Masks and Distancing Won't Be Enough to Prevent School Outbreaks, Latest Science Suggests
Never has the prospect of "back to school" seemed so ominous as it does in 2020. As the number of COVID-19 cases climb steadily in nearly every state, the prospect of in-person classes are filling students, parents, and faculty alike with a corresponding sense of dread.
The notion that children are immune or resistant to SARS-CoV-2 is demonstrably untrue.
The decision to resume classes at primary, secondary, and collegiate levels is not one that should be regarded lightly, particularly as coronavirus cases skyrocket across the United States.
What should be a measured, data-driven discussion that weighs risks and benefits has been derailed by political talking points. President Trump has been steadily advocating for an unfettered return to the classroom, often through imperative "OPEN THE SCHOOLS!!!" tweets. In July, Secretary of Education Betsy DeVos threatened to withhold funding from schools that did not reopen for full-time, in-person classes, despite not having the authority to do so. Like so many public health issues, opening schools in the midst of a generational pandemic has been politicized to the point that the question of whether it is safe to do so has been obscured and confounded. However, this question still deserves to be examined based on evidence.
What We Know About Kids and COVID-19
Some arguments for returning to in-person education have focused on the fact that children and young adults are less susceptible to severe disease. In some cases, people have stated that children cannot be infected, pointing to countries that have resumed in-person education with no associated outbreaks. However, those countries had extremely low community transmission and robust testing and surveillance.
The notion that children are immune or resistant to SARS-CoV-2 is demonstrably untrue: children can be infected, they can become sick, and, in rare cases, they can die. Children can also transmit the virus to others, especially if they are in prolonged proximity to them. A Georgia sleepaway camp was the site of at least 260 cases among mostly children and teenagers, some as young as 6 years old. Children have been shown to shed infectious virus in their nasal secretions and have viral loads comparable to adults. Children can unquestionably be infected with SARS-CoV-2 and spread it to others.
The more data emerges, the more it appears that both primary and secondary schools and universities alike are conducive environments for super-spreading. Mitigating these risks depends heavily on individual schools' ability to enforce reduction measures. So far, the evidence demonstrates that in most cases, schools are unable to adequately protect students or staff. A school superintendent from a small district in Arizona recently described an outbreak that occurred among staff prior to in-person classes resuming. Schools that have opened so far have almost immediately reported new clusters of cases among students or staff.
This is because it is impossible to completely eliminate risk even with the most thoughtful mitigation measures when community transmission is high. Risk can be reduced, but the greater the likelihood that someone will be exposed in the community, the greater the risk they might pass the virus to others on campus or in the classroom.
There are still many unknowns about SARS-CoV-2 transmission, but some environments are known risks for virus transmission: enclosed spaces with crowds of people in close proximity over extended durations. Transmission is thought to occur predominantly through inhaled aerosols or droplets containing SARS-CoV-2, which are produced through common school activities such as breathing, speaking, or singing. Masks reduce but do not eliminate the production of these aerosols. Implementing universal mask-wearing and physical distancing guidelines will furthermore be extraordinarily challenging for very young children.
Smaller particle aerosols can remain suspended in the air and accumulate over time. In an enclosed space where people are gathering, such as a classroom, this renders risk mitigation measures such as physical distancing and masks ineffective. Many classrooms at all levels of education are not conducive to improving ventilation through low-cost measures such as opening windows, much less installing costly air filtration systems.
As a risk reduction measure, ventilation greatly depends on factors like window placement, window type, room size, room occupancy, building HVAC systems, and overall airflow. There isn't much hard data on the specific effects of ventilation on virus transmission, and the models that support ventilation rely on assumptions based on scant experimental evidence that doesn't account for virologic parameters.
There is also no data about how effective air filtration or UV systems would be for SARS-CoV-2 transmission risk reduction, so it's hard to say if this would result in a meaningful risk reduction or not. We don't have enough data outside of a hospital setting to support that ventilation and/or filtration would significantly reduce risk, and it's impractical (and most likely impossible in most schools) to implement hospital ventilation systems, which would likely require massive remodeling of existing HVAC infrastructure. In a close contact situation, the risk reduction might be minimal anyway since it's difficult to avoid exposure to respiratory aerosols and droplets a person is exhaling.
You'd need to get very low rates in the local community to open safely in person regardless of other risk reduction measures, and this would need to be complemented by robust testing and contact tracing capacity.
Efforts to resume in-person education depend heavily on school health and safety plans, which often rely on self-reporting of symptoms due to insufficient testing capacity. Self-reporting is notoriously unreliable, and furthermore, SARS-CoV-2 can be readily transmitted by pre-symptomatic individuals who may be unaware that they are sick, making testing an essential component of any such plan. Primary and secondary schools are faced with limited access to testing and no funds to support it. Even in institutions that include a testing component in their reopening plans, this is still too infrequent to support the full student body returning to campus.
Economic Conflicts of Interest
Rebecca Harrison, a PhD candidate at Cornell University serving on the campus reopening committee, is concerned that her institution's plan places too much faith in testing capacity and is over-reliant on untested models. Harrison says that, as a result, students are being implicitly encouraged to return to campus and "very little has been done to actively encourage students who are safe and able to stay home, to actually stay home."
Harrison also is concerned that her institution "presumably hopes to draw students back from the safety of their parents' basements to (re)join the residential campus experience ... and drive revenue." This is a legitimate concern. Some schools may be actively thwarting safety plans in place to protect students based on financial incentives. Student athletes at Colorado State have alleged that football coaches told them not to report COVID-19 symptoms and are manipulating contact tracing reports.
Public primary and secondary schools are not dependent on student athletics for revenue, but nonetheless are susceptible to state and federal policies that tie reopening to budgets. If schools are forced to make decisions based on a balance sheet, rather than the health and safety of students, teachers, and staff, they will implement health and safety plans that are inadequate. Schools will become ground zero for new clusters of cases.
Looking Ahead: When Will Schools Be Able to Open Again?
One crucial measure is the percent positivity rate in the local community, the number of positive tests based on all the tests that are done. Some states, like California, have implemented policies guiding the reopening of schools that depend in part on a local community's percent positivity rate falling under 8 percent, among other benchmarks including the rate of new daily cases. Currently, statewide, test positivity is below 7%, with an average of 3 new daily cases per 1000 people per day. However, the California department of health acknowledges that new cases per day are underreported. There are 6.3 million students in the California public school system, suggesting that at any given time, there could be nearly 20,000 students who might be contagious, without accounting for presymptomatic teachers and staff. In the classroom environment, just one of those positive cases could spread the virus to many people in one day despite masks, distancing, and ventilation.
You'd need to get very low rates in the local community to open safely in person regardless of other risk reduction measures, and this would need to be complemented by robust testing and contact tracing capacity. Only with rapid identification and isolation of new cases, followed by contact tracing and quarantine, can we break chains of transmission and prevent further spread in the school and the larger community.
None of these safety concerns diminish the many harms associated with the sudden and haphazard way remote learning has been implemented. Online education has not been effective in many cases and is difficult to implement equitably. Young children, in particular, are deprived of the essential social and intellectual development they would normally get in a classroom with teachers and their peers. Parents of young children are equally unprepared and unable to provide full-time instruction. Our federal leadership's catastrophic failure to contain the pandemic like other countries has put us in this terrible position, where we must choose between learning or spreading a deadly pathogen.
Blame aside, parents, educators, and administrators must decide whether to resume in-person classes this fall. Those decisions should be based on evidence, not on politics or economics. The data clearly shows that community transmission is out of control throughout most of the country. Thus, we ignore the risk of school outbreaks at our peril.
[Editor's Note: Here's the other essay in the Back to School series: 5 Key Questions to Consider Before Sending Your Child Back to School.]
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.