Abstract
The chapter provides an in-depth overview and analysis for developing policies and strategies for managing a pandemic based on information and data. While looking for the credibility of an information source, various parameters are subjected for considerations (i.e., infection and death rates per given time, availability of personal protective equipment [PPE], overall population attitude, current strategy response rate, society behaviors, outcomes of policies interventions for curbing the spread of the virus, and many more). To critically analyze pandemic information and data usage along with issues and challenges that arise in collecting, extracting, or using various forms of information and data for pandemic management, numerous national action plans, world health databases, pandemic monitoring smart applications, government published infection-to-death ratios, and health cloud services are interpreted and discussed.
Top2. Historical Perspective
Nearly a century ago, 50 million people were killed due to the Spanish flu pandemic. Afterwards, tons of deaths and casualties were reported within specified premises due to Legionnaires' disease outbreak (Philadelphia, 1976), Hantavirus (South-western United States, 1993) and Influenza A (H1N1) pandemic (Argentina, 2009). Pandemic like acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), Ebola, and lethal influenza (avian and swine flu) are among the most recent and prominent pandemic widely reported that affected people for approximately two decades (Patrick & Daniel, 2016).
Legionnaires' disease outbreak took substantial attention from US health officials to obtain and use data for pandemic management (Thomas, Randi, Albert, Keith, & Philip, 2020). They targeted sources like local clinical data, designated treatment units, official epidemiologic survey results, and various specimens collected from sufferers. These data acquisitions couldn’t portray productive results due to lack of structured approach, data ambiguity and least computer and data model support.
Within the span of 4 years, Toxic Shock Syndrome (TSS) were widely reported within the same region (Byomakesh, Pooja, & Abhisek, 2020). This time, data and information was managed on mainframe computers while considering flaws that were initially reported during Legionnaires' disease outbreak pandemic data handling. Sequentially, one after another pandemic, computer technology, epidemiologic and laboratory methods for curtailing the upsurge was in place. However, a compatible data management strategy had not been established.
Key Terms in this Chapter
Epidemiologist: An expert which deals with the viral incidence, epidemic distribution, and possible strategies to control the diseases.
COVID: COVID is an abbreviation of 'CO' corona, 'VI' is for virus, and 'D' stands for disease. It be believed to be modified form of Severe Acute Respiratory Syndrome (SARS).
Personal Protective Equipment (PPE): PPE stands for any device or appliance specifically designed to be used, worn, or held by a concerning individual for protecting one or more health and related safety hazards.
Risk Perception Analysis: It is a technique that calculates current and upcoming risk factors to quantitatively analyse risk percentages for health data systems.
Wide-Scale Surveillance: It is a process adapted by the large scale health organizations in order to protect critical health assets and reduce security threats for the health environment.
Metadata: It is data about data. For example, a data that provides explicit information about other data under consideration.
Real-Time Data: A data that is available as soon as it is added into a database is considered as real time data. Real-time data does not need processing, storing, and configuration time.
Technology-Rich Crisis Management: A management that relies on online tools and technology for pandemic and crisis management along with saving utilization, processing, and execution time. Technology-rich crisis management is cost-effective and uses less resources in comparison with traditional crisis management.
Fragmented Data: A data that is distributed into different blocks/chunks for evenly storage on various memory structures. Fragmented data needs to be defragmented before proceeding towards processing stage.
Redundant Health Databases: Redundant data is created when a same set of information is either duplicated or stored in two different places/locations in single database. Redundant health databases add memory constraints as well as critical resource wastage.