Disease Forecasting: Enhancing Disease Surveillance through Predictive Analysis
Disease forecasting is essential for protecting the public from the spread of infectious diseases like influenza, COVID-19, and other communicable infections that can lead to illness. However, the accuracy of these reports can always be improved. The more accurate the report, the better state, local, and federal officials can prepare for a health emergency. Similar to all forecasts, disease forecasting aims to predict the unknown. Generating the report involves collecting and analyzing substantial amounts of personal health information. So, how can public health officials and disease surveillance professionals increase the accuracy of their disease forecasts?
It all starts with using the appropriate public health software program. By doing so, data collection workers can automate routine processes without compromising the integrity of the data. Customizable interfaces make it easy for workers to add supplementary information to electronic health records based on the current situation. Alerts and other built-in notification features can also prevent users from accidentally creating duplicate records while also allowing them to review their work.
Learn more about the disease forecasting process and how public health departments are responding to the daily challenges.
What is disease forecasting?
Disease forecasting entails generating a quantitative, probabilistic report that predicts the future spread of an infectious disease. It is similar to weather forecasting, which uses climate data to predict the weather to minimize the potential loss of life and property. The same idea applies to the spread of infectious disease.
Epidemiologists and disease surveillance experts use similar methods to predict future health events, which can help prevent the spread of infection, reduce the loss of life, and help the local healthcare system prepare for a surge in cases. Additionally, health experts create public awareness campaigns to educate individuals and businesses on protective measures to prevent the spread of disease. State and local governments must also report on the spread of various infectious diseases to the Centers for Disease Control and Prevention (CDC).
A disease forecast includes detailed information regarding the future spread of a disease, including the estimated number of people likely to be infected within a certain period of time. It offers insight into the timing, scale, and potential impact of the spread based on current conditions.
How does disease forecasting help with disease surveillance?
Disease forecasting focuses on the events that have yet to occur by making predictive statements based on existing data. Disease surveillance relies on similar data, but it is meant to track the spread of the disease in real-time. However, these systems are often delayed because the test results and laboratory reports can only track the disease after it has spread to individuals in the community. This poses challenges for public health officials in making timely decisions during a public health crisis. It also forces the department to make reactive decisions when time is of the essence, resulting in delays in deploying crucial mitigation resources.
Disease forecasting is vital to providing decision-makers with insights into the likely progression of disease spread. The forecast report offers information on the anticipated speed of disease transmission and the estimated number of people who may be affected. With this knowledge, the department can minimize the potential impact. This may include communicating the risks to the local population, shutting down schools and businesses, and encouraging people most at risk to get vaccinated. If the disease appears to be receding, the government can adopt a more relaxed approach, gradually easing restrictions on schools and businesses.
Knowing how the disease will spread helps disease surveillance experts prepare for the most likely outcome instead of having them react to what has already happened.
What data sources are used for disease forecasting?
A forecast report comprises a wide range of data regarding the ongoing or past spread of the disease. It includes information relevant to public and private healthcare providers who routinely test patients for infection. Ideally, the lab tests are sent electronically to a secure database of public health information. Individual health records within the database may contain details about how the patient caught the disease, the number of people the patient has come in contact with, and their existing symptoms. If a vaccine is available, the forecast also considers the number of people who have been or are projected to receive immunization in the near future when making its prediction.
As the department program collects this data using a disease surveillance platform, forecasters analyze it by comparing it to the existing infection pattern to calculate the increase or decrease intensity. The team may also compare it to previous waves of infection, past flu seasons, and other similar health events to determine how the current data correlates to the future. Notable public events where large crowds are likely to gather are also considered if epidemiologists believe they could influence the disease’s trajectory.
How accurate are disease forecasts?
Disease forecasts are only as accurate as the data they collect from participating providers and facilities. To create an accurate report, the department must collect health data from as many different sources as possible, including any entity involved in the treatment or vaccination process. Every entry must be included in the report without eliminating important details about the case. Environmental factors, including local events, the weather, and other seasonal differences should also be considered. The accuracy of these forecasts will vary widely based on the included data, the quality of the data verification system, and how long the disease has been in circulation.
These forecasts also need to meet the latest guidelines to ensure accuracy. The state and federal government has created a list of best practices for creating a disease forecast, but each state and jurisdiction has its own way of creating a forecast, which may or may not comply with the latest data collection guidelines.
It’s up to each department or disease surveillance unit to create a way to collect and analyze the data that goes into the report while adhering to the latest industry standards. The report may also be incompatible with certain information systems or interfaces, thus limiting accessibility when time is of the essence. The report should also be designed for the intended audience to maximize the potential impact of the findings. For example, if the language is overly complex, the report may be lost on nonscientific audiences.
Forecasts may look days, months, or even years into the future. The farther away the prediction, the harder it is to make an accurate prediction. The most accurate disease forecasts tend to focus on the coming days or weeks.
How to improve disease forecasting
Time is perhaps the biggest challenge when generating a disease forecast. The department needs to work fast to collect and analyze the incoming data, so they can distribute the report to the proper authorities as soon as possible to limit the number of infected people. Technology is speeding up the forecasting process thanks to automated data collection features and even artificial intelligence programs that can analyze the results in just a few seconds. However, these tools are only as good as the data that’s being collected, regardless of how the report is being generated.
When faced with an impending pandemic, the department must dramatically scale up its surveillance efforts. Officials can use an electronic disease surveillance system that automatically ingests and analyzes the data for accuracy while eliminating duplicate entries and notifying users of incomplete or missing fields. It should be easy for providers and community leaders to create an account on the system while validating their identity for privacy purposes. Users should also be able to customize the data collection interface to make room for supplemental information that may affect the accuracy of the report, such as the severity of the patient’s symptoms, how and where they spend their time, and whether they were vaccinated.
Disease forecasting is getting more advanced every year as public health officials incorporate new technologies into their operations. Future pandemics can be mitigated and even prevented with the right forecasting information. Creating an accurate report all comes down to having the right information at the right time. Contact SSG to learn more about disease monitoring and surveillance.