Update socioeconomic-data.md

_data/news.yml

@@ -141,4 +141,8 @@
 - name: "New page: Data Analysis of Pathogen Characterisation data"
   date: 2024-09-19
   linked_pr: 308
-  description: Content was added to the Pathogen Characterisation page on Data Analysis. [Discover the page here](/data-analysis/pathogen-characterisation)
+  description: Content was added to the Pathogen Characterisation page on Data Analysis. [Discover the page here](/data-analysis/pathogen-characterisation)
+- name: "New page: Data Analysis of Socioeconomic data"
+  date: 2024-10-01
+  linked_pr: 357
+  description: Content was added to the Socioeconomic data page on Data Analysis. [Discover the page here](/data-analysis/socioeconomic-data)

_data/sidebars/main.yml

@@ -16,6 +16,8 @@ subitems:
         url: /data-analysis/human-biomolecular-data
       - title: Pathogen characterisation
         url: /data-analysis/pathogen-characterisation
+      - title: Socioeconomic data
+        url: /data-analysis/socioeconomic-data
 
   - title: Data communication
     url: /data-communication/

_data/tool_and_resource_list.yml

@@ -1210,3 +1210,23 @@
   registry:
     biotools: enrichr
   url: https://github.com/guokai8/EnrichR
+- description: "Powerful free, open source tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data."
+  id: openrefine
+  name: OpenRefine
+  url: https://openrefine.org/
+- description: dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges.
+  id: dplyr
+  name: dplyr
+  url: https://dplyr.tidyverse.org/
+- description: Tidy data describes a standard way of storing data that is used wherever possible throughout the [tidyverse](https://www.tidyverse.org/). 
+  id: tidyr
+  name: tidyr
+  url: https://tidyr.tidyverse.org/
+- description: Trifacta is designed for analysts to explore, transform, and enrich raw data into clean and structured formats
+  id: trifacta
+  name: Trifacta
+  url: https://www.trifacta.com/
+- description: 
+  id: 
+  name: 
+  url: 

data-analysis/socioeconomic-data.md

@@ -1,27 +1,164 @@
 ---
 title: Socioeconomic data
 description: Generic workflows for different data types.
-contributors: []
-no_robots: true
-search_exclude: true
-sitemap: false
+contributors: [Simon Saldner, Diana Pilvar, Rafael Andrade Buono, Hedi Peterson, Patricia Palagi, Dimitra Kondyli]
 page_id: sed_data_analysis
 redirect_from: /socioeconomic-data/data-analysis
 rdmkit:
-  - name:
-    url:
+  - name: Data Analysis
+    url: https://rdmkit.elixir-europe.org/data_analysis
 training:
-  - name:
+  - name: CESSDA
     registry:
-    url:
+    url: https://www.cessda.eu/Training-Resources?keywords=Data%20Harmonization
+  - name: SERISS
+    registry:
+    url: https://seriss.eu/training/training-overview/
+
 # More information on how to fill in this metadata section can be found here https://www.infectious-diseases-toolkit.org/contribute/page-metadata
 ---
 
-**We are still working on the content for this page.** If you are interested in adding to the page, then:
+## Introduction
+
+The COVID-19 pandemic highlighted the crucial role of socioeconomic data in tackling infectious diseases, aiding in understanding their impact on diverse population segments and informing policy and resource allocation. This data analysis, encompassing factors such as income, employment, education, and healthcare access, is key to comprehending and mitigating disease spread and effects. Our guide focuses on the vital steps, tools, and best practices in socioeconomic data analysis, serving as a valuable resource for researchers, policymakers, and public health experts. 
+
+### General considerations
+
+Some important considerations for analysing socioeconomic data in the context of infectious disease research are:
+
+* **Contextual Understanding**:
+  *   Understand how the cultural, economic, and political context of the population under study affects how diseases spread and are prevented. For example, how people's travel patterns, willingness to get vaccinated, and trust in political leaders, affect the effectiveness of disease prevention. 
+  *   Acknowledge the global and local variations in socioeconomic determinants and their impact on infectious diseases. Socioeconomic status (e.g. influenced by education, income, and neighbourhood), is a commonly used socioeconomic indicator. The [Baseline Use Case report](https://zenodo.org/doi/10.5281/zenodo.12771702), describes this indicator and its relevance for health research in detail, including a list of important area-level indicators in table S2.
+
+
+* **Interdisciplinary Collaboration**:
+  *   Encourage collaboration between epidemiologists, social scientists, and healthcare professionals.
+  *   Integrate insights from various disciplines to form a comprehensive understanding of the issue.
+
+
+* **Longitudinal Analysis**:
+  *   Conduct longitudinal studies to capture changes in socioeconomic conditions over time.
+  *   Recognize that the impact of socioeconomic factors on infectious diseases may evolve.
+
+
+* **Ethical Considerations**:
+  *   Prioritise ethical considerations, ensuring participant confidentiality and informed consent.
+  *   Mitigate potential harm and consider the implications of research findings on vulnerable populations.
+
+
+* **Intersectionality**:
+
+  *   Recognize that individuals may experience multiple intersecting social identities, which can impact health outcomes.
+  *   Consider how factors such as race, gender, and socioeconomic status interact and influence disease risk.
+
+
+### Existing approaches
+
+The following are some sources providing introductions to why socioeconomic factors are important in health research:
+
+*   The BY-COVID's [Baseline Use Case on the integration of socioeconomic data](https://zenodo.org/doi/10.5281/zenodo.12771702) provides a detailed report describing how socioeconomic data can be used in health research 
+
+*   The World Health Organization (WHO) [Social Determinants of Health (SDH)](https://www.who.int/social_determinants/en/) team works to compile evidence on social, psychological and economic determinants of health, and contains multiple publications and other resources. 
+
+*   [Centers for Disease Control and Prevention (CDC)](https://www.cdc.gov/about/priorities/why-is-addressing-sdoh-important.html) lists multiple important socioeconomic factors in health research, and refers to other useful sources.
+
+
+## Preprocessing
+
+
+Data preprocessing is a crucial step in the analysis of socioeconomic data, encompassing a series of techniques and procedures aimed at enhancing the quality and usability of raw datasets. In the context of socioeconomic research, where data often originates from diverse sources and may be subject to various forms of incompleteness, noise, or inconsistencies, effective preprocessing is essential to ensure the reliability of subsequent analyses and interpretations. 
+
+### Considerations 
+
+Here are some common considerations involved in data preprocessing:
+
+*   Make your data FAIR:  In the field of infectious diseases, having data that is Findable, Accessible, Interoperable, and Reusable (FAIR) ensures that researchers and public health officials can easily access and utilise information essential for tracking outbreaks and developing interventions.  
+
+*   Define the data types: Accurately identifying and categorising the types of data available is necessary for analysing disease spread and impact. Refer to the [IDTk Socioeconomic Data Sources](/data-sources/socioeconomic-data) page for further information.
+
+*   Define the necessary data harmonisation and integration steps: Ensure your data is harmonised and made interoperable with other data. 
+
+*   Evaluate potential legal, privacy, and ethical considerations: Mind your legal and ethical obligations towards your research subjects, and any country or content-specific considerations. Refer to the [Ethical and Legal Issues](/ethical-legal-and-social-issues) page.  
+
+*   Data cleaning: Identify and handle missing or incomplete values to prevent biased analyses.
+
+*   Data transformation: Normalise or standardise variables to ensure a consistent scale.
+
+*   Data integration: Resolve discrepancies and ensure consistency with integrated datasets.
+
+*   Handling categorical data: Encode categorical variables into a suitable format (e.g., one-hot encoding).
+
+*   Dealing with outliers: These anomalies can indicate unique or extreme conditions that may significantly influence disease spread and severity, such as areas with exceptionally high poverty or overcrowding. Properly analysing and understanding these outliers helps to tailor public health interventions more effectively, ensuring resources are allocated where they are most needed to prevent and control outbreaks.
+
+
+### Existing approaches
+
+Use some of these available tools for socioeconomic data preprocessing:
+
+
+*   Python Libraries: {% tool "pandas" %} for data manipulation and cleaning, {% tool "numpy" %} for numerical operations and transformations, {% tool "scikit-learn" %} for preprocessing tasks like normalisation, encoding, and combining data from multiple sources.
+
+*   R Programming: Data cleaning and manipulation using packages such as {% tool "dplyr" %} and {% tool "tidyr" %}.
+
+*   Excel and Google Sheets: Basic data cleaning and transformation using built-in functions. Suitable for smaller datasets and quick exploratory analyses.
+
+*   {% tool "openrefine" %}: A powerful open-source tool for cleaning and transforming messy data. [Tutorial by Pilvar, D.](https://doi.org/10.5281/zenodo.10224703) could help you get started.
+
+*   {% tool "trifacta" %}: A data wrangling tool with a user-friendly interface for cleaning and transforming diverse datasets.
+
+*   SQL: Database queries and manipulations for preprocessing data stored in relational databases.
+
+
+## Analysis
+
+Socioeconomic analysis and associated methodologies are quite diverse and often complex, as they strive to investigate complex, changeable, and often intangible social phenomena such as trust, ideology, or socioeconomic status. Thus, social sciences use both quantitative and qualitative research methods, often in combination, ranging from large scale quantitative surveys, to interviews or ethnographic research. As such, socioeconomic research methodologies and data often differ significantly from those typically used in the life sciences. 
+
+### Considerations
+
+*   Demographic diversity: Consider demographic variables such as age, gender, and ethnicity to identify vulnerable populations.
+
+*   Geospatial analysis: Utilise geospatial data to understand the spatial distribution of infectious diseases and their correlation with socioeconomic factors.
+
+*   Temporal trends: Analyse temporal trends to identify patterns in the relationship between socioeconomic factors and disease prevalence.
+
+*   Income Disparities: Investigate the impact of income disparities on healthcare access, treatment adherence, and disease outcomes.
+
+*   Education levels: Examine the influence of education levels on awareness, preventive behaviours, and healthcare-seeking practices.
+
+*   Health literacy: Assess health literacy levels to understand how well communities comprehend and act upon health information.
+
+*   Institutional and interpersonal Trust: Trust in political institutions, media, science, and fellow citizens may affect the spread of diseases and the effectiveness of e.g. COVID-19 measures (see e.g. [Cohen, Péron, & Algan 2022](https://cepr.org/voxeu/columns/trust-other-factor-covid-19-crisis)).  
+
+*   Occupational risks: Explore occupational factors that may contribute to disease transmission and susceptibility in specific sectors, e.g. jobs involving lots of human contact.   
+*   Healthcare infrastructure: Assess the availability and quality of healthcare infrastructure, identifying areas with limited access.
+
+*   Public health policies: Evaluate the types and effectiveness of public  health policies in addressing socioeconomic determinants and mitigating disease risk.
+
+*   Behavioural factors: Analyse behavioural factors influenced by socioeconomic status, such as hygiene practices and compliance with preventive measures.
+
+*   Vaccine equity: Investigate disparities in vaccine access and uptake based on socioeconomic factors to guide equitable distribution strategies.
+
+*   Migration and urbanisation: Consider the impact of migration and urbanisation on disease spread, particularly in densely populated areas.
+
+*   Social network analysis: Employ social network analysis to understand how social connections may contribute to disease transmission.
+
+*   Cultural practices: Explore the influence of cultural practices on disease transmission and prevention within specific communities, e.g. celebrations or health-related traditions. 
+
+*   Environmental factors: Investigate the intersection of socioeconomic factors with environmental conditions that may influence disease dynamics.
+
+*   Disaster preparedness: Analyse the preparedness of communities with different socioeconomic statuses for health emergencies and disasters.
+
+*   Units of analysis: Analyse how the outcome you study varies at different levels of society such as country, municipality, or schools, e.g. using multilevel analysis/modelling  
+
+*   Long-term effects: Consider the long-term socioeconomic effects of infectious diseases, including economic consequences and recovery challenges.
+
+## Epidemiological Trend Analysis
 
-[Feel free to contribute](/contribute/){: class="btn btn-primary btn-lg rounded-pill"}
+Epidemiological Trend Analysis is vital to understanding and managing infectious diseases, particularly in pathogen characterisation. This analytical approach involves studying the patterns, causes, and effects of health conditions in specific populations, focusing on how diseases spread, evolve, and impact communities. It is crucial for identifying trends in disease transmission, assessing the effectiveness of public health interventions, and guiding the development of strategies to control and prevent the spread of pathogens. In the context of infectious diseases, such as during the COVID-19 pandemic, Epidemiological Trend Analysis provides essential data-driven insights that inform public health decisions and contribute to the broader understanding of a pathogen's characteristics and behaviour.
 
-This is a community-driven website, so contributions are welcome! You will, of course, be listed as a contributor on the page.
+### Existing approaches
 
-New content is announced on the [home page](/) and [news page](/about/news), so please check for updates there. You can also watch for changes on this page by using a free service like [Visual Ping](https://visualping.io/) or [Distill Web Monitor](https://distill.io/), or by using a [browser add-on](https://chrome.google.com/webstore/detail/distill-web-monitor/inlikjemeeknofckkjolnjbpehgadgge?hl=en).
+*   Tracking and Modeling Disease Spread: Utilising data from [WHO](https://data.who.int/dashboards/covid19/cases?n=c) and [IHME](https://www.healthdata.org/data-tools-practices/interactive-visuals/covid-19-projections), analysts tracked COVID-19 infection rates, reproduction numbers, and case fatality rates. Sophisticated models incorporating factors like population density and mobility data were developed to predict virus transmission and assess the potential impact of public health measures ([Institute for Health Metrics and Evaluation projections](https://covid19.healthdata.org/global?view=cumulative-deaths&tab=trend), [World Health Organization dashboard](https://data.who.int/dashboards/covid19/cases)).
+
 
+*  Evaluating Public Health Interventions: Time-series analyses were crucial in determining the effectiveness of interventions such as social distancing, lockdowns, and vaccination campaigns. These studies helped understand changes in infection rates following these measures, guiding future public health strategies [Our World in Data. Our World in Data Website](https://ourworldindata.org/coronavirus).