can
  • 17:38follow our progress as we go.
  • 17:40We will post these reports roughly
  • 17:43once every four to six weeks to
  • 17:46coincide with the governor's stated
  • 17:48reopening phase plans and so I will.
  • 17:50Paste a link here in the web and
  • 17:53our chat window if you'd like
  • 17:55to check out this website,
  • 17:57you don't have to copy down the URL.
  • 18:00Basically,
  • 18:00over the next few months will try to
  • 18:03provide actionable intelligence to
  • 18:05state decision makers so that they can
  • 18:07better plan the states response an reopening.
  • 18:10In this crisis.
  • 18:11And that's all I have for you.
  • 18:14Thank you very much.
  • 18:17Thank you very much, I'd like.