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Covid Exit Strategy & Covid Act Now are now one team

We’re excited to officially announce that Covid Exit Strategy (CES) and Covid Act Now (CAN) are joining forces. Updates to CES will continue until December 20, with the CES team joining CAN to grow the data and tools we provide for you. 

We have worked together to identify new official data sources and align on the thresholds for key indicators in order to make this unification possible. CES has been an invaluable resource to CAN, and we are especially grateful to have two key CES players join our team. CES’s founder Ryan Panchadsaram, currently at Kleiner Perkins and formerly United States Deputy CTO, will join CAN’s Board of Directors. Marta Wosińska of the Duke-Margolis Center for Health Policy and a CES advisor will join our Advisory Board. 

This consolidation represents months of collaboration between our two teams to create a unified COVID framework and resource that better helps Americans reach a shared understanding of this pandemic. 

Indigenous Peoples’ Day: Seeing COVID’s impact on majority Native American counties

On Indigenous Peoples’ Day, our team built a tool to help visualize the impact of COVID on majority Native American counties. 

Why did we build this feature?

As an organization, we are keenly aware of the disproportionate impact that COVID has on Native American populations. According to CDC data, Native Americans have 5.3 times higher rates of hospitalization, 1.4 times higher rates of death, and 2.8 times higher case incidence than their non-Hispanic, white peers. 

What can you do with the new feature?

With this launch, you can now see COVID cases and deaths for all counties in the U.S. with a majority Native American population (>50% American Indian or Alaskan Native). Moreover, users can compare this data against data for all counties in the U.S., which will help them understand how outcomes have differed for Native American communities.

What is our data source for this new feature?

Our team relied on the latest demographic data from the census. In order to best visualize the data within our current framework of county-based reporting, our team decided to look at the U.S. counties where the population was at least 50% Native American. 

What are the limitations of our new feature? 

Our team is fully aware that our new feature is an incomplete tool–U.S. counties where the population was at least 50% Native American only represent a small fraction of the total Native American population in the country. To be specific, these counties represent a population of 337,000 Americans, the majority of which are Native American

What’s next?

Next, we hope that COVID data is more widely reported and made accessible by race and ethnicity. By closing the gap on Native American data, we can share a more complete understanding about the virus and keep more people safe.

Some things we observed just today:

Native American majority counties had much higher rates of daily new cases than the national average.

Deaths in Native American Counties were higher than the national average.

Why are there such disproportionate impacts?

There are many possible reasons for this data, and we still don’t know all of them. However, research shows that contributing factors may include high prevalence of underlying health conditions, disproportionate rates of poverty, and a persistent underfunding of the health systems serving Native American communities. 

In general, the higher-than-average rate of underlying health conditions in Native American communities has existed long before COVID. According to the Indian Health Services (IHS), as of 2010, Native Americans suffered higher mortality rates than other ethnic groups in the U.S. for many health conditions, including chronic liver disease, type 2 diabetes, suicide, and lower respiratory diseases, leading to a life expectancy 5.5 years below all other ethnic groups. This increased severity of health risks faced by the Native community may be related to the fact that 26% of Native Americans nationwide live in poverty and on average receive significantly less spending on healthcare. Before the pandemic, Native American youth were already experiencing the fastest growing rate of disease of all demographic groups in the country. 

We are not experts in this space, the data that we have access to is limited, and interpreting the data is itself a complex challenge. However, we hope that the data we are surfacing today can serve as one of many steps needed to move towards better health outcomes and quality of life for more Americans. 

We visualized COVID’s spread across every U.S. state and county. Here’s what we discovered:

Just by looking at numbers and daily updates, it can be hard to decipher a clear narrative of how COVID has affected the U.S. and its diverse regions over time. 

To give you a better understanding, our team at Covid Act Now took all of the data we’ve been collecting and created a time-lapse of COVID’s spread across 3,000+ U.S. counties since March. Watch what happens:

Notice anything interesting? Here are 5 key patterns that our team saw:

1. More movement = higher COVID incidence.

Mobility data, or data relating to transportation, show a striking similarity between the percentage of people staying home and cases per 100,000 people (also known as incidence) during the month of August (source: U.S. Bureau of Transportation Statistics).

Especially in the Southeast (which includes states like Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, and Mississippi), only 5 percent to 10 percent of people are consistently staying home, and this is reflected in their red case density values:

2.Masks and other non-pharmaceutical interventions (NPIs) play a powerful role in controlling the spread of the virus.

As America’s viral epicenter, New York pioneered NPIs for COVID prevention. Starting on April 17, Gov. Andrew Cuomo enacted a mask requirement, and this, in addition to other NPIs in New York State, contributed to New York’s eventual containment of COVID (source: New York State). Some of New York’s NPIs include:

March 15: Schools close for the first time.March 28: New York State’s 2020 democratic primary is postponed to limit in-person social contact.April 16: Stay-at-home order and school closures are extended through May 15.April 22: Cuomo announces plans to begin a localized contact tracing program throughout New York State.

From this animation of New York, we can see the success of these policies in limiting the virus from March to June.

3. NPIs not only reduce the spread of COVID within states but also between them.

Throughout the animation, it is clear that COVID spreads rapidly within states, often going from a few red counties to an entire red state within weeks, not months.

The border between Arizona and New Mexico, however, is an example of how COVID oftentimes meets a metaphorical wall at state borders. With the Arizona side of the border solidly red in late July, New Mexico was able to maintain a handle on the virus.

The difference in COVID cases in the two states can be attributed to differing statewide policies:

Masks:New Mexico institutes a statewide mask requirement on May 16, with harsh monetary penalties for mask infractions (source: State of New Mexico).In contrast, Arizona, despite having some of the highest case density in the country, still only “recommends” mask wearing. In fact, until June 17,  Gov. Doug Ducey does not allow local governments to introduce mask mandates (source: Office of the Governor of Arizona).Quick response to outbreaks:It takes until July 9, after Arizona’s cases reach record highs and hospitals reach capacity, for Gov.Ducey to reduce restaurant dine-in services to 50 percent capacity.In comparison, indoor dining is not allowed in New Mexico until June 1, and Gov. Michelle Lujan Grisham quickly passes an executive order in July as Arizona’s cases spike, outlawing indoor dining, closing state parks to out-of-state visitors, and requiring masks while exercising.

The disparity in cases is well reflected in July, when Arizona (left) is almost entirely red, while New Mexico (right) has much more yellow and green:

4. Relaxing NPIs too early leads to flare-ups of COVID.

Let’s look at Georgia as an example here. Although Georgia initially implemented NPIs, such as stay-at-home orders and school closures, several premature reopening policies may have led to further outbreaks in the state. Let’s look at the timeline:

April 24-27: Gov. Brian Kemp allows the reopening of restaurants (including indoor dining), movie theaters, gyms, and more (Georgia ACLU).August 15: Gov. Kemp does not allow local governments (such as Atlanta) to create mask mandates until August 15. Up until this point, there are no mask mandates on either the state or local level. However, only cities and counties that have 19 or more cases per 100,000 people may enact mask requirements for public property. For reference, at 19 or more cases per 100,000, our animation displays dark orange. August 20: University of Georgia and Georgia Tech reopen for in-person classes.

These failures to maintain effective NPIs are reflected in the difference in Georgia between April (left) and August (right) where you can see the number of counties in the red go from approximately 25 percent to 90 percent:

5. When critical safety measures are taken, large, outdoor gatherings may not result in a spike in cases.

While many were concerned that the recent protests and gatherings for social justice would lead to a spike in COVID cases, our map shows that this was in fact not the case. The New York Times produced this map on June 6, when protesting peaked, showing where protests happened geographically:

Via the New York Times, “Black Lives Matter May Be the Largest Movement in U.S. History”

Regions that saw protests include California’s Bay Area and Los Angeles County, New York and Washington DC, and Minnesota/Wisconsin.

If the protests had caused outbreaks, the animation would have displayed meaningful color changes in regions that hosted large protests and gatherings. The lack of outbreaks demonstrates that if we take proper precautions, large gatherings that take place outdoors may not be as dangerous as we once thought.

Symptoms of COVID usually show up two to 14 days after infection (source: CDC). If the protests caused a spike in cases, we would have detected the increase in about two weeks. However, in all of the regions listed above, there was no spike after June 6. See screenshots from June 5 and two weeks later below:

By comparing state and local policies (including mask mandates, school closures, restrictions on indoor dining and other NPIs) to the geographic spread of COVID, a clear picture emerges of how COVID can be contained when society takes action. What’s more, our video depicts visually how states that lift stay-at-home mandates and other restrictions prematurely experience rapid flare-ups of COVID. In contrast, states that keep NPIs in place are even able to stop COVID from crossing their state border.

Check out our  Youtube Channel for our educational videos.

To learn more about Covid Act Now, visit our about page. For more information please reach out on our contact page or by email at info@covidactnow.org.

Sign up to our alerts to stay up-to-date on the COVID risk level in your area.

CAN Compare: See COVID’s Spread Across the U.S.

By: Lexie Kaplan

CAN Compare is our newest feature to give you a quick way to compare states and counties. Specifically, you can: 

Compare states by overall risk level and by each key indicator, e.g. incidence, infection growth rate, test positivity rate, ICU headroom, and tracers hiredCompare counties within a state or nationallyCompare a county to its neighboring countiesCompare just counties within metro areas or just counties outside of metro areasView which counties have significant college student populations

Let’s dive into a few of the more granular features within CAN Compare:

Metro vs. non-metro counties

With CAN Compare, you can look specifically at counties in metropolitan (metro) or non-metropolitan (non-metro) areas. 

So what’s the difference between metro and non-metro counties? We follow U.S. Census definition of a Metro Statistical Area (MSA) as one or more “urbanized areas” of 50,000 persons or more, as well as outlying areas that have strong economic ties to these central hubs. Outlying areas are deemed to be part of a metro area if 25 percent or more of their employed workforce commute to the central county, or if 25 percent or more of the outlying county’s employed labor force is made up of commuters from the central city.In CAN Compare, we define “metro” counties as those belonging to MSAs and “non-metro” counties as those not within an MSA. We think this filter allows for more meaningful comparisons, since metro counties with densely populated urban cores tend to experience COVID outbreaks differently than non-metro counties. 

College counties

In September, as our team was using CAN Compare, we noticed something in common among eight of the metro counties with the highest case incidence: On September 2, six were home to a large college or university that had recently reopened. Of those six, all had seen massive spikes in the past three weeks, coinciding with when many students were arriving on campus. So we decided to display tags for counties that have high college student populations (over 5 percent of the local population). The tags provide an extra layer of visibility to that trend. 

If you are a university administrator, you may want to know if there is a direct correlation between universities opening and local outbreaks so that you can implement more stringent policies for students and faculty living on campus. Likewise, if you are a college student or parent, you may want to know about this correlation so that you can make an informed decision to withdraw from in-person class and seek online alternatives.We’ve thus made it easy for you to see which counties are home to colleges. We hope that this helps you better understand the relationship between COVID spikes and college towns so that you can make more informed decisions for both yourself and your community. 

Nearby counties

In addition to understanding how a given county is performing in comparison to all the counties in the state, we’ve made it easy to rank and compare counties that are adjacent. This helps you quickly discover what areas around you are more or less impacted by COVID outbreaks. 

In September, we tested this feature for ourselves. When we looked at Bergen County, New Jersey, a county just outside of New York City, we used the “Nearby” filter to better understand how Bergen County was performing in relation to New York City and the other nearby suburbs that are likely to have similar conditions.

We found that of the adjacent counties, on September 17 Bergen County had the 11th highest case incidence, performing better than the other suburban counties, Westchester and Passaic, and city counties like Bronx County. 

Try out CAN Compare 

Our hope is that CAN Compare will help decision makers and the public contextualize the COVID risk scores and key indicators. For instance, local leaders may want to understand how their county’s current risk score compares to other similar counties in the state in order to enact appropriate policies. Similarly, residents can better understand how their state or county compares to others on key COVID indicators to know what behaviors or policies might be appropriate. 

You can try out CAN Compare here.

And if you want to get really deep into the data, we recommend you try CAN Analytics, a feature that lets you analyze raw COVID data. 

Infection Rate: Explained

The Infection Rate, or “R(t),” is the number of people one infected person goes on to infect in a specific area, over a specific time. The areas we look at are county and state. The period of time we look at is while a person is contagious (able to spread COVID).

In the picture to the right, R(t) is 3.

If the R(t) is 3, one person will most likely infect three other people, and those three people will each go on to infect three more people and so forth.

R(t) is one of the most important metrics Covid Act Now tracks because it tells us how fast COVID is spreading. R(t), in combination with daily new cases, tells us about how many people are spreading COVID and at what rate. R(t) also indicates risks associated with ICU headroom used.

Here is a video that helps explain infection rate:

R(t) can change based on factors like community behavior (whether there are large gatherings) and their intervention practices (whether people wear masks and maintain social distancing.) 

How Does Covid Act Now rate R(t)?

We use 0.9 as the cutoff for a green score because, at 0.9, the number of infected people significantly declines. This is because each infected person is spreading COVID to less than one other person. 

When looking at R(t), it is important to note both value and direction. In the graph above, on March 31, the R(t) value was critical, but decreasing. On June 7, the R(t) value was high and increasing.

**The dotted line on the right represents values that have yet to be finalized.

There are significant delays between when people change their behavior and when that behavior change is reflected in R(t). When people are infected, it takes time to develop symptoms, get tested, and receive results. Because of these delays, interventions like social distancing or mask orders will take at least a few days, if not weeks, to show a decrease in R(t).

Similarly, by the time you see cases rise in your county or state, COVID has likely been spreading for days or weeks. The lower test positivity is, the more accurate R(t) measurements are because the state or county is missing fewer positive cases.

Where does our data come from for this metric?

To calculate R(t), we use new positive cases and COVID death data from The New York Times.

Infection rate is just one of several important metrics to determine how well your state or county is doing in the fight against COVID. Learn more about daily new cases, test positivity, ICU headroom used, and contact tracing. For a description of assumptions and methodology, please see our references and assumptions document, along with our data sources presentation.

Check out our Youtube Channel for our educational videos.

To learn more about Covid Act Now, visit our about page. For more information please reach out on our contact page or by email at info@covidactnow.org.

Sign up to our alerts to stay up-to-date on the COVID risk level in your area.

ICU Headroom Used: Explained

ICU Headroom Used measures an Intensive Care Unit’s (ICU) ability to handle a surge of patients in the case of an outbreak. Metrics like daily new cases and R(t) inform the potential impact on the ICU headroom. You might see similar metrics called “Hospital Capacity.” At Covid Act Now, we chose to measure Hospital ICU capacity because ICUs treat the sickest patients with equipment, like ventilators, that can save the lives of the sickest patients. 

The White House’s reopening plan, AEI’s plan, and Harvard University’s Safra Center for Ethic’s plan all note ICU headroom used as a key metric for reopening safely. 

Here’s a video that helps explain ICU headroom used:

How Does Covid Act Now rate this metric?

The top row represents the ICU headroom available for COVID patients. If three out of five beds (or 60%) of headroom for COVID patients are in use, risk is considered high (orange).

ICU capacity can change suddenly,  depending on infection rate, daily new cases, test positivity, and reporting delays. We need enough ICU beds to treat patients until COVID spikes are found and controlled. We measure the percentage of ICU headroom that is available to treat COVID patients. 

We chose our cutoffs based on Resolve to Save Live’s policy recommendation, which says COVID cases in the ICU occupancy should be able to double for states to think about opening.

The graph below shows a spike in Georgia’s ICU headroom used in the last two weeks of July. See how quickly the percent of beds used went from 71% on July 19 to 93% Aug 3? If actions have not already been taken to lower infection rate and daily new cases, the ICU will reach full capacity and will likely have to resort to crisis standards of care.

Where does our data come from for this metric?

To calculate ICU headroom used, we use data from CovidCareMap.

ICU headroom used is just one of several important metrics to determine how well your state or county is doing in the fight against COVID. Learn more about daily new cases, test positivity, infection rate, and contact tracing. For a more technical, “kitchen sink,” description of assumptions and methodology, please see our references and assumptions document, along with our data sources presentation.

Check out our Youtube Channel for our educational videos.

To learn more about Covid Act Now, visit our about page. For more information please reach out on our contact page or by email at info@covidactnow.org.

Sign up to our alerts to stay up-to-date on the COVID risk level in your area.

Test Positivity: Explained

Test positivity is the percentage of COVID testing that comes back positive. It is not the total number of positive tests. If a state or county has a high test positivity rate, it is a sign of insufficient testing in that area.

To make sure there are no unknown infections that could lead to an outbreak, states and counties need to test a large enough percentage of the population. Test Positivity also alerts us to possible surges in ICU headroom used.

Let’s look at the pictures below. Red represents a positive test, green a negative test, and grey untested. There are two positive COVID tests in each of these pictures. In the first picture (left), test positivity is 20% because two people out of 10 tested positive. There are 90 people left who remain untested and could be infected. Now, look at the third picture (right), where the test positivity is 2%. The entire population, 100 out of 100 people were tested. The closer we get to testing everyone, the less likely we are to miss positive cases.

Here’s a video that helps explain test positivity:

How Does Covid Act Now rate this metric?

The ratings below are based upon recommendations from the World Health Organization, Harvard University epidemiologist William Hanague, Admiral Brett Giroir, and a member of the White House Coronavirus Task Force.

How do you know if your area is testing enough?

As Michael Ryan, executive director of the WHO Health Emergencies Program, says, “If 80-90% of the people test positive, you are probably missing a lot of cases.” The White House Coronavirus Task Force’s reopening guidelines also point to a low test positivity as a key metric for reopening. 

Countries like Taiwan, New Zealand, Australia, and South Korea were back to normal activity with test positivity rates below 3%. As of Aug 11, 2020, South Korea’s test positivity was 0.9%.

When test positivity decreases, we move from testing only the sickest people to those who are not infected, asymptomatic, or identified through contact tracing which is especially important since we know infected people can spread COVID with no symptoms.

It is critical, due to testing and reporting delays, that we get as much testing information as possible to track surges and act quickly to contain COVID.

Where does our data come from for this metric?

To calculate test positivity, Covid Act Now uses new positive cases and COVID death data from Corona Data Scraper.

Test positivity is just one of several important metrics to determine how well your state or county is doing in the fight against COVID. Learn more about daily new cases, infection rate, ICU headroom used, and contact tracing. For a description of assumptions and methodology, please see our references and assumptions document, along with our data sources presentation.

Check out our Youtube Channel for our educational videos.

To learn more about Covid Act Now, visit our about page. For more information please reach out on our contact page or by email at info@covidactnow.org.

Sign up to our alerts to stay up-to-date on the COVID risk level in your area.

Daily New Cases: Explained

Daily new cases, also known as incidence in epidemiology, is the number of new COVID cases per day per unit of population. We use a more clear term, “daily new cases” that doesn’t require expert knowledge and calculate it per 100,000 people in states and counties.

Daily new cases answers the question: “How many new infections are in my area each day?” Because we can’t test our entire population, we don’t know how many total cases are in a given population. Measuring daily new cases gives us a way to gauge new COVID cases without having to test an entire population.

In the example to the right, the number of daily new cases is 3. On day one, there is a single case, but each day there are 3 new cases x per 100k people.

You can see that daily new cases, or incidence, is not a measure of the total number of cases.

While the infection rate shows how many people one person will infect while contagious, daily new cases tells us how many newly infected people are spreading at that rate. The two metrics go hand-in-hand. 

Here’s a video that helps explain daily new cases:

How does Covid Act Now rate Daily New Cases?

Covid Act Now takes an average of new COVID cases over the past seven days and divide that by 100,000. This way, we can see how many new cases there are per 100,000 people in a county or state. 

Our risk levels are based on the percent of a population that will be infected if the rate continues for one year. If critical (red) rates continue, more than 50% of a population will be infected. If high (orange) rates continue, between 10-50% of the population will be infected. If medium (yellow) rates continue, 1-10% of the population will be infected. If low (green) rates continue, less than 1% of the population will be infected. 

Let’s look at daily new cases in Nevada on July 12, below. There were 24.2 daily new cases (per 100,000) and the infection rate was 1.13.  The population of Nevada is 3.08 million, so 27.3 people per 100,000 is 841 people.

Where does our data come from for this metric?

To calculate daily new cases, we use new positive cases from The New York Times.

Daily New Cases is just one of several important metrics to determine how well your state or county is doing in the fight against COVID. Learn more about test positivity, infection rate, ICU headroom used, and contact tracing. For a description of assumptions and methodology, please see our references and assumptions document, along with our data sources presentation.

Check out our Youtube Channel for our educational videos.

To learn more about Covid Act Now, visit our about page. For more information please reach out on our contact page or by email at info@covidactnow.org.

Sign up to our alerts to stay up-to-date on the COVID risk level in your area.

Recruiting A Volunteer Writer

About Covid Act Now

Covid Act Now was launched on March 20. Since then, the model has reached over 10-million Americans, was cited by many counties and states in their decisions to shut down, and has been referenced by Dr. Birx in a White House press briefing. We provide data directly to 10+ governors and state epidemiologists across the country, to the Department of Defense, and to numerous businesses, healthcare groups, and other entities.

Covid Act Now is run by a multidisciplinary team with 40 full time employees including Silicon Valley engineers and data scientists, Georgetown epidemiologists, Grand Rounds modelers, and other political and medical experts. We are also part of a consortium of institutions convened by Harvard’s Global Health Institute and the Edmond J. Safra Center for Ethics to set standards for evaluating COVID risk.

The goal of Covid Act Now is to supply disease intelligence to policymakers and the public to help them make the most informed, best possible decisions to stop COVID, save lives, and help the world return safely to a post-pandemic normal.

About The Position 

The writer will work with a team to strategize and create content for our website and CAN’s public audience. They will write researched pieces to educate the public about COVID, advocate for data-driven policies, explain CAN’s work, and flesh out technical subjects for the general public. They have an attention to detail, a flair for words, and the ability to work independently. 

Responsibilities:

Help write researched/journalistic pieces for CAN on a fairly regular basis. Blog posts provide educational material and memorialize improvements to the model. We are also hoping to add more researched/journalistic pieces that advocate for data driven policies indicated by our models. These projects may also involve conducting scientific literature reviews.Help edit our Daily Download Newsletter, which goes out to 100k+ email recipients, including every state legislator in the United States.Edit writing across the organization to make sure all writing is up to our standards of both clarity and panache.Work with a team to set and adjust style guidelines for the organization.Potentially help create scripts for video content.Potentially help write website copy.Based on interest and availability, become involved in other projects across CAN.

Qualifications

Preferred: experience in writing, journalism, research, science, or public healthRequired: excellent writer, attention to detail, interest in research, interest in public health, and a passion for explaining complicated subjects in a clear and lively way.

Expectations and Position

The volunteer position is minimum 10 hours a week, maximum as many as you’d like!

The writer must be able to think critically and creatively, collaborate with a team, conduct research, and write engaging material (sometimes with a quick turnaround time).

The writer must be able to commit to working at least through the end of October

The writer must be able to start immediately.

The project is very fast moving. A bit frenetic. Very startup-y.

The writer will report to Anna Blech. The writer will also talk and work closely with those across the organization, including the partnerships team, the media team, and the modeling team. They will help create educational material and translate CAN’s work to government partners and the public.

To apply, please submit a cover letter, resumé, and one or two writing samples to anna@covidactnow.org. We prefer writing samples that showcase a lively writing style, and are journalistic or have an audience of the general public.

Evolving data and assumptions: Why we are removing our old hospital projections

Covid Act Now (CAN) began as a spreadsheet model to forecast when hospitals would be overwhelmed, based on whether or not interventions (e.g. shelter-at-home) were put in place. We’ve come a long way since then. As of today, we’ll no longer be showing “Future Hospitalization Projections.”

These projections were focused on hospital bed capacity, but we’ve since learned that the number of staffed ICU beds is the critical limiting resource. We already have a key indicator for COVID ICU Headroom, which is designed to alert when ICUs might be running out of beds while leaving sufficient time for policymakers to react.

We will maintain the projections in our API.

Check out our Youtube Channel for our educational videos.

To learn more about Covid Act Now, visit our about page. For more information please reach out on our contact page or by email at info@covidactnow.org.

Sign up to our alerts to stay up-to-date on the COVID risk level in your area.

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