Covid-19

Subterms

    More stories

    • 125 Shares179 Views

      in Data Management & Statistics

      Study finds lockdowns effective at reducing travel in Sierra Leone

      by

      Throughout the Covid-19 pandemic, governments have used data on people’s movements to inform strategies for containing the spread of the virus. In Europe and the United States, for example, contact-tracing apps have used Bluetooth signals in smartphones to alert people when they’ve spent time near app users who have tested positive for Covid-19. 

      But how can governments make evidence-based decisions in countries where such fine-grained data isn’t available? In recent findings, MIT researchers, in collaboration with Sierra Leone’s government, use cell tower records in Sierra Leone to show that people were traveling less during lockdowns. “When the government implemented novel three-day lockdowns, there was a dual aim to reduce virus spread and also limit social impacts, like increased hunger or food insecurity,” says Professor Lily L. Tsai, MIT Governance Lab’s (MIT GOV/LAB) director and founder. “We wanted to know if shorter lockdowns would be successful.”   

      The research was conducted by MIT GOV/LAB and MIT’s Civic Data Design Lab (CDDL), in partnership with Sierra Leone’s Directorate for Science, Innovation and Technology (DSTI) and Africell, a wireless service provider. The findings will be published as a chapter in the book “Urban Informatics and Future Cities,” a selection of research submitted to the 2021 Computational Urban Planning and Urban Management conference. 

      A proxy for mobility: cell tower records

      Any time someone’s cellphone sends or receives a text, or makes or receives a call, the nearest cell tower is pinged. The tower collects some data (call-detail records, or CDRs), including the date and time of the event and the phone number. By tracking which towers a certain (anonymized) phone number pings, the researchers could approximately measure how much someone was moving around.  

      These measurements showed that, on average, people were traveling less during lockdowns than before lockdowns. Professor Sarah Williams, CDDL’s director, says the analysis also revealed frequently traveled routes, which “allow the government to develop region-specific lockdowns.” 

      While more fine-grained GPS data from smartphones paint a more accurate picture of movement, “there just isn’t a systematic effort in many developing countries to build the infrastructure to collect this data,” says Innocent Ndubuisi-Obi Jr., an MIT GOV/LAB research associate. “In many cases, the closest thing we can use as a proxy for mobility is CDR data.”

      Measuring the effectiveness of lockdowns

      Sierra Leone’s government imposed the three-day lockdown, which required people stay in their homes, in April 2020. A few days after the lockdown ended, a two-week inter-district travel ban began. “Analysis of aggregated CDRs was the quickest means to understanding mobility prior to and during lockdowns,” says Michala Mackay, DSTI’s director and chief operating officer. 

      The data MIT and DSTI received was anonymized — an essential part of ensuring the privacy of the individuals whose data was used. 

      Extracting meaning from the data, though, presented some challenges. Only about 75 percent of adults in Sierra Leone own cellphones, and people sometimes share phones. So the towers pinged by a specific phone might actually represent the movement of several people, and not everyone’s movement will be captured by cell towers. 

      Furthermore, some districts in Sierra Leone have significantly fewer towers than others. When the data were collected, Falaba, a rural district in the northeast, had only five towers, while over 100 towers were clustered in and around Freetown, the capital. In areas with very few towers, it’s harder to detect changes in how much people are traveling. 

      Since each district had a unique tower distribution, the researchers looked at each district separately, establishing a baseline for average distance traveled in each district before the lockdowns, then measuring how movement compared to this average during lockdowns. They found that travel to other districts declined in every district, by as much as 72 percent and by as little as 16 percent. Travel within districts also dropped in all but one district. 

      This map shows change in average distance traveled per trip to other districts in Sierra Leone in 2020.

      Image courtesy of the MIT GOV/LAB and CDDL.

      Previous item
      Next item

      Lockdowns have greater costs in poorer areas

      While movement did decline in all districts, the effect was less dramatic in poorer, more sparsely populated areas. This finding was to be expected; other studies have shown that poorer people often can’t afford to comply with lockdowns, since they can’t take time off work or need to travel to get food. Evidence showing how lockdowns are less effective in poorer areas highlights the importance of distributing resources to poorer areas during crises, which could both provide support during a particularly challenging time and make it less costly for people to comply with social distancing measures. 

      “In low-income communities that demonstrated moderate or low compliance, one of the most common reasons why people left their homes was to search for water,” says Mackay. “A policy takeaway was that lockdowns should only be implemented in extreme cases and for no longer than three days at a time.”

      Throughout the project, the researchers collaborated intimately with DSTI. “This meant government officials learned along with the MIT researchers and added crucial local knowledge,” says Williams. “We hope this model can be replicated elsewhere — especially during crises.” 

      The researchers will be developing an MITx course teaching government officials and MIT students how to collaboratively use CDR data during crises, with a focus on how to do the analysis in a way that protects people’s privacy.

      Ndubuisi-Obi Jr. also has led a training on CDR analysis for Sierra Leonean government officials and has written a guide on how policymakers can use CDRs safely and effectively. “Some of these data sets will help us answer really important policy questions, and we have to balance that with the privacy risks,” he says. More

    • 125 Shares189 Views

      in Data Management & Statistics

      Lincoln Laboratory convenes top network scientists for Graph Exploitation Symposium

      by

      As the Covid-19 pandemic has shown, we live in a richly connected world, facilitating not only the efficient spread of a virus but also of information and influence. What can we learn by analyzing these connections? This is a core question of network science, a field of research that models interactions across physical, biological, social, and information systems to solve problems.

      The 2021 Graph Exploitation Symposium (GraphEx), hosted by MIT Lincoln Laboratory, brought together top network science researchers to share the latest advances and applications in the field.

      “We explore and identify how exploitation of graph data can offer key technology enablers to solve the most pressing problems our nation faces today,” says Edward Kao, a symposium organizer and technical staff in Lincoln Laboratory’s AI Software Architectures and Algorithms Group.

      The themes of the virtual event revolved around some of the year’s most relevant issues, such as analyzing disinformation on social media, modeling the pandemic’s spread, and using graph-based machine learning models to speed drug design.

      “The special sessions on influence operations and Covid-19 at GraphEx reflect the relevance of network and graph-based analysis for understanding the phenomenology of these complicated and impactful aspects of modern-day life, and also may suggest paths forward as we learn more and more about graph manipulation,” says William Streilein, who co-chaired the event with Rajmonda Caceres, both of Lincoln Laboratory.

      Social networks

      Several presentations at the symposium focused on the role of network science in analyzing influence operations (IO), or organized attempts by state and/or non-state actors to spread disinformation narratives.  

      Lincoln Laboratory researchers have been developing tools to classify and quantify the influence of social media accounts that are likely IO accounts, such as those willfully spreading false Covid-19 treatments to vulnerable populations.

      “A cluster of IO accounts acts as an echo chamber to amplify the narrative. The vulnerable population is then engaging in these narratives,” says Erika Mackin, a researcher developing the tool, called RIO or Reconnaissance of Influence Operations.

      To classify IO accounts, Mackin and her team trained an algorithm to detect probable IO accounts in Twitter networks based on a specific hashtag or narrative. One example they studied was #MacronLeaks, a disinformation campaign targeting Emmanuel Macron during the 2017 French presidential election. The algorithm is trained to label accounts within this network as being IO on the basis of several factors, such as the number of interactions with foreign news accounts, the number of links tweeted, or number of languages used. Their model then uses a statistical approach to score an account’s level of influence in spreading the narrative within that network.

      The team has found that their classifier outperforms existing detectors of IO accounts, because it can identify both bot accounts and human-operated ones. They’ve also discovered that IO accounts that pushed the 2017 French election disinformation narrative largely overlap with accounts influentially spreading Covid-19 pandemic disinformation today. “This suggests that these accounts will continue to transition to disinformation narratives,” Mackin says.

      Pandemic modeling

      Throughout the Covid-19 pandemic, leaders have been looking to epidemiological models, which predict how disease will spread, to make sound decisions. Alessandro Vespignani, director of the Network Science Institute at Northeastern University, has been leading Covid-19 modeling efforts in the United States, and shared a keynote on this work at the symposium.

      Besides taking into account the biological facts of the disease, such as its incubation period, Vespignani’s model is especially powerful in its inclusion of community behavior. To run realistic simulations of disease spread, he develops “synthetic populations” that are built by using publicly available, highly detailed datasets about U.S. households. “We create a population that is not real, but is statistically real, and generate a map of the interactions of those individuals,” he says. This information feeds back into the model to predict the spread of the disease. 

      Today, Vespignani is considering how to integrate genomic analysis of the virus into this kind of population modeling in order to understand how variants are spreading. “It’s still a work in progress that is extremely interesting,” he says, adding that this approach has been useful in modeling the dispersal of the Delta variant of SARS-CoV-2. 

      As researchers model the virus’ spread, Lucas Laird at Lincoln Laboratory is considering how network science can be used to design effective control strategies. He and his team are developing a model for customizing strategies for different geographic regions. The effort was spurred by the differences in Covid-19 spread across U.S. communities, and what the researchers found to be a gap in intervention modeling to address those differences.

      As examples, they applied their planning algorithm to three counties in Florida, Massachusetts, and California. Taking into account the characteristics of a specific geographic center, such as the number of susceptible individuals and number of infections there, their planner institutes different strategies in those communities throughout the outbreak duration.

      “Our approach eradicates disease in 100 days, but it also is able to do it with much more targeted interventions than any of the global interventions. In other words, you don’t have to shut down a full country.” Laird adds that their planner offers a “sandbox environment” for exploring intervention strategies in the future.

      Machine learning with graphs

      Graph-based machine learning is receiving increasing attention for its potential to “learn” the complex relationships between graphical data, and thus extract new insights or predictions about these relationships. This interest has given rise to a new class of algorithms called graph neural networks. Today, graph neural networks are being applied in areas such as drug discovery and material design, with promising results.

      “We can now apply deep learning much more broadly, not only to medical images and biological sequences. This creates new opportunities in data-rich biology and medicine,” says Marinka Zitnik, an assistant professor at Harvard University who presented her research at GraphEx.

      Zitnik’s research focuses on the rich networks of interactions between proteins, drugs, disease, and patients, at the scale of billions of interactions. One application of this research is discovering drugs to treat diseases with no or few approved drug treatments, such as for Covid-19. In April, Zitnik’s team published a paper on their research that used graph neural networks to rank 6,340 drugs for their expected efficacy against SARS-CoV-2, identifying four that could be repurposed to treat Covid-19.

      At Lincoln Laboratory, researchers are similarly applying graph neural networks to the challenge of designing advanced materials, such as those that can withstand extreme radiation or capture carbon dioxide. Like the process of designing drugs, the trial-and-error approach to materials design is time-consuming and costly. The laboratory’s team is developing graph neural networks that can learn relationships between a material’s crystalline structure and its properties. This network can then be used to predict a variety of properties from any new crystal structure, greatly speeding up the process of screening materials with desired properties for specific applications.

      “Graph representation learning has emerged as a rich and thriving research area for incorporating inductive bias and structured priors during the machine learning process, with broad applications such as drug design, accelerated scientific discovery, and personalized recommendation systems,” Caceres says. 

      A vibrant community

      Lincoln Laboratory has hosted the GraphEx Symposium annually since 2010, with the exception of last year’s cancellation due to Covid-19. “One key takeaway is that despite the postponement from last year and the need to be virtual, the GraphEx community is as vibrant and active as it’s ever been,” Streilein says. “Network-based analysis continues to expand its reach and is applied to ever-more important areas of science, society, and defense with increasing impact.”

      In addition to those from Lincoln Laboratory, technical committee members and co-chairs of the GraphEx Symposium included researchers from Harvard University, Arizona State University, Stanford University, Smith College, Duke University, the U.S. Department of Defense, and Sandia National Laboratories. More

    • 100 Shares119 Views

      in Data Management & Statistics

      Lockdowns reveal inequities in opportunities for walking activities

      by

      Lockdowns saved lives during the global SARS-CoV-2 pandemic. But as much as they have slowed the spread of Covid-19, there have been some unintended consequences.

      New MIT research shows that lockdowns in 10 metropolitan areas throughout the United States led to a marked reduction in walking. These decreases were mostly seen among residents living in lower-income areas of the city, effectively reducing access to physical activity for minorities and people suffering from illnesses such as obesity and diabetes.

      “Walking is the cheapest, most accessible physical exercise that you can do,” says Esteban Moro, visiting research scientist in the MIT Connection Science Group and senior author on the Nature Communications paper published on June 16. “Places in which people have lower incomes, less park access, and more obesity prevalence were more affected by this walking reduction — which you can think of as another pandemic, the lack of access to affordable exercise.”

      The research focused on recreational versus utilitarian walking done by residents in the U.S. cities of New York, Los Angeles, Chicago, Boston, Miami, Dallas, San Francisco, Seattle, Philadelphia, and Washington D.C. (Utilitarian walking is defined as having a goal; for example, walking to the store or to public transportation. Recreational walking is a walk meant for leisure or exercise.)

      Comparing cellphone data from February 2020 to different time points throughout 2020 lockdowns, the researchers saw an average 70 percent decrease in the number of walks — which remained down by about 18 percent after loosened restrictions — a 50 percent decrease in distance walked, and a 72 percent decrease in utilitarian walking — which remained down by 39 percent even after restrictions were lifted.

      On their face, these findings may not be surprising. When people couldn’t leave their homes, they walked less. But digging deeper into the data yields troubling insights. For example, people in lower-income regions are more likely to rely on public transportation. Lockdowns cut back on those services, meaning fewer people walking to trains and buses.

      Another statistic showed that people in higher-income areas reduced their number of utilitarian walks but were able to replace some of the lost movement with recreational walks around their neighborhoods or in nearby parks.

      “People in higher-income areas generally not only have a park nearby, but also have jobs that give them a degree of flexibility. Jobs that permit them to take a break and walk,” says Moro. “People in the low-income regions often don’t have the ability, the opportunity or even the facilities to actually do this.”

      How it was done

      The researchers used de-identified mobile data obtained through a partnership within the company Cuebiq’s Data for Good COVID-19 Collaborative program. The completely anonymized dataset consisted of GPS locations gathered from smartphone accelerometers from users who opted into the program. Moro and his collaborators took these data and, using specifically designed algorithms, determined when people walked, for how long, and for what purpose. They compared this information from before the pandemic, at different points throughout lockdown, and at a point when most restrictions had been eased. They matched the GPS-identified locations of the smartphones with census data to understand income level and other demographics.

      To make sure their dataset was robust, they only used information from areas that could reasonably be considered pedestrian. The researchers also acknowledge that the dataset may be incomplete, considering people may have occasionally walked without their phones on them.

      Leisure versus utilitarian walks were separated according to distance and/or destination. Utilitarian walks are usually shorter and involve stops at destinations other than the starting point. Leisure walks are longer and usually happen closer to home or in dedicated outdoor spaces.

      For example, many of the walks recorded pre-Covid-19 were short and occurred at around 7 a.m. and between 3 and 5 p.m., which would indicate a walking commute. These bouts of walking were replaced on weekends by short walks around noon.

      The key takeaway is that most walking in cities occurs with the goal of getting to a place. If people don’t have the opportunity to walk to places they need to go, they will reduce their walking activity overall. But when provided opportunity and access, people can supplement utilitarian activity with leisure walking.

      What can be done about it

      Taking into account the public health implications of physical inactivity, the authors argue a reduction in access to walking should be considered a second pandemic and be addressed with the same rigor as the Covid-19 pandemic.

      They suggest several tactical urbanization strategies (defined as non-permanent but easily accessible measures) to increase safety and appeal for both utilitarian and recreational walkers. Many of these have already been implemented in various cities around the world to ease economic and other hardships of the pandemic. Sections of city streets have been closed off to cars on weekends or other non-busy times to allow for pedestrian walking areas. Restaurants have been given curb space to allow for outdoor dining.

      “But most of these pop-up pedestrian areas happen in downtown, where people are high-income and have easier access to more walking opportunities,” notes Moro.

      The same attention needs to be paid to lower-income areas, the researchers argue. This study’s data showed that people explored their own neighborhoods in a recreational way more during lockdown than pre-pandemic. Such wanderings, the researcher say, should be encouraged by making any large, multi-lane intersections safer to cross for the elderly, sick, or those with young children. And local parks, usually seen as places for running laps, should be made more attractive destinations by adding amenities like water fountains, shaded pavilions, and hygiene and sanitation spaces.

      This study was unique in that its data came straight from mobile devices, rather than being self-reported in surveys. This more reliable method of tracking made this study more data-driven than other, similar efforts. And the geotagged data allowed the researchers to dig into socioeconomic trends associated with the findings.

      This is the team’s first analysis of physical activity during and just after lockdown. They hope to use lessons learned from this and planned follow-ups to encourage more permanent adoption of pedestrian-friendly pandemic-era changes.

      The Connection Science Group, co-led by faculty member Alex “Sandy” Pentland — who, along with Moro was a co-author on the paper along with six others from the UK, Brazil, and Australia — is part of the MIT Sociotechnical Systems Research Center within the MIT Institute for Data, Systems, and Society. The collaborative research exemplified in this study is core to the mission of the SSRC; in pairing computer science with public health, the group not only observes trends but also contextualizes data and use them to make improvements for everyone.

      “SSRC merges both the social and technological components of the research,” says Moro. “We’re not only building an analysis, but going beyond that to propose new policies and interventions to change what we are seeing for the better.” More