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Related Concept Videos

Principles of Disease Surveillance01:26

Principles of Disease Surveillance

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Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
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Data Collection I01:30

Data Collection I

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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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Data Collection III01:05

Data Collection III

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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
The principles to begin the physical assessment include conducting a comprehensive or problem-related history in a quiet, well-lit room, emphasizing privacy and comfort for the...
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Data Collection II01:29

Data Collection II

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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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Data Collection by Observations01:08

Data Collection by Observations

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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Data Collection by Survey01:07

Data Collection by Survey

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The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
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Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs
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Lessons from COVID-19 for rescalable data collection.

Sangeeta Bhatia1, Natsuko Imai2, Oliver J Watson3

  • 1MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; NIHR Health Protection Research Unit in Modelling and Health Economics, Imperial College London, UK Health Security Agency, and London School of Hygiene & Tropical Medicine, London, UK; Modelling and Economics Unit, UK Health Security Agency, London, UK.

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Public health response to COVID-19 relied on novel data and scaled surveillance systems. A cost-effective surveillance system for SARS-CoV-2 is crucial for future global health security.

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Area of Science:

  • Public Health
  • Epidemiology
  • Health Informatics

Background:

  • The COVID-19 pandemic necessitated rapid adaptation of public health surveillance.
  • Existing systems were enhanced, and new data sources were developed to address urgent questions.

Purpose of the Study:

  • To describe routine and novel data used during the pandemic.
  • To identify challenges in data generation, sustainability, and equity.
  • To highlight lessons for future scalable data collection systems.

Main Methods:

  • Review of data streams utilized for COVID-19 surveillance.
  • Analysis of challenges in data generation and sustainability.
  • Identification of lessons learned for public health crisis response.

Main Results:

  • Novel data and scaled surveillance were critical for the pandemic response.
  • Challenges identified include sustainability and equity in data generation.
  • Lessons learned emphasize the need for scalable data systems.

Conclusions:

  • Ongoing SARS-CoV-2 surveillance requires a minimal, cost-effective system.
  • Retrospective evaluation of data stream cost-benefit is recommended.
  • Preparedness for future public health crises hinges on robust data infrastructure.