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BackgroundIrritable bowel syndrome (IBS) is characterized by abdominal discomfort and irregular bowel movements and stool consistency. Because there are different symptoms associated with IBS, it is difficult to diagnose the role of the microbiome in IBS.ObjectiveHere, we present a study that includes metagenomic sequencing of stool samples from subjects with the predominant subtypes of IBS and a healthy cohort. We collected longitudinal samples from individuals with IBS who took daily made-to-order precision probiotic and prebiotic supplementation throughout the study.Materials and MethodsThis study includes a population of 489 individuals with IBS and 122 healthy controls. All stool samples were subjected to shotgun metagenomic sequencing. Precision probiotics and prebiotics were formulated for all subjects with longitudinal timepoints.ResultsThere was significant variation explained in the microbiome between the healthy and IBS cohorts. Individuals with IBS had a lower gut microbiome diversity and reduced anti-inflammatory microbes compared to the healthy controls. Eubacterium rectale and Faecalibacterium prausnitzii were associated with healthy microbiomes while Shigella species were associated with IBS. Pathway analysis indicated a functional imbalance of short chain fatty acids, vitamins, and a microbial component of Gram-negative bacteria in IBS compared to healthy controls. In the longitudinal dataset, there was a significant difference in microbiome composition between timepoints 1 and 3. There was also a significant increase in the overall microbiome score and relative abundances of probiotic species used to target the symptoms associated with IBS.ConclusionsWe identified microbes and pathways that differentiate healthy and IBS microbiomes. In response to precision probiotic supplementation, we identified a significant improvement in the overall microbiome score in individuals with IBS. These results suggest an important role for probiotics in managing IBS symptoms and modulation of the microbiome as a potential management strategy.ImportanceAn estimated 35 million people in the United States and 11.5% of the population globally are affected by IBS. Immunity, genetics, environment, diet, small intestinal bacterial overgrowth (SIBO), and the gut microbiome are all factors that contribute to the onset or triggers of IBS. With strong supporting evidence that the gut microbiome may influence symptoms associated with IBS, elucidating the important microbes that contribute to the symptoms and severity is important to make decisions for targeted treatment. As probiotics have become more common in treating IBS symptoms, identifying effective probiotics may help inform future studies and treatment.

Machine learning methods and digital data sources improve influenza forecasting at the city and state levels in the United States., Mitigating the effects of disease outbreaks with timely and effective interventions requires accurate real-time surveillance and forecasting of disease activity, but traditional health care–based surveillance systems are limited by inherent reporting delays. Machine learning methods have the potential to fill this temporal “data gap,” but work to date in this area has focused on relatively simple methods and coarse geographic resolutions (state level and above). We evaluate the predictive performance of a gated recurrent unit neural network approach in comparison with baseline machine learning methods for estimating influenza activity in the United States at the state and city levels and experiment with the inclusion of real-time Internet search data. We find that the neural network approach improves upon baseline models for long time horizons of prediction but is not improved by real-time internet search data. We conduct a thorough analysis of feature importances in all considered models for interpretability purposes.

The ability to transfer adversarial attacks from one model (the surrogate) to another model (the victim) has been an issue of concern within the machine learning (ML) community. The ability to successfully evade unseen models represents an uncomfortable level of ease toward implementing attacks. In this work we note that as studied, current transfer attack research has an unrealistic advantage for the attacker: the attacker has the exact same training data as the victim. We present the first study of transferring adversarial attacks focusing on the data available to attacker and victim under imperfect settings without querying the victim, where there is some variable level of overlap in the exact data used or in the classes learned by each model. This threat model is relevant to applications in medicine, malware, and others. Under this new threat model attack success rate is not correlated with data or class overlap in the way one would expect, and varies with dataset. This makes it difficult for attacker and defender to reason about each other and contributes to the broader study of model robustness and security. We remedy this by developing a masked version of Projected Gradient Descent that simulates class disparity, which enables the attacker to reliably estimate a lower-bound on their attack's success., Comment: to appear in Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security

Multiple digital data streams forecast COVID-19 activity weeks before traditional epidemiological surveillance., Given still-high levels of coronavirus disease 2019 (COVID-19) susceptibility and inconsistent transmission-containing strategies, outbreaks have continued to emerge across the United States. Until effective vaccines are widely deployed, curbing COVID-19 will require carefully timed nonpharmaceutical interventions (NPIs). A COVID-19 early warning system is vital for this. Here, we evaluate digital data streams as early indicators of state-level COVID-19 activity from 1 March to 30 September 2020. We observe that increases in digital data stream activity anticipate increases in confirmed cases and deaths by 2 to 3 weeks. Confirmed cases and deaths also decrease 2 to 4 weeks after NPI implementation, as measured by anonymized, phone-derived human mobility data. We propose a means of harmonizing these data streams to identify future COVID-19 outbreaks. Our results suggest that combining disparate health and behavioral data may help identify disease activity changes weeks before observation using traditional epidemiological monitoring.

Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the usefulness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.3 to 4.8 million, with possibly as many as 7.6 million cases, up to 25 times greater than the cumulative confirmed cases of about 311,000. Extending our methods to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 4.9 to 10.1 million, as opposed to 1.5 million positive test counts. The proposed combination of approaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems., Author summary Accurate estimates of the weekly incidence of COVID-19 in the United States is essential for planning and researching effective public health responses. Because of systematic testing shortages across the United States, official positive COVID-19 test counts are an unreliable indicator of true incidence. In this study, we present four alternative approaches for estimating cumulative incidence, which leverage different data sources and assumptions. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.3 to 4.8 million, with possibly as many as 7.6 million cases, up to 25 times greater than the cumulative confirmed cases of about 311,000. We emphasize that comparing multiple models rather than relying on a single method gives more reliable estimates of COVID-19 incidence. Our approaches could be useful for tracking the resurgence of COVID-19 in the United States as well as in other countries.

Successful malware attacks on information technology systems can cause millions of dollars in damage, the exposure of sensitive and private information, and the irreversible destruction of data. Anti-virus systems that analyze a file's contents use a combination of static and dynamic analysis to detect and remove/remediate such malware. However, examining a file's entire contents is not always possible in practice, as the volume and velocity of incoming data may be too high, or access to the underlying file contents may be restricted or unavailable. If it were possible to obtain estimates of a file's relative likelihood of being malicious without looking at the file contents, we could better prioritize file processing order and aid analysts in situations where a file is unavailable. In this work, we demonstrate that file names can contain information predictive of the presence of malware in a file. In particular, we show the effectiveness of a character-level convolutional neural network at predicting malware status using file names on Endgame's EMBER malware detection benchmark dataset.

Deep neural networks (DNNs) are vulnerable to subtle adversarial perturbations applied to the input. These adversarial perturbations, though imperceptible, can easily mislead the DNN. In this work, we take a control theoretic approach to the problem of robustness in DNNs. We treat each individual layer of the DNN as a nonlinear dynamical system and use Lyapunov theory to prove stability and robustness locally. We then proceed to prove stability and robustness globally for the entire DNN. We develop empirically tight bounds on the response of the output layer, or any hidden layer, to adversarial perturbations added to the input, or the input of hidden layers. Recent works have proposed spectral norm regularization as a solution for improving robustness against l2 adversarial attacks. Our results give new insights into how spectral norm regularization can mitigate the adversarial effects. Finally, we evaluate the power of our approach on a variety of data sets and network architectures and against some of the well-known adversarial attacks.

Understanding the behavior of emerging disease outbreaks in, or ahead of, real-time could help healthcare officials better design interventions to mitigate impacts on affected populations. Most healthcare-based disease surveillance systems, however, have significant inherent reporting delays due to data collection, aggregation, and distribution processes. Recent work has shown that machine learning methods leveraging a combination of traditionally collected epidemiological information and novel Internet-based data sources, such as disease-related Internet search activity, can produce meaningful “nowcasts” of disease incidence ahead of healthcare-based estimates, with most successful case studies focusing on endemic and seasonal diseases such as influenza and dengue. Here, we apply similar computational methods to emerging outbreaks in geographic regions where no historical presence of the disease of interest has been observed. By combining limited available historical epidemiological data available with disease-related Internet search activity, we retrospectively estimate disease activity in five recent outbreaks weeks ahead of traditional surveillance methods. We find that the proposed computational methods frequently provide useful real-time incidence estimates that can help fill temporal data gaps resulting from surveillance reporting delays. However, the proposed methods are limited by issues of sample bias and skew in search query volumes, perhaps as a result of media coverage., Author summary Public health officials regularly make choices about treatment and prevention in disease outbreaks that have the potential to impact entire affected populations. Often these decisions are based on incomplete or unreliable information due to inherent reporting delays in healthcare-based disease surveillance systems. This issue of public health decision-making based on limited data is even more salient in emerging outbreaks, which are typically characterized by uncertain disease dynamics and limited surveillance capacity. We demonstrate the potential for using digital trace data—in this case, Internet-based information from Google search trends—for estimating disease activity in emerging outbreaks in the absence of accurate real-time healthcare-based data sources. We evaluate how data-driven methods leveraging search trend data would have performed in real-time in five recent outbreaks (yellow fever in Angola, Zika in Colombia, Ebola in the DRC, plague in Madagascar, and cholera in Yemen), and find that the methods frequently provide useful signals of disease activity ahead of standard healthcare-based surveillance methods.

Traditional pharmacovigilance systems rely on adverse event reports received by regulatory authorities such as the United States Food and Drug Administration (FDA). These traditional systems suffer from underreporting and are not timely due to their reliance on third-party sentinels. To address these issues, the MedWatcher Social system for monitoring adverse events through automated processing of digital social media data and crowdsourcing was launched in 2012 by Boston Children’s Hospital and the FDA. The system is rooted in the well-established FDA MedWatch system.MedWatcher Social uses an indicator score approach to identify adverse events. This study evaluates the MedWatcher Social adverse event classifier’s performance on Twitter data and proposes an enhancement to the indicator score method that results in improved adverse event identification.Our research suggests that automatic pharmacovigilance systems using the original indicator score approach should be updated. Careful consideration of modeling assumptions is critical when designing algorithms for computational epidemiology, and algorithms should be regularly reevaluated to identify enhancements and to remedy concept drift.

Accurate real-time monitoring systems of influenza outbreaks help public health officials make informed decisions that may help save lives. We show that information extracted from cloud-based electronic health records databases, in combination with machine learning techniques and historical epidemiological information, have the potential to accurately and reliably provide near real-time regional estimates of flu outbreaks in the United States.

Background: Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact. Objective: Describe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions. Methods: We describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), Influenzanet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using Influenzanet and Flu Near You). Results: WISDM-based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. The Influenzanet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities, and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates with the observed CDC ILI rates is 0.99 for 2013-2015. Additional data sources lead to an error reduction of about 40% when compared to the estimates of the model that only incorporates CDC historical information. Conclusions: While the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, it is limited by a lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long-term forecasting of influenza activity in data-poor parts of the world. [JMIR Public Health Surveill 2017;3(4):e83]

Objectives. We summarized Flu Near You (FNY) data from the 2012–2013 and 2013–2014 influenza seasons in the United States. Methods. FNY collects limited demographic characteristic information upon registration, and prompts users each Monday to report symptoms of influenza-like illness (ILI) experienced during the previous week. We calculated the descriptive statistics and rates of ILI for the 2012–2013 and 2013–2014 seasons. We compared raw and noise-filtered ILI rates with ILI rates from the Centers for Disease Control and Prevention ILINet surveillance system. Results. More than 61 000 participants submitted at least 1 report during the 2012–2013 season, totaling 327 773 reports. Nearly 40 000 participants submitted at least 1 report during the 2013–2014 season, totaling 336 933 reports. Rates of ILI as reported by FNY tracked closely with ILINet in both timing and magnitude. Conclusions. With increased participation, FNY has the potential to serve as a viable complement to existing outpatient, hospital-based, and laboratory surveillance systems. Although many established systems have the benefits of specificity and credibility, participatory systems offer advantages in the areas of speed, sensitivity, and scalability.

We present a machine learning-based methodology capable of providing real-time (“nowcast”) and forecast estimates of influenza activity in the US by leveraging data from multiple data sources including: Google searches, Twitter microblogs, nearly real-time hospital visit records, and data from a participatory surveillance system. Our main contribution consists of combining multiple influenza-like illnesses (ILI) activity estimates, generated independently with each data source, into a single prediction of ILI utilizing machine learning ensemble approaches. Our methodology exploits the information in each data source and produces accurate weekly ILI predictions for up to four weeks ahead of the release of CDC’s ILI reports. We evaluate the predictive ability of our ensemble approach during the 2013–2014 (retrospective) and 2014–2015 (live) flu seasons for each of the four weekly time horizons. Our ensemble approach demonstrates several advantages: (1) our ensemble method’s predictions outperform every prediction using each data source independently, (2) our methodology can produce predictions one week ahead of GFT’s real-time estimates with comparable accuracy, and (3) our two and three week forecast estimates have comparable accuracy to real-time predictions using an autoregressive model. Moreover, our results show that considerable insight is gained from incorporating disparate data streams, in the form of social media and crowd sourced data, into influenza predictions in all time horizons., Author Summary The aggregated activity patterns of Internet users have enabled the detection and tracking of multiple population-wide events such as disease outbreaks, financial markets performance, and preferences in online movie selections. As a consequence, a collection of mathematical models aiming at monitoring and predicting these events in real-time have been proposed in the past decade. As we discover new methods and data sources suitable to track these events, it is not clear whether more information will lead to improved predictions. In the context of digital disease detection at the population level, we show that it is advantageous to combine the information from multiple flu activity predictors in the US instead of simply choosing the best performing flu predictor. Our findings suggest that the information from multiple data sources such as Google searches, Twitter microblogs, nearly real-time hospital visit records, and data from a participatory surveillance system, complement one another and produce the most accurate and robust set of flu predictions when combined optimally.