The relationship between epidemiology forecasting and data sources is a complex one. Generating accurate forecasts relies heavily on data, and factors such as real-world data and artificial intelligence (AI) have an increasing impact.

Factors affecting forecast quality and accuracy

There are a few factors that contribute to the accuracy of epidemiological forecasts. Firstly, the availability of data, which varies based on the disease and the country in question. Countries like the US tend to have more data available, while data from emerging markets is often limited. The epidemiological model itself also affects accuracy, with more granular models requiring more specific data.

The best way to compensate for data limitations is to combine different data sources. By integrating epi data, patient records, claims data, and market research, forecasters can obtain a better understanding of disease dynamics and patient populations.

Why are epidemiological models so important?

Epidemiological models help you to understand the underlying drivers of diseases and to predict market dynamics. They delve deeper than sales-based models to help explain changes in patient populations, identify key drivers behind market size, and allow for scenario planning based on potential future changes.

Pharma companies can use the three model types – epi-based or cross-sectional, opportunity-based, and patient flow modelling to inform their strategic planning and decision-making processes, even in situations where sales data is limited or unavailable.

By considering factors such as prevalence rates, diagnosis rates, and aging populations, epi models can accurately describe the drivers of market size and structure. As a forecaster, you can then evaluate the impact of various inputs on sales and anticipate changes in market dynamics. So, if disease prevalence varies by age, by segmenting the forecast by age cohorts you can better understand the impact of an aging population on market size.

Leveraging technology for improved epidemiology forecasting

Factors like AI and machine learning (ML) offer real opportunities for improving epidemiological forecasting. AI models can leverage big data to predict disease outbreaks and understand their potential behaviour in the future. This rapidly evolving technology also has the potential to shorten R&D timelines, identify new treatment options, reduce drug development costs, and enable earlier diagnosis.

These advancements necessitate the use of good epidemiological models to accurately incorporate the impacts of AI and technology-driven changes in disease management. By employing advanced forecast modelling software, forecasters can better analyse the impact of earlier diagnosis and improved cure rates on market size and patient outcomes.

Challenges and future perspectives

Despite the promise of advanced technologies and the increasing availability of data, there are still challenges in leveraging them for epidemiological forecasting. The availability and quality of data vary across countries and diseases. Data protection regulations, such as those in Germany, can pose hurdles in using available data for disease management and forecasting. And rare disease populations present a challenge for AI models that rely heavily on big data.

How Evaluate supports epidemiology forecasting

Our innovative forecasting solutions help you gain deeper insights for more effective epidemiology forecasting.

• Epi+ forecasting software eliminates the need for external specialist support and streamlines the forecasting process. With built-in risk and demand analysis, consolidation, and peak share prediction to enhance your forecasting capabilities.
• Evaluate Epi is an evidence-based source for patient populations that provides the foundation of accurate and robust asset valuations and forecasts, Access detailed patient segmentation data, covering over 530 diseases and over 14,500 sub-populations, to measure the true value of a product.

If you’d like to know more about how Evaluate and J+D Forecasting can help you to harness the power of epidemiological forecasting, you can get in touch with the team here.