Pandemic Predictions

Why outbreaks can still happen, even if most people get vaccinated — study

A 2019 disease reveals a key lesson about herd immunity in 2020.

Before 2019, the United States was tantalizingly close to achieving herd immunity for measles.

Approximately 94.7 percent of kindergarteners had received both doses of their measles vaccines for the 2018-2019 school year, putting the nation within the 95 percent range needed to achieve herd immunity for that disease. Then, a measles outbreak tore through 31 states, ending with over 1,200 cases – the most since 1992.

New research suggests this outbreak, while much smaller than the case counts driven by the novel coronavirus pandemic, is a teachable moment for the present. It's not enough for some people to be vaccinated when illness tears through a community. To achieve herd immunity on a community level, everyone needs to get the shot.

A paper published Monday shows that even if 99 percent of people in a small city are vaccinated against measles, there’s still a chance that outbreaks could occur, because of these unvaccinated clusters. We could appear to achieve herd immunity as a country or even a city – but not actually have gotten there in every community.

Case in point: Although almost all kindergarteners received their measles vaccines, that national trend didn’t reflect the whole story. Despite high overall vaccination rates, insular communities with low vaccination rates allowed the disease to resurge. In Clark County, Washington, where the measles outbreak began, 7.9 percent of kindergarteners hadn’t received the measles vaccine.

Nina Masters, the study’s first author and a Ph.D. student at the University of Michigan, tells Inverse that her findings apply to the current pandemic. Even if most people get a coronavirus vaccine on the national level it may not guarantee herd immunity from the virus on a local level.

Herd immunity is the idea that, if enough people are vaccinated, even those who are unvaccinated are protected as the disease becomes less common. It’s been a central feature of the quest for a Covid-19 vaccine.

The World Health Organization notes that about 60 to 80 percent of people need to be vaccinated to get to herd immunity for Covid-19 – but scientists are still trying to pin this number down. Meanwhile, a study released last week in the journal Nature found that, across 19 countries, 71.5 percent of people surveyed were "very or somewhat likely" to take a Covid-19 vaccine.

“There’s much talk about ‘herd immunity’ for COVID-19, but I think the lessons from measles, and what we show in this paper, is that herd immunity shouldn’t be seen as a magic number to hit on a state or national scale,” Masters says.

The paper was published in the Proceedings of the National Academy of Sciences.

Achieving local herd immunity – Non-vaccination tends to happen in clusters within larger communities, Masters explains.

Her team's paper examined different patterns of clustering of unvaccinated groups (all concentrated in one area, for example, or spread in “patchwork” fashion). Then, the researchers looked at the risk of a measles outbreak in an imaginary city of 256,000 people – about the size of Buffalo, New York.

You can see the clusters of anti-vaccine populations in the grids below. The whole grid is a city and each square is a block. In every case, 95 percent of the community is vaccinated, but if you look at the city as a whole, hot spots of unvaccinated clusters are obscured.

Anti-vaccine clusters are hard to spot unless you're reporting data the local level – school district level data is fine enough to detect these patterns, Masters says.

The findings suggest that even if 99 percent of people in a city are vaccinated, there's an 18.1 percent chance of a measles outbreak of 20 or more cases. If that percentage drops to 95 percent (12,800 unvaccinated people, in the model) there’s an 87.4 percent chance of an outbreak of 20 or more cases.

Lessons for Covid-19 — Masters says it’s likely that resistance to the Covid-19 vaccine will also lead to small groups of people living close together who will refuse the vaccine:

“Vaccine hesitancy often stems from fear of side effects, distrust in government and medical providers; low perceived risk of the disease itself. Many of those concerns are amplified right now, with a highly politicized national vaccination development program, an anti-science administration, and many circulating conspiracy theories.”

Taken together, Masters also says that the paper also suggests that you can't pin down one universal number for herd immunity, even for a city. Instead, when we roll out the vaccine we should be thinking about herd immunity on the level of towns, census tracts, or school districts.

“Herd immunity thresholds, to be effective, should hold at small spatial scales, which would ideally be part of a vaccination rollout plan," she says.

Importantly, measles is far more infectious than Covid-19. One person with measles can infect up to 18 others in the right circumstances, whereas the CDC’s best estimate for Covid-19 was 2.5, as of September. This paper, in turn, is not a perfect model of what might happen as a Covid-19 vaccine is rolled out, although helpful in sorting through how we should think about herd immunity and vaccines.

The key lesson: even if 95 percent of people are vaccinated at the global, national, county, or even city level, that small percentage of unvaccinated people could be clustered together in a way that makes outbreaks possible.

How to stop an outbreak – We can establish thresholds for herd immunity by reporting data in a more nuanced way, Masters says.

Vaccination data is reported at the school level because children typically require vaccines to enroll. Knowing vaccination rates at the school-district would be fine enough to establish local thresholds for herd immunity, she explains.

A protestor at a London rally against Covid-19 vaccines and restrictions.

Getty Images/ Justin Tallis

That data isn’t always made publicly available, however, making it hard to know which local regions might be the best footholds for diseases preventable by vaccines.

Still, the Covid-19 pandemic has shown us the importance of local-level data in determining risk. Masters sees signs that this trend could carry over into the future as part of a team that updates a Covid-19 map in Michigan. The map reports Covid-19 cases in areas a granular as 10 kilometer segments, based on address-level data from Michigan.

Bigger initiatives, like the Johns Hopkins Coronavirus Resource Center, disease data as granular as the county level. Masters would like to see public health authorities get even more specific for vaccination rates – allowing each place to know what their true immunity threshold is.

“We should really invest in collecting, maintaining, and disseminating finer-scale vaccination data to create ‘susceptibility maps’ which can better direct resources to areas at highest risk of outbreaks," she says.

Abstract: The United States experienced historically high numbers of measles cases in 2019, despite achieving national measles vaccination rates above the World Health Organization recommendation of 95% coverage with two doses. Since the COVID-19 pandemic began, resulting in suspension of many clinical preventive services, pediatric vaccination rates in the United States have fallen precipitously, dramatically increasing risk of measles resurgence. Previous research has shown that measles outbreaks in high-coverage contexts are driven by spatial clustering of nonvaccination, which decreases local immunity below the herd immunity threshold. However, little is known about how to best conduct surveillance and target interventions to detect and address these high-risk areas, and most vaccination data are reported at the state level—a resolution too coarse to detect community-level clustering of nonvaccination characteristic of recent outbreaks. In this paper, we perform a series of computational experiments to assess the impact of clustered nonvaccination on outbreak potential and magnitude of bias in predicting disease risk posed by measuring vaccination rates at coarse spatial scales. We find that, when nonvaccination is locally clustered, reporting aggregate data at the state- or county-level can result in substantial underestimates of outbreak risk. The COVID-19 pandemic has shone a bright light on the weaknesses in US infectious disease surveillance and a broader gap in our understanding of how to best use detailed spatial data to interrupt and control infectious disease transmission. Our research clearly outlines that finer-scale vaccination data should be collected to prevent a return to endemic measles transmission in the United States.
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