scientificamerican--2020-11-20--Doing the Touchy Math on Who Should Get a COVID Vaccine First

If the book of nature is written in the language of mathematics , as Galileo once declared , the Covid-19 pandemic has brought that truth home for the world 's mathematicians , who have been galvanized by the rapid spread of the coronavirus .
So far this year , they have been involved in everything from revealing how contagious the novel coronavirus is , how far we should stand from each other , how long an infected person might shed the virus , how a single strain spread from Europe to New York and then burst across America , and @ @ @ @ @ @ @ ' to save hundreds of thousands of lives .
Modeling also helped persuade the Centers for Disease Control and Prevention that the virus can be airborne and transmitted by aerosols that stay aloft for hours .
And at the moment many are grappling with a particularly urgent—and thorny—area of research : modeling the optimal rollout of a vaccine .
Because vaccine supply will be limited at first , the decisions about who gets those first doses could save tens of thousands of lives .
This is critical now that promising early results are @ @ @ @ @ @ @ Pfizer and BioNTech and one from Moderna—that may be highly effective and for which the companies may apply for emergency authorization from the Food and Drug Administration .
But figuring out how to allocate vaccines—there are close to 50 in clinical trials on humans—to the right groups at the right time is “ a very complex problem , ” says Eva Lee , director of the Center for Operations Research in Medicine and Health Care at the Georgia Institute of Technology .
Lee has modeled dispensing strategies for vaccines and medical supplies for @ @ @ @ @ @ @ and is now working on Covid-19 .
The coronavirus is “ so infectious and so much more deadly than influenza , ” she says .
“ We have never been challenged like that by a virus.
” Howard Forman , a public health professor at Yale University , says “ the last time we did mass vaccination with completely new vaccines , '' was with smallpox and polio .
`` We are treading into an area we are not used to.
” All the other vaccines of the last decades have either been @ @ @ @ @ @ @ slowly , he says .
Because Covid-19 is especially lethal for those over 65 and those with other health problems such as obesity , diabetes , or asthma , and yet is spread rapidly and widely by healthy young adults who are more likely to recover , mathematicians are faced with two conflicting priorities when modeling for vaccines : Should they prevent deaths or slow transmission ?
The consensus among most modelers is that if the main goal is to slash mortality rates , officials must prioritize vaccinating those who are older , @ @ @ @ @ @ @ , they must target younger adults .
“ Almost no matter what , you get the same answer , ” says Harvard epidemiologist Marc Lipsitch .
Vaccinate the elderly first to prevent deaths , he says , and then move on to other , healthier groups or the general population .
One recent study modeled how Covid-19 is likely to spread in six countries—the U.S. , India , Spain , Zimbabwe , Brazil , and Belgium—and concluded that if the primary goal is to reduce mortality rates , adults over 60 should be @ @ @ @ @ @ @ , by Daniel Larremore and Kate Bubar of the University of Colorado Boulder , Lipsitch , and their colleagues , has been published as a preprint , meaning it has not yet been peer reviewed .
Of course , when considering Covid-19 's outsized impact on minorities—especially Black and Latino communities—additional considerations for prioritization come into play .
Most modelers agree that “ everything is changing with coronavirus at the speed of light , ” as applied mathematician Laura Matrajt , a research associate at the Fred Hutchinson Cancer Research Center in Seattle @ @ @ @ @ @ @ That includes our understanding of how the virus spreads , how it attacks the body , how having another disease at the same time might raise the risk , and what leads to super-spreader events .
So far , the research has yielded some surprising results .
While children are usually prioritized for flu vaccine , for example , experts say the very young should be a lower priority for Covid-19 vaccines in the United States , because thus far young adults have been primary drivers of transmission .
( This is not @ @ @ @ @ @ @ India , for instance , where multiple generations often live together in smaller spaces , new research shows both children and young adults are spreading much of the virus in the two states studied . )
In addition , several models suggest that significant headway can be made against the pandemic even with lower deployment of a vaccine that is only partly effective .
And several others emphasize the importance of local infection and transmission rates .
According to Lee , whose early assessments of the pandemic 's origin , virulence , and @ @ @ @ @ @ @ accurate , New York could potentially contain the virus if about 40 percent of the population were vaccinated , because local transmission of the virus is fairly low ( a positivity rate of a little below 3 percent as of Nov. 16 ) , and around 20 percent have already been infected .
“ The higher the fraction of people in the population who already have antibodies , the more bang for your buck , ” says Larremore , because you can prioritize giving vaccines to those who don ’ t have antibodies @ @ @ @ @ @ @ , “ at the end of the day , you will never have enough vaccines for the entire population , '' says Lee—and not all Americans will take it .
In fact , the World Health Organization recently predicted that healthy young adults may not even be able to get a vaccine until 2022 , after the elderly , health care workers , and other high-risk groups are vaccinated .
To model the rollout of vaccines , mathematicians must build formulas that reflect the starburst of human life and our complex interactions , @ @ @ @ @ @ @ , daily habits , age , and health risks .
But first they establish how contagious the virus is—its reproductive rate , or `` R-naught . ''
This represents the number of people that one infected person can be expected to transmit the infection to .
When some fraction ( depending on R-naught ) of people are immune ( either by recovering from natural infection , if that grants immunity , or through vaccination ) , herd immunity has been achieved .
That means that while small outbreaks may still occur , the @ @ @ @ @ @ @ .
Given the R-naught of SARS-CoV-2 , the virus that causes Covid-19 , the World Health Organization has estimated that 65 percent to 70 percent of the population needs to be immune before this can be achieved .
Modeling vaccine rollout requires a complex acrobatics , and while the models to flatten the curve that mesmerized the public last spring took weeks to craft , vaccine distribution models take many months .
There are innumerable practical challenges facing modelers .
For one thing , many of the vaccines currently in the pipeline—including the @ @ @ @ @ @ @ Moderna—require two shots , several weeks apart , which involve registries and follow-up to ensure that people get the second , critical booster shot .
And as The New York Times noted in late September , “ Companies may have to transport tiny glass vials thousands of miles while keeping them as cold as the South Pole in the depths of winter.
” There is also the question of vaccine efficacy .
Will a given vaccine provide robust immunity , and in all groups ?
Or will it primarily shorten duration of infection @ @ @ @ @ @ @ be of great value in reducing mortality as well as transmission ?
And what if a vaccine is less effective among the elderly , as is often the case ?
At the moment , vaccines using messenger RNA ( including those produced by Moderna and Pfizer and BioNTech ) are “ looking pretty good in older adults , ” according to Kathleen Neuzil , director of the Center for Vaccine Development and Global Health at the University of Maryland School of Medicine .
Preliminary analyses of both vaccine candidates show that they may @ @ @ @ @ @ @ Finally , there is also the vexing question of how long immunity might last after infection .
For some viruses , such as the varicella-zoster virus that causes chickenpox , immunity can last for decades .
For others , such as the family of coronaviruses that includes SARS-CoV-2 and the common cold , the virus has a relatively high mutation rate that may protect novel strains from our antibodies .
That uncertainty is difficult to model precisely , so many modelers assume that , for the time being at least , those who @ @ @ @ @ @ @ , of the Fred Hutchinson Cancer Center in Seattle , remembers vividly how hard it was to begin to construct a model out of thin air when she began working with colleagues on a vaccination model this past April .
There were “ so many uncertainties , ” she recalls .
Together , the researchers developed algorithms based on an astonishing 440 or so combinations of parameters , from transmission to immunity to age groups and mortality .
Their computers spent nearly 9,000 hours running equations , and their model , published in @ @ @ @ @ @ @ if there is only a low supply of vaccine at first , older adults should be prioritized if the goal is to reduce deaths .
But for vaccines that are at least 60 percent effective , once there is enough to cover at least half the population , switching to target healthy individuals ages 20 to 50 as well as children would minimize deaths .
The model also predicts how many deaths can be averted with different amounts of vaccine coverage .
For instance , if 20 percent of the population has already @ @ @ @ @ @ @ could be halved by vaccinating just 35 percent of the remainder , if the vaccine is at least 50 percent effective .
In the model by Matrajt and her colleagues , herd immunity is achieved once 60 percent of the population is immune .
“ It is completely normal that different models will give different numbers , ” she says , explaining why her estimate varies slightly from the WHO figure of 65 percent .
The model does “ a really nice job looking at a large number of plausible cases , ” @ @ @ @ @ @ @ resource economist at the University of California , Davis , who just finished his own model with Jack Buckner , a colleague at UC Davis , and Gerardo Chowell , a mathematical epidemiologist at Georgia State University .
Their study , released in preprint , also suggests the power of careful initial targeting in reducing deaths .
The models suggest that even a partially-effective vaccine given to just part of the population , says Springborn , “ can go a really long way to reducing infections and reducing deaths.
” Lee 's modeling @ @ @ @ @ @ @ in 2003 , in conjunction with the CDC , for dispensing of supplies in natural disasters and pandemics , analyzes how the disease might be contained in areas with different infection rates and initially scarce vaccine supplies .
In New York City , which was hit so hard in the spring , her model predicts that roughly 60 percent of the population may need immunity to contain the pandemic .
Assuming 20 percent are already infected , about 40 percent would need to be vaccinated .
In San Diego , however , where @ @ @ @ @ @ @ ’ s model suggests that 65 percent will need to achieve immunity through infection or vaccination .
In Houston , the figure may be as high as 73 percent because the infection has persisted at a “ slow burn ” and because of the city ’ s large , vulnerable Latino and African American populations , who have borne disproportionate risk .
Lee cautions that these results do not mean you can suddenly go to a football game in Houston or Broadway show in New York , but it does mean that with @ @ @ @ @ @ @ be contained with the percentages given in her models , until more vaccine arrives .
Though their results vary , most models agree that certain factors are critical , notably age group , which changes the risk of contracting , spreading , and dying from a virus .
It 's not always predictable : The swine flu , for instance , spared older adults to some degree , while SARS-CoV-2 has severely affected those over 65 .
Adults 65 and older compose 16 percent of the U.S. population but account for about 80 @ @ @ @ @ @ @ , age indirectly influences transmission patterns .
In 2009 , Yale epidemiologists Alison Galvani and Jan Medlock published a mathematical model in Science , showing that targeting flu vaccines to children and young adults ( in addition to the elderly ) could have slashed swine flu infections from 59 million to 44 million ; and for seasonal influenza , 83 million infections could plunge to 44 million .
Children , it turns out , drive a disproportionate amount of flu transmission , and protecting them protects society at large .
The study , @ @ @ @ @ @ @ change in CDC policy to prioritize vaccinating children .
“ It was a revolution in how we think about vaccines , ” says Larremore .
Vaccination models now routinely consider the power of indirect protection of the most vulnerable by vaccinating those most responsible for spread .
Age also intersects , in complex ways , with social connectivity in different regions .
For instance , African American and Latino communities in the United States have been disproportionately hit by Covid-19 , in part because of the prevalence of multiple generations living together : @ @ @ @ @ @ @ the young adults who might be the likeliest carriers of infection .
Modeling connectivity requires drawing grids that represent how we live and move among each other .
In 2008 , a landmark paper built a grid that epidemiologists everywhere still use today .
It stratified people into groups based on age , from birth to 70 years old and up .
In the study , more than 7,000 individuals kept a diary of their contacts—nearly 98,000 of them—over the course of one day .
Contacts were sorted by place ( home , @ @ @ @ @ @ @ by nature ( physical or nonphysical , brief or longer lasting ) .
The model found that 5- to 19-year-olds tend to experience the highest incidence of infection when a new pathogen begins to spread in a completely susceptible population , possibly because of their more frequent and physical contact with others .
It also showed how profoundly a society ’ s grids of connection influence transmission .
The model was expanded globally in 2017 , with contact rates for 152 countries .
“ It ’ s what we all use , ” @ @ @ @ @ @ @ s the best thing we have to identify how people contact each other.
” She incorporated the contact grid into her model .
For example , “ if kids are really the hubs around which society is built , ” Larremore says , “ so that if you vaccinate the kids , you fragment that transmission network , then that ’ s going to give us a totally different way of rolling out this vaccine.
” The original grid relied on diaries .
Today , our ability to gather data through real time @ @ @ @ @ @ @ greater .
When social distancing became widespread this past spring , it dramatically altered the input into the typical transmission model , says Springborn .
Data from the Institute for Health Metrics and Evaluation at the University of Washington shows the power of social distancing in reducing transmission .
The contact grids in previous studies are “ from pre-pandemic times , ” Springborn wrote in an email .
“ We know that contact rates are very different under social distancing and we want to account for that .
And we expect social distancing @ @ @ @ @ @ @ falls .
Human nature : As risk falls , so does risk-mitigating behavior.
” That needs to be modeled as well .
And it will influence the expectations for a vaccine ’ s rollout and success .
In fact , Lee maintains , if we had 90 percent compliance with face masks and social distancing right now , we could contain the virus without a vaccine .
In the study by Springborn , Buckner , and Chowell , social distancing is modeled by creating age-stratified categories for both essential and nonessential workers .
@ @ @ @ @ @ @ , and many schoolteachers , among others—are at high risk for infection because they can not socially distance .
This model finds that deaths , as well as total years of life lost , are dramatically decreased when essential workers are prioritized to receive the vaccine .
Older essential workers between 40 and 59 should be prioritized first if the goal is to minimize deaths , the authors maintain .
With no vaccine , about 179,000 people may die in the first six months of 2021 , Springborn says .
His team ’ @ @ @ @ @ @ @ to about 88,000 simply by introducing a vaccine gradually , giving it to 10 percent of the population each month , and distributing it uniformly without prioritizing any groups .
But distributing vaccines in a targeted way , based on people ’ s ages and whether they are essential workers , could save another 7,000 to 37,000 lives , depending on the situation .
There are other methods of teasing out social connectivity beyond diaries and cellphone data .
Census and other data reflect age , profession , and socioeconomic status , and @ @ @ @ @ @ @ “ The zip code gives you a huge amount of information , ” she says .
Public health data on disease prevalence and hospitalizations can tease out the other unrelated diseases that Covid-19 patients have , as well as vulnerabilities in a given area .
Even information on a city ’ s housing , whether skyscrapers or single-family homes , can give a clue to how closely people are packed together and how likely they are to interact .
Inputting this kind of data allows for a vaccine rollout that is sensitive to @ @ @ @ @ @ @ model about 500 representative cities around the U.S. , she says , to cover the country accurately .
As powerful as the models can be , they are an imperfect guide .
Inevitably they intersect with deep and broad social concerns .
The pandemic has disproportionately harmed and killed minorities and those with lower incomes .
For that reason , various groups are looking into the ethical principles that should frame vaccine allocation , according to Hanna Nohynek , deputy head of the Infectious Diseases Control and Vaccinations Unit at the Finnish Institute @ @ @ @ @ @ @ member of the WHO ’ s SAGE Working Group on Covid-19 vaccines .
In the U.S. , the National Academies of Sciences , Engineering , and Medicine has begun to model an equitable allocation of a vaccine .
In addition , two other important models have emerged , one associated with University of Pennsylvania School of Medicine , and the other with Johns Hopkins University .
Both are guided by concerns about ethics , fairness , maximizing benefits , building trust and the greater public good .
But building trust can be challenging @ @ @ @ @ @ @ ’ s widely acknowledged that Black people have experienced hospitalization and death at disproportionately high rates compared to White people .
Yet when ethicists begin to talk about prioritizing Black people for vaccines , it can be perceived as an intent to experiment on them by pushing them to the head of the line .
If there is concern among African Americans , it ’ s a logical reaction to “ a vast history of centuries of abuse of African Americans in the medical sphere , ” says medical ethicist Harriet Washington , @ @ @ @ @ @ @ , both ethical and mathematical models have to face real-world practicalities .
“ It ’ s hard because math essentially boils down to a utilitarian calculus , ” says Lipsitch , the Harvard epidemiologist .
Nonetheless , says Larremore , the models will help guide us in the uncertain early days .
“ Vaccines take a while to roll out , ” he says .
`` We can ’ t let our foot off the gas the moment a vaccine is announced.
” This article was originally published on Undark .
Read the @ @ @

If the book of nature is written in the language of mathematics, as Galileo once declared, the Covid-19 pandemic has brought that truth home for the world's mathematicians, who have been galvanized by the rapid spread of the coronavirus.
So far this year, they have been involved in everything from revealing how contagious the novel coronavirus is, how far we should stand from each other, how long an infected person might shed the virus, how a single strain spread from Europe to New York and then burst across America, and how to ‘'flatten the curve’' to save hundreds of thousands of lives. Modeling also helped persuade the Centers for Disease Control and Prevention that the virus can be airborne and transmitted by aerosols that stay aloft for hours.
And at the moment many are grappling with a particularly urgent—and thorny—area of research: modeling the optimal rollout of a vaccine. Because vaccine supply will be limited at first, the decisions about who gets those first doses could save tens of thousands of lives. This is critical now that promising early results are coming in about two vaccine candidates—one from Pfizer and BioNTech and one from Moderna—that may be highly effective and for which the companies may apply for emergency authorization from the Food and Drug Administration.
But figuring out how to allocate vaccines—there are close to 50 in clinical trials on humans—to the right groups at the right time is “a very complex problem,” says Eva Lee, director of the Center for Operations Research in Medicine and Health Care at the Georgia Institute of Technology. Lee has modeled dispensing strategies for vaccines and medical supplies for Zika, Ebola, and influenza, and is now working on Covid-19. The coronavirus is “so infectious and so much more deadly than influenza,” she says. “We have never been challenged like that by a virus.”
Howard Forman, a public health professor at Yale University, says “the last time we did mass vaccination with completely new vaccines,'' was with smallpox and polio. "We are treading into an area we are not used to.” All the other vaccines of the last decades have either been tested for years or were introduced very slowly, he says.
Because Covid-19 is especially lethal for those over 65 and those with other health problems such as obesity, diabetes, or asthma, and yet is spread rapidly and widely by healthy young adults who are more likely to recover, mathematicians are faced with two conflicting priorities when modeling for vaccines: Should they prevent deaths or slow transmission?
The consensus among most modelers is that if the main goal is to slash mortality rates, officials must prioritize vaccinating those who are older, and if they want to slow transmission, they must target younger adults.
“Almost no matter what, you get the same answer,” says Harvard epidemiologist Marc Lipsitch. Vaccinate the elderly first to prevent deaths, he says, and then move on to other, healthier groups or the general population. One recent study modeled how Covid-19 is likely to spread in six countries—the U.S., India, Spain, Zimbabwe, Brazil, and Belgium—and concluded that if the primary goal is to reduce mortality rates, adults over 60 should be prioritized for direct vaccination. The study, by Daniel Larremore and Kate Bubar of the University of Colorado Boulder, Lipsitch, and their colleagues, has been published as a preprint, meaning it has not yet been peer reviewed. Of course, when considering Covid-19's outsized impact on minorities—especially Black and Latino communities—additional considerations for prioritization come into play.
Most modelers agree that “everything is changing with coronavirus at the speed of light,” as applied mathematician Laura Matrajt, a research associate at the Fred Hutchinson Cancer Research Center in Seattle, put it in an email. That includes our understanding of how the virus spreads, how it attacks the body, how having another disease at the same time might raise the risk, and what leads to super-spreader events.
So far, the research has yielded some surprising results. While children are usually prioritized for flu vaccine, for example, experts say the very young should be a lower priority for Covid-19 vaccines in the United States, because thus far young adults have been primary drivers of transmission. (This is not necessarily true across the globe; in India, for instance, where multiple generations often live together in smaller spaces, new research shows both children and young adults are spreading much of the virus in the two states studied.)
In addition, several models suggest that significant headway can be made against the pandemic even with lower deployment of a vaccine that is only partly effective. And several others emphasize the importance of local infection and transmission rates. According to Lee, whose early assessments of the pandemic's origin, virulence, and probable global trajectory proved to be strikingly accurate, New York could potentially contain the virus if about 40 percent of the population were vaccinated, because local transmission of the virus is fairly low (a positivity rate of a little below 3 percent as of Nov. 16), and around 20 percent have already been infected.
“The higher the fraction of people in the population who already have antibodies, the more bang for your buck,” says Larremore, because you can prioritize giving vaccines to those who don’t have antibodies.
All these findings are important because, “at the end of the day, you will never have enough vaccines for the entire population," says Lee—and not all Americans will take it. In fact, the World Health Organization recently predicted that healthy young adults may not even be able to get a vaccine until 2022, after the elderly, health care workers, and other high-risk groups are vaccinated.
To model the rollout of vaccines, mathematicians must build formulas that reflect the starburst of human life and our complex interactions, using data like housing and socioeconomic status, daily habits, age, and health risks. But first they establish how contagious the virus is—its reproductive rate, or "R-naught." This represents the number of people that one infected person can be expected to transmit the infection to.
When some fraction (depending on R-naught) of people are immune (either by recovering from natural infection, if that grants immunity, or through vaccination), herd immunity has been achieved. That means that while small outbreaks may still occur, the pandemic will not take off globally again. Given the R-naught of SARS-CoV-2, the virus that causes Covid-19, the World Health Organization has estimated that 65 percent to 70 percent of the population needs to be immune before this can be achieved.
Modeling vaccine rollout requires a complex acrobatics, and while the models to flatten the curve that mesmerized the public last spring took weeks to craft, vaccine distribution models take many months. There are innumerable practical challenges facing modelers. For one thing, many of the vaccines currently in the pipeline—including the two candidates from Pfizer and BioNTech and Moderna—require two shots, several weeks apart, which involve registries and follow-up to ensure that people get the second, critical booster shot. And as The New York Times noted in late September, “Companies may have to transport tiny glass vials thousands of miles while keeping them as cold as the South Pole in the depths of winter.”
There is also the question of vaccine efficacy. Will a given vaccine provide robust immunity, and in all groups? Or will it primarily shorten duration of infection and lessen symptoms, which would still be of great value in reducing mortality as well as transmission? And what if a vaccine is less effective among the elderly, as is often the case? At the moment, vaccines using messenger RNA (including those produced by Moderna and Pfizer and BioNTech) are “looking pretty good in older adults,” according to Kathleen Neuzil, director of the Center for Vaccine Development and Global Health at the University of Maryland School of Medicine. Preliminary analyses of both vaccine candidates show that they may be more than 90 percent effective.
Finally, there is also the vexing question of how long immunity might last after infection. For some viruses, such as the varicella-zoster virus that causes chickenpox, immunity can last for decades. For others, such as the family of coronaviruses that includes SARS-CoV-2 and the common cold, the virus has a relatively high mutation rate that may protect novel strains from our antibodies. That uncertainty is difficult to model precisely, so many modelers assume that, for the time being at least, those who have been infected are immune.
Matrajt, of the Fred Hutchinson Cancer Center in Seattle, remembers vividly how hard it was to begin to construct a model out of thin air when she began working with colleagues on a vaccination model this past April. There were “so many uncertainties,” she recalls. Together, the researchers developed algorithms based on an astonishing 440 or so combinations of parameters, from transmission to immunity to age groups and mortality. Their computers spent nearly 9,000 hours running equations, and their model, published in August as a preprint, shows that if there is only a low supply of vaccine at first, older adults should be prioritized if the goal is to reduce deaths.
But for vaccines that are at least 60 percent effective, once there is enough to cover at least half the population, switching to target healthy individuals ages 20 to 50 as well as children would minimize deaths. The model also predicts how many deaths can be averted with different amounts of vaccine coverage. For instance, if 20 percent of the population has already been infected and is immune, deaths could be halved by vaccinating just 35 percent of the remainder, if the vaccine is at least 50 percent effective.
In the model by Matrajt and her colleagues, herd immunity is achieved once 60 percent of the population is immune. “It is completely normal that different models will give different numbers,” she says, explaining why her estimate varies slightly from the WHO figure of 65 percent.
The model does “a really nice job looking at a large number of plausible cases,” says Michael Springborn, an environmental and resource economist at the University of California, Davis, who just finished his own model with Jack Buckner, a colleague at UC Davis, and Gerardo Chowell, a mathematical epidemiologist at Georgia State University. Their study, released in preprint, also suggests the power of careful initial targeting in reducing deaths.
The models suggest that even a partially-effective vaccine given to just part of the population, says Springborn, “can go a really long way to reducing infections and reducing deaths.”
Lee's modeling, created with software she first developed in 2003, in conjunction with the CDC, for dispensing of supplies in natural disasters and pandemics, analyzes how the disease might be contained in areas with different infection rates and initially scarce vaccine supplies. In New York City, which was hit so hard in the spring, her model predicts that roughly 60 percent of the population may need immunity to contain the pandemic. Assuming 20 percent are already infected, about 40 percent would need to be vaccinated. In San Diego, however, where infection rates have been lower, Lee’s model suggests that 65 percent will need to achieve immunity through infection or vaccination. In Houston, the figure may be as high as 73 percent because the infection has persisted at a “slow burn” and because of the city’s large, vulnerable Latino and African American populations, who have borne disproportionate risk.
Lee cautions that these results do not mean you can suddenly go to a football game in Houston or Broadway show in New York, but it does mean that with ongoing precautions, the virus might well be contained with the percentages given in her models, until more vaccine arrives.
Though their results vary, most models agree that certain factors are critical, notably age group, which changes the risk of contracting, spreading, and dying from a virus. It's not always predictable: The swine flu, for instance, spared older adults to some degree, while SARS-CoV-2 has severely affected those over 65. Adults 65 and older compose 16 percent of the U.S. population but account for about 80 percent of Covid-19 deaths.
In addition, age indirectly influences transmission patterns. In 2009, Yale epidemiologists Alison Galvani and Jan Medlock published a mathematical model in Science, showing that targeting flu vaccines to children and young adults (in addition to the elderly) could have slashed swine flu infections from 59 million to 44 million; and for seasonal influenza, 83 million infections could plunge to 44 million. Children, it turns out, drive a disproportionate amount of flu transmission, and protecting them protects society at large.
The study, and others like it, inspired a change in CDC policy to prioritize vaccinating children. “It was a revolution in how we think about vaccines,” says Larremore. Vaccination models now routinely consider the power of indirect protection of the most vulnerable by vaccinating those most responsible for spread.
Age also intersects, in complex ways, with social connectivity in different regions. For instance, African American and Latino communities in the United States have been disproportionately hit by Covid-19, in part because of the prevalence of multiple generations living together: Older individuals are much more exposed to the young adults who might be the likeliest carriers of infection.
Modeling connectivity requires drawing grids that represent how we live and move among each other. In 2008, a landmark paper built a grid that epidemiologists everywhere still use today. It stratified people into groups based on age, from birth to 70 years old and up. In the study, more than 7,000 individuals kept a diary of their contacts—nearly 98,000 of them—over the course of one day. Contacts were sorted by place (home, school, work, leisure) and by nature (physical or nonphysical, brief or longer lasting). The model found that 5- to 19-year-olds tend to experience the highest incidence of infection when a new pathogen begins to spread in a completely susceptible population, possibly because of their more frequent and physical contact with others. It also showed how profoundly a society’s grids of connection influence transmission.
The model was expanded globally in 2017, with contact rates for 152 countries. “It’s what we all use,” says Matrajt, “because it’s the best thing we have to identify how people contact each other.” She incorporated the contact grid into her model.
For example, “if kids are really the hubs around which society is built,” Larremore says, “so that if you vaccinate the kids, you fragment that transmission network, then that’s going to give us a totally different way of rolling out this vaccine.”
The original grid relied on diaries. Today, our ability to gather data through real time cellphone and online activity may be even greater.
When social distancing became widespread this past spring, it dramatically altered the input into the typical transmission model, says Springborn. Data from the Institute for Health Metrics and Evaluation at the University of Washington shows the power of social distancing in reducing transmission. The contact grids in previous studies are “from pre-pandemic times,” Springborn wrote in an email. “We know that contact rates are very different under social distancing and we want to account for that. And we expect social distancing to soften as the number of infections falls. Human nature: As risk falls, so does risk-mitigating behavior.”
That needs to be modeled as well. And it will influence the expectations for a vaccine’s rollout and success. In fact, Lee maintains, if we had 90 percent compliance with face masks and social distancing right now, we could contain the virus without a vaccine.
In the study by Springborn, Buckner, and Chowell, social distancing is modeled by creating age-stratified categories for both essential and nonessential workers. Essential workers—health care workers, grocery workers, and many schoolteachers, among others—are at high risk for infection because they cannot socially distance. This model finds that deaths, as well as total years of life lost, are dramatically decreased when essential workers are prioritized to receive the vaccine. Older essential workers between 40 and 59 should be prioritized first if the goal is to minimize deaths, the authors maintain.
With no vaccine, about 179,000 people may die in the first six months of 2021, Springborn says. His team’s model suggests that deaths could decline to about 88,000 simply by introducing a vaccine gradually, giving it to 10 percent of the population each month, and distributing it uniformly without prioritizing any groups. But distributing vaccines in a targeted way, based on people’s ages and whether they are essential workers, could save another 7,000 to 37,000 lives, depending on the situation.
There are other methods of teasing out social connectivity beyond diaries and cellphone data. Census and other data reflect age, profession, and socioeconomic status, and Lee includes them in her models. “The zip code gives you a huge amount of information,” she says. Public health data on disease prevalence and hospitalizations can tease out the other unrelated diseases that Covid-19 patients have, as well as vulnerabilities in a given area. Even information on a city’s housing, whether skyscrapers or single-family homes, can give a clue to how closely people are packed together and how likely they are to interact. Inputting this kind of data allows for a vaccine rollout that is sensitive to local conditions. Lee would need to model about 500 representative cities around the U.S., she says, to cover the country accurately.
As powerful as the models can be, they are an imperfect guide. Inevitably they intersect with deep and broad social concerns. The pandemic has disproportionately harmed and killed minorities and those with lower incomes. For that reason, various groups are looking into the ethical principles that should frame vaccine allocation, according to Hanna Nohynek, deputy head of the Infectious Diseases Control and Vaccinations Unit at the Finnish Institute for Health and Welfare, and a member of the WHO’s SAGE Working Group on Covid-19 vaccines.
In the U.S., the National Academies of Sciences, Engineering, and Medicine has begun to model an equitable allocation of a vaccine. In addition, two other important models have emerged, one associated with University of Pennsylvania School of Medicine, and the other with Johns Hopkins University. Both are guided by concerns about ethics, fairness, maximizing benefits, building trust and the greater public good.
But building trust can be challenging in practice. For instance, it’s widely acknowledged that Black people have experienced hospitalization and death at disproportionately high rates compared to White people. Yet when ethicists begin to talk about prioritizing Black people for vaccines, it can be perceived as an intent to experiment on them by pushing them to the head of the line. If there is concern among African Americans, it’s a logical reaction to “a vast history of centuries of abuse of African Americans in the medical sphere,” says medical ethicist Harriet Washington, author of “Medical Apartheid.”
Ultimately, both ethical and mathematical models have to face real-world practicalities. “It’s hard because math essentially boils down to a utilitarian calculus,” says Lipsitch, the Harvard epidemiologist.
Nonetheless, says Larremore, the models will help guide us in the uncertain early days. “Vaccines take a while to roll out,” he says. "We can’t let our foot off the gas the moment a vaccine is announced.”
This article was originally published on Undark. Read the original article.


scientificamerican--2020-11-20--Storm-Surfing Parasites Spread Widely

Storm-Surfing Parasites Spread Widely

Just after researchers wrapped up fieldwork for the season in the turquoise waters between the Virgin Islands and Puerto Rico in 2017 , back-to-back major hurricanes tore through their study sites and laboratories .
It was a serious setback in the team 's work with little-known parasites called gnathiids , whose young latch onto saltwater fish and feed on their blood .
But the misfortune also presented a rare opportunity to study how catastrophic hurricanes impact marine animal populations , says molecular ecologist Juan Andrés Pagán , a graduate student at the University of Porto in Portugal and lead author @ @ @ @ @ @ @ .
Every year since 2013 , the team—which calls itself Gnathiid Gnation—had collected a species it named for Bob Marley ( Gnathia marleyi ) , found in coral reefs .
After the hurricanes the researchers returned to collect more specimens .
They braved jellyfish and sharks to set traps for the gnathiids , which are most active at night .
For their new study , the scientists sequenced gnathiid DNA from population samples collected at the various sites before and after the hurricanes , which were the most intense consecutive storms to hit @ @ @ @ @ @ @ were able to pretty clearly establish that , prior to the hurricanes , these populations were stable , ” Pagán says , meaning the parasites remained in the same isolated areas year after year .
Gnathiids are bad long-distance swimmers .
Under normal conditions they stick to small areas , similar to other invertebrates such as crustaceans and mollusks .
But G. marleyi rode the 2017 hurricanes over unusually long distances , with some apparently traveling more than 250 kilometers .
The researchers found that this displacement led to a “ mixing of @ @ @ @ @ @ @ , a more genetically similar gnathiid population , says team leader Paul Sikkel , an Arkansas State University marine ecologist and study co-author .
“ The hurricanes homogenized all that , ” Sikkel says .
“ At any one site the diversity became higher .
But overall [ the populations ] became more even because things were being moved around.
” This may be the first clear , genetics-based evidence that hurricanes affect the genetic diversity of a marine species , he says .
“ The kicker here for me is climate change @ @ @ @ @ @ @ marine biologist at the Florida Institute of Technology , who was not involved in the study .
If hurricanes increase in intensity as predicted , Aronson says , the study suggests there may be more genetic homogenization among some marine animals that would otherwise remain in isolated populations .
This would make peripheral populations less likely to cleave off and create new species , he adds .
The researchers are also investigating whether the parasites spread blood-borne pathogens that could harm fish populations .
If that is the case , Sikkel says , @ @ @ @ @ @ @ parasite-borne disease outbreaks in the wake of future major hurricanes .

Just after researchers wrapped up fieldwork for the season in the turquoise waters between the Virgin Islands and Puerto Rico in 2017, back-to-back major hurricanes tore through their study sites and laboratories. It was a serious setback in the team's work with little-known parasites called gnathiids, whose young latch onto saltwater fish and feed on their blood.
But the misfortune also presented a rare opportunity to study how catastrophic hurricanes impact marine animal populations, says molecular ecologist Juan Andrés Pagán, a graduate student at the University of Porto in Portugal and lead author on a recent study in Scientific Reports. Every year since 2013, the team—which calls itself Gnathiid Gnation—had collected a species it named for Bob Marley (Gnathia marleyi), found in coral reefs. After the hurricanes the researchers returned to collect more specimens. They braved jellyfish and sharks to set traps for the gnathiids, which are most active at night.
For their new study, the scientists sequenced gnathiid DNA from population samples collected at the various sites before and after the hurricanes, which were the most intense consecutive storms to hit the area in decades. “We were able to pretty clearly establish that, prior to the hurricanes, these populations were stable,” Pagán says, meaning the parasites remained in the same isolated areas year after year.
After the storms, things changed.
Gnathiids are bad long-distance swimmers. Under normal conditions they stick to small areas, similar to other invertebrates such as crustaceans and mollusks. But G. marleyi rode the 2017 hurricanes over unusually long distances, with some apparently traveling more than 250 kilometers. The researchers found that this displacement led to a “mixing of the pool” and, in turn, a more genetically similar gnathiid population, says team leader Paul Sikkel, an Arkansas State University marine ecologist and study co-author. “The hurricanes homogenized all that,” Sikkel says. “At any one site the diversity became higher. But overall [the populations] became more even because things were being moved around.” This may be the first clear, genetics-based evidence that hurricanes affect the genetic diversity of a marine species, he says.
“The kicker here for me is climate change,” says Richard Aronson, a marine biologist at the Florida Institute of Technology, who was not involved in the study. If hurricanes increase in intensity as predicted, Aronson says, the study suggests there may be more genetic homogenization among some marine animals that would otherwise remain in isolated populations. This would make peripheral populations less likely to cleave off and create new species, he adds.
The researchers are also investigating whether the parasites spread blood-borne pathogens that could harm fish populations. If that is the case, Sikkel says, this study may illustrate the potential for parasite-borne disease outbreaks in the wake of future major hurricanes.


scientificamerican--2020-11-20--Deep Frozen Arctic Microbes Are Waking Up

In August 2019 , Iceland held a funeral for the Okjökull Glacier , the first Icelandic glacier lost to climate change .
The community commemorated the event with a plaque in recognition of this irreversible change and the grave impacts it represents .
Globally , glacier melt rates have nearly doubled in the last five years , with an average loss of 832 mmw.e .
( millimeters water equivalent ) in 2015 , increasing to 1,243 mmw.e .
in 2020 ( WGMS ) .
This high rate of loss decreases glacial stores of freshwater and changes the structure of the @ @ @ @ @ @ @ years , warming in the Arctic has outpaced projections so rapidly that scientists are now suggesting that the poles are warming four times faster than the rest of the globe .
This has led to glacier melt and permafrost thaw levels that weren ’ t forecast to happen until 2050 or later .
In Siberia and northern Canada , this abrupt thaw has created sunken landforms , known as thermokarst , where the oldest and deepest permafrost is exposed to the warm air for the first time in hundreds or even thousands of @ @ @ @ @ @ @ to warm , many questions remain about the periglacial environment .
Among them : as water infiltration increases , will permafrost thaw more rapidly ?
And , if so , what long-frozen organisms might “ wake up ” ?
Permafrost covers 24 percent of the Earth ’ s land surface , and the soil constituents vary with local geology .
Arctic lands offer unexplored microbial biodiversity and microbial feedbacks , including the release of carbon to the atmosphere .
In some locations , hundreds of millions of years ’ worth of carbon is @ @ @ @ @ @ @ ancient frozen microbes , Pleistocene megafauna and even buried smallpox victims .
As the permafrost thaws with increasing rapidity , scientists ’ emerging challenge is to discover and identify the microbes , bacteria and viruses that may be stirring .
Some of these microbes are known to scientists .
Methanogenic Archaea , for example metabolize soil carbon to release methane , a potent greenhouse gas .
Other permafrost microbes ( methanotrophs ) consume methane .
The balance between these microbes plays a critical role in determining future climate warming .
Others are known @ @ @ @ @ @ @ New evidence of genes moving between thawing ecosystems indicates a restructuring at multiple levels .
In the Arctic Ocean , planktonic Chloroflexi bacteria recently acquired genes used for degrading carbon from land-based Actinobacteria species .
As melt-swollen Arctic rivers carried sediments from thawing permafrost to the sea , the genes for processing permafrost carbon were also transported .
Permafrost thaw in Siberia led to a 2018 anthrax outbreak and the death of 200,000 reindeer and a child .
But the hardy spores of Bacillus anthracis may represent an exception to the brutal freeze-thaw @ @ @ @ @ @ @ viral pathogens .
Their adaptable characteristics have allowed them to remain frozen and viable over centuries of inactivity .
Organisms that co-evolved within now-extinct ecosystems from the Cenozoic to the Pleistocene may also emerge and interact with our modern environment in entirely novel ways .
A potential example , the emerging Orthopoxvirus species Alaskapox causing skin lesions , has appeared and disappeared in Alaska twice in the last five years .
It is possible that the virus was transmitted through animal-human contact , but this novel virus ’ s origin remains unknown .
@ @ @ @ @ @ @ and tenacious cold-adapted microbes .
Some species survive as psychrophiles , a type of specialist specieshighly adapted to prolonged exposure to subfreezing conditions .
These species may be lost with warming .
Others survive by being highly adaptable , inhabiting many , varied niches .
Understanding more about these generalists ’ ecology and genomic diversity offers a window into the microbiome of the New Arctic .
These generalist microbes that adapt to diverse conditions are the likely winners , as we lose the cryosphere .
And then there are microbes that are entirely @ @ @ @ @ @ @ a novel threat .
It is clear that the warmer we make the Arctic , the weirder it will get , as temperatures at the surface become more extreme and thawing deepens .
With the coalescence of microbes reawakening from the deep and surface conditions unprecedented in human history , it is challenging to assess risks accurately without improved Arctic microbial datasets .
We should pay attention to both known unknowns , such as antibiotic-resistant bacteria , and unknown unknowns , including the potential risks from the resurrection of ancient and poorly described @ @ @ @ @ @ @ biologists .
For all of these reasons , we must come up with guidelines for future Arctic research .
As travel through the region increases , the likelihood of pathogen export and import rises as well .
The planetary protection guidelines that space agencies follow to prevent interplanetary contamination can provide a framework for how microbial investigation can safely continue .
Biosurveillance measures must be put into place to protect communities in the Arctic and beyond .
As the Arctic continues to transform , one thing is clear : as climate change warms @ @ @ @ @ @ @ , the full range of consequences is yet to be told .
Funding provided by the Jet Propulsion Laboratory , California Institute of Technology , under a con-tract with NASA ( 80NM0018D0004 ) , UK NERC ( NE/S1001034/1 ) and Research Council of Norway BIOICE .
© 2020 .
California Institute of Technology .
Government sponsorship acknowledged .
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the policy or position of any other agency or institution .

In August 2019, Iceland held a funeral for the Okjökull Glacier, the first Icelandic glacier lost to climate change. The community commemorated the event with a plaque in recognition of this irreversible change and the grave impacts it represents. Globally, glacier melt rates have nearly doubled in the last five years, with an average loss of 832 mmw.e. (millimeters water equivalent) in 2015, increasing to 1,243 mmw.e. in 2020 (WGMS). This high rate of loss decreases glacial stores of freshwater and changes the structure of the surrounding ecosystem.
In the last 10 years, warming in the Arctic has outpaced projections so rapidly that scientists are now suggesting that the poles are warming four times faster than the rest of the globe. This has led to glacier melt and permafrost thaw levels that weren’t forecast to happen until 2050 or later. In Siberia and northern Canada, this abrupt thaw has created sunken landforms, known as thermokarst, where the oldest and deepest permafrost is exposed to the warm air for the first time in hundreds or even thousands of years.
As the global climate continues to warm, many questions remain about the periglacial environment. Among them: as water infiltration increases, will permafrost thaw more rapidly? And, if so, what long-frozen organisms might “wake up”?
Permafrost covers 24 percent of the Earth’s land surface, and the soil constituents vary with local geology. Arctic lands offer unexplored microbial biodiversity and microbial feedbacks, including the release of carbon to the atmosphere. In some locations, hundreds of millions of years’ worth of carbon is buried. The layers may still contain ancient frozen microbes, Pleistocene megafauna and even buried smallpox victims. As the permafrost thaws with increasing rapidity, scientists’ emerging challenge is to discover and identify the microbes, bacteria and viruses that may be stirring.
Some of these microbes are known to scientists. Methanogenic Archaea, for example metabolize soil carbon to release methane, a potent greenhouse gas. Other permafrost microbes (methanotrophs) consume methane. The balance between these microbes plays a critical role in determining future climate warming.
Others are known but have unpredictable behavior after release. New evidence of genes moving between thawing ecosystems indicates a restructuring at multiple levels. In the Arctic Ocean, planktonic Chloroflexi bacteria recently acquired genes used for degrading carbon from land-based Actinobacteria species. As melt-swollen Arctic rivers carried sediments from thawing permafrost to the sea, the genes for processing permafrost carbon were also transported.
Permafrost thaw in Siberia led to a 2016 anthrax outbreak and the death of 200,000 reindeer and a child.* But the hardy spores of Bacillus anthracis may represent an exception to the brutal freeze-thaw cycle that degrades more delicate bacterial and viral pathogens. Their adaptable characteristics have allowed them to remain frozen and viable over centuries of inactivity.
Organisms that co-evolved within now-extinct ecosystems from the Cenozoic to the Pleistocene may also emerge and interact with our modern environment in entirely novel ways. A potential example, the emerging Orthopoxvirus species Alaskapox causing skin lesions, has appeared and disappeared in Alaska twice in the last five years. It is possible that the virus was transmitted through animal-human contact, but this novel virus’s origin remains unknown.
The microbiomes of the Arctic contain resilient and tenacious cold-adapted microbes. Some species survive as psychrophiles, a type of specialist specieshighly adapted to prolonged exposure to subfreezing conditions. These species may be lost with warming. Others survive by being highly adaptable, inhabiting many, varied niches. Understanding more about these generalists’ ecology and genomic diversity offers a window into the microbiome of the New Arctic. These generalist microbes that adapt to diverse conditions are the likely winners, as we lose the cryosphere.
And then there are microbes that are entirely unfamiliar to scientists, which may represent a novel threat.
It is clear that the warmer we make the Arctic, the weirder it will get, as temperatures at the surface become more extreme and thawing deepens. With the coalescence of microbes reawakening from the deep and surface conditions unprecedented in human history, it is challenging to assess risks accurately without improved Arctic microbial datasets. We should pay attention to both known unknowns, such as antibiotic-resistant bacteria, and unknown unknowns, including the potential risks from the resurrection of ancient and poorly described viral genomes from Arctic ice by synthetic biologists.
For all of these reasons, we must come up with guidelines for future Arctic research. As travel through the region increases, the likelihood of pathogen export and import rises as well. The planetary protection guidelines that space agencies follow to prevent interplanetary contamination can provide a framework for how microbial investigation can safely continue. Biosurveillance measures must be put into place to protect communities in the Arctic and beyond. As the Arctic continues to transform, one thing is clear: as climate change warms this microbial repository during the 21st century, the full range of consequences is yet to be told.
*Editor’s Note (11/23/20): This sentence was edited after posting to correct the timing of the anthrax outbreak; it occurred in 2016, not 2018.
Funding provided by the Jet Propulsion Laboratory, California Institute of Technology, under a con-tract with NASA (80NM0018D0004), UK NERC (NE/S1001034/1) and Research Council of Norway BIOICE. © 2020. California Institute of Technology. Government sponsorship acknowledged.
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the policy or position of any other agency or institution.


scientificamerican--2020-11-20--New Scientist-Candidates for U.S. Congress Fared Worse Than Expected in 2020

In this month ’ s election , where health and science issues such as the COVID pandemic and President Donald Trump ’ s handling of it played enormous roles , new science-oriented candidates for U.S. Congress had a large number of losses .
Despite national events , many voters apparently were not swayed by the pro-science campaigns of these newcomers .
Incumbents with science backgrounds did see more success .
This year 27 candidates—incumbents as well as new challengers—were endorsed by the science advocacy group 314 Action .
The group recruits people with backgrounds in science , technology , engineering @ @ @ @ @ @ @ endorses candidates and has a political action committee that contributes to their campaigns .
In the House of Representatives , just two endorsed challengers out of eight won , though one race remains too close to call because mailed ballots are still being counted .
Among endorsed Senate challengers , former astronaut Mark Kelly won in Arizona , and former geologist John Hickenlooper succeeded in Colorado , where he was previously the state ’ s governor .
Chris Coons , an endorsed Senate incumbent from Delaware , also won .
The victories were @ @ @ @ @ @ @ was in Kansas where Barbara Bollier , a former physician and state legislator , was beaten ; another was in Alaska , where orthopedic surgeon Al Gross lost .
In the House , endorsed incumbents with STEM backgrounds , several of whom were first elected in 2018 , won 12 of 14 contests , reflecting the easier road that incumbents usually have to reelection .
( One of those races remains too close to call as well . )
Party affiliation overlapped with science advocacy in many races , and that may have @ @ @ @ @ @ @ made science a bigger point in their campaigns—including concerns about climate change as well as COVID—were all Democrats .
Republicans rarely run on their science experience or advocacy , and 314 Action has never endorsed one .
Overall in Congress this year , Republicans picked up seats from Democrats , and Shaughnessy Naughton , 314 Action ’ s founder and president , says it is hard to make sense of the election ’ s mixed outcomes .
“ Why did [ voters ] hold the president accountable for what was going on but @ @ @ @ @ @ @ ’ s going to take a while to unpack that , ” she says .
Issues unrelated to science “ may have had an effect on some Democrats running , including our scientists , ” she says .
Several pro-science candidates , for instance , voiced support for police reform and the Black Lives Matter movement , and some of their more conservative rivals went in the opposite direction .
Matt Motta , a political scientist at Oklahoma State University , says the outcomes are very possibly “ less about specific things Democrats @ @ @ @ @ @ @ we underestimated support for some of the policies that Donald Trump and others in the GOP espouse.
” Motta has , however , found in surveys that self-described conservatives are more likely to hold negative views of scientists than self-described liberals , and such differences increase after scientists engage in political activism .
“ It was not a good year for Democrats in the House rather than any reflection on science , ” says Bill Foster , a physicist who was elected to Congress in Illinois ’ s 11th District in 2012 and @ @ @ @ @ @ @ notes that some Republicans in the House have been supportive of science .
“ There were Republican proposals in the House Science Committee to double the basic scientific research budgets in the United States , ” he says .
“ Part of the justification [ was ] to find new technologies to mitigate CO2 emissions , which is quite remarkable as a change of the traditional stance of the Republicans in Congress.
” Foster says that makes him optimistic that similar proposals will get bipartisan support in the coming year .
Naughton says @ @ @ @ @ @ @ of the goals of getting scientists to run for office is to make science issues more prominent in policy discussions .
And she thinks the congressional contests accomplished that aim .
“ We definitely saw politicians talking about science in a way that we ’ ve never seen before , ” Naughton says .
She notes that Lee Zeldin , a longtime Republican incumbent from New York State ’ s First Congressional District , who is the likely winner over chemist Nancy Goroff , “ claimed to do more for science than the @ @ @ @ @ @ @ says she wants to “ make it so that it is no longer acceptable for politicians to deny scientific consensus and undermine scientific advancement.
” That is also the goal of Nikema Williams , one of the few 314 Action–endorsed challengers to win .
Williams , who buried her opponent for the Georgia seat formerly held by the late Representative John Lewis in a landslide , is a former vice president of public policy at Planned Parenthood Southeast and majored in biology as an undergraduate .
She says she is “ very grounded @ @ @ @ @ @ @ scientific experts in how we move and make decisions ” and adds that electing people who follow evidence-based thinking “ will impact everything from health care to climate change to getting our children back to school safely.
” That effect does not seem to be happening right away , however .
“ It would be great if we could find some bipartisan things to do , and if [ Senate Majority Leader ] Mitch McConnell should see the light or if it should become politically expedient for him to do something on climate @ @ @ @ @ @ @ of the Energy Security and Climate Initiative at the Brookings Institution .
“ But I don ’ t see us being ready just yet.
” President-elect Joe Biden can take action to mitigate effects of climate change by issuing executive orders that counteract the greenhouse-gas-friendly policies of his predecessor , she says , but legislation would be more durable .
Gross says that if Biden winds up dealing with a Republican Senate , he likely will not be able to get climate change legislation passed even if Foster ’ s hoped-for bipartisan accord @ @ @ @ @ @ @ ’ s hard to imagine Mitch McConnell giving in and allowing climate legislation to go forward , ” she adds .
“ That ’ s why the makeup of the Senate is so important.
” Two Georgia Senate runoffs are set for early January 2021 .
Gross says that if the two Democrats on the ballot win , they could shift the balance of power—and will be “ incredibly important ” for climate policy going forward .

In this month’s election, where health and science issues such as the COVID pandemic and President Donald Trump’s handling of it played enormous roles, new science-oriented candidates for U.S. Congress had a large number of losses. Despite national events, many voters apparently were not swayed by the pro-science campaigns of these newcomers. Incumbents with science backgrounds did see more success.
This year 27 candidates—incumbents as well as new challengers—were endorsed by the science advocacy group 314 Action. The group recruits people with backgrounds in science, technology, engineering or medicine to run for office, endorses candidates and has a political action committee that contributes to their campaigns. In the House of Representatives, just two endorsed challengers out of eight won, though one race remains too close to call because mailed ballots are still being counted. Among endorsed Senate challengers, former astronaut Mark Kelly won in Arizona, and former geologist John Hickenlooper succeeded in Colorado, where he was previously the state’s governor. Chris Coons, an endorsed Senate incumbent from Delaware, also won. The victories were countered by two Senate losses. One was in Kansas where Barbara Bollier, a former physician and state legislator, was beaten; another was in Alaska, where orthopedic surgeon Al Gross lost. In the House, endorsed incumbents with STEM backgrounds, several of whom were first elected in 2018, won 12 of 14 contests, reflecting the easier road that incumbents usually have to reelection. (One of those races remains too close to call as well.)
Party affiliation overlapped with science advocacy in many races, and that may have been a factor. The candidates who made science a bigger point in their campaigns—including concerns about climate change as well as COVID—were all Democrats. Republicans rarely run on their science experience or advocacy, and 314 Action has never endorsed one. Overall in Congress this year, Republicans picked up seats from Democrats, and Shaughnessy Naughton, 314 Action’s founder and president, says it is hard to make sense of the election’s mixed outcomes. “Why did [voters] hold the president accountable for what was going on but not Republican representatives? I think it’s going to take a while to unpack that,” she says. Issues unrelated to science “may have had an effect on some Democrats running, including our scientists,” she says. Several pro-science candidates, for instance, voiced support for police reform and the Black Lives Matter movement, and some of their more conservative rivals went in the opposite direction.
Matt Motta, a political scientist at Oklahoma State University, says the outcomes are very possibly “less about specific things Democrats did and more about the fact that we underestimated support for some of the policies that Donald Trump and others in the GOP espouse.” Motta has, however, found in surveys that self-described conservatives are more likely to hold negative views of scientists than self-described liberals, and such differences increase after scientists engage in political activism.
Credit: Amanda Montañez
“It was not a good year for Democrats in the House rather than any reflection on science,” says Bill Foster, a physicist who was elected to Congress in Illinois’s 11th District in 2012 and won his 2020 reelection handily. He notes that some Republicans in the House have been supportive of science. “There were Republican proposals in the House Science Committee to double the basic scientific research budgets in the United States,” he says. “Part of the justification [was] to find new technologies to mitigate CO2 emissions, which is quite remarkable as a change of the traditional stance of the Republicans in Congress.” Foster says that makes him optimistic that similar proposals will get bipartisan support in the coming year.
Naughton says that even without election victories, one of the goals of getting scientists to run for office is to make science issues more prominent in policy discussions. And she thinks the congressional contests accomplished that aim. “We definitely saw politicians talking about science in a way that we’ve never seen before,” Naughton says. She notes that Lee Zeldin, a longtime Republican incumbent from New York State’s First Congressional District, who is the likely winner over chemist Nancy Goroff, “claimed to do more for science than the scientists” during the race. Naughton says she wants to “make it so that it is no longer acceptable for politicians to deny scientific consensus and undermine scientific advancement.”
That is also the goal of Nikema Williams, one of the few 314 Action–endorsed challengers to win. Williams, who buried her opponent for the Georgia seat formerly held by the late Representative John Lewis in a landslide, is a former vice president of public policy at Planned Parenthood Southeast and majored in biology as an undergraduate. She says she is “very grounded in my belief of following data and scientific experts in how we move and make decisions” and adds that electing people who follow evidence-based thinking “will impact everything from health care to climate change to getting our children back to school safely.”
That effect does not seem to be happening right away, however. “It would be great if we could find some bipartisan things to do, and if [Senate Majority Leader] Mitch McConnell should see the light or if it should become politically expedient for him to do something on climate,” says Samantha Gross, director of the Energy Security and Climate Initiative at the Brookings Institution. “But I don’t see us being ready just yet.” President-elect Joe Biden can take action to mitigate effects of climate change by issuing executive orders that counteract the greenhouse-gas-friendly policies of his predecessor, she says, but legislation would be more durable.
Gross says that if Biden winds up dealing with a Republican Senate, he likely will not be able to get climate change legislation passed even if Foster’s hoped-for bipartisan accord occurs in the House. “It’s hard to imagine Mitch McConnell giving in and allowing climate legislation to go forward,” she adds. “That’s why the makeup of the Senate is so important.” Two Georgia Senate runoffs are set for early January 2021. Gross says that if the two Democrats on the ballot win, they could shift the balance of power—and will be “incredibly important” for climate policy going forward.