Learning on Health Fairness and Environmental Justice via Interactive Visualization

This paper introduces an interactive visualization interface with a machine learning consensus analysis that enables the researchers to explore the impact of atmospheric and socioeconomic factors on COVID-19 clinical severity by employing multiple Recurrent Graph Neural Networks. We designed and implemented a visualization interface that leverages coordinated multi-views to support exploratory and predictive analysis of hospitalizations and other socio-geographic variables at multiple dimensions, simultaneously. By harnessing the strength of geometric deep learning, we build a consensus machine learning model to include knowledge from county-level records and investigate the complex interrelationships between global infectious disease, environment, and social justice. Additionally, we make use of unique NASA satellite-based observations which are not broadly used in the context of climate justice applications. Our current interactive interface focus on three US states (California, Pennsylvania, and Texas) to demonstrate its scientific value and presented three case studies to make qualitative evaluations. © 2022 IEEE.

Evaluating COVID-19's impacts on Puerto Rican's travel behaviors

The COVID-19 pandemic has completely disrupted and possibly permanently changed the way humans travel. In Puerto Rico, major travel restrictions to the island have persisted at different levels since March 2020, which heavily influenced residents' travel behaviors. However, it remains unclear about how big the impact is and how inequitable it might be. The goal of this study is to evaluate COVID-19's impacts on Puerto Rican's travel behaviors by analyzing travel flows from Puerto Rico to the contiguous US with a modified gravity model. The roles of socioeconomic factors regarding the Puerto Rican travelers and COVID-19 factors regarding the destination US states have been assessed. COVID-19 was a strong deterring factor of travel at the beginning of the pandemic and also in the winter of 2020, but it did not keep Puerto Ricans from traveling during the summer 2020 when most travel restrictions were lifted. We found that the elderly population of Puerto Rico, despite being more vulnerable to COVID-19, were much more likely to travel during the pandemic. We also found that, during the holiday season in 2020, some socioeconomically disadvantaged populations were more likely to be traveling, a direct contradiction to their travel flows the year prior. These findings shed light on about how disproportionately affected populations behavior changed from pre-pandemic to after the pandemic started. With the continuance of the pandemic, this information is extremely valuable for future planning with respect to emergency management, travel regulation, and social benefit. © 2023 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group.

Factors associated with the use of public eHealth services in Poland - a 2022 nationwide cross-sectional survey.

INTRODUCTION AND OBJECTIVE: Poland is an example of a European country that has made significant progress in digitizing healthcare during the last 5 years. There is limited data on the use of eHealth services by different socio-economic groups in Poland during the COVID-19 pandemic The aim of the study was to characterize public attitudes towards the use of e-Health services in Poland, as well as to identify factors associated with the use of e-Health services among adults in Poland. MATERIAL AND METHODS: A questionnaire-based survey was carried out during 9-12 September 2022. A computer-assisted web interview methodology was used. A nationwide random quota sample of 1,092 adult Poles was selected. Questions on the use of 6 different public eHealth services in Poland and soci-economic characteristics were addressed. RESULTS: Two-thirds of participants (67.1%) had received an e-prescription in the last 12 months. More than half of the participants used the Internet Patient Account (58.2%) or the patient.gov.pl website (54.9%). One-third of the participants had teleconsultation with a doctor (34.4%), and approximately one-quarter of participants had received electronic sick leave (26.9%) or used electronic information about treatment dates (26.7%). Of the ten different socio-economic factors analyzed in this study, educational level, and place of residence (p<0.05) were the most important factors associated with the use of public eHealth services among adults in Poland. CONCLUSIONS: Living in rural areas or small cities is associated with a lower level of public eHealth services utilization. A relatively high interest in health education through eHealth methods was observed.

The long-term dynamic relationship between communicable disease spread, economic prosperity, greenhouse gas emissions, and government health expenditures: preparing for COVID-19-like pandemics.

The spread of communicable diseases, such as COVID-19, has a detrimental effect on our socio-economic structure. In a dynamic log-run world, socio-economic and environmental factors interact to spread communicable diseases. We investigated the long-term interdependence of communicable disease spread, economic prosperity, greenhouse gas emissions, and government health expenditures in India's densely populated economy using a variance error correction (VEC) approach. The VEC model was validated using stationarity, cointegration, autocorrelation, heteroscedasticity, and normality tests. Our impulse response and variance decomposition analyses revealed that economic prosperity (GNI) significantly impacts the spread of communicable diseases, greenhouse gas emissions, government health expenditures, and GNI. Current health expenditures can reduce the need for future increases, and the spread of communicable diseases is detrimental to economic growth. Developing economies should prioritize economic growth and health spending to combat pandemics. Simultaneously, the adverse effects of economic prosperity on environmental degradation should be mitigated through policy incentives.

Examining post-pandemic behaviors influencing human mobility trends

COVID-19 unleashed a global pandemic that has resulted in human, economic, and social crises of unprecedented scale. While the efficacy of mobility restrictions in curbing contagion has been scientifically and empirically acknowledged, a deeper understanding of the human behavioral trends driving the mixed adoption of mobility restrictions will aid future policymaking. In this paper, we employ associative rule-mining and regression to pinpoint socioeconomic and demographic factors influencing the evolving mobility trends. We compare and contrast short-distance and long-distance trips by analyzing Chicago county-level and US state-level mobility. Our study yields rules that explain the changing propensity in trip length and the collective effect of population density, economic standing, COVID testing, and the number of infected cases on mobility decisions. Through regression and correlation analysis, we show the influence of ethnic and demographic factors and perception of infection on short and long-distance trips. We find that the new mobility rules correspond to reduced long-And short-distance trip frequencies. We graphically demonstrate a marked decline in the proportion of long county-level trips but a minor change in the distribution of state-level trips. Our correlation study highlights it is hard to characterize the effect of perception of infection spread on mobility decisions. We conclude the paper with a discussion on the overlap between the analysis in the existing literature on both during-And post-lockdown mobility trends and our findings. © 2022 ACM.

An In-Depth Analysis Investigating the Ethnical Disparities of COVID-19 Cases and Death Rates in Georgia and Mississippi based on Socioeconomic Factors

This research aims to quantify racial disparities associated with COVID-19 cases and deaths in Georgia and Mississippi. It investigates ethnic disparities at the county level, based on socioeconomic factors. The factors used include the county population, median income, percentage of the county population per ethnic group, and county presidential election party major. In addition, COVID-19 cases and death rates by ethnicity were provided. The combined data was used for K-means clustering analysis and Analysis of Variances, to investigate the differences due to ethnicity per county and the differences due to aggregated cases and death rates per county. The results showed a significant difference in the ethnic group's COVID-19 cases and deaths as well as the socioeconomic factors that might have affected these rates. Specifically, counties with the Republican party as the presidential political party majority had significantly more cases and deaths for American Indian and Alaskan Native (AIAN), Black, and White ethnic groups in Mississippi and Georgia. There was no significant statistical difference between the Asian and Latinx groups. This research concluded that there is a significant difference in the COVID-19 deaths and cases based on the ethnic groups due to socioeconomic factors and the political party majority of the counties. In addition, counties with significant cases and death rates consist of large proportions of people of color than their population representation percentage based on the 2020 Census. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

Note: CORONOSIS: Corona Prognosis via a Global Lens to Enable Efficient Policy-making Both at Global and Local Levels

Epidemics and pandemics have been affecting human lives since time, and have sometimes altered the course of history. At this very moment, Coronavirus (COVID-19) pandemic has been the defining global health crisis. Now, perhaps for the first time in history, humanity as a whole has undergone major disruptions to life and some form of lockdown. New policies need to be forged by policy-makers for various sectors such as trading, banking, education, etc., to lessen losses and to heal quickly. For efficient policy-making, in turn, some prerequisites needed are historical trend analysis on the pandemic spread, future forecasting, the correlation between the spread of the disease and various socio-economic and environmental factors, etc. Besides, all of these need to be presented in an integrated manner in real-time to facilitate efficient policy-making. Therefore, in this work, we developed a web-based integrated real-time operational dashboard as a one-stop decision support system for COVID-19. In our study, we conducted a detailed data-driven analysis based on available data from multiple authenticated sources to predict the upcoming consequences of the pandemic through rigorous modeling and statistical analyses. We also explored the correlations between disease spread and diverse socio-economic as well as environmental factors. Furthermore, we presented how the outcomes of our work can facilitate both contemporary and future policy-making. © 2022 ACM.

Revealing Influences of Socioeconomic Factors over Disease Outbreaks

The recent Covid-19 pandemic elucidates the need for a better disease outbreak analysis and surveillance system, which can harness state-of-the-art data mining and machine learning techniques to produce better forecasting. In this regard, understanding the correlation between disease outbreaks and socioeconomic factors should pave the way for such systems by providing useful indicators, which are yet to be explored in the literature to the best of our knowledge. Therefore, in this study, we accumulated data on 72 infectious diseases and their outbreaks all over the globe over a period of 23 years as well as corresponding different socioeconomic data. We, then, performed point-biserial and spearman correlation analysis over the collected data. Our analysis of the obtained correlations demonstrates that various disease outbreak attributes are positively and negatively correlated with different socioeconomic indicators. For example, indicators such as lifetime risk of maternal death, adolescent fertility rate, etc., are positively correlated, while indicators such as life expectancy at birth, measles immunization, etc., are negatively correlated, with disease outbreaks that affect the digestive organ system. In this paper, we find and summarize the correlations between 126 outbreak attributes derived from the characteristics of the 72 diseases in consideration and 192 socioeconomic factors which is a novel contribution to the field of disease outbreak analysis and prediction. © 2022 ACM.

Attitudes Towards the Use of COVID-19 Apps and Its Associated Factors

Since early 2020, the COVID-19 pandemic has been significantly changing people’s daily lives as social activities are limited to slow down the spread of the novel coronavirus. New technologies, especially mobiles apps, have been widely applied to help with reducing the spread of the pandemic. However, although these apps bring many benefits, it also raises privacy issues given the amount of user information being collected and shared. The goal of this study is to understand individuals’ attitudes towards the privacy concerns on using COVID-19 apps, and their expectations on the privacy protections. By conducting the survey and collecting responses, results found that majority of the participants expressed privacy concerns on COVID-19 apps, and participants with different socioeconomic status may have different levels of willingness to use the app. Results from this study not only provide guidance for the government and app service providers on the implementation of appropriate safeguards, but also address on the needs of privacy protections for the vulnerable groups. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model

COVID-19 pandemic has caused unprecedented negative impacts on our society, including further exposing inequity and disparity in public health. To study the impact of socioeconomic factors on COVID transmission, we first propose a spatial-temporal model to examine the socioeconomic heterogeneity and spatial correlation of COVID-19 transmission at the community level. Second, to assess the individual risk of severe COVID-19 outcomes after a positive diagnosis, we propose a dynamic, varying-coefficient model that integrates individual-level risk factors from electronic health records (EHRs) with community-level risk factors. The underlying neighborhood prevalence of infections (both symptomatic and pre-symptomatic) predicted from the previous spatial-temporal model is included in the individual risk assessment so as to better capture the background risk of virus exposure for each individual. We design a weighting scheme to mitigate multiple selection biases inherited in EHRs of COVID patients. We analyze COVID transmission data in New York City (NYC, the epicenter of the first surge in the United States) and EHRs from NYC hospitals, where time-varying effects of community risk factors and significant interactions between individual- and community-level risk factors are detected. By examining the socioeconomic disparity of infection risks and interaction among the risk factors, our methods can assist public health decision-making and facilitate better clinical management of COVID patients. © 2021 Neural information processing systems foundation. All rights reserved.

Forecasting High-risk Areas of COVID-19 Infection Through Socioeconomic and Static Spatial Analysis

Existing COVID-19 prediction models focus on studying the dynamic nature of the virus spread by using pandemic-related temporal data. In this paper, we present a work that exclusively uses comprehensive socioeconomic factors to predict the high risk areas of COVID-19 infection based on fine-grained static spatial analysis. Moreover, the most and least influential socioeconomic factors on COVID-19 spread are identified. This paper uses a uniquely built dataset by combining local states' cumulative COVID-19 statistics and their associated socioeconomic features on the zip code level. Further, the work solves the lack of data by augmentation. To evaluate the work, four case studies are conducted on Florida, Illinois, Minnesota, and Virginia. Experimental results show that the study provides accurate predictions with respect to ground truth data. By identifying high risk areas and socioeconomic factors, policymakers can use this study to take necessary measures to help disadvantaged communities. © 2021 IEEE.

Covid Vaccine Risk Stratification

In response to the pandemic caused by the rapidly spreading COVID-19 virus, several highly effective vaccines have been developed by Pfizer, Moderna, and Janssen. Despite the promising efficacy of those vaccines, there remains the challenge of properly distributing vaccines to those who need it most in the US. of particular concern are individuals who are at higher risk due to underlying medical conditions which have been shown to exacerbate COVID-19 symptoms and at times lead to fatal illnesses. In addition to this, a variety of socioeconomic factors have been linked to increased COVID-19 rates and increased mortality, such as race, age, income, mobility, and education level.This project aims to develop an information system to help advise vaccine distributors and state governments on how to effectively distribute vaccines to prioritize high risk individuals. The information system incorporates state-level data of the population with underlying medical conditions, demographics, overall state income, education level, and state mobility to formulate a mortality index. State-level data on the number of vaccines available and doses already administered are also incorporated into the information system to generate a vaccine index. The mortality and vaccine indices for each state are coupled to generate a vaccine priority ranking which can be used to advise vaccine distribution.The prototype can successfully link the data described above to a map of the US and then color code states according to the vaccine priority ranking. Implementation of this prototype will enable optimal vaccine distribution and reduce instances of severe or fatal COVID-19 illnesses as well as reduce costs associated with oversupply of vaccines in a single region. Future work will focus on improving the granularity of data down to the county-level, as well as increasing the scope of the system to the global scale. Additionally, the team plans to expand the application space of this information system to other diseases. © 2021 IEEE.

Towards a Model of Social Responsibility for Peruvian Companies in Response to the Covid-19 Pandemic: The Case of FARVET SAC

The COVID-19 crisis has brought with it a series of effects beyond the public health implications, others related to socioeconomic factors, as companies have had to reconcile business resilience with social resilience, thus becoming a complex challenge, where Social and business responsibility (CSR) plays an important role because it allows us to know the responsibility that each organization has with the environment in which it operates and with the society of which it is a part. The objective of this study was to identify the response capacity on the social responsibility of Peruvian businesses in the face of the COVID-19 pandemic, analyzing the particular case of the local company FARVET SAC, for which the different CSR activities of five main companies were analyzed. : Buenaventura, San Fernando, Sociedad Agrícola Virú and Arca Continental Lindley, as well as the analysis of the case study, obtaining as results that the types of response by these five companies were classified as selective, supportive and reactive;Regarding the case study, the company's CSR activities were described, with an emphasis on donations of health products, donations in kind, scientific research on COVID-19 and support for its collaborators. Additionally, a CSR model is proposed, based on a strategic organizational program that allows for sustainability to the actions that are being carried out, taking into account CSR indicators that ensure the monitoring of progress and objectives achieved, so that corrective measures or changes strategies can be identified and implemented in a timely manner. © 2021 IEEE.

Analysis of COVID-19 Mortality in Japan by Using Support Vector Machine

To look for factors of the COVID-19 spreading in the whole world currently, an empirical study has been tried by using a multi-regression analysis for mortality rates of 47 prefectures as an objective variable, and various indices as the explanatory variables. A support vector machine method was applied to deal with a nonlinear relationship between objective and explanatory variables, and a sensitivity analysis was applied to search the factors of the COVID-19 mortality. Welfare, urbanization, poverty rate, service industry, and sex ratio were obtained as dangerous factors which increase mortality, while single-person households, meals, and sleep were obtained as defensing factors which decrease mortality. Novel and useful knowledge for prevention measure of the COVID-19 was obtained: three factors of urbanization, service industry, and single-person household relating to the Three Cs contribute largest to the mortality, and two factors of welfare and poverty rate, reflecting the reality' of the poor people also contribute.

Path Analysis to Assess Socio-Economic and Mitigation Measure Determinants for Daily Coronavirus Infections.

(1) Background: With the rapid global spread of the coronavirus disease 2019 (COVID-19) and the relatively high daily cases recorded in a short time compared to other types of seasonal flu, the world remains under continuous threat unless we identify the key factors that contribute to these unexpected records. This identification is important for developing effective criteria and plans to reduce the spread of the COVID-19 pandemic and can guide national authorities to tighten or reduce mitigation measures, in addition to spreading awareness of the important factors that contribute to the propagation of the disease. (2) Methods: The data represents the daily infections (210 days) in four different countries (China, Italy, Iran, and Lebanon) taken approximately in the same duration, between January and March 2020. Path analysis was implemented on the data to detect the significant factors that affect the daily COVID-19 infections. (3) Results: The path coefficients show that quarantine commitment (ß = -0.823) and full lockdown measures (ß = -0.775) have the largest direct effect on COVID-19 daily infections. The results also show that more experience (ß = -0.35), density in society (ß = -0.288), medical resources (ß = 0.136), and economic resources (ß = 0.142) have indirect effects on daily COVID-19 infections. (4) Conclusions: The COVID-19 daily infections directly decrease with complete lockdown measures, quarantine commitment, wearing masks, and social distancing. COVID-19 daily cases are indirectly associated with population density, special events, previous experience, technology used, economic resources, and medical resources.

The short-term effects of COVID-19 outbreak on dietary diversity and food security status of Iranian households (A case study in Tehran province).

Currently, the COVID-19 outbreak is spreading fast in 185 countries and has engaged most people around the world. COVID-19 imposes severe and tragic consequences on people's health due to the high rate of spread and potentially fatal impacts. In this study, the association of socio-economic factors with food security and dietary diversity is assessed before and during the COVID-19 pandemic. Data from 299 respondents were collected by an online standard questionnaire. Household Dietary Diversity Score (HDDS) and Household Food Insecurity Access Scale (HFIAS) were calculated. A multinomial regression model was applied to determine factors associated with HDDS and HFIAS before and during COVID-19 outbreak. Food security of Iranian households improved during the initial COVID-19 pandemic period (P < 0.001). Households reduced consumption of some food groups during the COVID-19 pandemic compared to the pre-COVID-19 period. Key socio-economic factors associated with food insecurity during the COVID-19 pandemic included personal savings, household income, employment status of head of household, and nutrition knowledge of head of household. During the COVID-19 outbreak, household size, head of household's occupation, personal savings, and number of male children were significantly associated with dietary diversity. Distributing free food baskets to poor households, extending e-marketing, providing nutrition consultations, and organizing donations to support infected households may increase household dietary diversity and improve food security status during a pandemic such as COVID-19. Vulnerable populations in countries experiencing food insecurity, such as Iran, should be supported - not just by providing medical care and personal protective equipment, but also with flexible safety nets and food-based intervention programs to respond to population needs.