Data-Driven Approach to Raise the Marketing and Trade Strategy based on Halal Food Product Transactions of China Market

The adaptation of halal food to Chinese food culture was investigated in a meta-analysis of this China e-commerce study. Indoctrination is a sociocultural transformation in the Coronavirus pandemic in which a community of people or an in-dividual integrates into a different culture or country by adopting the host culture's linguistic skills, social features, nutritional pat-terns, and the current growth of e-commerce consumption. We ap-ply a data-driven marketing approach and modify the STP model in marketing to analyze the product differentiation parameters ac-cording to the research. Consuming halal food prepared following Islamic standards is a religious dietary obligation imposed on all Muslims, regardless of where they reside or travel. In Arabic, the term 'halal food' refers to food that is legal and allowed to ingest. Among many other dietary requirements, Islamic tenets ban the consumption of porcine food items, dead animal meat, animal blood, and any quantity of alcohol. Islam and halal eating prac-tices were introduced to China in the early seventh century by Cen-tral Asians and Muslim missionaries traveling through the peri-lous ancient desert. Throughout its history, China has been a non-Muslim country, but halal food practice has been successfully assimilated and even flourished as a key component of contemporary Chinese cuisine culture. This paper addresses the two most significant variables that have contributed to the successful cultural as-similation of halal food into the new Chinese food culture in the digital era through key Chinese e-commerce sites. © 2022 IEEE.

Social Determinants of COVID-19 Vaccination Rates: A Time-Constrained Multiple Mediation Analysis.

Objective To estimate the multiple direct/indirect effects of social, environmental, and economic factors on COVID-19 vaccination rates (series complete) in the 3109 continental counties in the United States (U.S.). Study design  The dependent variable was the COVID-19 vaccination rates in the U.S. (April 15, 2022). Independent variables were collected from reliable secondary data sources, including the Census and CDC. Independent variables measured at two different time frames were utilized to predict vaccination rates. The number of vaccination sites in a given county was calculated using the geographic information system (GIS) packages as of April 9, 2022. The Internet Archive (Way Back Machine) was used to look up data for historical dates. Methods  A chain of temporally-constrained least absolute shrinkage and selection operator (LASSO) regressions was used to identify direct and indirect effects on vaccination rates. The first regression identified direct predictors of vaccination rates. Next, the direct predictors were set as response variables in subsequent regressions and regressed on variables that occurred before them. These regressions identified additional indirect predictors of vaccination. Finally, both direct and indirect variables were included in a network model. Results  Fifteen variables directly predicted vaccination rates and explained 43% of the variation in vaccination rates in April 2022. In addition, 11 variables indirectly affected vaccination rates, and their influence on vaccination was mediated by direct factors. For example, children in poverty rate mediated the effect of (a) median household income, (b) children in single-parent homes, and (c) income inequality. For another example, median household income mediated the effect of (a) the percentage of residents under the age of 18, (b) the percentage of residents who are Asian, (c) home ownership, and (d) traffic volume in the prior year. Our findings describe not only the direct but also the indirect effect of variables. Conclusions  A diverse set of demographics, social determinants, public health status, and provider characteristics predicted vaccination rates. Vaccination rates change systematically and are affected by the demographic composition and social determinants of illness within the county. One of the merits of our study is that it shows how the direct predictors of vaccination rates could be mediators of the effects of other variables.

A Physics-Based Data-Driven Approach for Finite Time Estimation of Pandemic Growth

COVID-19 is a global health crisis that has had unprecedented, widespread impact on households across the United States and has been declared a global pandemic on March 11, 2020 by World Health Organization (WHO). According to Centers for Disease Control and Prevention (CDC), the spread of COVID-19 occurs through person-to-person transmission i.e. close contact with infected people through contaminated surfaces and respiratory fluids carrying infectious virus. This paper presents a data-driven physics-based approach to analyze and predict the rapid growth and spread dynamics of the pandemic. Temporal and spatial conservation laws are used to model the evolution of the COVID-19 pandemic. We integrate quadratic programming and neural networks to learn the parameters and estimate the pandemic growth. The proposed prediction model is validated through finite-time estimation of the pandemic growth using the total number of cases, deaths and recoveries in the United States recorded from March 12, 2020 until October 1, 2021. © 2022 Elsevier B.V.. All rights reserved.

epiDAMIK 5.0: The 5th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery

Similar to previous iterations, the epiDAMIK@KDD workshop is a forum to promote data driven approaches in epidemiology and public health research. Even after the devastating impact of COVID-19 pandemic, data driven approaches are not as widely studied in epidemiology, as they are in other spaces. We aim to promote and raise the profile of the emerging research area of data-driven and computational epidemiology, and create a venue for presenting state-of-the-art and in-progress results-in particular, results that would otherwise be difficult to present at a major data mining conference, including lessons learnt in the 'trenches'. The current COVID-19 pandemic has only showcased the urgency and importance of this area. Our target audience consists of data mining and machine learning researchers from both academia and industry who are interested in epidemiological and public-health applications of their work, and practitioners from the areas of mathematical epidemiology and public health. Homepage: https://epidamik.github.io/. © 2022 Owner/Author.

Occupant density impact on building maintenance: Data-driven approach for university buildings

Building maintenance needs in public buildings depend on occupant activities and presence. Research should understand how different types of occupant density patterns can be used to forecast the likelihood of specific kinds of maintenance requests. This research adopts a data-driven approach to evaluate experimental-based correlations between maintenance work orders number (relating to a set of Italian university buildings as a relevant case study) and occupant density, thanks to exceptional conditions due to COVID-19 pandemic, which significantly altered building use. Results offer a power-law-based correlation model, confirming that the reduction of occupant density in the COVID-19 lock-down phases impacted the number and perceived severity, but not the typologies, of maintenance work orders. The retrieved correlation model occupant could be directly used to define and prioritize maintenance strategies given occupant density. Future research could use the model to define outsourcing and contract definitions starting from historical data on maintenance actions.

A Dynamic Social Vulnerability Index to COVID-19 in Mexico

Several months have passed since the appearance of COVID-19, populations that were the most vulnerable at the beginning might not be anymore, and vice-versa. Government interventions, people behaviours and vaccination policies, change the social vulnerability. Our work proposes a complementary framework to the classic vulnerability indexes which aggregate structural variables into composite indexes. We define a Dynamic Vulnerability Index as an evolving relation between structural indicators and mortality ratio, we construct this index using a data-driven approach that updates the mortality ratio and uses Partial Least Squares to find a weighting of the structural variables at each municipality. Our index is able to distinguish at any given time between zones that are potentially vulnerable but do not exhibit a high exposure, and zones that are not as vulnerable in terms of their structural variables but present higher levels of exposure. The southwest part of the country, comprising the states of Chiapas, Guerrero and Oaxaca, exhibits low Dynamic Vulnerability for most of the study period despite being one of the poorest regions in the country. This happens because most of the region is relatively isolated and doesn’t have a great influx of people that could carry the virus. On the contrary, the Central Region where the capital (Mexico City) is located and has been the epicenter of the pandemic in Mexico, has remained with a high vulnerability for the whole period, even if it is not particularly poor. Our index represents a complement to the static view of vulnerability in the context of an evolving pandemic. While static vulnerability highlights regions that could experience a strong impact, the dynamic vulnerability highlights regions where there is a strong relationship between the fixed structural conditions and the evolving epidemic. This complementary picture allows decision makers to take more focused actions. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

From Deinstitutionalization to Community-Based Urban Development: Investigating Accessibility of Urban Systems in Calabria through Network Analytics

Community-based urban development is an inclusive approach for local service provision and management centered on the proactive partnerships between urban communities and local governments. Rooted in the deinstitutionalization of public services, the European Union and national policy effort is pushing towards the organization of community-based alternatives in response to the evolving needs of local communities. As the pandemic has shown, service accessibility has proven to be a key concern element that needs to be addressed to increase communities’ and cities’ resilience. In this direction, the paper aims to propose data-driven alternative approaches to assess urban systems’ accessibility and connectivity as an element of leveraging the resilience-oriented planning process and facilitating community-based development. The methodological approach focuses on the case of the Calabria region, where community-based alternatives for the provision of public services found difficulties to be operationalized through an integrated planning approach. The case study is explored by experimenting on the spatial connections of two purposefully selected clusters to assess the accessibility and connectivity of urban systems within the region through network analysis visualization tools: health and social-related services and transportation and logistics. The analytical approach outlines the accessibility level of urban systems in the region examined, proving its relevance in detecting social, economic, and environmental dynamics. This approach shows how using non-traditional data-driven perspectives can detect development dynamics—which affect local community’s needs—and their limitations in the organization of community-based development alternatives.

Modelling COVID-19 Scenarios for the States and Federal Territories of Malaysia.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 disease, which has become pandemic since December 2019. In the recent months, among five countries in the Southeast Asia, Malaysia has the highest per-capita daily new cases and daily new deaths. A mathematical modelling approach using a Singular Spectrum Analysis (SSA) technique was used to generate data-driven 30-days ahead forecasts for the number of daily cases in the states and federal territories in Malaysia at four consecutive time points between 27 July 2021 and 26 August 2021. Each forecast was produced using SSA prediction model of the current major trend at each time point. The objective is to understand the transition dynamics of COVID-19 in each state by analysing the direction of change of the major trends during the period of study. The states and federal territories in Malaysia were grouped in four categories based on the nature of the transition. Overall, it was found that the COVID-19 spread has progressed unevenly across states and federal territories. Major regions like Selangor, Kuala Lumpur, Putrajaya and Negeri Sembilan were in Group 3 (fast decrease in infectivity) and Labuan was in Group 4 (possible eradication of infectivity). Other states e.g. Pulau Pinang, Sabah, Sarawak, Kelantan and Johor were categorised in Group 1 (very high infectivity levels) with Perak, Kedah, Pahang, Terengganu and Melaka were classified in Group 2 (high infectivity levels). It is also cautioned that SSA provides a promising avenue for forecasting the transition dynamics of COVID-19; however, the reliability of this technique depends on the availability of good quality data.

The Role of Societal Aspects in the Formation of Official COVID-19 Reports: A Data-Driven Analysis.

This paper investigates the role of socioeconomic considerations in the formation of official COVID-19 reports. To this end, we employ a dataset that contains 1159 pre-processed indicators from the World Bank Group GovData360 and TCdata360 platforms and an additional 8 COVID-19 variables generated based on reports from 138 countries. During the analysis, a rank-correlation-based complex method is used to identify the time- and space-varying relations between pandemic variables and the main topics of World Bank Group platforms. The results not only draw attention to the importance of factors such as air traffic, tourism, and corruption in report formation but also support further discipline-specific research by mapping and monitoring a wide range of such relationships. To this end, a source code written in R language is attached that allows for the customization of the analysis and provides up-to-date results.

A joint dataset of official COVID-19 reports and the governance, trade and competitiveness indicators of World Bank group platforms.

The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries (European Centre for Disease Prevention and Control, 2020 [1] and Beltekian et al. [2]), as well as an additional 2203 governance, trade, and competitiveness indicators from the World Bank Group GovData360(World Bank Group, 2020 [3]) and TCdata360(World Bank Group, 2020 [4]) platforms. From these platforms, only annual indicators from 2015 and later were collected, and their missing values were replaced with previous annual values, in descending order by year, until 2015. During preprocessing, indicators (columns) were filtered out when the ratio of missing values exceeded 50%. Then, the same filtration was applied for the ratio of missing values above 25% in the case of countries (rows). Finally, duplicated variables were removed from the dataset. As a result of these steps, the missing value rate of the employed indicators was reduced to 4.25% on average. In addition to the database, the Kendall rank correlation matrix is provided to facilitate subsequent analysis. The dataset and the correlation matrix can be updated and customized with an R Notebook file, which is also available publicly in Mendeley Data (Kurbucz, 2020 [5]).