Related Experiment Video
Updated: Oct 12, 2025
08:53
Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
Published on: May 31, 2019
5.3K
Modeling epidemic recovery: An expert elicitation on issues and approaches
Monica Schoch-Spana1, Sanjana J Ravi1, Elena K Martin1
1The Johns Hopkins Center for Health Security, 621 East Pratt Street, Pier IV Building, Suite 210, Baltimore, MD, 21202, USA.
Social Science & Medicine (1982)
|November 23, 2021
Summary
Developing effective epidemic recovery requires expert insights beyond medical concerns. This study outlines principles for a holistic, community-level recovery model distinct from natural disaster response.
Area of Science:
- Public Health
- Disaster Management
- Epidemiology
Background:
- The COVID-19 pandemic highlighted the need for structured post-epidemic recovery plans.
- Existing disaster recovery models are insufficient for complex, long-duration epidemics.
- Expert judgment is crucial for developing recovery strategies when data is limited.
Purpose of the Study:
- To identify key principles and priority issues for an epidemic recovery model.
- To inform the development of a holistic, community-level post-epidemic recovery framework.
- To differentiate epidemic recovery from natural disaster recovery.
Main Methods:
- Expert elicitation process involving fourteen key informants.
- Informants specialized in infectious diseases, disaster recovery, public health, and policymaking.
- Qualitative data collection on design principles, priority issues, and field experiences.
Main Results:
- Epidemic recovery is distinct from natural disasters due to prolonged impact and psychological effects.
- Recovery is a complex, nonlinear process spanning political, economic, sociocultural, infrastructural, and health domains.
- An effective model must be multi-domain, consider social determinants of health, and be user-specific.
Conclusions:
- A comprehensive epidemic recovery model must extend beyond medical aspects.
- The model should specify units of interest and account for diverse recovery trajectories.
- This study initiates efforts to create indicators for transformative community-level epidemic recovery.
Related Concept Videos
01:18Steps in Outbreak Investigation
253
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:
253
01:26Modeling in Therapy
167
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
167
01:25Statistical Methods for Analyzing Epidemiological Data
601
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:
601
01:21Causality in Epidemiology
1.1K
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...
1.1K
01:25Strategies for Assessing and Addressing Confounding
173
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
173
01:26Introduction to Epidemiology
1.1K
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
1.1K