Weather forecasting has seen significant improvements in accuracy over the years, thanks to advancements in technology and data sources. In comparison, disease forecasting and outbreak prediction still have a long way to go. However, we can learn valuable lessons from weather forecasting to enhance disease forecasting and response to health emergencies. In this article, Dr. Dylan George, head of CDC’s Center for Forecasting and Outbreak Analytics (CFA), shares insights on how disease forecasting can follow the lead of weather forecasting.
Lesson 1: More data sources lead to greater accuracy
Weather forecasting has benefited from an explosion in the volume and variety of data sources. In addition to traditional weather station data, satellite imagery, remote sensors, radar stations, and other sources contribute to more accurate forecasts. Similarly, disease surveillance data has been expanding, with the addition of syndromic and wastewater surveillance data, as well as non-traditional sources like internet search trends and social media surveys. To improve disease forecasting, there is a need for continued growth in the volume and variety of disease surveillance data, as well as investment in harmonizing these disparate sources into a unified view of community infection.
Lesson 2: Innovative modeling enables advanced disease surveillance
Advances in weather modeling and simulation, driven by machine learning algorithms and increased computing power, have significantly improved weather forecasting. Disease forecasting, on the other hand, still relies largely on traditional epidemiological models. However, recent developments incorporating machine learning algorithms have shown improvements in forecast accuracy. Continued progress in developing innovative modeling techniques will be crucial for robust disease forecasting and outbreak prediction. Public health authorities, researchers, and corporations can collaborate to advance the application of advanced analytics to disease surveillance.
Lesson 3: Modern platforms deliver data and insights to the public
The availability of accurate weather forecasts is now widespread, thanks to modern technology infrastructure like the internet and mobile applications. In contrast, disease forecasts are not readily accessible to the public. Public health authorities need to invest in modern platforms that can process data, generate actionable insights, and disseminate them to the public. This will enable individuals to adjust their plans and behaviors to minimize morbidity and mortality related to infectious disease. The CDC’s Data Modernization Initiative and public-private collaborations, such as IBM’s partnership with Canadian health authorities, demonstrate the potential for modern platforms in disease surveillance.
Investments in improving the accuracy and availability of disease forecasts would not only save lives but also reduce the economic burden of unmitigated infectious disease outbreaks, as shown by the impact of accurate weather forecasting. By applying the lessons learned from weather forecasting, we can enhance disease forecasting and better respond to health emergencies.
In this article, Dr. Dylan George, head of CDC’s Center for Forecasting and Outbreak Analytics (CFA), discusses the lessons that can be learned from weather forecasting to improve disease forecasting and outbreak prediction. The three key lessons include the importance of more data sources for greater accuracy, the role of innovative modeling techniques in advanced disease surveillance, and the significance of modern platforms for delivering data and insights to the public. By applying these lessons, we can enhance disease forecasting and mitigate the impact of infectious disease outbreaks.
1. Why are weather forecasts more accurate than disease forecasts?
Weather forecasts have seen significant advancements in technology and data sources, allowing for greater accuracy. Disease forecasting still relies on traditional methods and has a limited volume of surveillance data. However, there is ongoing progress in incorporating advanced modeling techniques and expanding data sources for disease forecasting.
2. Can non-traditional data sources improve disease surveillance?
Non-traditional data sources, such as syndromic surveillance, wastewater surveillance, internet search trends, and social media user surveys, have the potential to provide real-time and hyperlocal information for disease surveillance. Integrating these data sources with traditional case reporting can enhance disease forecasting and outbreak prediction.
3. How can modern platforms improve the availability of disease forecasts?
Investing in modern platforms that can process data, generate actionable insights, and disseminate them to the public is crucial for improving the availability of disease forecasts. Similar to weather forecasting, a robust technology infrastructure can ensure that disease forecasts are readily accessible to individuals, allowing them to make informed decisions to minimize the impact of infectious diseases.