Assessing vulnerability for future Zika virus outbreaks using seroprevalence data and environmental suitability maps.

The 2015-17 Zika virus (ZIKV) epidemic in the Americas subsided faster than expected and evolving population immunity was postulated to be the main reason. Herd immunization is suggested to occur around 60-70% seroprevalence, depending on demographic density and climate suitability. However, herd immunity was only documented for a few cities in South America, meaning a substantial portion of the population might still be vulnerable to a future Zika virus outbreak. The aim of our study was to determine the vulnerability of populations to ZIKV by comparing the environmental suitability of ZIKV transmission to the observed seroprevalence, based on published studies. Using a systematic search, we collected seroprevalence and geospatial data for 119 unique locations from 37 studies. Extracting the environmental suitability at each location and converting to a hypothetical expected seroprevalence, we were able to determine the discrepancy between observed and expected. This discrepancy is an indicator of vulnerability and divided into three categories: high risk, low risk, and very low risk. The vulnerability was used to evaluate the level of risk that each location still has for a ZIKV outbreak to occur. Of the 119 unique locations, 69 locations (58%) fell within the high risk category, 47 locations (39%) fell within the low risk category, and 3 locations (3%) fell within the very low risk category. The considerable heterogeneity between environmental suitability and seroprevalence potentially leaves a large population vulnerable to future infection. Vulnerability seems to be especially pronounced at the fringes of the environmental suitability for ZIKV (e.g. Sao Paulo, Brazil). The discrepancies between observed and expected seroprevalence raise the question: "why did the ZIKV epidemic stop with large populations unaffected?". This lack of understanding also highlights that future ZIKV outbreaks currently cannot be predicted with confidence.

A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA.

With COVID-19 affecting every country globally and changing everyday life, the ability to forecast the spread of the disease is more important than any previous epidemic. The conventional methods of disease-spread modeling, compartmental models, are based on the assumption of spatiotemporal homogeneity of the spread of the virus, which may cause forecasting to underperform, especially at high spatial resolutions. In this paper, we approach the forecasting task with an alternative technique-spatiotemporal machine learning. We present COVID-LSTM, a data-driven model based on a long short-term memory deep learning architecture for forecasting COVID-19 incidence at the county level in the USA. We use the weekly number of new positive cases as temporal input, and hand-engineered spatial features from Facebook movement and connectedness datasets to capture the spread of the disease in time and space. COVID-LSTM outperforms the COVID-19 Forecast Hub's Ensemble model (COVIDhub-ensemble) on our 17-week evaluation period, making it the first model to be more accurate than the COVIDhub-ensemble over one or more forecast periods. Over the 4-week forecast horizon, our model is on average 50 cases per county more accurate than the COVIDhub-ensemble. We highlight that the underutilization of data-driven forecasting of disease spread prior to COVID-19 is likely due to the lack of sufficient data available for previous diseases, in addition to the recent advances in machine learning methods for spatiotemporal forecasting. We discuss the impediments to the wider uptake of data-driven forecasting, and whether it is likely that more deep learning-based models will be used in the future.

Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions.

Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19, which is a challenging task, especially at high spatial resolutions. In this study, we develop a Spatiotemporal autoregressive model to predict county-level new cases of COVID-19 in the coterminous US using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features. We capture human interactions through 1) Facebook- and 2) cell phone-derived measures of connectivity and human mobility, and use them in two separate models for predicting county-level new cases of COVID-19. We evaluate the model on 14 forecast dates between 2020/10/25 and 2021/01/24 over one- to four-week prediction horizons. Comparing our predictions with a Baseline model developed by the COVID-19 Forecast Hub indicates an average 6.46% improvement in prediction Mean Absolute Errors (MAE) over the two-week prediction horizon up to 20.22% improvement in the four-week prediction horizon, pointing to the strong predictive power of our model in the longer prediction horizons.

Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awareness.

Various domain users are increasingly leveraging real-time social media data to gain rapid situational awareness. However, due to the high noise in the deluge of data, effectively determining semantically relevant information can be difficult, further complicated by the changing definition of relevancy by each end user for different events. The majority of existing methods for short text relevance classification fail to incorporate users' knowledge into the classification process. Existing methods that incorporate interactive user feedback focus on historical datasets. Therefore, classifiers cannot be interactively retrained for specific events or user-dependent needs in real-time. This limits real-time situational awareness, as streaming data that is incorrectly classified cannot be corrected immediately, permitting the possibility for important incoming data to be incorrectly classified as well. We present a novel interactive learning framework to improve the classification process in which the user iteratively corrects the relevancy of tweets in real-time to train the classification model on-the-fly for immediate predictive improvements. We computationally evaluate our classification model adapted to learn at interactive rates. Our results show that our approach outperforms state-of-the-art machine learning models. In addition, we integrate our framework with the extended Social Media Analytics and Reporting Toolkit (SMART) 2.0 system, allowing the use of our interactive learning framework within a visual analytics system tailored for real-time situational awareness. To demonstrate our framework's effectiveness, we provide domain expert feedback from first responders who used the extended SMART 2.0 system.

The Validity, Generalizability and Feasibility of Summative Evaluation Methods in Visual Analytics.

Many evaluation methods have been used to assess the usefulness of Visual Analytics (VA) solutions. These methods stem from a variety of origins with different assumptions and goals, which cause confusion about their proofing capabilities. Moreover, the lack of discussion about the evaluation processes may limit our potential to develop new evaluation methods specialized for VA. In this paper, we present an analysis of evaluation methods that have been used to summatively evaluate VA solutions. We provide a survey and taxonomy of the evaluation methods that have appeared in the VAST literature in the past two years. We then analyze these methods in terms of validity and generalizability of their findings, as well as the feasibility of using them. We propose a new metric called summative quality to compare evaluation methods according to their ability to prove usefulness, and make recommendations for selecting evaluation methods based on their summative quality in the VA domain.

VASSL: A Visual Analytics Toolkit for Social Spambot Labeling.

Social media platforms are filled with social spambots. Detecting these malicious accounts is essential, yet challenging, as they continually evolve to evade detection techniques. In this article, we present VASSL, a visual analytics system that assists in the process of detecting and labeling spambots. Our tool enhances the performance and scalability of manual labeling by providing multiple connected views and utilizing dimensionality reduction, sentiment analysis and topic modeling, enabling insights for the identification of spambots. The system allows users to select and analyze groups of accounts in an interactive manner, which enables the detection of spambots that may not be identified when examined individually. We present a user study to objectively evaluate the performance of VASSL users, as well as capturing subjective opinions about the usefulness and the ease of use of the tool.

MetricsVis: A Visual Analytics System for Evaluating Employee Performance in Public Safety Agencies.

Evaluating employee performance in organizations with varying workloads and tasks is challenging. Specifically, it is important to understand how quantitative measurements of employee achievements relate to supervisor expectations, what the main drivers of good performance are, and how to combine these complex and flexible performance evaluation metrics into an accurate portrayal of organizational performance in order to identify shortcomings and improve overall productivity. To facilitate this process, we summarize common organizational performance analyses into four visual exploration task categories. Additionally, we develop MetricsVis, a visual analytics system composed of multiple coordinated views to support the dynamic evaluation and comparison of individual, team, and organizational performance in public safety organizations. MetricsVis provides four primary visual components to expedite performance evaluation: (1) a priority adjustment view to support direct manipulation on evaluation metrics; (2) a reorderable performance matrix to demonstrate the details of individual employees; (3) a group performance view that highlights aggregate performance and individual contributions for each group; and (4) a projection view illustrating employees with similar specialties to facilitate shift assignments and training. We demonstrate the usability of our framework with two case studies from medium-sized law enforcement agencies and highlight its broader applicability to other domains.

FT-IR study and solvent-implicit and explicit effect on stepwise tautomerism of Guanylurea: M06-2X as a case of study.

All 66 conformers of guanylurea were optimized and frequency calculations were performed at M06-2X/6-311++G(d,p) level of theory. Theses conformers were categorized into five tautomers, and the most stable conformer of each tautomer were found. Geometrical parameters indicated that these tautomers have almost planar structure. Complete stepwise tautomerism were studied through both intramolecular proton transfer routs and internal rotations. Results indicated that the proton transfer routs involving four-membered heterocyclic structures were rate-determining steps. Also, intramolecular proton movement having six-membered transition state structures had very low energy barrier comparable to the transition states of internal rotation routs. Differentiation of studied tautomers could easily be done through their FT-IR spectra in the range of 3200 to 3900cm-1 by comparing absorption bands and intensity of peaks. Solvent-implicit effects on the stability of tautomers were also studied through re-optimization and frequency calculation in four solvents. Water, DMSO, acetone and toluene had stabilization effect on all considered tautomers, but the order of stabilization effect was as follows: water>DMSO>acetone>toluene. Finally, solvent-explicit, base-explicit and acid-explicit effect were also studied by taking place of studied tautomer nearside of acid, base or solvent and optimization of them. Frequency calculation for proton movement by contribution of explicit effect showed that formic acid had a very strong effect on proton transfer from tautomer A1 to tautomer D8 by lowering the energy barrier from 42.57 to 0.8kcal/mol. In addition, ammonia-explicit effect was found to lower the barrier from 42.57 to 22.46kcal/mol, but this effect is lower than that of water and methanol-explicit effect.

A green route for the cross-coupling of azide anions with aryl halides under both base and ligand-free conditions: exceptional performance of a Cu2O-CuO-Cu-C nanocomposite.

A convenient, inexpensive and effective route for the preparation of a Cu2O-CuO-Cu-C nanocomposite is described here by applying Cu(ii) as a source of copper. Characterization of the nanocomposite was performed with X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), high-resolution TEM (HR-TEM), field emission scanning electron microscopy (FE-SEM), X-ray photoelectron spectroscopy (XPS), and energy-dispersive X-ray spectroscopy (EDX). Analysis of the data showed that the particles of the nanocomposite are uniformly distributed and show high catalytic activity in the cross-coupling of sodium azide with various aryl iodides and bromides. This nanocomposite has a high level of performance, and even led to the synthesis of the products at room temperature. In addition, this is the first report of the synthesis of aryl azides under both base- and ligand-free conditions. For the first time, both ligand- and base-free conditions were applied for the synthesis of aryl azides, which implies exceptional performance of the Cu2O-CuO-Cu-C nanocomposite. Simultaneous removal of the base and ligand in a green solvent is the main advantage of this reaction. Unfortunately, aryl bromides and aryl iodides with electron-withdrawing functional groups in their scaffold did not give the desired aryl azides.