Annette Regan, University of California, Los Angeles

In February 2025, flu rates spiked to the highest levels seen in at least 15 years, with flu outpacing COVID-19 infections and hospitalizations for the first time since the beginning of the COVID-19 pandemic. The Centers for Disease Control and Prevention has classified this flu season as having “high” severity across the U.S.

The Conversation asked epidemiologist Annette Regan to explain why this flu season is different from last year’s and what people can do to help reduce the spread.

How do flu cases and hospitalizations this year compare with previous years?

Beginning in late January and extending through February 2025, flu hospitalizations have been higher than any other week since before 2009.

Most flu cases appear to be from influenza A strains, with a split between influenza A/H3N2 and influenza A/H1N1. These are two different subtypes of the influenza A virus.

Researchers believe that historically seasons that are predominated by influenza A/H3N2 infections tend to be more severe, but infections from influenza A/H1N1 can still be very severe.

This year’s season is also peaking “late” compared with the past three flu seasons, which peaked in early or late December.

Unfortunately, there have been a number of deaths from flu too this season. Since Jan. 1, 2025, alone, over 4,000 people, including 68 children, have died from flu. While the number of deaths do not mark a record number, it shows that flu can be a serious illness, even in children.

Why are flu cases so high this year?

There are a number of factors behind any severe season, including poor community protection from low immunization rates and low natural immunity, virus characteristics, vaccine effectiveness and increased human contact via travel, office work or schools.

Unfortunately, flu vaccination rates have declined since the COVID-19 pandemic. At the end of the 2023-24 flu season, 9.2 million fewer doses were administered in pharmacies and doctors’ offices compared with an average year before the pandemic.

In addition, since 2022, fewer and fewer doses of flu vaccine have been distributed by private manufacturers. Flu vaccination rates for adults have historically been in the 30% to 60% range, much lower than the recommended 70%. Before the COVID-19 pandemic, flu vaccination rates were increasing by around 1% to 2% every year.

Flu vaccination rates began dropping after the COVID-19 pandemic, especially in higher-risk groups. Flu vaccination in children has dropped from 59% in 2019-20 to 46% in 2024-25. In adults 65 years and older, the group with the greatest risk of hospitalization and death, flu vaccination rates dropped from 52% in 2019-20 to 43% in 2024-25.

Lower vaccination rates mean a greater portion of the population is not protected by vaccines. Data shows that vaccination reduces the risk of flu hospitalization. Even if a vaccinated person gets infected, they may be less likely to experience severe illness. As a result, low vaccination rates could contribute to higher flu severity this season.

However, low vaccination rates are probably not the only reason for the high rates of flu this season. In previous severe seasons, genetic changes to the viruses have made them better at infecting people and more likely to cause severe illness.

The effectiveness of annual flu vaccines varies depending on how well the vaccine matches the circulating virus. The effectiveness of vaccines ranges from 19% to 60% in any given season. In the 2023-24 flu season, the vaccine was 42% effective.

Similarly, early 2024-25 data from the U.S. shows that the vaccine was 41% to 55% effective against flu hospitalizations in adults and 63% to 78% effective against flu hospitalizations in children.

How do seasonal flu symptoms differ from COVID-19 and other illnesses?

It’s important to remember that people often incorrectly refer to “the flu” when they have a common cold. Flu is caused only by the influenza virus, which tends to be more severe than common colds and more commonly causes a fever.

Many of the signs and symptoms for flu, COVID-19 and other respiratory viruses are the same and can range from mild coldlike symptoms to pneumonia and respiratory distress. Common flu symptoms are fever, cough and fatigue, and may also include shortness of breath, a sore throat, nasal congestion, muscle aches and headache.

Some symptoms, such as changes in or loss of taste and smell, are more common for COVID-19. For both COVID-19 and flu, the symptoms do not start until about one to four days after infection, and symptoms seem to last longer for COVID-19.

The only way to know what virus is causing an infection is to test. This can be done using a rapid test, some of which now test for flu and COVID-19 together, or by seeing a doctor and getting tested using a nasal swab. There are prescription antiviral medications available to treat flu and COVID-19, but they need to be taken near the time that symptoms start.

Some people are at high risk of severe flu and COVID-19, such as those who are immunosuppressed, have diabetes or have chronic heart or lung conditions. In these cases, it is important to seek early care and treatment from a health care professional. Some doctors will also prescribe via telehealth calls, which can help reduce the strain on doctors’ offices, urgent care centers and emergency rooms when infection rates are high.

What can people do now to help steer clear of the flu?

There are a number of ways people can reduce their risk of getting or spreading flu. Since the flu season is still underway, it’s not too late to get a flu vaccine. Even in seasons when the vaccine’s effectiveness is low, it is likely to offer better protection compared with remaining unvaccinated.

Handwashing and disinfecting high-traffic surfaces can help reduce contact with the flu virus. Taking efforts to avoid contact with sick people can also help, including wearing a mask when in health care facilities.

Finally, remember to take care of yourself. Exercising, eating healthy and getting sufficient sleep all help support a healthy immune system, which can help reduce chances of infection.

Those who have been diagnosed with flu or are experiencing flu-like symptoms should avoid contact with other people, especially in crowded spaces. Covering coughs and sneezes can help reduce the amount of virus that is spread.

Annette Regan, Adjunct Associate Professor of Epidemiology, University of California, Los Angeles

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Preston Green III, University of Connecticut and Suzanne Eckes, University of Wisconsin-Madison

In April 2025, the Supreme Court will hear arguments about whether the nation’s first religious charter school can open in Oklahoma. The St. Isidore of Seville Catholic Virtual School would be funded by taxpayer money but run by a local archdiocese and diocese.

The case is often discussed in terms of religion, and a decision in the school’s favor could allow government dollars to directly fund faith-based charter schools nationwide. In part, the justices must decide whether the First Amendment’s prohibition on government establishing religion applies to charter schools. But the answer to that question is part of an even bigger issue: Are charters really public in the first place?

As two professors who study education law, we believe the Supreme Court’s decision will impact issues of religion and state, but could also ripple beyond – determining what basic rights students and teachers do or don’t have at charter schools.

Dueling arguments

In June 2023, the Oklahoma Statewide Virtual Charter School Board approved St. Isidore’s application to open as an online K-12 school. The following year, however, the Oklahoma high court ruled that the proposal was unconstitutional. The justices concluded that charter schools are public under state law, and that the First Amendment’s establishment clause forbids public schools from being religious. The court also found that a religious charter school would violate Oklahoma’s constitution, which specifically forbids public money from benefiting religious organizations.

On appeal, the charter school is claiming that charter schools are private, and so the U.S. Constitution’s establishment clause does not apply.

Moreover, St. Isidore argues that if charter schools are private, the state’s prohibition on religious charters violates the First Amendment’s free exercise clause, which bars the government from limiting “the free exercise” of religion. Previous Supreme Court cases have found that states cannot prevent private religious entities from participating in generally available government programs solely because they are religious.

In other words, while St. Isidore’s critics argue that opening a religious charter school would violate the First Amendment, its supporters claim the exact opposite: that forbidding religious charter schools would violate the First Amendment.

Are charters public?

The question of whether an institution is public or private turns on a legal concept known as the “state action doctrine.” This principle provides that the government must follow the Constitution, while private entities do not have to. For example, unlike students in public schools, students in private schools do not have the constitutional right to due process for suspensions and expulsions – procedures to ensure fairness before taking disciplinary action.

Charter schools have some characteristics of both public and private institutions. Like traditional public schools, they are government-funded, free and open to all students. However, like private schools, they are free from many laws that apply to public schools, and they are independently run.

Because of charters’ hybrid nature, courts have had a hard time determining whether they should be considered public for legal purposes. Many charter schools are overseen by private corporations with privately appointed boards, and it is unclear whether these private entities are state actors. Two federal circuit courts have reached different conclusions.

In Caviness v. Horizon Learning Center, a case from 2010, the 9th Circuit held that an Arizona charter school corporation was not a state actor for employment purposes. Therefore, the board did not have to provide a teacher due process before firing him. The court reasoned that the corporation was a private actor that contracted with the state to provide educational services.

In contrast, the 4th Circuit ruled in 2022 that a North Carolina charter school board was a state actor under the equal protection clause of the Fourteenth Amendment. In this case, Peltier v. Charter Day School, students challenged the dress code requirement that female students wear skirts because they were considered “fragile vessels.”

The court first reasoned that the board was a state actor because North Carolina had delegated its constitutional duty to provide education. The court observed that the charter school’s dress code was an inappropriate sex-based classification, and that school officials engaged in harmful gender stereotyping, violating the equal protection clause.

If the Supreme Court sides with St. Isidore – as many analysts think is likely – then all private charter corporations might be considered nonstate actors for the purposes of religion.

But the stakes are even greater than that. State action involves more than just religion. Indeed, teachers and students in private schools do not have the constitutional rights related to free speech, search and seizure, due process and equal protection. In other words, if charter schools are not considered “state actors,” charter students and teachers may eventually shed constitutional rights “at the schoolhouse gate.”

Amtrak: An alternate route?

When courts have held that charter schools are not public in state law, some legislatures have made changes to categorize them as public. For example, California passed a law to clarify that charter school students have the same due process rights as traditional public school students after a court ruled otherwise.

Likewise, we believe states looking to clear up charter schools’ ambiguous state actor status under the Constitution can amend their laws. As we explain in a recent legal article, a 1995 Supreme Court case involving Amtrak illustrates how this can be done.

Lebron v. National Railroad Passenger Corporation arose when Amtrak rejected a billboard ad for being political. The advertiser sued, arguing that the corporation had violated his First Amendment right to free speech. Since private organizations are not required to protect free speech rights, the case hinged on whether Amtrak qualified as a government agency.

The court ruled in the plaintiff’s favor, reasoning that Amtrak was a government actor because it was created by special law, served important governmental objectives, and its board members were appointed by the government.

Courts have applied this ruling in other instances. For example, the 10th Circuit Court ruled in 2016 that the National Center for Missing and Exploited Children was a governmental agency and therefore was required to abide by the Fourth Amendment’s protection from unreasonable search and seizure.

Currently, we believe charter schools fail the test set out in the Amtrak decision. Charter schools do serve the governmental purpose of providing educational choice for students. However, charter school corporations are not created by special law. They also fall short because most have independent boards instead of members who are appointed and removed by government officials.

However, we would argue that states can amend their laws to comply with Lebron’s standard, ensuring that charter schools are public or state actors for constitutional purposes.

Preston Green III, John and Maria Neag Professor of Urban Education, University of Connecticut and Suzanne Eckes, Susan S. Engeleiter Professor of Education Law, Policy and Practice, University of Wisconsin-Madison

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Rutvik Desai, University of South Carolina

Generative AI systems like large language models and text-to-image generators can pass rigorous exams that are required of anyone seeking to become a doctor or a lawyer. They can perform better than most people in Mathematical Olympiads. They can write halfway decent poetry, generate aesthetically pleasing paintings and compose original music.

These remarkable capabilities may make it seem like generative artificial intelligence systems are poised to take over human jobs and have a major impact on almost all aspects of society. Yet while the quality of their output sometimes rivals work done by humans, they are also prone to confidently churning out factually incorrect information. Skeptics have also called into question their ability to reason.

Large language models have been built to mimic human language and thinking, but they are far from human. From infancy, human beings learn through countless sensory experiences and interactions with the world around them. Large language models do not learn as humans do – they are instead trained on vast troves of data, most of which is drawn from the internet.

The capabilities of these models are very impressive, and there are AI agents that can attend meetings for you, shop for you or handle insurance claims. But before handing over the keys to a large language model on any important task, it is important to assess how their understanding of the world compares to that of humans.

I’m a researcher who studies language and meaning. My research group developed a novel benchmark that can help people understand the limitations of large language models in understanding meaning.

Making sense of simple word combinations

So what “makes sense” to large language models? Our test involves judging the meaningfulness of two-word noun-noun phrases. For most people who speak fluent English, noun-noun word pairs like “beach ball” and “apple cake” are meaningful, but “ball beach” and “cake apple” have no commonly understood meaning. The reasons for this have nothing to do with grammar. These are phrases that people have come to learn and commonly accept as meaningful, by speaking and interacting with one another over time.

We wanted to see if a large language model had the same sense of meaning of word combinations, so we built a test that measured this ability, using noun-noun pairs for which grammar rules would be useless in determining whether a phrase had recognizable meaning. For example, an adjective-noun pair such as “red ball” is meaningful, while reversing it, “ball red,” renders a meaningless word combination.

The benchmark does not ask the large language model what the words mean. Rather, it tests the large language model’s ability to glean meaning from word pairs, without relying on the crutch of simple grammatical logic. The test does not evaluate an objective right answer per se, but judges whether large language models have a similar sense of meaningfulness as people.

We used a collection of 1,789 noun-noun pairs that had been previously evaluated by human raters on a scale of 1, does not make sense at all, to 5, makes complete sense. We eliminated pairs with intermediate ratings so that there would be a clear separation between pairs with high and low levels of meaningfulness.

We then asked state-of-the-art large language models to rate these word pairs in the same way that the human participants from the previous study had been asked to rate them, using identical instructions. The large language models performed poorly. For example, “cake apple” was rated as having low meaningfulness by humans, with an average rating of around 1 on scale of 0 to 4. But all large language models rated it as more meaningful than 95% of humans would do, rating it between 2 and 4. The difference wasn’t as wide for meaningful phrases such as “dog sled,” though there were cases of a large language model giving such phrases lower ratings than 95% of humans as well.

To aid the large language models, we added more examples to the instructions to see if they would benefit from more context on what is considered a highly meaningful versus a not meaningful word pair. While their performance improved slightly, it was still far poorer than that of humans. To make the task easier still, we asked the large language models to make a binary judgment – say yes or no to whether the phrase makes sense – instead of rating the level of meaningfulness on a scale of 0 to 4. Here, the performance improved, with GPT-4 and Claude 3 Opus performing better than others – but they were still well below human performance.

Creative to a fault

The results suggest that large language models do not have the same sense-making capabilities as human beings. It is worth noting that our test relies on a subjective task, where the gold standard is ratings given by people. There is no objectively right answer, unlike typical large language model evaluation benchmarks involving reasoning, planning or code generation.

The low performance was largely driven by the fact that large language models tended to overestimate the degree to which a noun-noun pair qualified as meaningful. They made sense of things that should not make much sense. In a manner of speaking, the models were being too creative. One possible explanation is that the low-meaningfulness word pairs could make sense in some context. A beach covered with balls could be called a “ball beach.” But there is no common usage of this noun-noun combination among English speakers.

If large language models are to partially or completely replace humans in some tasks, they’ll need to be further developed so that they can get better at making sense of the world, in closer alignment with the ways that humans do. When things are unclear, confusing or just plain nonsense – whether due to a mistake or a malicious attack – it’s important for the models to flag that instead of creatively trying to make sense of almost everything.

If an AI agent automatically responding to emails gets a message intended for another user in error, an appropriate response may be, “Sorry, this does not make sense,” rather than a creative interpretation. If someone in a meeting made incomprehensible remarks, we want an agent that attended the meeting to say the comments did not make sense. The agent should say, “This seems to be talking about a different insurance claim” rather than just “claim denied” if details of a claim don’t make sense.

In other words, it’s more important for an AI agent to have a similar sense of meaning and behave like a human would when uncertain, rather than always providing creative interpretations.

Rutvik Desai, Professor of Psychology, University of South Carolina

This article is republished from The Conversation under a Creative Commons license. Read the original article.