{
  "format": "dataset-manifest/1",
  "slug": "global-health-insights",
  "version": 4,
  "seed": "global-health-insights:1",
  "engine": 1,
  "engineVersion": "1.0.0",
  "spec": {
    "format": "dataset/1",
    "slug": "global-health-insights",
    "name": "Global Health Insights",
    "tagline": "Hospital capabilities, patient symptoms, vaccination rates, and border crossings during the Covid-19 pandemic.",
    "description": "Healthcare organizations and governments track Covid-19 metrics to shape effective public health responses. Hospitals play a critical role in this ecosystem, as they manage patient care while monitoring symptoms and administering tests. Each hospital is tied to a specific region, allowing for a detailed understanding of healthcare access and outcomes within those areas. Vaccination data complements this by highlighting immunization rates, which differ significantly across regions, particularly when contrasting urban centers with rural communities. The interplay between hospital resources, patient demographics, and vaccination rates shapes how effectively regions manage public health crises.\n\nEach row in the hospitals table represents a unique healthcare facility, detailing its capabilities such as bed count and the types of services provided. The regions table defines geographical areas and includes metrics like population size and healthcare access levels. Rows in the patients table capture individual patient data, encompassing demographics and reported symptoms, while vaccinations track the immunization status of these patients. Border crossings data illustrates movement patterns that might influence the spread of Covid-19, with links to regional health statistics.\n\nThis dataset enables practitioners to answer pressing questions such as: How do vaccination rates correlate with hospital admission rates in different regions? Are there emerging trends in patient symptoms that indicate a need for increased testing or healthcare resources? What effects do border crossings have on local Covid-19 case rates? The structure of this synthetic data allows for practice in relational database management, data visualization, and predictive analytics, fostering skills applicable in real-world health scenarios.",
    "domain": "saas",
    "shape": "slowly-changing",
    "useCases": [
      "etl-pipelines",
      "teaching"
    ],
    "keywords": [
      "covid-19",
      "healthcare",
      "vaccination",
      "hospital",
      "patient",
      "symptoms",
      "testing",
      "border",
      "analytics"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "hospitals": {
        "description": "This table contains information about hospitals, including their capacity and the number of Covid-19 patients treated. Each hospital is linked to its respective region, allowing for analysis of healthcare access.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each hospital."
          },
          "name": {
            "kind": "fullName",
            "description": "The full name of the hospital."
          },
          "capacity": {
            "kind": "int",
            "min": 50,
            "max": 2000,
            "description": "Total number of patients the hospital can accommodate.",
            "distribution": "normal"
          },
          "covid_patients": {
            "kind": "int",
            "min": 0,
            "max": 2000,
            "description": "Number of Covid-19 patients currently admitted.",
            "distribution": "long-tail"
          },
          "region_id": {
            "kind": "fk",
            "ref": "regions.id",
            "description": "Foreign key linking to the regions table."
          }
        }
      },
      "regions": {
        "description": "This table provides details about different regions, including population and vaccination rates. It connects hospitals to their respective geographic areas.",
        "rows": 50,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each region."
          },
          "name": {
            "kind": "title",
            "description": "The name of the region.",
            "pool": [
              "Africa",
              "Asia",
              "Europe",
              "North-America",
              "South-America",
              "Oceania",
              "Middle-East",
              "Southeast-Asia",
              "Eastern-Europe",
              "Western-Europe",
              "Central-America",
              "Caribbean",
              "Sub-Saharan",
              "Northern-America",
              "Southern-Europe",
              "South-Asia",
              "Pacific",
              "Indian-Ocean",
              "Arctic",
              "Antarctica",
              "Caucasus"
            ],
            "min": 1,
            "max": 3
          },
          "population": {
            "kind": "int",
            "min": 1000,
            "max": 10000000,
            "description": "Total population of the region.",
            "distribution": "heavy-tail"
          },
          "vaccination_rate": {
            "kind": "decimal",
            "min": 0,
            "max": 100,
            "scale": 2,
            "description": "Percentage of the population vaccinated against Covid-19.",
            "distribution": "high"
          }
        }
      },
      "patients": {
        "description": "This table includes data on patients who have been tested for Covid-19, detailing their symptoms and test results. It links to the hospitals where they received care.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each patient."
          },
          "hospital_id": {
            "kind": "fk",
            "ref": "hospitals.id",
            "description": "Foreign key linking to the hospitals table."
          },
          "symptoms": {
            "kind": "words",
            "max": 3,
            "description": "Symptoms experienced by the patient.",
            "nullable": true,
            "when": {
              "column": "test_result",
              "in": [
                "positive"
              ]
            },
            "pool": [
              "fever",
              "cough",
              "headache",
              "nausea",
              "fatigue",
              "dizziness",
              "chills",
              "muscle-pain",
              "sore-throat",
              "shortness-of-breath",
              "chest-pain",
              "joint-pain",
              "rash",
              "vomiting",
              "diarrhea",
              "sweating",
              "loss-of-smell",
              "loss-of-taste",
              "confusion",
              "abdominal-pain",
              "congestion"
            ],
            "min": 1
          },
          "test_result": {
            "kind": "enum",
            "values": [
              "positive",
              "negative"
            ],
            "description": "Result of the Covid-19 test.",
            "weights": [
              7,
              3
            ]
          },
          "test_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date when the test was administered.",
            "trend": "growing"
          }
        }
      },
      "vaccinations": {
        "description": "This table tracks vaccination events, including the date and type of vaccine administered to patients in different regions.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each vaccination event."
          },
          "patient_id": {
            "kind": "fk",
            "ref": "patients.id",
            "description": "Foreign key linking to the patients table."
          },
          "vaccine_type": {
            "kind": "enum",
            "values": [
              "Pfizer",
              "Moderna",
              "AstraZeneca",
              "Johnson & Johnson"
            ],
            "description": "Type of vaccine administered.",
            "weights": [
              5,
              4,
              2,
              1
            ]
          },
          "vaccination_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date when the vaccination was administered.",
            "trend": "seasonal"
          }
        }
      },
      "border_crossings": {
        "description": "This table records instances of border crossings, which may affect the spread of Covid-19. It links to regions to analyze travel impact.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each border crossing."
          },
          "region_id": {
            "kind": "fk",
            "ref": "regions.id",
            "description": "Foreign key linking to the regions table."
          },
          "crossing_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date of the border crossing.",
            "nullable": true,
            "when": {
              "column": "crossing_type",
              "in": [
                "entry"
              ]
            },
            "trend": "growing"
          },
          "crossing_type": {
            "kind": "enum",
            "values": [
              "entry",
              "exit"
            ],
            "description": "Type of border crossing.",
            "weights": [
              7,
              3
            ]
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "nullable",
      "timestamped"
    ],
    "analysisQuestions": [
      "How have vaccination rates evolved across different regions over time?",
      "What is the correlation between hospital capacity and the number of Covid-19 patients?",
      "Which symptoms are most commonly reported among Covid-19 patients?",
      "How do border crossings impact the spread of Covid-19 in various regions?",
      "What trends can be observed in test results over the pandemic period?"
    ],
    "suggestedQueries": [
      {
        "title": "Join hospitals and regions",
        "sql": "SELECT hospitals.name, regions.name FROM hospitals JOIN regions ON hospitals.region_id = regions.id;",
        "dialect": "postgres"
      },
      {
        "title": "Count Covid-19 patients by region",
        "sql": "SELECT regions.name, COUNT(patients.id) AS patient_count FROM patients JOIN hospitals ON patients.hospital_id = hospitals.id JOIN regions ON hospitals.region_id = regions.id GROUP BY regions.name;",
        "dialect": "postgres"
      },
      {
        "title": "Average vaccination rate per region",
        "sql": "SELECT regions.name, AVG(regions.vaccination_rate) AS avg_vaccination_rate FROM regions GROUP BY regions.name;",
        "dialect": "postgres"
      },
      {
        "title": "Symptoms reported by Covid-19 patients",
        "sql": "SELECT symptoms, COUNT(*) FROM patients WHERE test_result = 'positive' GROUP BY symptoms;",
        "dialect": "postgres"
      },
      {
        "title": "Border crossings and Covid-19 cases",
        "sql": "SELECT regions.name, COUNT(border_crossings.id) AS crossings, COUNT(patients.id) AS patients FROM border_crossings JOIN regions ON border_crossings.region_id = regions.id LEFT JOIN patients ON regions.id = patients.hospital_id GROUP BY regions.name;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Healthcare Analysis",
        "body": "Healthcare analysts can utilize the Global Health Insights dataset to evaluate the effectiveness of vaccination campaigns across various regions. By analyzing the relationship between vaccination rates and hospital capacity, they can identify areas needing additional resources and better health policies."
      },
      {
        "title": "Public Health Research",
        "body": "Researchers studying the effects of Covid-19 can use this dataset to explore trends in patient symptoms and test results over time. This analysis can inform public health strategies and improve responses to the pandemic by identifying common symptoms and their severity."
      },
      {
        "title": "Educational Purposes",
        "body": "Instructors can leverage the Global Health Insights dataset in academic settings to teach students about data analysis and SQL querying. By working with real-world healthcare data, students can gain practical experience in analyzing trends and drawing insights relevant to public health."
      },
      {
        "title": "Policy Making",
        "body": "Policymakers can access this dataset to understand the impact of border crossings on Covid-19 transmission. By evaluating the correlation between travel and infection rates, they can develop informed policies regarding travel restrictions and public health measures."
      }
    ]
  },
  "counts": {
    "regions": 50,
    "border_crossings": 150,
    "hospitals": 100,
    "patients": 200,
    "vaccinations": 300
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