COVID-GAN+: Estimating Human Mobility Responses to COVID-19 through Spatio-temporal Generative Adversarial Networks with Enhanced Features

Estimating human mobility responses to the large-scale spreading of the COVID-19 pandemic is crucial, since its significance guides policymakers to give Non-pharmaceutical Interventions, such as closure or reopening of businesses. It is challenging to model due to complex social contexts and limited training data. Recently, we proposed a conditional generative adversarial network (COVID-GAN) to estimate human mobility response under a set of social and policy conditions integrated from multiple data sources. Although COVID-GAN achieves a good average estimation accuracy under real-world conditions, it produces higher errors in certain regions due to the presence of spatial heterogeneity and outliers. To address these issues, in this article, we extend our prior work by introducing a new spatio-temporal deep generative model, namely, COVID-GAN+. COVID-GAN+ deals with the spatial heterogeneity issue by introducing a new spatial feature layer that utilizes the local Moran statistic to model the spatial heterogeneity strength in the data. In addition, we redesign the training objective to learn the estimated mobility changes from historical average levels to mitigate the effects of spatial outliers. We perform comprehensive evaluations using urban mobility data derived from cell phone records and census data. Results show that COVID-GAN+ can better approximate real-world human mobility responses than prior methods, including COVID-GAN.

Estimating The Distribution of Livestock In Space And Time: A Time-Series Contribution To Livestock Density Mapping

More than one billion people rely on livestock for income, nutrition, and social cohesion, however livestock keeping can facilitate disease transmission and contribute to climate change. While data on the distribution of livestock thus have broad utility across a range of applications, efforts to map the distribution of livestock on a large scale are limited to the Gridded Livestock of the World (GLW) project. We present a complimentary effort to map the distribution of cattle and pigs in Malawi, Uganda, Democratic Republic of Congo (DRC), and South Sudan. In contrast to GLW, which uses dasymmetric modeling applied to census data to produce time-stratified estimates of livestock counts and spatial density, our work uses complex survey data and distinct modeling methods to generate a time-series of livestock distribution, defining livestock density as the ratio of animals to humans. In addition to favorable cross-validation results and general agreement with national density estimates derived from external data on national human and livestock populations, our results demonstrate extremely good agreement with GLW-3 estimates, supporting the validity of both efforts. Our results furthermore offer a high-resolution time series result and employ a definition of density which is particularly well-suited to the study of livestock-origin zoonoses.

Beginning farmer and rancher credit usage by socially disadvantaged status

PurposeThe purpose of this paper is to examine credit usage by beginning farmers and ranchers (BFR). BFR credit usage is stratified by location (state) and by socially disadvantaged farmer and rancher (SDFR, also known as historically underserved) status. SDFR groups are defined to include women; individuals with Hispanic, Latino or Spanish Origin; individuals who identify as American Indian or Alaskan Native, Black or African American, Asian, Native Hawaiian or other Pacific Islander. Non-SDFR is defined as individuals who identify as non-Hispanic, White men.Design/methodology/approachThe US Department of Agriculture’s Census of Agriculture, Agricultural Resource Management Survey (ARMS) is linked with Farm Service Agency (FSA) loan program administrative data to estimate shares of BFR operations using FSA credit. Census data provided information on population changes in total farms and BFR operations from 2012 to 2017 which are compared by SDFR status.FindingsResults reveal differences among BFR operations active in agricultural credit markets by SDFR status and state. BFR were more common among SDFR groups as well as in regions where farms tend to be smaller, such as the Northeast, compared to a more highly agricultural upper Midwest. Among BFR, non-SDFR are more likely to utilize credit than SDFR, however, FSA appeared to be crucial in enabling BFR and especially beginning SDFR groups to access loans.Originality/valueThe results are timely and of keen interest to researchers, industry and policymakers and are expected to assist in developing and adjusting policies to effectively promote and improve BFR success in general and for beginning SDFR groups.

Socioeconomic Status and Group Belonging: Evidence from Early-Nineteenth-Century Colonial West Africa

This study provides a novel analysis of occupational stratification in Sierra Leone from a historical perspective. By employing census data for early-nineteenth-century colonial Sierra Leone, the present study offers a valuable snapshot of a colony characterized by a heterogenous population of indigenous and migratory origin. The study shows that an association between colonial group categorization and socioeconomic status existed despite the colony being of very recent foundation implying a hierarchical structure of the society. Although Europeans and “mulattoes” occupied most high-status positions, as common in the colonies, indigenous immigrants were also represented in high socioeconomic strata thanks to the opportunities stemming from long- and short-distance trading. However, later arrivals, especially liberated slaves, belonged within the lowest socioeconomic strata of the society and worked as farmers or unskilled labor, suggesting that the time component may also have influence socioeconomic opportunities.

Analysis and Design of Inpatient Daily Cense Information System in Bhayangkara Bengkulu Hospital

Background: Progressing technology in the world need to fast and accurate information in the hospital agencies as the basis for appropriate making decision. The inpatient daily census reporting of system Bhayangkara Hospital Bengkulu don't have utilized the Inpatient Daily census system electronically and still uses a manual system, so that the processing of report data is less than optimal. There are still a lot of inputting errors, inaccurate data, and inefficient time and energy. This study to aim design system information inpatient daily census reporting application at the Bhayangkara hospital to existing problems solving.Methods: The method used in designing and making this application is by utilizing software development methods, namely the waterfall method which includes identification, analysis, design or design, implementation and maintenance of the system.Results: The results this study is creation of an application to facilitys the processing of data into an inpatient daily census report that is needed and to overcome the problems that arise because of the report processing system manually. Design and Creation of Inpatient Daily Census Applications with Visual Basic 6.0 Programming at Bhayangkara Bengkulu Hospital have been made with the results of an analysis of existing systems and according to the method used, and the design of the forms that have been made in accordance with the manual form or home party needs sick and can simplify filling out forms and processing the data.Conclusions: At Bhayangkara Bengkulu Hospital still uses a manual inpatient daily census system, and not on time for reporting daily cencus patient data. The data structure contained in the ledger consists of patient identity, patient diagnosis, and others. There are three processes in the stage of analyzing the needs of the inpatient daily census system, namely the data input process, data processing and data output processes. ledger, patient data consisting of patient identity, doctor's name, patient diagnosis, treatment room, and treatment class. In designing the daily inpatient census system at Bhayangkara Bengkulu Hospit consists of patient data forms, incoming patients, outgoing patients, and patients moving. The implementation of the daily inpatient census system at the Bhayangkara Bengkulu Hospital has carried out socialization and discussions about the user interface design to officers or users of the electronic daily census system. And the maintenance of the daily inpatient census system is carried out in several stages (1) corrective, by correcting design and errors in the program, (2) adaptive, by modifying the system according to user needs, (3) perfective, namely processing census data computerized.

Calculating earthquake damage building by building: the case of the city of Cologne, Germany

The creation of building exposure models for seismic risk assessment is frequently challenging due to the lack of availability of detailed information on building structures. Different strategies have been developed in recent years to overcome this, including the use of census data, remote sensing imagery and volunteered graphic information (VGI). This paper presents the development of a building-by-building exposure model based exclusively on openly available datasets, including both VGI and census statistics, which are defined at different levels of spatial resolution and for different moments in time. The initial model stemming purely from building-level data is enriched with statistics aggregated at the neighbourhood and city level by means of a Monte Carlo simulation that enables the generation of full realisations of damage estimates when using the exposure model in the context of an earthquake scenario calculation. Though applicable to any other region of interest where analogous datasets are available, the workflow and approach followed are explained by focusing on the case of the German city of Cologne, for which a scenario earthquake is defined and the potential damage is calculated. The resulting exposure model and damage estimates are presented, and it is shown that the latter are broadly consistent with damage data from the 1978 Albstadt earthquake, notwithstanding the differences in the scenario. Through this real-world application we demonstrate the potential of VGI and open data to be used for exposure modelling for natural risk assessment, when combined with suitable knowledge on building fragility and accounting for the inherent uncertainties.

Suicidal ideation in patients with mental illness and concurrent substance use: analyses of national census data in Norway

Background Suicidal ideation may signal potential risk for future suicidal behaviors and death. We examined the prevalence of recent suicidal ideation in patients with mental illness and concurrent substance use and explored the clinical and sociodemographic factors associated with suicidal ideation in this patient subgroup, which represents a particular risk group for adverse psychiatric outcomes. Methods We used national cross-sectional census data in Norway collected from 25,525 patients in specialized mental health services. The analytic sample comprised 3,842 patients with concurrent substance use, defined as having a co-morbid substance use disorder or who reported recent regular alcohol use/occasional illicit drug use. Data included suicidal ideation measured in relation to the current treatment episode, sociodemographic characteristics and ICD-10 diagnoses. Bivariate and multivariate analyses were used to examine differential characteristics between patients with and without suicidal ideation. Results The prevalence of suicidal ideation was 25.8%. The suicidal ideation rates were particularly high for those with personality disorders, posttraumatic stress disorder, and depression, and for alcohol and sedatives compared with other substances. Patients with suicidal ideation were characterized by being younger, having single marital status, and having poorly perceived social relationships with family and friends. Conclusion Suicidal ideation in patients with mental illness and concurrent substance use was associated with a number of distinct characteristics. These results might help contribute to an increased focus on a subgroup of individuals at particular risk for suicidality and support suicide prevention efforts in specialized mental health services.

Evaluation of Knowledge and Practice of Yazd General Practitioners about Shade Selection of Fixed Dental Prosthesis in 2019

Introduction: The color matching of dental restorations is effective in the beauty of the smile and a dentist should be familiar with the subject of color and light to make a beautiful and natural prosthesis. The aim of this study was to investigate the knowledge and practice of general dentists in Yazd City about the choice of fixed prosthesis color in 2019. Methods: This was a descriptive-analytical cross-sectional study in which all the general dentists in Yazd City were included in the study by census. Data were collected in the field through a valid and reliable questionnaire containing 11 knowledge questions and 17 performance questions. Data were analyzed by SPSS software version 24 using t-test and ANOVA (α <0.05). Results: 150 dentists with a mean age of 39.14 ± 7.96 were studied in this study. The mean score of knowledge was 26.5 ± 3.32 out of 33 points. Mean knowledge score was not statistically significant with age, sex and dental work experience (P >0.05), but was associated with participation in retraining courses (P = 0.046). 79% of participants used brossage and prophylaxis before choosing a color, 31% used sunlight to choose a color, and 37% used the color of adjacent teeth as a color choice. Conclusion: The knowledge of general dentists in Yazd City in choosing the color of fixed prostheses was at a good level and the performance was relatively good. Participation in retraining courses had led to an increase in dentists' awareness in this field.

Using satellites and artificial intelligence to measure health and material-living standards in India

The application of deep learning methods to survey human development in remote areas with satellite imagery at high temporal frequency can significantly enhance our understanding of spatial and temporal patterns in human development. Current applications have focused their efforts in predicting a narrow set of asset-based measurements of human well-being within a limited group of African countries. Here, we leverage georeferenced village-level census data from across 30 percent of the landmass of India to train a deep-neural network that predicts 16 variables representing material conditions from annual composites of Landsat 7 imagery. The census-based model is used as a feature extractor to train another network that predicts an even larger set of developmental variables (over 90 variables) included in two rounds of the National Family Health Survey (NFHS) survey. The census-based model outperforms the current standard in the literature, night-time-luminosity-based models, as a feature extractor for several of these large set of variables. To extend the temporal scope of the models, we suggest a distribution-transformation procedure to estimate outcomes over time and space in India. Our procedure achieves levels of accuracy in the R-square of 0.92 to 0.60 for 21 development outcomes, 0.59 to 0.30 for 25 outcomes, and 0.29 to 0.00 for 28 outcomes, and 19 outcomes had negative R-square. Overall, the results show that combining satellite data with Indian Census data unlocks rich information for training deep learning models that track human development at an unprecedented geographical and temporal definition.

The Long-Run Effects of Recessions on Education and Income

This paper examines the long-run effects of the 1980–1982 recession on education and income. Using confidential census data, I estimate difference-in-difference regressions that exploit variation across counties in recession severity and across cohorts in age at the time of the recession. For individuals age 0–10 in 1979, a 10 percent decrease in earnings per capita in their county of birth reduces four-year college degree attainment by 15 percent and earnings in adulthood by 5 percent. Simple calculations suggest that in aggregate, the 1980–1982 recession led to 1.3–2.8 million fewer college graduates and $66–$139 billion less earned income per year. (JEL E32, I21, I26, J24, J31)