Related Experiment Video
Updated: May 15, 2026
08:52
Use of an Influenza Antigen Microarray to Measure the Breadth of Serum Antibodies Across Virus Subtypes
Published on: July 26, 2019
Risk-based input-output analysis of influenza epidemic consequences on interdependent workforce sectors
Joost R Santos1, Larissa May, Amine El Haimar
1Engineering Management and Systems Engineering, The George Washington University, 1776 G Street NW, Suite 101, Washington, DC, USA.
Summary
Epidemics cause severe economic and productivity losses through workforce absenteeism. Recovery strategies must balance economic impact with operational capacity, especially in critical sectors like healthcare.
Area of Science:
- Epidemiology
- Economics
- Public Health
Background:
- Epidemics pose significant, prolonged threats to populations and economies.
- Workforce absenteeism during epidemics creates interdependent economic and productivity risks.
Purpose of the Study:
- To develop a dynamic input-output model for quantifying sector-disaggregated economic losses from epidemic-induced workforce disruptions.
- To analyze the economic and inoperability impacts of the 2009 H1N1 pandemic in the National Capital Region (NCR).
Main Methods:
- Developed a dynamic input-output model to assess economic losses and inoperability across sectors.
- Conducted an ex post analysis of the 2009 H1N1 pandemic in the NCR.
- Simulated a mild pandemic scenario to compare economic loss and inoperability metrics.
Main Results:
- Economic loss metric highlighted sectors with high GDP contribution (e.g., federal government enterprises).
- Inoperability metric identified critical sectors involved in epidemic management (e.g., hospitals).
- Distinct sector rankings emerged based on the chosen metric.
Conclusions:
- Sector prioritization for recovery requires balancing economic loss, inoperability, and other objectives.
- The proposed methodology is adaptable for analyzing epidemic impacts in different regions.
Related Concept Videos
01:18Steps in Outbreak Investigation
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
01:27Influenza
Influenza is an acute, highly communicable viral disease that affects the respiratory tract and is responsible for seasonal epidemics worldwide. Influenza A is the most prevalent type associated with widespread outbreaks and is subtyped based on two surface glycoproteins: hemagglutinin (H) and neuraminidase (N), as in H1N1. These glycoproteins are essential for viral infectivity, transmission, and immune recognition. Transmission occurs primarily through respiratory droplets and contaminated...
01:25Statistical Methods for Analyzing Epidemiological Data
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
01:28Infectious Diseases and Their Occurrence
Infectious diseases appear in populations through various transmission patterns, influenced by pathogen characteristics, population immunity, environmental conditions, and social behavior. Understanding these patterns is essential for effective public health surveillance and intervention. These categories—sporadic, outbreak, epidemic, pandemic, and endemic—help frame the nature and scope of disease events.Sporadic diseases occur irregularly and infrequently, without a predictable temporal or...
01:21Causality in Epidemiology
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
01:23Investigation of Disease Outbreaks
Multistate foodborne outbreaks pose significant public health risks and require meticulous investigation to identify sources and implement control measures. The Centers for Disease Control and Prevention (CDC) utilizes a dynamic seven-step process for these investigations, integrating data from laboratories, interviews, and environmental assessments to protect public health.Outbreak Detection: The detection of multistate outbreaks typically begins with PulseNet, the CDC's national laboratory...