Disease Surveillance Tools Infographic for Forecasting - SSG, LLC
Disease surveillance tools are digital systems that collect, organize, and analyze epidemiological data in real time. They support disease forecasting by helping public health agencies spot trends, monitor outbreaks, and generate predictive insights that improve preparedness and response efforts.
Key Takeaways
- Effective disease forecasting depends on the ability to collect, standardize, and share surveillance data across systems in near real time, enabling earlier detection of emerging public health threats.
- Gaps in data quality, reporting delays, and disconnected systems significantly reduce forecasting accuracy, limiting the ability of public health teams to respond proactively.
- Modern disease surveillance tools improve forecasting outcomes by increasing data visibility, supporting trend analysis, and enabling more informed, timely decision-making.
Tools for Disease Forecasting: Using Systems to Make Forecasting Possible and Effective
What is Disease Forecasting?
Public health departments track various factors related to the spread of disease to predict where and how fast it will spread in the future, so officials can introduce policies and provide resources that reduce the risk of infection.
When done correctly, forecasting will reduce the economic impact of the outbreak, improve the healthcare industry’s understanding of the disease, and ultimately help save lives.
Challenges of Disease Forecasting
Many departments use outdated electronic surveillance systems that keep data siloed in separate databases, which delays the release of the disease forecast while limiting its accuracy.
The forecast is only as reliable as the data being used to create it.
How SSG’s Casetivity Improves Disease Forecasting
Casetivity is a customizable, modular low-code platform designed to help public health departments create a configurable end-to-end process for disease forecasting, which can then be automated for continuous monitoring and surveillance.
How to Forecast the Spread of Disease with Casetivity
1. Data Collection
Public Health collects data related to the spread of disease, including:
- Test results and infection rates
- Reported symptoms
- Vaccinations rates
- School/work absences
- Over-the-counter prescription sales
2. Data Sharing
Casetivity standardizes the format of the data before uploading it to a single interface for analysis and review.
Automatically cleanses and verifies the data for more accurate reporting
Complies with federal and state privacy laws while giving stakeholders access to essential information
Open and flexible business process engine for mapping out specific workflows and reconfiguring based on organizational needs
Allows staff to focus on the most important factors and variables for tracking the spread of disease
3. Forecasting Visibility
Casetivity provides data for disease forecasting and assessing the severity of the outbreak in the community.
If cases spike, the department can share its findings with the CDC and take steps to reduce the spread by focusing on the leading causes of infection.
The data informs public health announcements to give individuals and businesses up-to-date information on how they can protect themselves.
Ongoing analysis tracks trends over time to help providers and officials better understand the effects of the disease and how it spreads.
Contact SSG to learn more about Casetivity and how it is changing disease surveillance for the better.
FAQs
- What tools are used for disease forecasting in public health?
Public health agencies use a combination of disease surveillance platforms, analytics software, reporting systems, dashboards, and predictive modeling tools to support disease forecasting. These systems collect and process data from laboratories, hospitals, electronic health records, and public health reporting networks.
Platforms like Casetivity are often employed to automate these workflows, enabling continuous data ingestion, normalization, and visualization.
- How do disease surveillance tools improve forecast accuracy?
Disease surveillance tools improve forecast accuracy by providing timely, standardized, and comprehensive public health data. Real-time reporting and data integration help analysts identify trends earlier and reduce visibility gaps across jurisdictions. When agencies can access high-quality data quickly, forecasting models can generate more reliable projections and support faster decision-making.
- What is the difference between disease surveillance and disease forecasting?
Disease surveillance focuses on collecting, monitoring, and analyzing current public health data to identify health events and trends. Disease forecasting uses surveillance data to predict how diseases may spread or evolve in the future. In short, disease surveillance provides the foundation for forecasting by supplying the data needed to build predictive models and assess future risks.