See how Los Alamos National Laboratory scientists are using computational analysis and modeling to examine and predict how HIV spreads — and create a first-in-class preventive HIV vaccine now being tested for efficacy in humans.
Vaccines that induce protective T-cell responses could protect against members across the filovirus family, according to a study by Tomáš Hanke of the University of Oxford, Bette Korber of Los Alamos National Laboratory, and colleagues.
Researchers at Los Alamos National Laboratory (LANL) recently used computer modeling to map the process by which the Ebola and Zika viruses infiltrate host cells.
Mapping the body's battle with Ebola and Zika
The viruses that cause Ebola and Zika, daunting diseases that inspire concern at every outbreak, share a strong similarity in how they first infiltrate a host’s cells. Through the computer modeling capabilities of Los Alamos National Laboratory, the molecular calisthenics involved in invading a cell are visually documented, an essential step toward vaccine and therapeutic medicine development.
A team at Los Alamos National Laboratory, in collaboration with several institutions, is working to develop an innovative tool set for early and accurate diagnosis of the disease.
A research breakthrough allowing the first direct, empirical, blood-based, cow-side test for diagnosing bovine tuberculosis could spare ranchers and the agriculture industry from costly quarantines and the mass slaughter of animals infected with this easily spread disease.
This year’s flu shot is not as effective against one of the nastiest strains—but you should still get it
Los Alamos researcher Dave Osthus talks about how the flu season is unfolding and predicts when the peak of cases will hit.
At Los Alamos National Laboratory, researchers use mathematics, computer science, statistics and information about how disease develops and spreads to forecast the flu season and even next week’s sickness trends.
The Forecast Calls for Flu
Using real-time data from Wikipedia and social media, Sara del Valle and her team from Los Alamos National Laboratory have developed a global disease-forecasting system that will improve the way we respond to epidemics.
As a data scientist for Los Alamos National Laboratory, Sara Del Valle studies data from wide-ranging, public sources to identify patterns in hopes of being able to predict trends that could be a threat to global security.
Web searches, medical records and networks of local volunteers are enabling faster control of disease outbreaks. Epidemiologists anticipate a future in which they can track infectious diseases as do meteorologists mapping the weather.
Los Alamos National Laboratory has worked with Descartes Labs to come up with systems for analyzing on-the-ground conditions in Brazil in order to forecast dengue. The hope is to expand the work around the world.
Better tracking of infectious diseases can help us improve disease prediction and, consequently, more quickly stop their spread. Los Alamos National Laboratory has been using mathematics and computer modeling since to do exactly that.
Could we forecast disease like we forecast the weather?
scientists are improving disease-forecasting mathematical models by using clinical data—as well as internet data sources such as Wikipedia, Twitter, and Google—and pairing it with satellite imagery. The goal is to better understanding how diseases spread and, eventually, forecast disease outbreaks.
Measles outbreak hits 25-year high
The nationwide measles outbreak hit an alarming milestone. The CDC said 78 new cases were reported over the past week. That brings this year's total to 704 in 22 states. Most are in New York state where health officials have closed seven schools. Sara Del Valle talks about the possibility of someday predicting outbreaks.
Data-Driven Disease Forecasting
If disease outbreaks could be forecast like the weather, communities could set up protective measures to mitigate their impact. Los Alamos National Laboratory scientists are improving disease-forecasting mathematical models by using clinical and internet data sources coupled with satellite imagery.
Saturday Science with Geoff Fairchild
Computer scientist Geoff Fairchild talks about how his team is using Los Alamos National Laboratory’s supercomputing capabilities to data mine social media and reveal trends relating to health and safety.
The Forecast Calls for Flu
Using real-time data from Wikipedia and social media, Sara Del Valle and her team from Los Alamos National Laboratory have developed a global disease-forecasting system that will improve the way we respond to epidemics.
With Sara Del Valle, Mathematical Epidemiologist, and Nicholas Generous, Molecular Biologist, of the Mathematical and Computational Epidemiology Team at Los Alamos National Laboratory.
A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern
BIORXIV | May 2019
Sara Y. Del Valle, et al.
Epidemic forecasting and prediction tools have the potential to provide actionable information in the midst of emerging epidemics. While numerous predictive studies were published during the 2016-2017 Zika Virus (ZIKV) pandemic, it remains unknown how timely, reproducible and actionable the information produced by these studies was. To improve the functional use of mathematical modeling in support of future infectious disease outbreaks, we conducted a systematic review of all ZIKV prediction studies published during the recent ZIKV pandemic using the PRISMA guidelines. Using MEDLINE, EMBASE and grey literature review, we identified studies that forecasted, predicted or simulated ecological or epidemiological phenomenon related to the Zika pandemic that were published as of March 01, 2017. Eligible studies underwent evaluation of objectives, data sources, methods, timeliness, reproducibility, accessibility and clarity by independent reviewers.
ARXIV | APRIL 2019
WEndy K. Caldwell, Geoffrey Fairchild, Sara Y. Del Valle
Influenza epidemics result in a public health and economic burden around the globe. Traditional surveillance techniques, which rely on doctor visits, provide data with a delay of 1-2 weeks. A means of obtaining real-time data and forecasting future outbreaks is desirable to provide more timely responses to influenza epidemics. In this work, we present the first implementation of a novel data set by demonstrating its ability to supplement traditional disease surveillance at multiple spatial resolutions. We use Internet traffic data from the Centers for Disease Control and Prevention (CDC) website to determine the potential usability of this data source. We test the traffic generated by ten influenza-related pages in eight states and nine census divisions within the United States and compare it against clinical surveillance data. Our results yield r2=0.955 in the most successful case, promising results for some cases, and unsuccessful results for other cases.
Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human Behavior and Internet Data Streams in Epidemic Forecast
The Journal of Infectious Diseases | November 2016
Kelly R. Moran, Geoffrey Fairchild, Nicholas Generous, Dave Osthus, Reid Priedhorsky, James Hyman, Sara Y. Del Valle, et al.
Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection and Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. We conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting.
PLOS Computational Biology | May 2015
Geoffrey Fairchild, Reid Priedhorsky, Nicholas Generous, James M. Hyman, Alina Deshpande, Sara Y. Del Valle, et al.
Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.
A web-based disease-outbreak tool developed at Los Alamos National Laboratory can be used by diverse group of users, such as analysts, scientists, practitioners, decision makers and the public, at no cost.
Drawing on a classic definition that included the perception of the elements in one’s environment within a given time and space, Dr. Deshpande’s talk centered around the analytics used for investigating a disease outbreak, specifically how one might contextualize it.
New work at Los Alamos is narrowing down the confusion over Francisella bacteria, which include highly virulent human and animal pathogens, fish pathogens, opportunistic human pathogens, tick endosymbionts, and isolates in brackish water.
A new computer modeling study from Los Alamos National Laboratory is aimed at making epidemiological models more accessible and useful for public-health collaborators and improving disease-related decision making.
New molecular dynamics research into how RNA folds into hairpin-shaped structures called tetraloops could provide important insights into new treatments for retroviral diseases.
The virulent pathogen that causes “rabbit fever” is considered a potential bioweapon. Los Alamos National Laboratory researchers are working to use DNA markers to discern related but relatively harmless species as they are identified.
Scientists create first billion-atom biomolecular simulation
Researchers have created the largest simulation to date of an entire gene of DNA, a feat that required one billion atoms to model and will help researchers to better understand and develop cures for diseases like cancer.
Computer simulations unlock the secrets of antibiotic resistance
In a strategy to combat antibiotic resistant bacteria, theoretical biologists at Los Alamos National Laboratory are simulating how some microbes use protein structures to flush out the drugs before they kill the bacteria.
A universal biosensor for infectious disease
Whether in a rural village or an urban medical clinic, healthcare workers need diagnostics that provide answers, for any disease, in order to effectively treat individual patients or widespread outbreaks.
Mutational Signatures Mark Cancer's Smoking Gun
The research of LANL and its collaborators demonstrates, for the first time, that smoking increases cancer risk by causing somatic mutations in tissues directly and indirectly exposed to tobacco smoke.
Direct detection of bacteremia by exploiting host-pathogen interactions of lipoteichoic acid and lipopolysaccharide
Nature | April 2019
Dung M. Vu, Loreen R. Stromberg, Douglas J. Perkins, Benjamin H. McMahon, Harshini Mukundan, et al.
Bacteremia is a leading cause of death in sub-Saharan Africa where childhood mortality rates are the highest in the world. The early diagnosis of bacteremia and initiation of treatment saves lives, especially in high-disease burden areas. However, diagnosing bacteremia is challenging for clinicians, especially in children presenting with co-infections such as malaria and HIV. There is an urgent need for a rapid method for detecting bacteremia in pediatric patients with co-morbidities to inform treatment. In this manuscript, we have developed and clinically validated a novel method for the direct detection of amphiphilic pathogen biomarkers indicative of bacteremia, directly in aqueous blood, by mimicking innate immune recognition. Specifically, we have exploited the interaction of amphiphilic pathogen biomarkers such as lipopolysaccharides (LPS) from Gram-negative bacteria and lipoteichoic acids (LTA) from Gram-positive bacteria with host lipoprotein carriers in blood, in order to develop two tailored assays – lipoprotein capture and membrane insertion – for their direct detection.