America鈥檚 homeless services system relies on a massive amount of data, and at first glance, that data is exacting. Federal reports describe the country鈥檚 unhoused population in granular detail, listing precisely how many people are experiencing homelessness in each city along with detailed demographic data. Want to know how many people ages 55-64 slept outside in Spokane last year? A spreadsheet confidently provides the answer: 听

That data influences decisions at every level of government, from how the U.S. Department of Housing and Urban Development (HUD) distributes $3 billion in funding to how local service providers target their outreach efforts. It鈥檚 also . As a result, communities across the country 鈥� including King County 鈥� don’t really know exactly how many of their residents are unhoused and have a limited window into people鈥檚 circumstances and needs. 听

So, a team of 91探花 researchers designed a better way to count. Led by , a 91探花associate professor of sociology, and , professor emeritus of health systems and population health, researchers developed a method that taps into people鈥檚 social networks to generate a more representative sample, which they use to estimate the total unhoused population. Along the way, agency staff and volunteers gather information on people鈥檚 demographics, resources and needs. 听

The researchers launched this method in partnership with King County in 2022 and repeated the process in 2024, publishing their findings . 91探花News sat down with Almquist and Hagopian to discuss their new approach and how it could help close the gaps in our understanding of homelessness in America.听

Statistics on homelessness and the demographics of unhoused populations are often quite specific. The federal government reported that on a single night in January 2023, for example. How do we get these statistics, and how reliable are they?听听

Amy Hagopian: I鈥檓 always a little amused at numbers that create a false specificity; for example, an airline says my flight will arrive in Chicago at 11:33 a.m. Everyone knows that number isn鈥檛 true, except sometimes by accident, and yet we entertain the airline by pretending to believe the number. After all, there are no consequences for being wrong!听

The national count is an amalgamation of counts reported by each community鈥檚 jurisdiction, designed by the U.S. Department of Housing and Urban Development. Most jurisdictions are still attempting a single-night head count of people found by volunteers who move about in the dark with flashlights and clipboards 鈥� a highly problematic approach King County has abandoned in favor of our sampling method. When these numbers come in, HUD just adds them up, and of course the number won鈥檛 be round. We all know it鈥檚 way below the actual number, because a middle-of-the-night census isn鈥檛 going to find everyone.听

Zack Almquist: There is a common fiction that when we do a census it is exact, because government reports often do not provide a margin of error. I think if you asked many experts, they would say they know the reality is a range, not a single number. In fact, not providing a range provides a level of confidence that we really don鈥檛 have, regardless of how we get there.

One nice thing about using a statistical estimate is that people are trained to expect a margin of error or confidence interval. We can say, plus or minus 5%, or 100-200 people. In other words, by moving into a space where we expect to see a range, we can be more honest, and ideally be more prepared to handle the real situation.听

Why does it matter how accurate this data is?听

AH: America has the worst homelessness problem in the world created by an economic system 鈥� as opposed to war and other disasters 鈥� largely because we make no attempt to recognize the human right to housing as established by the United Nations. One reason to count by jurisdiction is to learn where the hot spots are, and which areas have managed to lower their counts, and why. 听

ZA: This is also an equity and respect issue for the people who are experiencing homelessness. We owe it to our community members to do our best to capture the real state of the problem in our area and to best represent their race, ethnicity, gender, disability status, and causal or associated factors like eviction. We cannot hope to adequately engage a problem if we can鈥檛 accurately quantify it.

Your team developed a new method to estimate the unhoused population. How does your method work, and how does it differ from the traditional PIT count?听听

ZA: Our method takes the approach that there is no reliable way for us to obtain a census of people living unsheltered in our community, and that we need to move from a biased counting exercise to an approach that leverages modern statistical methods to obtain a best estimate of the population given our current resources. Modern sampling methods can improve how we count people. Sampling is the process of selecting a small group from a larger population to study and make conclusions about the entire population.听

We leveraged a sampling strategy that comes out of public health literature and is endorsed by the National Institutes of Health and World Health Organization. First, we collect a roster and bed count from shelters. The HUD-mandated Point-in-Time count was always split between the roster or bed count and an unsheltered count; the latter was historically counted in King County by a visual census. So, the total number of people experiencing homelessness is the number of people in emergency shelters on a given night plus the number of people living outside on a given night. Through some ratios and algebra, we can estimate the total number of people if we know who slept in an emergency shelter and know from historical measures the relative proportion of people who slept outside. 听

Our sampling strategy of leveraging people鈥檚 social networks and peer referral allows us to estimate the proportion of people who slept outside to those who slept in an emergency shelter on a given night. Further, this allows us to better find and count people who would be hard to find in the traditional visual census 鈥� people living in the woods or hiding 鈥� and also provides a clear method for the margin of error of our estimate of the number of people experiencing unsheltered homelessness.听

Your count creates a more reliable estimate of the unhoused population, but that鈥檚 not all. What other information can you collect with this method, and how might it be useful?听听

AH: When other jurisdictions do their midnight census counts, they are just counting bodies seen. There is no opportunity to collect demographic or life history or health status data unless they shake people awake and interview them in the moment, which few people do. Instead, they conduct a post-count interview process in places like food banks. Our approach provides the opportunity to count people during daylight hours while also learning something about their life course and circumstances. This provides King County with some valuable information about the causes of homelessness. Once we move towards a quarterly count, we can also learn about the 鈥渃hurn鈥� —听the number of people moving into and out of homelessness and what the drivers are for those changes in circumstance.听

ZA: I think this point can鈥檛 be emphasized enough, as running a post-count survey is almost always conducted as a spatial convenience sample that surveys both those using emergency shelters and those who slept outside. It鈥檚 unlikely to include the same people who were in the one-night body count. 听

What have you heard from people who鈥檝e participated in your method? How do participants鈥� experiences differ from the old Point-In-Time count?听

AH: We conducted a couple of focus groups recently with people experiencing homelessness in Seattle. We asked them about their impressions of the recent methods change in how we count. We found people appreciated the motivations behind the change, and the more respectful approach we are now using.听听

ZA: I just want to second what Amy said, and to point out that people really appreciate being directly engaged with and having a chance to be paid for their time and effort.听

How else could this method be used? Are there potential applications outside of homelessness and housing services?听听

AH: I have helped conduct mortality counts in war zones, and some of the lessons learned from those experiences were helpful here. For example, in Iraq conducted a door-to-door survey to ask adult household members to tell us about the alive or dead status of their siblings. This allowed us to calculate a total war-related mortality rate for the country, as our sample was selected proportionate to size of the governorate sampled. 听

ZA: I think the basic ideas used here could end up influencing health and demography measurement efforts for several hard-to-estimate populations. For example, international migration can often be split between those we can count with high fidelity, like registered immigrants, and unregistered immigrants. Combining new sampling methods with administrative data to count hard-to-reach populations could be employed for a number of problems in industry, health and public policy. I hope to see these ideas picked up broadly.听

AH: We are grateful to the UW鈥檚 Population Health Initiative for the opportunity to develop these methods, and to our partners at King County Regional Homelessness Authority for being willing to try something new with us.听

For more information or to contact Hagopian and/or Almquist, contact Alden Woods at acwoods@uw.edu.听听

When 91探花 President Ana Mari Cauce launched the Population Health Initiative in 2016, she spoke in soaring, ambitious terms. 鈥淲e have an unprecedented opportunity to help people live longer, healthier, more productive lives 鈥� here and around the world,鈥� she said. 91探花researchers have leapt at that opportunity, forging connections across the university, working side by side with community partners and breaking down traditional barriers to improving public health. 听

In just eight years, the Initiative has funded 227 innovative, interdisciplinary projects. Many are focused right here in Western Washington, where projects have helped in South Seattle, identified soil contaminants in community gardens in the Duwamish Valley, and improved how community leaders along the Okanogan River . Other projects have reached across the globe, targeting health disparities in Somalia, Peru, Brazil and more.听听

鈥淚n this relatively short period of time, we鈥檝e demonstrated the power that accrues when faculty and staff across the various areas of our campuses are working together and also exposing students to the cutting-edge work of tackling grand challenges,鈥� Cauce said in her most recent .听

And they’re just getting started. Many PHI-funded projects are still in their earliest stages, leveraging initial funding to show proof-of-concept for their ideas and setting the stage for future work. Fourteen projects so far have received much larger grants to empower researchers and community partners to expand successful projects and scale up for greater impact.听

With the Initiative now a third of the way into its 25-year vision, 91探花News checked in with three projects that recently received funding to scale their efforts.听

Spotting potential memory health issues in rural Washington

Diagnosing memory health issues in the best of circumstances is extraordinarily difficult. Patients typically make multiple visits to their doctor and take a many of which can produce flawed results 鈥� people who take the same test more than once, for example, will often score higher, potentially masking memory loss. 听

It鈥檚 even harder in rural America, which has a Patients seeking memory care might have to make a long, expensive trip to a major city, which leads many people to wait until a problem becomes apparent. By then, it鈥檚 often too late 鈥� modern treatments can slow the progress of memory loss, but there鈥檚 no way to regain what鈥檚 been lost.听

鈥淪o, how do you catch it early?鈥� said , a 91探花associate professor of psychology. 鈥淲e give people an app to have them check for themselves.鈥澨�听

Stocco and , director of the 91探花Alzheimer鈥檚 Disease Research Center, together with Hedderik van Rijn of the University of Groningen in the Netherlands, led the development of an online program that can measure a person鈥檚 memory and predict their risk of memory disorders. Like a flash-card app that helps students cram for a test, the program shows pictures and asks the user to recall what they saw a few minutes earlier. The app records how quickly and accurately the user responds to each question and makes the next one a little easier or more difficult.听听

Researchers have long understood that a person鈥檚 ability to recall a specific memory tends to fade over time. This is called the 鈥�.鈥� In听 Stocco and van Rijn found that they could measure individual differences in the slopes of such curves.听 The app works by comparing a person鈥檚 responses to an internal model of forgetting and adjusting the slope of the model until it matches the responses. The resulting slope can be used to estimate the likelihood that their memory is fading faster than normal.听听

By taking the test regularly, a person can track their memory鈥檚 decline over time. But preliminary tests, Stocco said, have shown that even a single use can spot a potential problem.听

鈥淛ust by looking at a single lesson, based on the result, there鈥檚 almost a perfect correspondence between the speed of forgetting and your probability of being diagnosed by a doctor,鈥� Stocco said. 鈥淚t can be as accurate as the best clinical tests but, instead of taking two or three hours, this can be done in eight minutes, and you don鈥檛 need a doctor.鈥�听

A Tier 3 grant from the Population Health Initiative and a collaboration with the will allow the researchers to share the app with up to 500 people in rural and counties. Participants can take the test on their own time, and the results will be shared with researchers. If a potential problem emerges, the researchers plan to invite participants to Seattle for an in-person evaluation.听听

鈥淚t鈥檚 a solution that seems to solve these problems of early access and diagnostic bottlenecks,鈥� Stocco said. 鈥淚f this works, there鈥檚 no problem giving it to everybody in the state. We鈥檙e really interested in expanding and adding people from underrepresented populations and underrepresented areas, and the grant will allow us to do that.鈥�听

Nancy Spurgeon of the Central Washington Area Health Education Center is also a collaborator on the project to test the prototype app, which is not yet available to the public.听

Revamping the Point-In-Time Count to better understand King County鈥檚 unhoused population听

For years, volunteers fanned across King County on a cold night each January, flashlights and clipboards in hand, searching for people sleeping outside. They鈥檇 also gather the shelter head counts for that night. Officially called the , this effort attempted to tally the number of people who lacked stable housing. This endeavor was replicated in cities across the country, and the results were combined to create a national count that influences how the federal government allocates funding.听

There鈥檚 just one problem 鈥� the count is Volunteers can鈥檛 possibly find everybody. It captures only a single moment in time, and collects only limited data on people鈥檚 circumstances or personal needs. A person sleeping in their car might need different services than a person who sleeps in a tent, and the count didn鈥檛 fully capture that distinction.听

So, a team of 91探花researchers designed a better way to count. Their method, detailed in a published Sept. 4 in in the American Journal of Epidemiology, taps into people鈥檚 social networks to generate a more representative sample, which the researchers then ran through a series of calculations to estimate the total unhoused population.听听听

Called 鈥渞espondent-driven sampling,鈥� the method stations volunteers in common 鈥渉ubs,鈥� like libraries or community centers, and offers cash gift cards for in-person interviews and peer referrals. Volunteers collect detailed information on people鈥檚 circumstances and needs, giving each person three tickets to share with their unhoused peers. When those peers come in for an interview and show the ticket, the person who referred them receives another small reward. The new person gets a gift card and another three tickets.听

鈥淭his method gives people a more active voice in being counted. It鈥檚 a more humane way to count people, and it鈥檚 also voluntary,鈥� said , a 91探花associate professor of sociology and co-lead on the project. 鈥淭he regular PIT (Point-In-Time) count just counted people. Now we can collect all sorts of information from people on their circumstances and their needs. Should policymakers want to, they could leverage that data to change service offerings.鈥�听

The researchers received a Tier 2 grant to develop the system. They launched it in partnership with King County in 2022 and 2024, and were recently awarded a Tier 3 grant to test out the feasibility of running it quarterly.听听

鈥淩unning the count quarterly allows us to estimate how many people move in and out of homelessness and whether there are seasonal changes, which are rarely measured,鈥� Almquist said. 鈥淎lso, people鈥檚 needs change depending on the time of year, and this method will help us better understand those rhythms.鈥澨�听

Other cities and counties have expressed interest, the researchers said. The team has also begun to expand the effort, aiming to improve data across the broad spectrum of housing and homelessness services.听听

鈥淎 very important byproduct of this work across schools and departments at 91探花is that we can create an ecosystem of people and projects,鈥� said , a 91探花professor emeritus of health systems and population health and co-lead on the project. 鈥淲e鈥檝e spun off projects on sleep assessments, relationships with organizations that collect data on homelessness, and we鈥檙e mapping the sweeps of encampments in relationship to where people choose to be located. We have a whole network of homelessness-related research now.听

鈥淭hese PHI grants gave us the fuel to ignite these projects.鈥�听

Other collaborators are of the 91探花Department of Health Systems and Population Health and of the VA Health Services Research and Development; of the 91探花Departments of Sociology and Statistics; of the Center for Studies in Demography & Ecology and the eScience Institute; and Owen Kajfasz, Janelle Rothfolk and Cathea Carey of the King County Regional Homelessness Authority.听

Engaging community to mitigate flood risk in the Duwamish Valley听

More than a century ago, Seattle leaders set out to control and redirect the Duwamish River. They dredged the riverbed and dug out its twists and turns. Wetlands were filled in, the valley was paved over and a system of hydrology was severed. What had been a wild, winding river valley with regular flooding became an angular straightaway built for industry. But when 91探花postdoctoral scholar looks out at the Duwamish, she sees the river fighting back.听听

鈥淭he water was always there,鈥� Jeranko said, 鈥渁nd now it鈥檚 fighting to come back up.鈥澨�听

The river returned with devastating effect in December 2022, when a king tide and heavy rainfall , submerging homes and shuttering local businesses. The underserved neighborhood faces a significant risk of future floods.听

To mitigate that risk, the City of Seattle has updated the neighborhood鈥檚 stormwater drainage system and launched a new flood-warning system. But the , a nonprofit focused on river pollution and environmental health, saw an opportunity for something greater. The DRCC asked a team of 91探花researchers to help develop flood adaptation plans that are community-based, culturally responsive and that enrich the local environment.听听

鈥淚n the community, people don鈥檛 think there鈥檚 been enough engagement. There鈥檚 all this talk about flood mitigation, but all they see are sandbags,鈥� Jeranko said. 鈥淪o DRCC was like, 鈥楲ook, we really need the people who live in the flood zone to understand the solutions.鈥� Because we have this long-lasting relationship with them, they see us as someone who鈥檚 able to provide a list of solutions, not favor one over the others, and do it in an informative way.鈥�听

Boosted by a Tier 3 grant from the PHI, Jeranko and a team representing five 91探花departments, the Burke Museum and the DRCC are engaging with the community. This fall, the team will present the neighborhood with an expansive list of flood mitigation options and encourage city leaders to consider people鈥檚 preferences. Early work shows the community would favor nature-based solutions, Jeranko said. Floodable parks, for example, would provide ecological, recreational and public health benefits to the entire community, while storing flood water during storms.听听

鈥淚t has been wonderful to collaborate with the 91探花team on this to make sure we are centering community voices in every single step of the planning for climate resilience,鈥� said Paulina L贸pez, executive director of the DRCC. 鈥淐ommunity leadership and representation is indispensable to bring climate justice to the Duwamish Valley.鈥�听

Jeranko hopes their community-based model will be replicated by communities across the country facing similar risks from climate change and sea level rise.听

鈥淓ven though 91探花and a lot of other universities really support and invest in community-engaged work, a lot of times it鈥檚 fundamentally hard to make that research happen,鈥� Jeranko said. 鈥淏ut the Population Health Initiative grant was about supporting all those things.鈥�听

Other collaborators on the project are , and of the Department of Environmental & Occupational Health Sciences; of the Department of Landscape Architecture; of the Department of Civil & Environmental Engineering, of the School of Environmental and Forest Sciences; of the Quaternary Research Center and the Burke Museum; and L贸pez and Robin Schwartz of the DRCC.听

For more information on any of the projects mentioned, or to learn more about the 91探花Population Health Initiative, visit the Initiative’s website or contact Alden Woods at acwoods@uw.edu.听听

Social cohesion, normally associated with positive outcomes in physical and mental health, can be a liability during a pandemic, according to new research by the University of California, Irvine, and the 91探花.

That鈥檚 because social connections 鈥� which generally ensure access to support, information and resources 鈥� can also provide pathways to infection, especially for vulnerable individuals.

The study, published recently in the , points to a hidden driver of disparities in the COVID-19 pandemic, particularly among marginalized communities living in densely populated urban areas.

鈥淲ith this study, we wanted to better understand factors that led to differences in who became infected early on in the pandemic,鈥� says lead author Loring Thomas, a Ph.D. candidate in sociology at UC Irvine. 鈥淥ur computational models found that communities whose members belonged to groups that were, on average, slightly more cohesive, experienced a much higher infection hazard, especially before non-pharmaceutical interventions like masking were widespread.鈥�

The researchers focused on San Francisco, combining demographic and housing data from the U.S. Census with observed infection cases among Black, Latinx, Asian and white racial and ethnic groups. They then used computational modelling to understand 1,225 trajectories 鈥� or pandemic histories 鈥� of individual infections that occurred before March 24, 2020.

“This paper shows the power of computational models to further our understanding on how small racial/ethnic disparities can result in large real-world outcomes such as what we have seen in this pandemic’s timing and exposure to COVID-19,” said co-author , an assistant professor of sociology at the UW.

A previous paper from this research team, published during the first year of the pandemic, used Census tract demographics, simulation techniques and COVID-19 case data to examine where and how quickly the coronavirus could spread through Seattle and 18 other major cities. The team created a new model of virus diffusion, showing how infection could peak in some neighborhoods faster than others, based in part on social and geographic connections.

In drilling down on San Francisco for the latest study, researchers found that differences in social cohesion among demographic groups 鈥� the strength of relationships and the sense of solidarity among members of a community 鈥� as well as other factors such as housing arrangements, affected infection rates in the pandemic鈥檚 first months.

鈥淎听key risk factor of early infection during the beginning of the pandemic was not merely having numerous contacts, but being embedded in locally cohesive parts of the contact network 鈥� that is, having many contacts in a community who themselves have many contacts within that same community,鈥� said co-author Carter Butts, a UC Irvine professor of sociology.

When the data was broken down further by race and location, researchers discovered that Black and Latinx populations housed in the city鈥檚 center had the highest infection rates, followed by Asian and white population groups.

Butts added that these results can also help those preparing for future emergencies to prioritize warning messages or interventions for high-risk groups when outbreaks of a potentially serious disease are first detected.

Additional co-authors were Peng Huang, Fan Yin, Junlan Xu and John Hipp, all of UC Irvine. The study was funded by the National Science Foundation, the National Institutes of Health and a UCI Council on Research, Computing and Libraries grant.

For more information, contact Almquist at zalmquist@uw.edu.

Adapted from a UC Irvine press release.

The coronavirus doesn鈥檛 spread uniformly through a community.

But in the world of disease modeling, many projections take a high-level approach to a geographic area, like a county or state, and forecast based on a general idea that a virus will take root and spread at an equal rate until it reaches its peak of infection.

A research team led by UC Irvine and the 91探花 has created a new model of coronavirus diffusion through a community. This approach, Sept. 10 in the Proceedings of the National Academy of Sciences, 听factors in network exposure 鈥� whom one interacts with 鈥� and demographics to simulate at a more detailed level both where and how quickly the coronavirus could spread through Seattle and 18 other major cities.

The team used U.S. Census Bureau tract demographics, simulation techniques and COVID-19 case data from spring 2020 to estimate a range of days for the virus to spread within a given city.

The result: Some neighborhoods peak sooner than others. And in every city, the virus sticks around far longer than some might expect.

鈥淭he most basic takeaway from this research is risk. People are at risk longer than they think, the virus will last longer than expected, and the point at which you think you don鈥檛 need to be vigilant means that it just hasn鈥檛 happened to you yet,鈥� said co-author , an assistant professor of sociology at the UW.

Almquist and the team took on their study with two basic premises: Account for the social and geographic connections within a tract that could affect the course of the virus; and assume no vaccine or other major intervention alters its path. Then, based on actual COVID-19 and demographic data, project a likely scenario for spread over time.

Take Seattle. The study鈥檚 map of the city outlines each census tract and provides a color-coded range of days each tract could take to reach peak infection before the virus goes into a low remission. The overall range is vast, from neighborhoods with the fastest peak 鈥� 83 days 鈥� to those that take more than 1,000. That鈥檚 more than three years, assuming there is no significant intervention to stem the spread.

Denser neighborhoods in Seattle, such as Capitol Hill or the University District, reach peak infection rate earlier. But simulations predict that even nearby neighborhoods won鈥檛 reach peak infection until weeks or even years later. These models predict more 鈥渂urst-like鈥� behavior of the virus鈥� spread than standard models 鈥� with short, sudden episodes of infection across the city, Almquist said.

In the study鈥檚 model of Washington, D.C., census tracts also appear to reach peak infection rates at different times.

Again, denser areas tend to peak sooner. But the network connections can cause 鈥渂ursty鈥� peak infection days, with some areas seeing early peak infections and others seeing it much later based on the neighborhoods鈥� relative connections with each other, Almquist said.

Projecting the path of the virus can help estimate the impact on local hospitals. Researchers predicted this in several ways, such as modeling the number of cases per hospital over time and the number of days a hospital is at peak capacity.

The model of projected hospital cases shows how the geographic variations in the timing in peak COVID-19 infections could affect hospitals in different areas. Without outside intervention, some hospitals would remain at capacity for years, especially those farthest from major population centers.

These types of models are important because they provide a more detailed and nuanced prediction of an unknown like the novel coronavirus, said Almquist. Gauging how the virus might spread throughout a city and strain its hospitals can help local officials and health care providers plan for many scenarios. And while this study assumes no major interventions will rein in the virus, it鈥檚 reasonable to believe the virus will linger to some degree, even with solutions such as a vaccine, according to Almquist.

鈥淚f you project these models for what it means over the country, we might expect to see some areas, such as rural populations, not see infection for months or even years before their peak infection occurs,鈥� Almquist said. 鈥淭hese projections, as well as others, are beginning to suggest that it could take years for the spread of COVID-19 to reach saturation in the population, and even if it does so it is likely to become endemic without a vaccine.鈥�

Co-authors are Loring Thomas, Peng Huang, Fan Yin, Xiaoshuang Iris Luo, John Hipp and Carter Butts, all of UC Irvine. The study was funded by the National Science Foundation and UC Irvine.

For more information, contact Almquist at zalmquist@uw.edu.

The 91探花鈥檚 , or CSDE, along with partners in the Center for Statistics and the Social Sciences and the , is among eight awardees across the country selected to develop training programs in advanced data analytics for population health through the National Institutes of Health鈥檚 Office of Behavioral and Social Sciences Research.

This five-year, $1.8 million training program at the 91探花will fund 25 academic-year graduate fellowships, develop a new training curriculum and contribute to methodological advances in health research at the intersection of demography and data science.

The new training program will be led by , assistant professor of sociology, and will build on CSDE鈥檚 graduate certificate in demographic methods by integrating training in advanced statistics and computational methods.

The inaugural cohort will begin the program in October and is composed of graduate students Ian Kennedy, Neal Marquez and Crystal Yu, all in sociology; Emily Pollock in anthropology; and Aja Sutton in geography.

鈥淥ur faculty are at the forefront of research programs grounded in advanced data analytics,鈥� said Robert Stacey, dean of the UW鈥檚 College of Arts and Sciences. 鈥淭his grant recognizes the important interdisciplinary work happening across the UW, and particularly in the social sciences, to build this knowledge into much-needed education and training programs.鈥�

, associate professor of sociology and statistics, and , professor of statistics and biostatistics, led听the grant application with support from , director of the CSDE and a professor of international studies, public policy and sociology, along with faculty affiliated with CSDE, CSSS and the eScience Institute.

The NIH review praised UW鈥檚 plans. 鈥淭he leadership team has well-established credentials, complementary expertise, and a strong track record and the proposed program builds on an existing program with demonstrable record of success,鈥� noted reviewers. 鈥淭he curriculum 鈥� which offers coursework in statistical methods, machine learning, coding, databases, data visualization and data ethics 鈥� is well-thought-out and will provide trainees with numerous immersive opportunities.鈥�

This funding was designed to fill educational gaps and needs in the behavioral and social sciences research community that are not being addressed by existing educational opportunities, according to the Office of Behavioral and Social Sciences Research. The other institutions awarded similar grants include Emory University; Johns Hopkins University; Stanford University; University of Arkansas Medical Center; the University of California, Berkeley; UC San Diego; and UC San Francisco. More information about the national initiative can be found .

For more information, contact Curran at scurran@uw.edu or Almquist at zalmquist@uw.edu.

Adapted from information provided by the 91探花Center for Studies in Demography & Ecology.