Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data.

Topic models are a useful and popular method to find latent topics of documents. However, the short and sparse texts in social media micro-blogs such as Twitter are challenging for the most commonly used Latent Dirichlet Allocation (LDA) topic model. We compare the performance of the standard LDA topic model with the Gibbs Sampler Dirichlet Multinomial Model (GSDMM) and the Gamma Poisson Mixture Model (GPM), which are specifically designed for sparse data. To compare the performance of the three models, we propose the simulation of pseudo-documents as a novel evaluation method. In a case study with short and sparse text, the models are evaluated on tweets filtered by keywords relating to the Covid-19 pandemic. We find that standard coherence scores that are often used for the evaluation of topic models perform poorly as an evaluation metric. The results of our simulation-based approach suggest that the GSDMM and GPM topic models may generate better topics than the standard LDA model.

Simulation Analysis of Infectious Disease Trend Based on Improved SEIR Model

Scholars have carried out a lot of research in the field of using data processing methods to analyze the evolution characteristics and development trends of infectious diseases. The research on data model method is more in-depth, that is, according to the specific characteristics of infectious diseases, suitable data models are designed and combined with different parameters to analyze infectious diseases, mainly including infectious disease data models based on statistical theory or dynamic theory. The former is mostly used in the case of insufficient initial data. Local analysis is carried out by means of a priori or assumptions to achieve global prediction. The latter mainly includes SIR model, complex network model, and cellular automata model. SIR model is the most in-depth research. Scholars have constructed or optimized Si model, SIS model, SEIR model, IR model, and other derivative models based on SIR model in combination with the characteristics of viruses. In this paper, the data source is Wuhan epidemic information released by Health Commission of Hubei Province. Combined with the specific characteristics of COVID-19, the traditional dynamic propagation model is optimized, and an improved SEIR model is constructed. The results of the improved SEIR model are in good agreement with the actual epidemic trend in Wuhan. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Covid-19 descriptive analytics visualization of pandemic and hospitalization data

This chapter will introduce readers to the kinds of data visualizations that can be made from a data set. From these data visualizations, readers can form some important insights about the nature of the data sets even before analysis begins. Based on these insights, analytical models can be designed to generate other insights valuable for understanding various phenomena in the data set. © 2023 Elsevier Inc. All rights reserved.

Forecasting the Temporal Evolution of COVID-19

COVID-19 epidemic has resulted in severe chaos across the globe. Complex frameworks can be investigated and studied using mathematical models, which are reliable and efficient. The objective of this research is to scrutinize the progression and prediction of parameters that evaluate the emergence and transmission of COVID-19 in the two most affected nations, i.e., the USA and India. Five models including the standard and hybrid epidemic models, viz, SIR (Susceptible-Infectious-Removed), SIRD (Susceptible-Infectious-Recovered-Death), SIRD with vaccination, SIRD with vital dynamics (i.e., including birth rate and death rate) and, SIRD with vital dynamics and vaccination have been developed. Worldwide statistics have been observed utilizing graphical layouts. Model evaluation measures such as Mean Absolute error (MAE), Mean-square error (MSE), and Root Mean Square Error (RMSE) for different parameters namely infection rate, recovery rate, and death rate have been estimated. © 2022 IEEE.

Assessing the accuracy of California county level COVID-19 hospitalization forecasts to inform public policy decision making.

BACKGROUND: The COVID-19 pandemic has highlighted the role of infectious disease forecasting in informing public policy. However, significant barriers remain for effectively linking infectious disease forecasts to public health decision making, including a lack of model validation. Forecasting model performance and accuracy should be evaluated retrospectively to understand under which conditions models were reliable and could be improved in the future. METHODS: Using archived forecasts from the California Department of Public Health's California COVID Assessment Tool ( https://calcat.covid19.ca.gov/cacovidmodels/ ), we compared how well different forecasting models predicted COVID-19 hospitalization census across California counties and regions during periods of Alpha, Delta, and Omicron variant predominance. RESULTS: Based on mean absolute error estimates, forecasting models had variable performance across counties and through time. When accounting for model availability across counties and dates, some individual models performed consistently better than the ensemble model, but model rankings still differed across counties. Local transmission trends, variant prevalence, and county population size were informative predictors for determining which model performed best for a given county based on a random forest classification analysis. Overall, the ensemble model performed worse in less populous counties, in part because of fewer model contributors in these locations. CONCLUSIONS: Ensemble model predictions could be improved by incorporating geographic heterogeneity in model coverage and performance. Consistency in model reporting and improved model validation can strengthen the role of infectious disease forecasting in real-time public health decision making.

Forecasting the Temporal Evolution of COVID-19

COVID-19 epidemic has resulted in severe chaos across the globe. Complex frameworks can be investigated and studied using mathematical models, which are reliable and efficient. The objective of this research is to scrutinize the progression and prediction of parameters that evaluate the emergence and transmission of COVID-19 in the two most affected nations, i.e., the USA and India. Five models including the standard and hybrid epidemic models, viz, SIR (Susceptible-Infectious-Removed), SIRD (Susceptible-Infectious-Recovered-Death), SIRD with vaccination, SIRD with vital dynamics (i.e., including birth rate and death rate) and, SIRD with vital dynamics and vaccination have been developed. Worldwide statistics have been observed utilizing graphical layouts. Model evaluation measures such as Mean Absolute error (MAE), Mean-square error (MSE), and Root Mean Square Error (RMSE) for different parameters namely infection rate, recovery rate, and death rate have been estimated. © 2022 IEEE.

Investigating Technology Transfer Gaps Through Farmers Field School

Technology transfer is one of the core elements in a rapidly changing agricultural sector. However, the booming of agricultural innovation is not followed by the generation of methodological tools able to diffuse innovation in farmers and other stakeholders. For the last decades, Farmers Field School (FFS) approach is offering technology transfer and co-generation, infused by agricultural extension. Traditional FFS form is a learning by doing method and farmers are learning from other experienced farmers. Even though FFS has various forms which are trying to cover gaps between science and practice, there are still different methodological challenges in each FFS form. In this research, we propose a Hybrid FFS strategy, assembled by the strengths of various FFS forms and trying to close these gaps. We review and implement a meta-analysis of FFS forms, investigating these gaps. Afterwards, a comprehensive, holistic and dynamic conceptual and methodological model, derived from meta-analysis is proposed to cover the technology transfer methodological gaps. Our Hybrid FFS strategy highlight strategic questions which offer the appropriate background for establishing a strong educational strategy and overcome possible challenges. "Learning by doing” is supported from farmers to farmers as well as from experts to experts. Various stakeholders from value chain are promoted to use and be familiarized with new technologies, practical tools and the internet, as well as develop their managerial skills in value chain products. Modules cover the gaps of recent FFS approaches, by incorporating issues of sustainability and certification of value chain products, with business and entrepreneurship. Flexibility of a hybrid (virtual and physical) environment resolve complex situations (i.e. COVID-19). This methodology can be useful to policy makers managers or agricultural extension researchers, in order to construct, implement and evaluate an FFS agricultural program. Hybrid FFS strategy describes how agricultural education approaches of the past can create educational environments of the future and lead learning accelerators in agricultural sector. © 2022, Bucharest University of Economic Studies. All rights reserved.

What are people asking about COVID-19? A question classification dataset

We present COVID-Q, a set of 1,690 questions about COVID-19 from 13 sources, which we annotate into 15 question categories and 207 question clusters. The most common questions in our dataset asked about transmission, prevention, and societal effects of COVID, and we found that many questions that appeared in multiple sources were not answered by any FAQ websites of reputable organizations such as the CDC and FDA. We post our dataset publicly at https://github.com/JerryWei03/COVID-Q. For classifying questions into 15 categories, a BERT baseline scored 58.1% accuracy when trained on 20 examples per category, and for a question clustering task, a BERT + triplet loss baseline achieved 49.5% accuracy. We hope COVID-Q can help either for direct use in developing applied systems or as a domain-specific resource for model evaluation. © ACL 2020.All right reserved.

Model Evaluation of Secondary Chemistry due to Disinfection of Indoor Air with Germicidal Ultraviolet Lamps

Air disinfection using germicidal ultraviolet light (GUV) has received increasing attention during the COVID-19 pandemic. GUV uses UVC lamps to inactivate microorganisms, but it also initiates photochemistry in air. However, GUV's indoor-air-quality impact has not been investigated in detail. Here, we model the chemistry initiated by GUV at 254 ("GUV254”) or 222 nm ("GUV222”) in a typical indoor setting for different ventilation levels. Our analysis shows that GUV254, usually installed in the upper room, can significantly photolyze O3, generating OH radicals that oxidize indoor volatile organic compounds (VOCs) into more oxidized VOCs. Secondary organic aerosol (SOA) is also formed as a VOC-oxidation product. GUV254-induced SOA formation is of the order of 0.1-1 μg/m3 for the cases studied here. GUV222 (described by some as harmless to humans and thus applicable for the whole room) with the same effective virus-removal rate makes a smaller indoor-air-quality impact at mid-to-high ventilation rates. This is mainly because of the lower UV irradiance needed and also less efficient OH-generating O3 photolysis than GUV254. GUV222 has a higher impact than GUV254 under poor ventilation due to a small but significant photochemical production of O3 at 222 nm, which does not occur with GUV254. © 2022 American Chemical Society.

Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review.

BACKGROUND: Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed such that models are fit for purpose and remain relevant in the long-term. We aimed to present an overview of methodological guidance for the evaluation (i.e., validation and impact assessment) and updating of clinical prediction models. METHODS: We systematically searched nine databases from January 2000 to January 2022 for articles in English with methodological recommendations for the post-derivation stages of interest. Qualitative analysis was used to summarize the 70 selected guidance papers. RESULTS: Key aspects for validation are the assessment of statistical performance using measures for discrimination (e.g., C-statistic) and calibration (e.g., calibration-in-the-large and calibration slope). For assessing impact or usefulness in clinical decision-making, recent papers advise using decision-analytic measures (e.g., the Net Benefit) over simplistic classification measures that ignore clinical consequences (e.g., accuracy, overall Net Reclassification Index). Commonly recommended methods for model updating are recalibration (i.e., adjustment of intercept or baseline hazard and/or slope), revision (i.e., re-estimation of individual predictor effects), and extension (i.e., addition of new markers). Additional methodological guidance is needed for newer types of updating (e.g., meta-model and dynamic updating) and machine learning-based models. CONCLUSION: Substantial guidance was found for model evaluation and more conventional updating of regression-based models. An important development in model evaluation is the introduction of a decision-analytic framework for assessing clinical usefulness. Consensus is emerging on methods for model updating.

Teaching Modeling, Simulation, and Performance Evaluation Course Online with Jupyter Notebook: Course Development and Lessons Learned

This innovative practice full paper presents a case of teaching modeling, simulation, and performance evaluation course online during the COVID-19 pandemic for graduate students. The course includes theoretical and practical sessions with varying complexity. Students should have a good background in math, statistics, and probability. Students must also have good experience in one of the computer programming languages to solve the homework and work on the term project. The challenge is how to teach these topics online and engage the students in the course as they learn in face-to-face classes. Delivering the course using PowerPoint slides and a whiteboard is not suitable for teaching the class online. Therefore, there must be an alternative way to deliver the course online. We noticed a growing interest in using Jupyter Notebook in teaching, which motivated us to apply it to the mentioned course with some innovations. Jupyter Notebook is an open-source web application that allows us to create and share documents that contain live code, equations, visualizations, and narrative text. We want to share our experience teaching this course online using Jupyter Notebook in this innovative practice. We will share the course development plan, delivery mode, lessons learned, and student feedback. I will also highlight maximizing the benefits of the Jupyter Notebook using add-ins and tools useful for teaching, such as converting the Jupyter Notebook to a slide show. Developing courses in Jupyter Notebook could be time-consuming and frustrating, especially if there are a lot of math equations, tables, and drawings. This effort pays off in terms of the quality of the instruction and learning, and it gives students a tool to help them practice and engage with the course material. © 2022 IEEE.

Comparison of Hawkes and Seir Models for the Spread of Covid-19

Two models that capture the spread of infectious diseases, the Hawkes point process model and the SEIR compartmental model, are compared with regard to their use in modeling the COVID-19 pandemic. The physical plausibility of the SEIR model is weighed against the parsimony and flexibility of the Hawkes model. The mathematical connection between Hawkes and SEIR models is described.

A Simple Framework for Calibrating Hydraulic Flood Inundation Models using Crowd-sourced Water Levels

Floods are the most commonly occurring natural disaster, with the Centre for Research on the Epidemiology of Disasters 2021 report on “The Non-COVID Year in Disasters” estimating economic losses worth over USD 51 million and over 6000 fatalities in 2020. The hydrodynamic models which are used for flood forecasting need to be evaluated and constrained using observations of water depth and extent. While remotely sensed estimates of these variables have already facilitated model evaluation, citizen sensing is emerging as a popular technique to complement real-time flood observations. However, its value for hydraulic model evaluation has not yet been demonstrated. This paper tests the use of crowd-sourced flood observations to quantitatively assess model performance for the first time. The observation set used for performance assessment consists of 32 distributed high water marks and wrack marks provided by the Clarence Valley Council for the 2013 flood event, whose timings of acquisition were unknown. Assuming that these provide information on the peak flow, maximum simulated water levels were compared at observation locations, to calibrate the channel roughness for the hydraulic model LISFLOOD-FP. For each realization of the model, absolute and relative simulation errors were quantified through the root mean squared error (RMSE) and the mean percentage difference (MPD). Similar information was extracted from 11 hydrometric gauges along the Clarence River and used to constrain the roughness parameter. The calibrated parameter values were identical for both data types and a mean RMSE value of ∼50 cm for peak flow simulation was obtained across all gauges. Results indicate that integrating uncertain flood observations from crowd-sourcing can indeed generate a useful dataset for hydraulic model calibration in ungauged catchments, despite the lack of associated timing information.

Computational Pipeline for Reference-Free Comparative Analysis of RNA 3D Structures Applied to SARS-CoV-2 UTR Models.

RNA is a unique biomolecule that is involved in a variety of fundamental biological functions, all of which depend solely on its structure and dynamics. Since the experimental determination of crystal RNA structures is laborious, computational 3D structure prediction methods are experiencing an ongoing and thriving development. Such methods can lead to many models; thus, it is necessary to build comparisons and extract common structural motifs for further medical or biological studies. Here, we introduce a computational pipeline dedicated to reference-free high-throughput comparative analysis of 3D RNA structures. We show its application in the RNA-Puzzles challenge, in which five participating groups attempted to predict the three-dimensional structures of 5'- and 3'-untranslated regions (UTRs) of the SARS-CoV-2 genome. We report the results of this puzzle and discuss the structural motifs obtained from the analysis. All simulated models and tools incorporated into the pipeline are open to scientific and academic use.

Convection-Permitting Models for Climate Research

4th International Convection-Permitting Modeling Workshop for Climate Research What: The purpose of the workshop was to discuss the performance of convection-permitting models (<4-km horizontal grid spacing) at global and local scales and also to discuss the potential of CPMs data for hazard and impact studies. Because of the rapid development of the convection-permitting modeling (CPM) field, we felt the need to host a virtual workshop this year to maintain community interactions and to provide a forum where scientific advances are presented and discussed. [...]the use of satellite observations and targeted model experiments that make use of field campaign data were discussed for evaluating global CPMs. High-resolution and high-quality observations were identified as crucial for a better understanding of processes and phenomena that cause extreme events and for supporting the development of parameterization schemes. Since rainfall is expected to intensify at small spatial and temporal scales in future climates, the impact of precipitation on the initiation of landslides in small river catchments becomes increasingly important.

Machine Learning for Exhibition Recommendation in a Museum's Virtual Tour Application

The museum visit is having a crisis during the COVID-19 pandemic. SMBII Museum in Palembang has a remarkable decrease of visitors up to 90%. A strategy is needed to increase museum visits and enable educational and tourism roles in a pandemic situation. This paper evaluates the machine learning model for exhibition recommendations given to visitors through virtual tour applications. Exploring unfamiliar museum exhibitions to visitors through virtual museum applications will be tedious. If virtual collections are ancient and do not display any interest, they will quickly lead to boredom and reluctance to explore virtual museums. For this reason, an effective method is needed to provide suggestions or recommendations that meet the interests of visitors based on the profiles of museum visitors, making it easier for visitors to find exciting exhibition rooms for learning and tourism. Machine learning has proven its effectiveness for predictions and recommendations. This study evaluates several machine learning classifiers for exhibition recommendations and development of virtual tour applications that applied machine learning classifiers with the best performance based on the model evaluation. The experimental results show that the KNN model performs best for exhibition recommendations with cross-validation accuracy = 89.09% and F-Measure = 90.91%. The SUS usability evaluation on the exhibition recommender feature in the virtual tour application of SMBII museum shows average score of 85.83. The machine learning-based recommender feature usability is acceptable, making it easy and attractive for visitors to find an exhibition that might match their interests. © 2022. All Rights Reserved.

Ozone Trends and the Ability of Models to Reproduce the 2020 Ozone Concentrations in the South Coast Air Basin in Southern California under the COVID-19 Restrictions

The current U.S. emission control requirements for on-road motor vehicles are driven by the ozone problem in the South Coast Air Basin (SoCAB) in southern California. Based on ozone modeling performed for Air Quality Management Plans (AQMPs), the SoCAB ozone attainment plan requires large (>80%) amounts of emission reductions in oxides of nitrogen (NOx) from current levels with more modest (similar to 40%) controls on Volatile Organic Compounds (VOC). The shelter in place orders in response to the 2020 COVID-19 pandemic resulted in an immediate reduction in emissions, but instead of ozone being reduced, in 2020 the SoCAB saw some of the highest observed ozone levels in decades. We used the abrupt emissions reductions from 2019 to 2020 caused by COVID-19 to conduct a dynamic model evaluation of the Community Multiscale Air Quality (CMAQ) model to evaluate whether the models used to develop ozone control plans can correctly simulate the ozone response to the emissions reductions. Ozone modeling was conducted for three scenarios: 2019 Base, 2020 business-as-usual (i.e., without COVID reductions), and 2020 COVID. We found that modeled ozone changes between 2019 and 2020 were generally consistent with the observed ozone changes. We determined that meteorology played the major role in the increases in ozone between 2019 and 2020;however, the reduction in NOX emissions also caused ozone increases in Los Angeles County and into western San Bernardino County, with more widespread ozone decreases further to the east.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Selection of the Optimal Diagnosis Model for COVID-19 Using the SAW and PROMETHEE II Methods

The COVID-19 outbreak is one thing that is the main focus for all countries in the world, including Indonesia. The diagnosis of COVID-19 can be done in various ways, such as checking symptoms, rapid-testing, swab-testing, and checking with X-rays. The application of machine learning in diagnosing a disease is one way that can be used to maximize the diagnosis of COVID-19 disease. This is what lies behind the need for optimization in the application of machine learning. This research goal is to determine the most optimal algorithm for diagnosing COVID-19 disease with a dataset in the form of Xray photos by using a decision-making methodology. The dataset in the form of Xray images will be processed utilizing image preprocessing (resize, grayscale, augmentation) and feature extraction (GLCM) methods. The researcher uses the Backpropagation algorithm, Recurrent Neural Network, SVM Linear, SVM Non-Linear, and Naive Bayes. The decision-making methodology used is Simple Additive Weighting and PROMETHEE II. Results of research in the form of ranking the optimal algorithm model in diagnosing COVID-19 according to the two decision making methodologies used. The five algorithms will take feature extraction of data as input and output 9 assessment criteria, including Accuracy, Precision, Recall Metric, ROC Curve, F1 Score, TNR, FPR, FNR, and time. The decision-making methodology used is Simple Additive Weighting and PROMETHEE II with 9 evaluation criteria as parameters. Results of research in the form of ranking the optimal algorithm model in diagnosing COVID-19 according to the two decision making methodologies used. The conclusion is that Backpropagation was the most optimal algorithm model in diagnosing COVID-19 disease with evaluation criteria values are AUC / ROC 0.935;Accuracy 0.893;F-score 0.888;Precision 0.908;Recalls 0.869;FPR 0.084;FNR 0.131;TNR 0.916;Time 1.023. © 2021 IEEE.

Model of COVID-19 Surveillance System for a Community-industry Setting

This study aimed to design and test a COVID-19 surveillance system model for community-industry population. A prospective cohort study was conducted from May to December, 2020. Researchers designed a COVID-19 surveillance system and presented it to stakeholders from the community-industry setting in Lamphun and Chiang Mai provinces, Thailand. The model was adjusted following feedback and tested. The model was an Active surveillance for early Alert and rapid Action using Big data and mobile phone application technology for a Community-industry setting (3ABC model). The major components were active surveillance, community-based surveillance, event-based surveillance, and early warning and rapid response. A drive-thru testing unit was operated to enable early detection. Alerts and recommended action on individual and administrative levels were sent via an application and networks. In the testing of the model, risk assessment was initially conducted with regard to COVID-19 transmission in the factories. Researchers provided recommendations based on findings. The improvements included human resource management, systems, and structure. The 3ABC model work well as designed. The participants actively reported events daily including prevention and control activities, animal diseases (foot-and-mouth disease in buffalos and hog cholera), human diseases (dengue and chikungunya), and absent of COVID-19 outbreak. Only five quarantined COVID-19 cases whom were monitored. Daily reports of no abnormal event was also high (70.2% to 71.1%). It is practical and feasible to implement the 3ABC model in a community-industry setting. A further study for a longer period to verify its level of effectiveness should be done. © 2021. Author (s). This is an open access article distributed under the term of the Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as you give appropriate credit to the original author (s) and the source.