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Two unfolding outbreaks continue to command global attention. As a hantavirus outbreak tied to a cruise ship appears to be petering out, Ebola cases continue to mount in Africa. Alongside them have emerged familiar artifacts of the Covid era, including dashboards, trackers, maps, risk estimates, and a polarized mix of alarming and dismissive takes.

Once again, we’re able to watch disease spread in almost real time. Yet despite all the information, many people are left asking the same questions: what can I trust? How bad is this, really? What should I do?

It’s a lesson we still haven’t fully absorbed: data doesn’t speak for itself.

Rewind to 2014, when the last major Ebola outbreak dominated headlines. Most of us encountered that crisis through journalists and public health officials who helped us interpret complex information. These experts provided important details. They acknowledged caveats. They connected relative risks to appropriate actions.

By 2020, those supports were already weakening. The Covid-19 pandemic turned millions of people into direct consumers of data dashboards, statistical models, and risk calculations. The Johns Hopkins dashboard alone received billions of data requests a day. The pandemic also turned social media into a machine for stripping numbers of context and recirculating them as certainty. We had never had more access to information, or less help making sense of it. Since then, the interpretive infrastructure has only continued to fragment and collapse.

Today’s high-profile outbreaks will eventually fade. But more are coming. Researchers put the odds at greater than one in five for another pandemic killing at least 25 million people within the next decade. Meanwhile, we’re already dealing with persistent measles outbreaks across parts of the US and the world – a disease so contagious that nine out of 10 unvaccinated people exposed will contract it, and one for which we have effective prevention. The challenge there is largely one of communication.

Preparing for future outbreaks will require not only containing viruses but managing the information environment around them. We’ve now accumulated more than enough lessons to draw from.

Credible channels

Deep cuts at the CDC, HHS and NIH, plus the dismantling of USAID and our country’s withdrawal from the WHO, have undermined systems that track and respond to infectious disease. Less discussed is the parallel gutting of communication capacity within those organizations and the concurrent demise of local and national newsrooms. The US newspaper industry has lost more than three quarters of its jobs in the past two decades.

As those channels have eroded, people have grown more reliant on rapid, context-thin streams of information on social media feeds and AI-generated summaries. Social media rewards certainty, not the nuance of relative versus absolute risk or the transmission dynamics of a virus. AI’s confident-sounding summaries may, too, omit the very caveats that determine whether a statistic is meaningful or misleading. This problem runs deeper than conspiracy theories, although a vacuum of trustworthy information does give misinformation room to spread.

There’s no returning to the old media landscape. But some of what’s been lost can be restored.

Investing in original reporting is a necessary foundation. As the New York Times publisher AG Sulzberger recently argued, AI products rely on journalism. Without strong reporting, they will eventually have little of value to synthesize. Communication teams need rebuilding, too. One underappreciated consequence of US withdrawal from the WHO is that we stepped away from one of the world’s primary efforts to coordinate health messaging. And we reduced its capacity for everyone. Before ties were cut, the WHO had begun partnering with platforms such as TikTok to reach wider audiences.

Scientists, doctors and other trusted voices can also do more to communicate directly with the public. We saw this work during Covid, when researchers used social media to walk people through concepts such as the logarithmic scale and “flattening the curve”. One study found that short videos by doctors and nurses ahead of the winter holidays reduced travel and subsequent Covid infections.

Ultimately, it’s about meeting people where they are. At the center of the Ebola outbreak in the Democratic Republic of Congo, a radio station has dedicated daily programming to answering questions and correcting rumors about the virus, in hopes of winning over residents who’ve grown distrustful of authorities.

Context and caveats

Even with ample words or minutes of video, framing shapes what people understand – and misunderstand. Distortions can take different forms, such as missing context that reverses a finding, definitions that subtly shift, and labels that project confidence not supported by the underlying numbers.

During Covid, some messengers cited data showing higher death rates among vaccinated people than unvaccinated people. Obscured was the fact that older adults were both more likely to be vaccinated and more likely to die from Covid. The relationship reversed once the data was broken down by age. Early hantavirus statistics carry a similar blind spot. Commonly cited death rates of 30% to 40% may overstate the true risk, since milder infections may go undiagnosed and shrink the denominator.

Geography can disappear from the picture, too. A region may hit the vaccination threshold for herd immunity on paper while unprotected pockets within it act as kindling, letting a virus spread between vulnerable clusters. Scientists believe this local variability is driving measles resurgence. Yet that nuance rarely reaches the public, potentially leaving people with a false sense of security.

Framing can also project false certainty. In January 2020, the WHO tweeted that preliminary investigations from Chinese authorities had found “no clear evidence of human-to-human transmission”. In fuller statements, the WHO acknowledged transmission was possible and would not be surprising. But those caveats disappeared when the information was condensed for the public. When a CDC official this May described an US cruise passenger as testing “mildly” positive for hantavirus, the phrase muddled the distinction between test result and disease severity. As one Facebook commenter put it: “Is mildly positive like saying kinda pregnant?” The test was simply inconclusive.

Technical terms can mislead by triggering the wrong associations entirely. The WHO’s declaration of the current Ebola outbreak as a “public health emergency of international concern” prompted headlines and posts suggesting global danger. In reality, the designation is a mechanism for mobilizing resources and coordination. A sharp drop in official Ebola case counts in early June had a similar optics problem. It looked like good news but actually reflected a definitional shift from suspected to confirmed cases. The outbreak had not suddenly become less dangerous, and confirmed counts have since continued to rise. Explaining such terms up front, and acknowledging uncertainty, makes later revisions look less like reversals.

All of this is compounded by how data currently travels. The most trusted Covid data dashboards specified “confirmed cases” and “confirmed deaths”. They also provided both absolute and relative case counts, explained methodologies, and annotated anomalies. But even the best ones couldn’t control what happened next. A figure that carried caveats on a dashboard could lose them the moment it hit a social feed.

Calibrated concern and response

Good risk communication helps people understand what actions are proportionate to their actual risk – whether that means getting vaccinated, monitoring symptoms, avoiding close contact or resisting the urge to panic.

Covid showed what happens when officials translate uncertainty into rules without clear reasoning. In February 2020, the US surgeon general tweeted: “Seriously people – STOP BUYING MASKS!” He stated they were not effective in preventing the public from catching Covid. Two months later, when the CDC recommended face coverings, people were less willing to trust the message, and the messenger. Officials also marked out 6ft intervals with precision, closed beaches and trails without always distinguishing risks of crowded gatherings versus solitary outdoor time, and urged intensive surface cleaning after shared indoor air appeared to be the greater threat.

The hantavirus response, too, has included mixed messages. Even as officials maintained that the virus required prolonged close contact to spread, passengers from the same cruise ship faced strikingly different protocols: some placed in quarantine, others asked to self-isolate at home. The divergent reactions reflected genuine uncertainty about whether the Andes strain could spread across a room and whether people are infectious before symptoms appear. But that uncertainty was rarely communicated explicitly, leaving people to draw their own conclusions from seemingly arbitrary rules.

Inconsistency can look like incompetence, and it can invite distrust. Research from Covid and earlier outbreaks linked greater trust in public institutions, medical experts, and media with greater adherence to public health guidance and lower anxiety.

Measles is a case in point. We have a highly effective vaccine and decades of knowledge about transmission. Yet outbreaks continue because communication and trust determine whether people act on that knowledge. As the US hosts millions of visitors for the 2026 World Cup, amid persistent measles outbreaks around the world, that gap becomes more dangerous.

Strong surveillance systems and coordinated responses will not be enough for the next outbreak. We also need to re-establish systems that help people understand what the evidence means – and what to do with it.