Application of One-, Three-, and Seven-Day Forecasts During Early Onset on the COVID-19 Epidemic Dataset Using Moving Average, Autoregressive, Autoregressive Moving Average, Autoregressive Integrated Moving Average, and Naïve Forecasting Methods

The coronavirus disease 2019 (COVID-19) spread rapidly across the world since its appearance in December 2019. This data set creates one-, three-, and seven-day forecasts of the COVID-19 pandemic\u27s cumulative case counts at the county, health district, and state geographic levels for the state of Virginia. Forecasts are created over the first 46 days of reported COVID-19 cases using the cumulative case count data provided by The New York Times as of April 22, 2020. From this historical data, one-, three-, seven, and all-days prior to the forecast start date are used to generate the forecasts. Forecasts are created using: (1) a Naïve approach; (2) Holt-Winters exponential smoothing (HW); (3) growth rate (Growth); (4) moving average (MA); (5) autoregressive (AR); (6) autoregressive moving average (ARMA); and (7) autoregressive integrated moving average (ARIMA). Median Absolute Error (MdAE) and Median Absolute Percentage Error (MdAPE) metrics are created with each forecast to evaluate the forecast with respect to existing historical data. These error metrics are aggregated to provide a means for assessing which combination of forecast method, forecast length, and lookback length are best fits, based on lowest aggregated error at each geographic level. The data set is comprised of an R-Project file, four R source code files, all 1,329,404 generated short-range forecasts, MdAE and MdAPE error metric data for each forecast, copies of the input files, and the generated comparison tables. All code and data files are provided to provide transparency and facilitate replicability and reproducibility. This package opens directly in RStudio through the R Project file. The R Project file removes the need to set path locations for the folders contained within the data set to simplify setup requirements. This data set provides two avenues for reproducing results: 1) Use the provided code to generate the forecasts from scratch and then run the analyses; or 2) Load the saved forecast data and run the analyses on the stored data. Code annotations provide the instructions needed to accomplish both routes. This data can be used to generate the same set of forecasts and error metrics for any US state by altering the state parameter within the source code. Users can also generate health district forecasts for any other state, by providing a file which maps each county within a state to its respective health-district. The source code can be connected to the most up-to-date version of The New York Times COVID-19 dataset allows for the generation of forecasts up to the most recently reported data to facilitate near real-time forecasting

Augmenting Bottom-Up Metamodels with Predicates

Metamodeling refers to modeling a model. There are two metamodeling approaches for ABMs: (1) top-down and (2) bottom-up. The top down approach enables users to decompose high-level mental models into behaviors and interactions of agents. In contrast, the bottom-up approach constructs a relatively small, simple model that approximates the structure and outcomes of a dataset gathered fromthe runs of an ABM. The bottom-up metamodel makes behavior of the ABM comprehensible and exploratory analyses feasible. Formost users the construction of a bottom-up metamodel entails: (1) creating an experimental design, (2) running the simulation for all cases specified by the design, (3) collecting the inputs and output in a dataset and (4) applying first-order regression analysis to find a model that effectively estimates the output. Unfortunately, the sums of input variables employed by first-order regression analysis give the impression that one can compensate for one component of the system by improving some other component even if such substitution is inadequate or invalid. As a result the metamodel can be misleading. We address these deficiencies with an approach that: (1) automatically generates Boolean conditions that highlight when substitutions and tradeoffs among variables are valid and (2) augments the bottom-up metamodel with the conditions to improve validity and accuracy. We evaluate our approach using several established agent-based simulations

Temporal and Spatiotemporal Investigation of Tourist Attraction Visit Sentiment on Twitter

In this paper, we propose a sentiment-based approach to investigate the temporal and spatiotemporal effects on tourists\u27 emotions when visiting a city\u27s tourist destinations. Our approach consists of four steps: data collection and preprocessing from social media; visitor origin identification; visit sentiment identification; and temporal and spatiotemporal analysis. The temporal and spatiotemporal dimensions include day of the year, season of the year, day of the week, location sentiment progression, enjoyment measure, and multi-location sentiment progression. We apply this approach to the city of Chicago using over eight million tweets. Results show that seasonal weather, as well as special days and activities like concerts, impact tourists\u27 emotions. In addition, our analysis suggests that tourists experience greater levels of enjoyment in places such as observatories rather than zoos. Finally, we find that local and international visitors tend to convey negative sentiment when visiting more than one attraction in a day whereas the opposite holds for out of state visitors

A Structured Narrative Prompt for Prompting Narratives from Large Language Models: Sentiment Assessment of ChatGPT-Generated Narratives and Real Tweets

Large language models (LLMs) excel in providing natural language responses that sound authoritative, reflect knowledge of the context area, and can present from a range of varied perspectives. Agent-based models and simulations consist of simulated agents that interact within a simulated environment to explore societal, social, and ethical, among other, problems. Simulated agents generate large volumes of data and discerning useful and relevant content is an onerous task. LLMs can help in communicating agents\u27 perspectives on key life events by providing natural language narratives. However, these narratives should be factual, transparent, and reproducible. Therefore, we present a structured narrative prompt for sending queries to LLMs, we experiment with the narrative generation process using OpenAI\u27s ChatGPT, and we assess statistically significant differences across 11 Positive and Negative Affect Schedule (PANAS) sentiment levels between the generated narratives and real tweets using chi-squared tests and Fisher\u27s exact tests. The narrative prompt structure effectively yields narratives with the desired components from ChatGPT. In four out of forty-four categories, ChatGPT generated narratives which have sentiment scores that were not discernibly different, in terms of statistical significance (alpha level α = 0.05), from the sentiment expressed in real tweets. Three outcomes are provided: (1) a list of benefits and challenges for LLMs in narrative generation; (2) a structured prompt for requesting narratives of an LLM chatbot based on simulated agents\u27 information; (3) an assessment of statistical significance in the sentiment prevalence of the generated narratives compared to real tweets. This indicates significant promise in the utilization of LLMs for helping to connect a simulated agent\u27s experiences with real people

Iron supplementation and altitude: Decision making using a regression tree

[No abstract available

Pre-Altitude Serum Ferritin Levels and Daily Oral Iron Supplement Dose Mediate Iron Parameter and Hemoglobin Mass Responses to Altitude Exposure

Purpose : To investigate the influence of daily oral iron supplementation on changes in hemoglobin mass (Hbmass) and iron parameters after 2–4 weeks of moderate altitude exposure.Methods :Hematological data collected from 178 athletes (98 males, 80 females) exposed to moderate altitude (1,350–3,000 m) were analysed using linear regression to determine how altitude exposure combined with oral iron supplementation influenced Hbmass, total iron incorporation (TII) and blood iron parameters [ferritin and transferrin saturation (TSAT)]. Results :Altitude exposure (mean ± s: 21 ± 3 days) increased Hbmass by 1.1% [-0.4, 2.6], 3.3% [1.7, 4.8], and 4.0% [2.0, 6.1] from pre-altitude levels in athletes who ingested nil, 105 mg and 210 mg respectively, of oral iron supplement daily. Serum ferritin levels decreased by -33.2% [-46.9, -15.9] and 13.8% [-32.2, 9.7] from pre-altitude levels in athletes who supplemented with nil and 105 mg of oral iron supplement daily, but increased by 36.8% [1.3, 84.8] in athletes supplemented with 210 mg of oral iron daily. Finally, athletes who ingested either 105 mg or 210 mg of oral iron supplement daily had a greater TII compared with non-supplemented athletes (0 versus 105 mg: effect size (d) = -1.88 [-2.56, -1.17]; 0 versus 210 mg: effect size (d) = -2.87 [-3.88, -1.66]). Conclusion :Oral iron supplementation during 2–4 weeks of moderate altitude exposure may enhance Hbmass production and assist the maintenance of iron balance in some athletes with low pre-altitude iron stores

A Generative Model of the Mutual Escalation of Anxiety Between Religious Groups

We propose a generative agent-based model of the emergence and escalation of xenophobic anxiety in which individuals from two different religious groups encounter various hazards within an artificial society. The architecture of the model is informed by several empirically validated theories about the role of religion in intergroup conflict. Our results identify some of the conditions and mechanisms that engender the intensification of anxiety within and between religious groups. We define mutually escalating xenophobic anxiety as the increase of the average level of anxiety of the agents in both groups overtime. Trace validation techniques show that the most common conditions under which longer periods of mutually escalating xenophobic anxiety occur are those in which the difference in the size of the groups is not too large and the agents experience social and contagion hazards at a level of intensity that meets or exceeds their thresholds for those hazards. Under these conditions agents will encounter out-group members more regularly, and perceive them as threats, generating mutually escalating xenophobic anxiety. The model\u27s capacity to grow the macro-level emergence of this phenomenon from micro-level agent behaviors and interactions provides the foundation for future work in this domain

Understanding and Assessing Demographic (In)Equity Resulting From Extreme Heat Exposure Due to Lack of Tree Canopies in Norfolk, VA Using Agent-Based Modeling

Prolonged exposure to extreme heat can result in illness and death. In urban areas of dense concentrations of pavement, buildings, and other surfaces that absorb and retain heat, extreme heat conditions can arise regularly and create harmful environmental exposures for residents daily during certain parts of the year. Tree canopies provide shade and help to cool the environment, making mature trees with large canopies a simple and effective way to reduce urban heat. We develop a demographically representative 1 (agent): 1 (person) agent-based model to understand the extent to which different demographics of residents in Norfolk, VA are equitably shaded from extreme heat conditions during a walk on a clear summer day. We use the model to assess the extent to which the city\u27s Tree Planting Plan will be effective in remediating any existing inequities. Our results show that inequitable conditions exist for residents (1) at different education levels, (2) at different income levels and, (3) living in different census tracts. Norfolk\u27s Tree Planting Program effectively reduces the distance residents of all demographics walk in extreme heat. However, residents of the city at lower income levels still experience statistically significantly more extreme heat exposure due to a lack of tree canopies in summer months than those at higher income levels

Performance on Indirect Measures of Race Evaluation Predicts Amygdala Activation

We used fMRI to explore the neural substrates involved in the unconscious evaluation of Black and White social groups. Specifically, we focused on the amygdala, a subcortical structure known to play a role in emotional learning and evaluation. In Experiment 1, White American subjects observed faces of unfamiliar Black and White males. The strength of amygdala activation to Black-versus-White faces was correlated with two indirect (unconscious) measures of race evaluation (Implicit Association Test [IAT] and potentiated startle), but not with the direct (conscious) expression of race attitudes. In Experiment 2, these patterns were not obtained when the stimulus faces belonged to familiar and positively regarded Black and White individuals. Together, these results suggest that amygdala and behavioral responses to Black-versus-White faces in White subjects reflect cultural evaluations of social groups modified by individual experience.Psycholog

Estimating the Health Effects of Adding Bicycle and Pedestrian Paths at the Census Tract Level: Multiple Model Comparison

Background: Adding additional bicycle and pedestrian paths to an area can lead to improved health outcomes for residents over time. However, quantitatively determining which areas benefit more from bicycle and pedestrian paths, how many miles of bicycle and pedestrian paths are needed, and the health outcomes that may be most improved remain open questions. Objective: Our work provides and evaluates a methodology that offers actionable insight for city-level planners, public health officials, and decision makers tasked with the question “To what extent will adding specified bicycle and pedestrian path mileage to a census tract improve residents’ health outcomes over time?” Methods: We conducted a factor analysis of data from the American Community Survey, Center for Disease Control 500 Cities project, Strava, and bicycle and pedestrian path location and use data from two different cities (Norfolk, Virginia, and San Francisco, California). We constructed 2 city-specific factor models and used an algorithm to predict the expected mean improvement that a specified number of bicycle and pedestrian path miles contributes to the identified health outcomes. Results: We show that given a factor model constructed from data from 2011 to 2015, the number of additional bicycle and pedestrian path miles in 2016, and a specific census tract, our models forecast health outcome improvements in 2020 more accurately than 2 alternative approaches for both Norfolk, Virginia, and San Francisco, California. Furthermore, for each city, we show that the additional accuracy is a statistically significant improvement (P2 weeks of poor physical health days in the census tract within 1.83% (SD 0.57%). For San Francisco (n=49 census tracts), our approach estimates, on average, that the percentage of individuals who had a stroke in the census tract is within 1.81% (SD 0.52%), and the percentage of individuals with diabetes in the census tract is within 1.26% (SD 0.91%). Conclusions: We propose and evaluate a methodology to enable decision makers to weigh the extent to which 2 bicycle and pedestrian paths of equal cost, which were proposed in different census tracts, improve residents’ health outcomes; identify areas where bicycle and pedestrian paths are unlikely to be effective interventions and other strategies should be used; and quantify the minimum amount of additional bicycle path miles needed to maximize health outcome improvements. Our methodology shows statistically significant improvements, compared with alternative approaches, in historical accuracy for 2 large cities (for 2016) within different geographic areas and with different demographics