Mining and Validating Social Media Data for COVID-19-Related Human Behaviors Between January and July 2020: Infodemiology Study.

BACKGROUND: Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak. OBJECTIVE: We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications. METHODS: We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. RESULTS: We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to -0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States. CONCLUSIONS: Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors.

Google Health Trends performance reflecting dengue incidence for the Brazilian states.

BACKGROUND: Dengue fever is a mosquito-borne infection transmitted by Aedes aegypti and mainly found in tropical and subtropical regions worldwide. Since its re-introduction in 1986, Brazil has become a hotspot for dengue and has experienced yearly epidemics. As a notifiable infectious disease, Brazil uses a passive epidemiological surveillance system to collect and report cases; however, dengue burden is underestimated. Thus, Internet data streams may complement surveillance activities by providing real-time information in the face of reporting lags. METHODS: We analyzed 19 terms related to dengue using Google Health Trends (GHT), a free-Internet data-source, and compared it with weekly dengue incidence between 2011 to 2016. We correlated GHT data with dengue incidence at the national and state-level for Brazil while using the adjusted R squared statistic as primary outcome measure (0/1). We used survey data on Internet access and variables from the official census of 2010 to identify where GHT could be useful in tracking dengue dynamics. Finally, we used a standardized volatility index on dengue incidence and developed models with different variables with the same objective. RESULTS: From the 19 terms explored with GHT, only seven were able to consistently track dengue. From the 27 states, only 12 reported an adjusted R squared higher than 0.8; these states were distributed mainly in the Northeast, Southeast, and South of Brazil. The usefulness of GHT was explained by the logarithm of the number of Internet users in the last 3 months, the total population per state, and the standardized volatility index. CONCLUSIONS: The potential contribution of GHT in complementing traditional established surveillance strategies should be analyzed in the context of geographical resolutions smaller than countries. For Brazil, GHT implementation should be analyzed in a case-by-case basis. State variables including total population, Internet usage in the last 3 months, and the standardized volatility index could serve as indicators determining when GHT could complement dengue state level surveillance in other countries.

Defragmentation of lysozyme derived Amyloid ß fibril using Biocompatible Magnetic fluid.

We present here a modulating effect on lysozyme derived Amyloid ß fibrils by aqueous magnetic fluid. This non-conventional approach of treatment of lysozyme derived Amyloid ß fibrils showed lysing of Amyloid fibrils to its secondary structures which can be seen using optical microscope and scanning electron microscopic image. The size of lysozyme derived amyloid fibrils before and after treatment was measured using dynamic light scattering technique. The mechanism of defragmentation of lysozyme derived Amyloid ß fibrils by magnetic fluid is explained. This is a first report to identify the secondary structure of protein using Fourier Transform Infrared (FTIR) and Circular Dichroism (CD) spectra after lysing. The cyto-toxicity study of this magnetic fluid on neuronal (SH-SY5Y) and non-neuronal (NRK) cell lines shows non-toxicity up to a concentration of 250 µg/mL. The study indicates a novel and unique complementary approach to treat the amyloidogenic brain diseases.

Long-latency reflexes of elbow and shoulder muscles suggest reciprocal excitation of flexors, reciprocal excitation of extensors, and reciprocal inhibition between flexors and extensors.

Postural corrections of the upper limb are required in tasks ranging from handling an umbrella in the changing wind to securing a wriggling baby. One complication in this process is the mechanical interaction between the different segments of the arm where torque applied at one joint induces motion at multiple joints. Previous studies have shown the long-latency reflexes of shoulder muscles (50-100 ms after a limb perturbation) account for these mechanical interactions by integrating information about motion of both the shoulder and elbow. It is less clear whether long-latency reflexes of elbow muscles exhibit a similar capability and what is the relation between the responses of shoulder and elbow muscles. The present study utilized joint-based loads tailored to the subjects' arm dynamics to induce well-controlled displacements of their shoulder and elbow. Our results demonstrate that the long-latency reflexes of shoulder and elbow muscles integrate motion from both joints: the shoulder and elbow flexors respond to extension at both joints, whereas the shoulder and elbow extensors respond to flexion at both joints. This general pattern accounts for the inherent flexion-extension coupling of the two joints arising from the arm's intersegmental dynamics and is consistent with spindle-based reciprocal excitation of shoulder and elbow flexors, reciprocal excitation of shoulder and elbow extensors, and across-joint inhibition between the flexors and extensors.

Technique to optimize magnetic response of gelatin coated magnetic nanoparticles.

The paper describes the results of optimization of magnetic response for highly stable bio-functionalize magnetic nanoparticles dispersion. Concentration of gelatin during in situ co-precipitation synthesis was varied from 8, 23 and 48 mg/mL to optimize magnetic properties. This variation results in a change in crystallite size from 10.3 to 7.8 ± 0.1 nm. TEM measurement of G3 sample shows highly crystalline spherical nanoparticles with a mean diameter of 7.2 ± 0.2 nm and diameter distribution (σ) of 0.27. FTIR spectra shows a shift of 22 cm(-1) at C=O stretching with absence of N-H stretching confirming the chemical binding of gelatin on magnetic nanoparticles. The concept of lone pair electron of the amide group explains the mechanism of binding. TGA shows 32.8-25.2% weight loss at 350 °C temperature substantiating decomposition of chemically bind gelatin. The magnetic response shows that for 8 mg/mL concentration of gelatin, the initial susceptibility and saturation magnetization is the maximum. The cytotoxicity of G3 sample was assessed in Normal Rat Kidney Epithelial Cells (NRK Line) by MTT assay. Results show an increase in viability for all concentrations, the indicative probability of a stimulating action of these particles in the nontoxic range. This shows the potential of this technique for biological applications as the coated particles are (i) superparamagnetic (ii) highly stable in physiological media (iii) possibility of attaching other drug with free functional group of gelatin and (iv) non-toxic.

"Thought I'd Share First" and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study.

BACKGROUND: The COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts. OBJECTIVE: The aim of this study is to use Twitter data to explore methods to characterize and classify four COVID-19 conspiracy theories and to provide context for each of these conspiracy theories through the first 5 months of the pandemic. METHODS: We began with a corpus of COVID-19 tweets (approximately 120 million) spanning late January to early May 2020. We first filtered tweets using regular expressions (n=1.8 million) and used random forest classification models to identify tweets related to four conspiracy theories. Our classified data sets were then used in downstream sentiment analysis and dynamic topic modeling to characterize the linguistic features of COVID-19 conspiracy theories as they evolve over time. RESULTS: Analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to nonmisinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events. CONCLUSIONS: Although we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will need to target evolving conspiracy theories and the new facets of each as they become incorporated.

Improving Detection of Disease Re-emergence Using a Web-Based Tool (RED Alert): Design and Case Analysis Study.

BACKGROUND: Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This practice may lead to ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence. OBJECTIVE: Our objective is to bring together a variety of disease-related data and analytics needed to help public health analysts answer the following 3 primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence? METHODS: We collected and cleaned disease-related data (eg, case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the World Health Organization (WHO), Pan American Health Organization (PAHO), World Bank, and Gideon. We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for the following 4 diseases: measles, cholera, dengue, and yellow fever. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies. RESULTS: Our supervised learning models were able to identify 82%-90% of the local re-emergence events, although with 18%-31% (except 46% for dengue) false positives. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. The review of the web-based tool through case studies showed that local re-emergence detection was possible and that the tool provided actionable information about potential factors contributing to the local disease re-emergence and trends in global disease re-emergence. CONCLUSIONS: To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above.

Meta-Analysis of Prevalence of Triple-Negative Breast Cancer and Its Clinical Features at Incidence in Indian Patients With Breast Cancer.

PURPOSE: Breast cancer is the most common cancer in women in India, with higher incidence rates of aggressive subtypes, such as triple-negative breast cancer (TNBC). METHODS: A systematic review was performed to compute pooled prevalence rates of TNBC among patients with breast cancer, and clinical features at presentation were systematically compared with non-TNBC in an Indian cohort of 20,000 patients. RESULTS: Combined prevalence of TNBC among patients with breast cancer was found to be on the higher side (27%; 95% CI, 24% to 31%). We found that the estrogen receptor (ER) expression cutoff used to determine ER positivity had an influence on the pooled prevalence and ranged from 30% (ER/progesterone receptor [PR] cut ff at 1%) to 24% (ER/PR cutoff at 10%). Odds for TNBC to present in the younger age-group were significantly higher (pooled odds ratio [OR], 1.35; 95% CI, 1.08 to 1.69), with a significantly younger mean age of incidence (weighted mean difference, -2.75; 95% CI, -3.59 to -1.92). TNBC showed a significantly higher odds of presenting with high grade (pooled OR, 2.57; 95% CI, 2.12 to 3.12) and lymph node positivity (pooled OR, 1.39; 95% CI, 1.21 to 1.60) than non-TNBC. CONCLUSION: Systematic review and meta-analysis of 34 studies revealed a high degree of heterogeneity in prevalence of TNBC within Indian patients with breast cancer, yet pooled prevalence of TNBC is high in India. High proportions of patients with TNBC present with aggressive features, such as high grade and lymph node positivity, compared with patients without TNBC. We emphasize the need for standardized methods for accurate diagnosis in countries like India.

Development of a Supervised Learning Algorithm for Detection of Potential Disease Reemergence: A Proof of Concept.

Infectious disease reemergence is an important yet ambiguous concept that lacks a quantitative definition. Currently, reemergence is identified without specific criteria describing what constitutes a reemergent event. This practice affects reproducible assessments of high-consequence public health events and disease response prioritization. This in turn can lead to misallocation of resources. More important, early recognition of reemergence facilitates effective mitigation. We used a supervised machine learning approach to detect potential disease reemergence. We demonstrate the feasibility of applying a machine learning classifier to identify reemergence events in a systematic way for 4 different infectious diseases. The algorithm is applicable to temporal trends of disease incidence and includes disease-specific features to identify potential reemergence. Through this study, we offer a structured means of identifying potential reemergence using a data-driven approach.

Summarizing Simulation Results using Causally-relevant States.

As increasingly large-scale multiagent simulations are being implemented, new methods are becoming necessary to make sense of the results of these simulations. Even concisely summarizing the results of a given simulation run is a challenge. Here we pose this as the problem of simulation summarization: how to extract the causally-relevant descriptions of the trajectories of the agents in the simulation. We present a simple algorithm to compress agent trajectories through state space by identifying the state transitions which are relevant to determining the distribution of outcomes at the end of the simulation. We present a toy-example to illustrate the working of the algorithm, and then apply it to a complex simulation of a major disaster in an urban area.

A comparison of multiple behavior models in a simulation of the aftermath of an improvised nuclear detonation.

We describe a large-scale simulation of the aftermath of a hypothetical 10kT improvised nuclear detonation at ground level, near the White House in Washington DC. We take a synthetic information approach, where multiple data sets are combined to construct a synthesized representation of the population of the region with accurate demographics, as well as four infrastructures: transportation, healthcare, communication, and power. In this article, we focus on the model of agents and their behavior, which is represented using the options framework. Six different behavioral options are modeled: household reconstitution, evacuation, healthcare-seeking, worry, shelter-seeking, and aiding & assisting others. Agent decision-making takes into account their health status, information about family members, information about the event, and their local environment. We combine these behavioral options into five different behavior models of increasing complexity and do a number of simulations to compare the models.

Modeling the effect of transient populations on epidemics in Washington DC.

Large numbers of transients visit big cities, where they come into contact with many people at crowded areas. However, epidemiological studies have not paid much attention to the role of this subpopulation in disease spread. We evaluate the effect of transients on epidemics by extending a synthetic population model for the Washington DC metro area to include leisure and business travelers. A synthetic population is obtained by combining multiple data sources to build a detailed minute-by-minute simulation of population interaction resulting in a contact network. We simulate an influenza-like illness over the contact network to evaluate the effects of transients on the number of infected residents. We find that there are significantly more infections when transients are considered. Since much population mixing happens at major tourism locations, we evaluate two targeted interventions: closing museums and promoting healthy behavior (such as the use of hand sanitizers, covering coughs, etc.) at museums. Surprisingly, closing museums has no beneficial effect. However, promoting healthy behavior at the museums can both reduce and delay the epidemic peak. We analytically derive the reproductive number and perform stability analysis using an ODE-based model.

PLANNING AND RESPONSE IN THE AFTERMATH OF A LARGE CRISIS: AN AGENT-BASED INFORMATICS FRAMEWORK*

We present a synthetic information and modeling environment that can allow policy makers to study various counter-factual experiments in the event of a large human-initiated crisis. The specific scenario we consider is a ground detonation caused by an improvised nuclear device in a large urban region. In contrast to earlier work in this area that focuses largely on the prompt effects on human health and injury, we focus on co-evolution of individual and collective behavior and its interaction with the differentially damaged infrastructure. This allows us to study short term secondary and tertiary effects. The present environment is suitable for studying the dynamical outcomes over a two week period after the initial blast. A novel computing and data processing architecture is described; the architecture allows us to represent multiple co-evolving infrastructures and social networks at a highly resolved temporal, spatial, and individual scale. The representation allows us to study the emergent behavior of individuals as well as specific strategies to reduce casualties and injuries that exploit the spatial and temporal nature of the secondary and tertiary effects. A number of important conclusions are obtained using the modeling environment. For example, the studies decisively show that deploying ad hoc communication networks to reach individuals in the affected area is likely to have a significant impact on the overall casualties and injuries.