Related Experiment Video
Updated: May 19, 2026
14:56
Remote Laboratory Management: Respiratory Virus Diagnostics
Published on: April 6, 2019
Real-time modelling used for outbreak management during a cholera epidemic, Haiti, 2010-2011
J Y Abrams1, J R Copeland, R V Tauxe
1Centers for Disease Control and Prevention, Atlanta, GA 30333, USA. JAbrams@cdc.gov
Epidemiology and Infection
|September 1, 2012
Summary
Real-time cholera outbreak projections in Haiti guided public health responses. These estimates, crucial for resource allocation, proved to be reasonable approximations of the epidemic's scale.
Area of Science:
- Epidemiology
- Public Health
- Disaster Response
Background:
- The 2010 Haiti earthquake exacerbated existing vulnerabilities, leading to an epidemic of cholera.
- Limited healthcare infrastructure and resources necessitated accurate outbreak forecasting for effective relief coordination.
Purpose of the Study:
- To provide timely and realistic projections of the cholera epidemic's extent in Haiti.
- To inform government and partner organizations for coordinated relief and response efforts.
Main Methods:
- Initial projections were developed 5 days after the first reported case.
- Projections were progressively updated using more advanced methods as surveillance data increased.
- Multiple projection methods were employed to estimate cases and hospitalizations.
Main Results:
- The first projection estimated 105,000 cholera cases in the first year.
- Subsequent projections estimated up to 652,000 cases and 163,000-247,000 hospitalizations.
- Surveillance data indicated these projections were reasonable approximations of the observed epidemic.
Conclusions:
- Real-time epidemic projections were vital for planning and implementing response measures.
- Effective forecasting enabled better coordination between Haitian ministries and external aid organizations.
- The study highlights the importance of predictive modeling in managing public health crises.
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:
01:25Cholera
Cholera is an acute gastrointestinal disease caused by the Gram-negative bacterium Vibrio cholerae. It is transmitted primarily via the fecal-oral route through the ingestion of contaminated water or food.Vibrio cholerae is a motile, Gram-negative bacterium of the family Vibrionaceae, primarily associated with waterborne outbreaks in areas with inadequate sanitation. Although over 200 serogroups of V. cholerae exist, only O1 and O139 are responsible for epidemic cholera. The O1 serogroup,...
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...
01:26Principles of Disease Surveillance
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...
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...
01:25Modeling with Differential Equations
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...