A future app may help you avoid getting the flu by informing you of your local risk on a given day.
Applied mathematician Sara del Valle works at the U.S.'s foremost nuclear weapons lab: Los Alamos. Once colloquially called Atomic City, it's a hidden place 45 minutes into the mountains northwest of Santa Fe. Here, engineers developed the first atomic bomb.
Like AccuWeather, an app for disease prediction could help people alter their behavior to live better lives.
Today, Los Alamos still a small science town, though no longer a secret, nor in the business of building new bombs. Instead, it's tasked with, among other things, keeping the stockpile of nuclear weapons safe and stable: not exploding when they're not supposed to (yes, please) and exploding if someone presses that red button (please, no).
Del Valle, though, doesn't work on any of that. Los Alamos is also interested in other kinds of booms—like the explosion of a contagious disease that could take down a city. Predicting (and, ideally, preventing) such epidemics is del Valle's passion. She hopes to develop an app that's like AccuWeather for germs: It would tell you your chance of getting the flu, or dengue or Zika, in your city on a given day. And like AccuWeather, it could help people alter their behavior to live better lives, whether that means staying home on a snowy morning or washing their hands on a sickness-heavy commute.
Sara del Valle of Los Alamos is working to predict and prevent epidemics using data and machine learning.
Since the beginning of del Valle's career, she's been driven by one thing: using data and predictions to help people behave practically around pathogens. As a kid, she'd always been good at math, but when she found out she could use it to capture the tentacular spread of disease, and not just manipulate abstractions, she was hooked.
When she made her way to Los Alamos, she started looking at what people were doing during outbreaks. Using social media like Twitter, Google search data, and Wikipedia, the team started to sift for trends. Were people talking about hygiene, like hand-washing? Or about being sick? Were they Googling information about mosquitoes? Searching Wikipedia for symptoms? And how did those things correlate with the spread of disease?
It was a new, faster way to think about how pathogens propagate in the real world. Usually, there's a 10- to 14-day lag in the U.S. between when doctors tap numbers into spreadsheets and when that information becomes public. By then, the world has moved on, and so has the disease—to other villages, other victims.
"We found there was a correlation between actual flu incidents in a community and the number of searches online and the number of tweets online," says del Valle. That was when she first let herself dream about a real-time forecast, not a 10-days-later backcast. Del Valle's group—computer scientists, mathematicians, statisticians, economists, public health professionals, epidemiologists, satellite analysis experts—has continued to work on the problem ever since their first Twitter parsing, in 2011.
They've had their share of outbreaks to track. Looking back at the 2009 swine flu pandemic, they saw people buying face masks and paying attention to the cleanliness of their hands. "People were talking about whether or not they needed to cancel their vacation," she says, and also whether pork products—which have nothing to do with swine flu—were safe to buy.
At the latest meeting with all the prediction groups, del Valle's flu models took first and second place.
They watched internet conversations during the measles outbreak in California. "There's a lot of online discussion about anti-vax sentiment, and people trying to convince people to vaccinate children and vice versa," she says.
Today, they work on predicting the spread of Zika, Chikungunya, and dengue fever, as well as the plain old flu. And according to the CDC, that latter effort is going well.
Since 2015, the CDC has run the Epidemic Prediction Initiative, a competition in which teams like de Valle's submit weekly predictions of how raging the flu will be in particular locations, along with other ailments occasionally. Michael Johannson is co-founder and leader of the program, which began with the Dengue Forecasting Project. Its goal, he says, was to predict when dengue cases would blow up, when previously an area just had a low-level baseline of sick people. "You'll get this massive epidemic where all of a sudden, instead of 3,000 to 4,000 cases, you have 20,000 cases," he says. "They kind of come out of nowhere."
But the "kind of" is key: The outbreaks surely come out of somewhere and, if scientists applied research and data the right way, they could forecast the upswing and perhaps dodge a bomb before it hit big-time. Questions about how big, when, and where are also key to the flu.
A big part of these projects is the CDC giving the right researchers access to the right information, and the structure to both forecast useful public-health outcomes and to compare how well the models are doing. The extra information has been great for the Los Alamos effort. "We don't have to call departments and beg for data," says del Valle.
When data isn't available, "proxies"—things like symptom searches, tweets about empty offices, satellite images showing a green, wet, mosquito-friendly landscape—are helpful: You don't have to rely on anyone's health department.
At the latest meeting with all the prediction groups, del Valle's flu models took first and second place. But del Valle wants more than weekly numbers on a government website; she wants that weather-app-inspired fortune-teller, incorporating the many diseases you could get today, standing right where you are. "That's our dream," she says.
This plot shows the the correlations between the online data stream, from Wikipedia, and various infectious diseases in different countries. The results of del Valle's predictive models are shown in brown, while the actual number of cases or illness rates are shown in blue.
(Courtesy del Valle)
The goal isn't to turn you into a germophobic agoraphobe. It's to make you more aware when you do go out. "If you know it's going to rain today, you're more likely to bring an umbrella," del Valle says. "When you go on vacation, you always look at the weather and make sure you bring the appropriate clothing. If you do the same thing for diseases, you think, 'There's Zika spreading in Sao Paulo, so maybe I should bring even more mosquito repellent and bring more long sleeves and pants.'"
They're not there yet (don't hold your breath, but do stop touching your mouth). She estimates it's at least a decade away, but advances in machine learning could accelerate that hypothetical timeline. "We're doing baby steps," says del Valle, starting with the flu in the U.S., dengue in Brazil, and other efforts in Colombia, Ecuador, and Canada. "Going from there to forecasting all diseases around the globe is a long way," she says.
But even AccuWeather started small: One man began predicting weather for a utility company, then helping ski resorts optimize their snowmaking. His influence snowballed, and now private forecasting apps, including AccuWeather's, populate phones across the planet. The company's progression hasn't been without controversy—privacy incursions, inaccuracy of long-term forecasts, fights with the government—but it has continued, for better and for worse.
Disease apps, perhaps spun out of a small, unlikely team at a nuclear-weapons lab, could grow and breed in a similar way. And both the controversies and public-health benefits that may someday spin out of them lie in the future, impossible to predict with certainty.
There is a lot to be optimistic about regarding the new safe and highly effective vaccines, which are moving society closer toward the goal of close human contact once again.
To be clear, these vaccine candidates for COVID-19, both authorized and not yet authorized, are highly effective and safe. In fact, across all trials and sites, all six vaccines were 100% effective in preventing hospitalizations and death from COVID-19.
All Vaccines' Phase 3 Clinical Data
Complete protection against hospitalization and death from COVID-19 exhibited by all vaccines with phase 3 clinical trial data.
This astounding level of protection from SARS-CoV-2 from all vaccine candidates across multiple regions is likely due to robust T cell response from vaccination and will "defang" the virus from the concerns that led to COVID-19 restrictions initially: the ability of the virus to cause severe illness. This is a time of hope and optimism. After the devastating third surge of COVID-19 infections and deaths over the winter, we finally have an opportunity to stem the crisis – if only people readily accept the vaccines.
Amidst these incredible scientific advancements, however, public health officials and politicians have been pushing downright discouraging messaging. The ubiquitous talk of ongoing masks and distancing restrictions without any clear end in sight threatens to dampen uptake of the vaccines. It's imperative that we break down each concern and see if we can revitalize our public health messaging accordingly.
The first concern: we currently do not know if the vaccines block asymptomatic infection as well as symptomatic disease, since none of the phase 3 vaccine trials were set up to answer this question. However, there is biological plausibility that the antibodies and T-cell responses blocking symptomatic disease will also block asymptomatic infection in the nasal passages. IgG immunoglobulins (generated and measured by the vaccine trials) enter the nasal mucosa and systemic vaccinations generate IgA antibodies at mucosal surfaces. Monoclonal antibodies given to outpatients with COVID-19 hasten viral clearance from the airways.
Although it is prudent for those who are vaccinated to wear masks around the unvaccinated in case a slight risk of transmission remains, two fully vaccinated people can comfortably abandon masking around each other.
Moreover, data from the AztraZeneca trial (including in the phase 3 trial final results manuscript), where weekly self-swabbing was done by participants, and data from the Moderna trial, where a nasal swab was performed prior to the second dose, both showed risk reductions in asymptomatic infection with even a single dose. Finally, real-world data from a large Pfizer-based vaccine campaign in Israel shows a 50% reduction in infections (asymptomatic or symptomatic) after just the first dose.
Therefore, the likelihood of these vaccines blocking asymptomatic carriage, as well as symptomatic disease, is high. Although it is prudent for those who are vaccinated to wear masks around the unvaccinated in case a slight risk of transmission remains, two fully vaccinated people can comfortably abandon masking around each other. Moreover, as the percentage of vaccinated people increases, it will be increasingly untenable to impose restrictions on this group. Once herd immunity is reached, these restrictions can and should be abandoned altogether.
The second concern translating to "doom and gloom" messaging lately is around the identification of troubling new variants due to enhanced surveillance via viral sequencing. Four major variants circulating at this point (with others described in the past) are the B.1.1.7 variant ("UK variant"), B.1.351 ("South Africa variant), P.1. ("Brazil variant"), and the L452R variant identified in California. Although the UK variant is likely to be more transmissible, as is the South Africa variant, we have no reason to believe that masks, distancing and ventilation are ineffective against these variants.
Moreover, neutralizing antibody titers with the Pfizer and Moderna vaccines do not seem to be significantly reduced against the variants. Finally, although the Novavax 2-dose and Johnson and Johnson (J&J) 1-dose vaccines had lower rates of efficacy against moderate COVID-19 disease in South Africa, their efficacy against severe disease was impressively high. In fact J&J's vaccine still prevented 100% of hospitalizations and death from COVID-19. When combining both hospitalizations/deaths and severe symptoms managed at home, the J&J 1-dose vaccine was 85% protective across all three sites of the trial: the U.S., Latin America (including Brazil), and South Africa.
In South Africa, nearly all cases of COVID-19 (95%) were due to infection with the B.1.351 SARS-CoV-2 variant. Finally, since herd immunity does not rely on maximal immune responses among all individuals in a society, the Moderna/Pfizer/J&J vaccines are all likely to achieve that goal against variants. And thankfully, all of these vaccines can be easily modified to boost specifically against a new variant if needed (indeed, Moderna and Pfizer are already working on boosters against the prominent variants).
The third concern of some public health officials is that people will abandon all restrictions once vaccinated unless overly cautious messages are drilled into them. Indeed, the false idea that if you "give people an inch, they will take a mile" has been misinforming our messaging about mitigation since the beginning of the pandemic. For example, the very phrase "stay at home" with all of its non-applicability for essential workers and single individuals is stigmatizing and unrealistic for many. Instead, the message should have focused on how people can additively reduce their risks under different circumstances.
The public will be more inclined to trust health officials if those officials communicate with nuanced messages backed up by evidence, rather than with broad brushstrokes that shame. Therefore, we should be saying that "vaccinated people can be together with other vaccinated individuals without restrictions but must protect the unvaccinated with masks and distancing." And we can say "unvaccinated individuals should adhere to all current restrictions until vaccinated" without fear of misunderstandings. Indeed, this kind of layered advice has been communicated to people living with HIV and those without HIV for a long time (if you have HIV but partner does not, take these precautions; if both have HIV, you can do this, etc.).
Our heady progress in vaccine development, along with the incredible efficacy results of all of them, is unprecedented. However, we are at risk of undermining such progress if people balk at the vaccine because they don't believe it will make enough of a difference. One of the most critical messages we can deliver right now is that these vaccines will eventually free us from the restrictions of this pandemic. Let's use tiered messaging and clear communication to boost vaccine optimism and uptake, and get us to the goal of close human contact once again.
