Relevant bibliographies by topics / Pandemic Data

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Pandemic Data'

Author: Grafiati
Published: 5 June 2025
Last updated: 24 June 2025

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Journal articles on the topic "Pandemic Data"

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Fein, Rebecca, and Leila R. Kalankesh. "Data Fuels Detection: How to Prevent Epidemics Using Data." Frontiers in Health Informatics 10, no. 1 (2021): 59. http://dx.doi.org/10.30699/fhi.v10i1.269.

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Data for prevention and tracking of disease should begin prior to the outbreak. The bottleneck for early detecting outbreaks is data. The data are collected from different points of care and aggregated, then analyzed centrally to warn us about what is happening. However, this current pandemic has not utilized data for prevention and tracking in a meaningful way. We believe the prevention problem is the data problem and it should be addressed to prevent the future pandemics in an effective way.
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Chen, Kai, Bing Yang, Miao Hao, Hong Yang, Meiyuan Qin, and Chengmei Zhang. "Epidemiological Investigation Model of Novel Coronavirus Pneumonia Based on Data Mining Technology." E3S Web of Conferences 292 (2021): 03026. http://dx.doi.org/10.1051/e3sconf/202129203026.

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With its extraordinary rapidity of transmission, the COVID-19 pandemic demonstrates the vulnerability of a globalized and networked world. The first months of the pandemic were marked by a significant strain on health-care systems. Since the prospect of pandemics has elevated public health concerns, it is critical to revisit this issue. The primary goal of this essay is to employ data mining technologies and methodologies to do investigative analysis on publicly available information. In this article we shared ways and techniques to handle and control this pandemic in the best possible way using data mining techniques and models. Researchers and scientists will be able to use the results of our poll to come up with new approaches to combat the pandemic.
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Phiri, Esabel, Ntsieni Fitzgerald Ramasimu, and Godfrey Maake. "Survival strategies and failure of SMMEs during the COVID-19 pandemic in Africa: secondary data." International Journal of Business Ecosystem & Strategy (2687-2293) 7, no. 2 (2025): 01–13. https://doi.org/10.36096/ijbes.v7i2.784.

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Pandemics result in significant devastation, leaving considerable destruction in their wake. Numerous researchers contend that the coronavirus disease 19 (COVID-19) pandemic exceeded prior pandemics in various aspects. The COVID-19 pandemic significantly impacted global economies, businesses, and societies, resulting in substantial damage and lasting effects. The aviation industry experienced a significant near-collapse for the first time since the advent of aeroplanes, attributed to the COVID-19 pandemic. This research was initiated to synthesise existing studies on the strategies utilised by Small, Medium, and Micro Enterprises (SMMEs) to sustain operations during disasters such as COVID-19 in South Africa. This systematic literature review analyses the survival strategies and failures of SMMEs in Africa in response to the economic disruptions induced by the COVID-19 pandemic. Sixteen accredited journals were incorporated into the data analysis. The thematic analysis identified several themes from the original data: Networking and Accessing Financial Support, Response to Crisis Conditions, Digital Technology, Disruption of Supply Chains, and Inadequate and Insufficient Government Support. This study synthesises existing research to identify factors that supported SMMEs in sustaining operations and recovering from challenges induced by the pandemic. This study offers significant insights into the survival strategies and challenges faced by SMMEs during the COVID-19 pandemic in Africa. African leaders should supervise the execution of digital transformation initiatives, focused financial assistance, and strategies for long-term business continuity.
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Lim, Hyungjin, and Jeong Lim Kim. "The Impact of COVID-19 on Crime - Focused on 112 Report Data -." Korean Security Science Review 63, no. 2 (2020): 233–54. http://dx.doi.org/10.36623/kssr.2020.pandemic.233.

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Silverman, David. "Collecting qualitative data during a pandemic." Communication and Medicine 17, no. 2 (2021): 199–202. http://dx.doi.org/10.1558/cam.20978.

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I think it is fair to say that the thoughtful comments on my paper were less ‘rejoinders’ than additions and elaborations. In that spirit, what follows is a summary of the arguments made and the critical questions that remain, also drawing on some personal communications I have received.
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Canhoto, Ana Isabel, and Aaron R. Brough. "The Pandemic-Induced Personal Data Explosion." Social Marketing Quarterly 28, no. 1 (2022): 78–86. http://dx.doi.org/10.1177/15245004221076858.

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Background Government and private responses to the COVID-19 pandemic resulted in the generation and dissemination of personal data not previously available in the public sphere. Focus of the Article This “Notes from the Field” paper reflects on the implications of this surge of new data for the study and practice of social marketing. The paper examines how this phenomenon impacts on the following aspects of social marketing: (1) Setting of explicit social goals; (2) citizen orientation and focus; (3) value proposition delivery via the social marketing intervention mix; (4) theory-, insight-, data-, and evidence-informed audience segmentation; (5) competition/barrier and asset analysis; and (6) critical thinking, reflexivity, and being ethical. Research Question How are the government and private responses to the pandemic shaping the generation and use of personal data, and what are the implications of this eruption of data for the social marketing scholarly community? Approach The paper highlights how the pandemic resulted in significant changes in behavior of government and citizens alike, and how these changes, in turn, spurred the generation and dissemination of new personal data. Subsequently, we draw on the Core Social Marketing Concepts framework to explore how the aforementioned data explosion impacts on the six dimensions of this central framework. Importance to the Social Marketing Field The COVID-19 pandemic is more than a temporary public health event. Therefore, it is important to consider the lasting consequences that may stem from the pandemic-induced personal data explosion, for both consumers and social marketing scholars and practitioners. Methods This paper comments on a topical matter, and discusses its implications for the social marketing community. Results We find that the data explosion creates conflicting social marketing goals, and that inequalities in access to digital technology are increasingly impacting what voices are heard, and which concerns are prioritized. Moreover, new innovations may be enabled or needed, leading to the improvement of firms' ability to create value for individual citizens; the creation of new datasets—particularly among demographics that previously had a limited digital footprint—enhances the ability to segment markets and target social marketing activities. Furthermore, the pandemic-induced data explosion may lead to the identification of additional barriers to positive social behaviors that have emerged, diminished, or even disappeared during the pandemic; but researchers need to critically examine the consequences of the government and private behaviors at the macro, meso, and micro levels. Recommendations for Research or Practice We propose a research agenda for the social marketing community, consisting of 21 research questions. Limitations Our analysis focuses on the behavior of government and citizens in North America and Western Europe.
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Pagel, Christina, and Christian A. Yates. "Tackling the pandemic with (biased) data." Science 374, no. 6566 (2021): 403–4. http://dx.doi.org/10.1126/science.abi6602.

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Philippidis, Alex. "Synthetic Data for a Real Pandemic." GEN Edge 3, no. 1 (2021): 42–47. http://dx.doi.org/10.1089/genedge.3.1.007.

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Knowles, Scott. "Pandemic Data Collection in Authoritarian Regimes." American Scientist 109, no. 6 (2021): 331. http://dx.doi.org/10.1511/2021.109.6.331.

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Lewis-Hughes, Peter, and Peter Brooks. "Pandemic planning: data, information and evidence." Australian Health Review 47, no. 1 (2023): 67–71. http://dx.doi.org/10.1071/ah22236.

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In this article, we examine the role and effectiveness of the Centers for Disease Control and Prevention in the USA and Europe and consider possible lessons for future pandemic planning in Australia. We also ‘map’ the interjurisdictional communication pathways that have been secured since the election of the new Commonwealth government. We suggest a number of steps that could be taken to upgrade the collection, distribution, accessibility and timelines of key information required to improve pandemic management and national health outcomes. While it may be hard to contemplate a move to a fully integrated National capacity when we are only just emerging from the pandemic, we do have a unique opportunity to at least start the process of review. We should use the lessons we have learned to transform our systems, rather than ‘tinker’ with them and ensure we are better prepared for next time.
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Dissertations / Theses on the topic "Pandemic Data"

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Wang, Xinlei. "Electricity-Consumption Data Reveals the Economic Impact and Industry Recovery during the Pandemic." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29233.

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Coping with the outbreak of Coronavirus disease 2019 (COVID-19), many countries have implemented public-health measures and movement restrictions to prevent the spread of the virus. However, the strict mobility control also brought about production stagnation and market disruption, resulting in a severe worldwide economic crisis. Quantifying the economic stagnation and predicting post-pandemic recovery are imperative issues. Besides, it is significant to examine how the impact of COVID-19 on economic activities varied with industries. As a reflection of enterprises' production output, high-frequency electricity-consumption data is an intuitive and effective tool for evaluating the economic impact of COVID-19 on different industries. In the thesis, we quantify and compare economic impacts on the electricity consumption of different industries in eastern China. In order to address this problem, we conduct causal analysis using a difference-in-difference (DID) estimation model to analyze the effects of multi-phase public-health measures. Our model employs the electricity-consumption data ranging from 2019 to 2020 of 96 counties in the Eastern China region, which covers three main economic sectors and their 53 sub-sectors. The results indicate that electricity demand of all industries (other than the information transfer industry) rebounded after the initial shock, and is back to pre-pandemic trends after easing the control measures at the end of May 2020. Emergency response, the combination of all countermeasures to COVID-19 in a certain period, affected all industries, and the higher level of emergency response with stricter movement control resulted in a greater decrease in electricity consumption and production. The pandemic outbreak has a negative-lag effect on industries, and there is greater resilience in industries that are less dependent on human mobility for economic production and activities.
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Long, Zijian. "Towards a Tweet Analysis System to Study Human Needs During COVID-19 Pandemic." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41210.

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Governments and municipalities need to understand their citizens’ psychological needs in critical times and dangerous situations. COVID-19 brings lots of challenges to deal with. We propose NeedFull, an interactive and scalable tweet analysis platform, to help governments and municipalities to understand residents’ real psychological needs during those periods. The platform mainly consists of four parts: data collection module, data storage module, data analysis module and data visualization module. The whole process of how data flows in the system is illustrated as follows: Our crawlers in the data collection module gather raw data from a popular social network website Twitter. Then the data is fed into our human need detection model in the data analysis module before stored into the database. When a user enters a query through the user interface, they will get all the related items in the database by the index system of the data storage module and a comprehensive human needs analysis of these items is then presented and depicted in the data visualization module. We employed the proposed platform to investigate the reaction of people in four big regions including New York, Ottawa, Toronto and Montreal to the ongoing worldwide COVID-19 pandemic by collecting tweets posted during this period. The results show that the most pronounced human need in these tweets is relatedness with 51.32%, followed by autonomy with 22.56% and competence with 18.82%. And the percentages of tweets expressing frustration are larger than those of tweets expressing satisfaction for each psychological need in general.
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Vicente, Tomás Ferreira Martins Pereira. "The impact of Covid-19 on transaction data in Portugal." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/20705.

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Mestrado em Finanças<br>Esta dissertação analisa o impacto da pandemia COVID-19 em dados regionais de transações em Portugal. Mais especificamente, o impacto regional é ponderado, assim como o impacto em diversas características dos municípios. Os dados usados são provenientes do INE, PORDATA e DGS e consideramos características económicas, demográficas e sociais em 278 concelhos de Portugal continental de 2015 a 2020. O método OLS de regressão é usado para efetuar a analise estatística. Estudos anteriormente realizados sugerem um aumento das transações antes da pandemia, como forma de açambarcar, mas também sugerem que o consumo iria diminuir durante o período subsequente à declaração do estado de emergência. Neste estudo analisamos três modelos distintos para compreender os três canais de transações: Levantamentos em ATM, pagamentos usando cartão português e pagamentos usando cartão estrangeiro. Testamos estes três modelos de forma a compreender os efeitos regionais causados pelo COVID-19. Observamos que as regiões com maior número de pacientes infetados com COVID-19 têm um impacto negativo em todos os canais de transação e que os meses de verão fazem aumentar valor de transações dos três canais considerados. Controlamos também diversas outras características regionais como a demográficas, económicas e sociais.<br>This dissertation aims to analyze the impact of COVID-19 on regional transaction data in Portugal. More specifically, the regional impact is assessed, as well as the impact on several characteristics of these counties. The data used is from INE, PORDATA and DGS and we consider economic, demographic and social characteristics in 278 counties in mainland Portugal from 2015 to 2020. An OLS regression method is used to perform the analytic analysis. Previous studies suggest an increase in transactions prior to the pandemic, as a stockpiling behavior, while also suggesting that the overall consumption drops in the months following the emergency state. In this study we analyze three different models to comprehend the three different transaction channels: Automated Teller Machine withdrawals, payments using Portuguese card and payments using foreign card. We use data from the counties in Portugal mainland in order to understand the regional effects caused by COVID-19. We found that regions with more COVID-19 infected people have a negative impact on all transactions and that summertime increases all three transaction channels in consideration. We also control for several other characteristics of each region like demographic, economic and social.<br>info:eu-repo/semantics/publishedVersion
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Cinelli, Ester. "Syndemic : A design prototype of a dashboard to understand pandemics beyond epidemiology." Thesis, Malmö universitet, Institutionen för konst, kultur och kommunikation (K3), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43624.

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This study wants to investigate how Interaction Design techniques can contribute to giving meaning to data visualization in a syndemic dashboard and to gain understanding from it. I am going to present to you a Syndemic Dashboard that has the goal of helping researchers to find trends, patterns and make predictions of the spread of Covid-19 in the Swedish context, collaborating with K3, IUR, DVMT, and the University of Oxford. In order to do this, I will first give an overview of what a dashboard is, dashboarding practices and interaction techniques, cognitive aspects involved to generate meaning, and relevant theories to gain understanding from Big Data. Consequently, I will explain the process and the methodologies applied to achieve the final result. The thesis ends with a discussion about the final result and proposes future investigations.
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Davis, Carisa Renee. "Pandemic Vibrio parahaemolyticus: Defining Strains Using Molecular Typing and a Growth Advantage at Lower Temperatures." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002531.

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Mansiaux, Yohann. "Analyse d'un grand jeu de données en épidémiologie : problématiques et perspectives méthodologiques." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066272/document.

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L'augmentation de la taille des jeux de données est une problématique croissante en épidémiologie. La cohorte CoPanFlu-France (1450 sujets), proposant une étude du risque d'infection par la grippe H1N1pdm comme une combinaison de facteurs très divers en est un exemple. Les méthodes statistiques usuelles (e.g. les régressions) pour explorer des associations sont limitées dans ce contexte. Nous comparons l'apport de méthodes exploratoires data-driven à celui de méthodes hypothesis-driven.Une première approche data-driven a été utilisée, évaluant la capacité à détecter des facteurs de l'infection de deux méthodes de data mining, les forêts aléatoires et les arbres de régression boostés, de la méthodologie " régressions univariées/régression multivariée" et de la régression logistique LASSO, effectuant une sélection des variables importantes. Une approche par simulation a permis d'évaluer les taux de vrais et de faux positifs de ces méthodes. Nous avons ensuite réalisé une étude causale hypothesis-driven du risque d'infection, avec un modèle d'équations structurelles (SEM) à variables latentes, pour étudier des facteurs très divers, leur impact relatif sur l'infection ainsi que leurs relations éventuelles. Cette thèse montre la nécessité de considérer de nouvelles approches statistiques pour l'analyse des grands jeux de données en épidémiologie. Le data mining et le LASSO sont des alternatives crédibles aux outils conventionnels pour la recherche d'associations. Les SEM permettent l'intégration de variables décrivant différentes dimensions et la modélisation explicite de leurs relations, et sont dès lors d'un intérêt majeur dans une étude multidisciplinaire comme CoPanFlu<br>The increasing size of datasets is a growing issue in epidemiology. The CoPanFlu-France cohort(1450 subjects), intended to study H1N1 pandemic influenza infection risk as a combination of biolo-gical, environmental, socio-demographic and behavioral factors, and in which hundreds of covariatesare collected for each patient, is a good example. The statistical methods usually employed to exploreassociations have many limits in this context. We compare the contribution of data-driven exploratorymethods, assuming the absence of a priori hypotheses, to hypothesis-driven methods, requiring thedevelopment of preliminary hypotheses.Firstly a data-driven study is presented, assessing the ability to detect influenza infection determi-nants of two data mining methods, the random forests (RF) and the boosted regression trees (BRT), ofthe conventional logistic regression framework (Univariate Followed by Multivariate Logistic Regres-sion - UFMLR) and of the Least Absolute Shrinkage and Selection Operator (LASSO), with penaltyin multivariate logistic regression to achieve a sparse selection of covariates. A simulation approachwas used to estimate the True (TPR) and False (FPR) Positive Rates associated with these methods.Between three and twenty-four determinants of infection were identified, the pre-epidemic antibodytiter being the unique covariate selected with all methods. The mean TPR were the highest for RF(85%) and BRT (80%), followed by the LASSO (up to 78%), while the UFMLR methodology wasinefficient (below 50%). A slight increase of alpha risk (mean FPR up to 9%) was observed for logisticregression-based models, LASSO included, while the mean FPR was 4% for the data-mining methods.Secondly, we propose a hypothesis-driven causal analysis of the infection risk, with a structural-equation model (SEM). We exploited the SEM specificity of modeling latent variables to study verydiverse factors, their relative impact on the infection, as well as their eventual relationships. Only thelatent variables describing host susceptibility (modeled by the pre-epidemic antibody titer) and com-pliance with preventive behaviors were directly associated with infection. The behavioral factors des-cribing risk perception and preventive measures perception positively influenced compliance with pre-ventive behaviors. The intensity (number and duration) of social contacts was not associated with theinfection.This thesis shows the necessity of considering novel statistical approaches for the analysis of largedatasets in epidemiology. Data mining and LASSO are credible alternatives to the tools generally usedto explore associations with a high number of variables. SEM allows the integration of variables des-cribing diverse dimensions and the explicit modeling of their relationships ; these models are thereforeof major interest in a multidisciplinary study as CoPanFlu
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Widing, Härje. "Business analytics tools for data collection and analysis of COVID-19." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176514.

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The pandemic that struck the entire world 2020 caused by the SARS-CoV-2 (COVID-19) virus, will have an enormous interest for statistical and economical analytics for a long time. While the pandemic of 2020 is not the first that struck the entire world, it is the first pandemic in history where the data were gathered to this extent. Most countries have collected and shared its numbers of cases, tests and deaths related to the COVID-19 virus using different storage methods and different data types. Gaining quality data from the COVID-19 pandemic is a problem most countries had during the pandemic, since it is constantly changing not only for the current situation but also because past values have been altered when additional information has surfaced. The importance of having the latest data available for government officials to make an informed decision, leads to the usage of Business Intelligence tools and techniques for data gathering and aggregation being one way of solving the problem. One of the mostly used software to perform Business Intelligence is the Microsoft develop Power BI, designed to be a powerful visualizing and analysing tool, that could gather all data related to the COVID-19 pandemic into one application. The pandemic caused not only millions of deaths, but it also caused one of the largest drops on the stock market since the Great Recession of 2007. To determine if the deaths or other reasons directly caused the drop, the study modelled the volatility from index funds using Generalized Autoregressive Conditional Heteroscedasticity. One question often asked when talking of the COVID-19 virus, is how deadly the virus is. Analysing the effect the pandemic had on the mortality rate is one way of determining how the pandemic not only affected the mortality rate but also how deadly the virus is. The analysis of the mortality rate was preformed using Seasonal Artificial Neural Network. Forecasting deaths from the pandemic using the Seasonal Artificial Neural Network on the COVID-19 daily deaths data.
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Alencar, Medeiros Gabriel Henrique. "ΡreDiViD Τοwards the Ρredictiοn οf the Disseminatiοn οf Viral Disease cοntagiοn in a pandemic setting". Electronic Thesis or Diss., Normandie, 2025. http://www.theses.fr/2025NORMR005.

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Les systèmes de surveillance basés sur les événements (EBS) sont essentiels pour détecter et suivre les phénomènes de santé émergents tels que les épidémies et crises sanitaires. Cependant, ils souffrent de limitations, notamment une forte dépendance à l’expertise humaine, des difficultés à traiter des données textuelles hétérogènes et une prise en compte insuffisante des dynamiques spatio-temporelles. Pour pallier ces limites, nous proposons une approche hybride combinant des méthodologies guidées par les connaissances et les données, ancrée dans l’ontologie des phénomènes de propagation (PropaPhen) et le cadre Description-Detection-Prediction Framework (DDPF), afin d’améliorer la description, la détection et la prédiction des phénomènes de propagation. PropaPhen est une ontologie FAIR conçue pour modéliser la propagation spatio-temporelle des phénomènes et a été spécialisée pour le biomédical grâce à l’intégration de UMLS et World-KG, menant à la création du graphe BioPropaPhenKG. Le cadre DDPF repose sur trois modules : la description, générant des ontologies spécifiques ; la détection, appliquant des techniques d'extraction de relations sur des textes hétérogènes ; et la prédiction, utilisant des méthodes avancées de clustering. Expérimenté sur des données du COVID-19 et de la variole du singe et validé avec les données de l’OMS, DDPF a démontré son efficacité dans la détection et la prédiction de clusters spatio-temporels. Son architecture modulaire assure son évolutivité et son adaptabilité à divers domaines, ouvrant des perspectives en santé publique, environnement et phénomènes sociaux<br>Event-Based Surveillance (EBS) systems are essential for detecting and tracking emerging health phenomena such as epidemics and public health crises. However, they face limitations, including strong dependence on human expertise, challenges processing heterogeneous textual data, and insufficient consideration of spatiotemporal dynamics. To overcome these issues, we propose a hybrid approach combining knowledge-driven and data-driven methodologies, anchored in the Propagation Phenomena Ontology (PropaPhen) and the Description-Detection-Prediction Framework (DDPF), to enhance the description, detection, and prediction of propagation phenomena. PropaPhen is a FAIR ontology designed to model the spatiotemporal spread of phenomena. It has been specialized in the biomedical domain through the integration of UMLS and World-KG, leading to the creation of the BioPropaPhenKG knowledge graph. The DDPF framework consists of three modules: description, which generates domain-specific ontologies; detection, which applies relation extraction techniques to heterogeneous textual sources; and prediction, which uses advanced clustering methods. Tested on COVID-19 and Monkeypox datasets and validated against WHO data, DDPF demonstrated its effectiveness in detecting and predicting spatiotemporal clusters. Its modular architecture ensures scalability and adaptability to various domains, opening perspectives in public health, environmental monitoring, and social phenomena
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Bäckström, Pernilla. "Covid-19 pandemins konsekvenser av mäns våld mot kvinnor i nära relationer : data från 9 länder." Thesis, Högskolan i Skövde, Institutionen för hälsovetenskaper, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19812.

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Introduktion: Mäns våld mot kvinnor är ett globalt folkhälsoproblem och ett brott mot mänskliga rättigheter. Under 2020-2021, påverkas hela världen av covid-19-pandemin, med restriktioner som hemkarantän och arbeta hemifrån har detta även resulterat i en social isolering, minskat socialt stödsystem samt ökat våld mot kvinnor. Vilket i sin tur innebär att situationen för våldsutsatta kvinnor riskerar att förvärras. Av de kvinnor som utsätts för våldsbrott, inträffar tre av fyra incidenter i kvinnans egen bostad. Detta innebär att för en kvinna är det hennes egna hem som i statistiken är den farligaste platsen för henne att befinna sig. Syfte: Studiens syfte är att belysa covid-19-pandemins konsekvenser av mäns våld mot kvinnor i nära relationer. Metod: En systematisk litteraturstudie med en tematisk analys baserad på tio vetenskapliga originalartiklar. Resultat: Samtliga artiklar rapporterade psykiskt våld som den formen av våldshandling som både ökat och nyttjades mest av män i våld mot kvinnor, men mycket tyder på att mörkertalet för mäns våld mot kvinnor i nära relationer i samband med covid-19 är globalt mycket större än vad som framkommit i dessa studier. Slutsats: Den aktuella studiens resultat fann ett begränsat stöd för sambandet mellan hypoteser i förhållandet mellan olika samhällsåtgärder under covid-19-pandemin och vissa socioekonomiska faktorer, på mäns våld mot kvinnor. När de socioekonomiska faktorerna påverkades av en pandemi samtidigt som den ekonomiska stressen uppkom, ökade mäns våld mot kvinnor. Psykiskt våld var den formen som rapporterades både ökat och användes mest av män i utövandet av våld mot kvinnor under covid-19-pandemin.<br>Introduction: Men's violence against women is a global public health problem and a violation of human rights. In 2020-2021, the entire world is affected by the covid-19-pandemic. Restrictions such as home quarantine and working from home have resulted in social isolation, reduced social support, and increased violence against women. This indicates that the situation for abused women is in danger of deteriorating. Of women who are victims of violence, three of four incidents occur in the woman's own home. This means that for a woman, her own home is the most dangerous place for her to be. Aim: This analysis aims to clarify the covid-19-pandemic's consequences of men's violence against women in intimate relationships. Methods: A systematic review with a thematic analysis based on ten scientific original articles. Results: All articles reported psychological violence as the form of violence that increased and was used the most by men in violence against women. Data indicate that the magnitude of men's violence against women in connection with covid-19 is globally large. Conclusion: The results of the current study found limited support for the hypotheses in the relationship between different society restrictions in connections with the covid-19-pandemic and socio-economic factors on men's violence against women. When the socio-economic factors were affected by the pandemic and at the same time experienced economic stress, men's violence against women increased. Psychological violence was the form of violence that was, reported to be used the most by men in their violence against women during the covid-19-pandemic
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Gustafsson, Johan, and Petter Wallgren. "Utilizing modern technology topromote tourism and reducephysical contact." Thesis, KTH, Hälsoinformatik och logistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296412.

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Tourism is an important factor for economic growth. Unfortunately, the ongoingCOVID-19 pandemic has struck hard on the tourism sector due to the lockdowns andtravel restrictions. The lockdowns have also led to an increasing isolation amongpeople which in the long term can lead to a decline in people’s psychological wellbeing.Together with Cybercom Group AB, an idea to solve this problem was to developan application with the intention to nurture the tourism sector and get peopleout of their homes while keeping the human interactions at a satisfactory level. The main feature of the application developed was a scheduler that carefully plannedout people’s daily activities depending how crowded a specific location was. An applicationsuch as the one developed could lead to an increase in foot traffic whilesimultaneously decreasing the amount of physical contact between people. The result of this thesis mainly focuses on the developed application but more specificallythe developed algorithms to schedule your day using crowd data. The algorithmdeveloped, the Optimal Time Slot Algorithm, averaged a crowding value of18,8% while the average of the best possible crowding value was 17,8%.<br>Turism är en viktig faktor för ekonomisk tillväxt. Tyvärr så har den pågående COVID-19 pandemin slagit hårt mot turismsektorn till följd av nedstängningar och restriktionerpå resande. Nedstängningarna har även lett till en ökad isolering hos personersom långsiktigt kan leda till en försämring av människors psykologiska välmående.Tillsammans med Cybercom Group AB växte en idé fram om att utveckla enapplikation som har till uppgift att främja turismsektorn och hjälpa folk att ta sig utur sina hem samtidigt som de undviker trängsel. Huvudfunktionen hos den utvecklade applikationen var en planerare som noggrantplanerar en persons dagliga aktiviteter beroende på hur mycket folk det var på denspecifika platsen vid ett visst tillfälle. En applikation likt den som utvecklats kan ledatill en ökad mängd personer i rörelse i kombination med att minska mängden fysiskkontakt mellan människor. Resultatet av detta examensarbete fokuserar huvudsakligen på den utvecklade applikationenoch specifikt de algoritmer som utvecklats för att planera din dag genomträngseldata. Den framtagna algoritmen, Optimal Time Slot Algorithm, resulteradei ett trängselsnitt på 17,8% där 18,8% var snittet av det bästa möjliga resultatet.
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Books on the topic "Pandemic Data"

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Niranjanamurthy, M., Siddhartha Bhattacharyya, and Neeraj Kumar, eds. Intelligent Data Analysis for COVID-19 Pandemic. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1574-0.

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Hewage, Chaminda, Yogachandran Rahulamathavan, and Deepthi Ratnayake, eds. Data Protection in a Post-Pandemic Society. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34006-2.

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Gamerman, Dani, Marcos O. Prates, Thaís Paiva, and Vinícius D. Mayrink. Building a Platform for Data-Driven Pandemic Prediction. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003148883.

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Dishman, Lihua, and Morgyne Brantley. Conducting a Doctoral Qualitative Study During a Pandemic: Pivoting to Online Data Collections and Data Analyses. SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529600513.

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Mishra, Sushruta, Pradeep Kumar Mallick, Hrudaya Kumar Tripathy, Gyoo-Soo Chae, and Bhabani Shankar Prasad Mishra, eds. Impact of AI and Data Science in Response to Coronavirus Pandemic. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2786-6.

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Strangfeld, Jennifer. Going Remote: When a Global Pandemic Intervenes in Gathering Interview Data. SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529602173.

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Shibuya, Kazuhiko. The Rise of Artificial Intelligence and Big Data in Pandemic Society. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0950-4.

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Okthariza, Noory. Pergerakan orang di Jakarta saat pandemic COVID-19: Analisis data facebook disease prevention map. CSIS Indonesia, 2020.

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Shiu-Yee, Karen, Alice Gaughan, Laura Rush, and Ann McAlearney. Online Qualitative Data Collection and Management: Primary Care Physicians’ Response to the COVID-19 Pandemic. SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529603545.

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Datoon, Rodmyr, Aileen Lapitan, Mark Llanco, Jeanarah Gapas, Susan Bacud, and Dania Laborte. Collecting Socioeconomic Agricultural Data Through Internet-Based Surveys and FGDs During the COVID-19 Pandemic. SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529603682.

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Book chapters on the topic "Pandemic Data"

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Pastore y Piontti, Ana, Nicola Perra, Luca Rossi, Nicole Samay, and Alessandro Vespignani. "DATA, DATA, AND MORE DATA." In Charting the Next Pandemic. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93290-3_2.

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Schintler, Laurie A. "COVID-19 Pandemic." In Encyclopedia of Big Data. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-32001-4_542-2.

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Schintler, Laurie A. "COVID-19 Pandemic." In Encyclopedia of Big Data. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-32010-6_542.

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Schintler, Laurie A. "COVID-19 Pandemic." In Encyclopedia of Big Data. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-32001-4_542-1.

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Mahalle, Parikshit Narendra, Nancy Ambritta P., Sachin R. Sakhare, and Atul P. Kulkarni. "Pandemic Problems." In Studies in Autonomic, Data-driven and Industrial Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8828-8_7.

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Mahanti, Rupa. "Data Analytics and Pandemic." In How Data Can Manage Global Health Pandemics. Productivity Press, 2022. http://dx.doi.org/10.4324/9781003270911-6.

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Mahanti, Rupa. "Pandemic—An Introduction." In How Data Can Manage Global Health Pandemics. Productivity Press, 2022. http://dx.doi.org/10.4324/9781003270911-1.

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Mahanti, Rupa. "Disease and Pandemic Potential." In How Data Can Manage Global Health Pandemics. Productivity Press, 2022. http://dx.doi.org/10.4324/9781003270911-7.

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Mahanti, Rupa. "Pandemic Preparedness and Strategies." In How Data Can Manage Global Health Pandemics. Productivity Press, 2022. http://dx.doi.org/10.4324/9781003270911-9.

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Pastore y Piontti, Ana, Nicola Perra, Luca Rossi, Nicole Samay, and Alessandro Vespignani. "INFECTIOUS DISEASE SPREADING: FROM DATA TO MODELS." In Charting the Next Pandemic. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93290-3_1.

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Conference papers on the topic "Pandemic Data"

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Tomaščik, Lukáš, Norbert Ádám, Nikola Geciová, Branislav Madoš, Peter Poprík, and Heidar Khorshidiyeh. "ETL Model for Pandemic Data Processing." In 2025 IEEE 23rd World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2025. https://doi.org/10.1109/sami63904.2025.10883187.

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Berry, Colin. "COVID-19 Mobility Restrictions and Post-Pandemic Work from Home." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825616.

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Shuvo, Shaon Bhatta, Jyoti Das, Ziad Kobti, and Narayan Kar. "Advancing Pandemic Preparedness through a Data-Driven Hybrid Simulation Model." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650164.

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Pirouz, Matin, and Preston McCullough. "A Data-Centric Approach to Post-Pandemic Housing Market Analysis." In 2024 IEEE 15th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2024. http://dx.doi.org/10.1109/uemcon62879.2024.10754766.

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Easwaramoorthy, Sathishkumar Veerappampalayam, Neelakandan Subramani, and Angela Lee Siew Hoong. "Smart Contract-Driven Pandemic Management Using Blockchain Technology and Artificial Intelligence." In 2024 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2024. https://doi.org/10.1109/icdmw65004.2024.00057.

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Parillo-Chapman, Lisa, Marguerite Moore, and Yanan Yu. "Fashion Printing Technology Diffusion: Big Data Analytics." In Pivoting for the Pandemic. Iowa State University Digital Press, 2020. http://dx.doi.org/10.31274/itaa.11718.

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Pan, Zhenhe, Hoang Long Nguyen, Hashim Abu-gellban, and Yuanlin Zhang. "Google Trends Analysis of COVID-19 Pandemic." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377852.

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Woo, Hongjoo, and Sanghee Kim. "Big Data Analysis of the Second-Hand Apparel Market Trends Comparing 2014 and 2019." In Pivoting for the Pandemic. Iowa State University Digital Press, 2020. http://dx.doi.org/10.31274/itaa.11768.

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Shur, Michael. "Describing and Predicting COVID19 Evolution Using Pandemic Equation." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378174.

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Eliassi-Rad, Tina, Nitesh Chawla, Vittoria Colizza, et al. "Fighting a Pandemic." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2020. http://dx.doi.org/10.1145/3394486.3409586.

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Reports on the topic "Pandemic Data"

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Voss, Miranda, Louise Burke, Dr Mahlet (Milly) Zimeta, et al. Data on teachers’ lives during the pandemic [report]. Open Data Institute, 2021. http://dx.doi.org/10.61557/pgxs6045.

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Singer, Christine, Professor Jeanette Steemers, Professor Cynthia Carter, et al. Data about children’s lives in the pandemic: report. Open Data Institute, 2020. http://dx.doi.org/10.61557/kvsf2220.

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Carolus, Gabriela Penelopé. The ‘theater’ of the shadow pandemic: Lessons learned collecting data. World Evidence-based Healthcare Day, 2024. http://dx.doi.org/10.70253/kqwd2183.

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Ramesh, Aditi Ramesh, Andrew J. Zahuranec Zahuranec, Andrew Young Young, et al. The Use of Mobility Data for Responding to the COVID-19 Pandemic. GovLab, 2021. http://dx.doi.org/10.15868/socialsector.40378.

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Carson, Jessica, and Sarah Boege. New Data Show One-in-Six Children Were Poor Before COVID-19 Pandemic. University of New Hampshire Libraries, 2020. http://dx.doi.org/10.34051/p/2021.8.

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Hu, Bin, Jason Gans, Youzuo Lin, Po-E. Li, and Patrick Chain. Using Deep Mutational Data and Machine Learning to Guide Outbreak and Pandemic Response. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1844116.

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Heard, Helen. Consumer Handwashing Research: Handwashing in a Pandemic. Food Standards Agency, 2021. http://dx.doi.org/10.46756/sci.fsa.uny803.

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Between April 2020 and January 2021, the Food Standards Agency (FSA) with Ipsos MORI collected data on handwashing to understand how and why people wash their hands and the impact the pandemic has had on their handwashing behaviour. This report combines the findings from the qualitative and quantitative research conducted by the FSA alongside other literature available on the topic of hand hygiene to provide a comprehensive overview of consumer handwashing behaviour during the pandemic.
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Vesselinov, Velimir, Richard Middleton, and Carl Talsma. COVID-19: Spatiotemporal social data analytics and machine learning for pandemic exploration and forecasting. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1774409.

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Young, Andrew Young, Andrew J. Zahuranec Zahuranec, Cecilia Emilsson Emilsson, Felipe Gonzalez-Zapata Gonzalez-Zapata, Jacob Arturo Rivera Perez Perez, and Lucia Chauvet Chauvet. Open Data in action: Initiatives during the initial stage of the COVID-19 pandemic. GovLab, 2021. http://dx.doi.org/10.15868/socialsector.40377.

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Schmitt-Grohé, Stephanie, and Martín Uribe. What Do Long Data Tell Us About the Inflation Hike Post COVID-19 Pandemic? National Bureau of Economic Research, 2022. http://dx.doi.org/10.3386/w30357.

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