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

Transcript

• 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 occured.
• 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.