How do you cope with the complexity and dynamism of health systems and outcomes?

2 What are the challenges of forecast verification for health?

One of the main challenges of forecast verification for health is the availability and quality of data. Health data can be scarce, incomplete, inconsistent, or delayed, making it difficult to obtain and compare the observed values with the predicted ones. Moreover, health data can be affected by measurement errors, reporting biases, and definitional issues, which can introduce noise and variability in the data. Another challenge is the choice and interpretation of verification metrics. There are many different ways to measure the accuracy, skill, and value of forecasts, such as mean absolute error, root mean square error, correlation, reliability, resolution, and sharpness. However, these metrics can have different meanings and implications depending on the context, scale, and purpose of the forecasts. For example, a forecast that is accurate at the national level may not be useful at the local level, or a forecast that is reliable in the short term may not be skillful in the long term.

3 How to verify forecasts for health?

There is no single best way to verify forecasts for health, as different methods may suit different situations and objectives. However, some general steps and principles can guide the verification process. First, it is important to define the scope and goals of the verification, such as what are the variables, time horizons, and spatial scales of interest, and what are the criteria and standards for evaluating the forecasts. Second, it is essential to collect and prepare the data for verification, such as cleaning, aggregating, and aligning the data, and addressing any gaps or inconsistencies. Third, it is advisable to use multiple verification metrics and methods, such as graphical, statistical, and probabilistic techniques, to capture different aspects and dimensions of the forecast performance. Fourth, it is necessary to interpret and communicate the verification results, such as explaining the meaning and implications of the metrics, highlighting the sources and levels of uncertainty and error, and providing feedback and recommendations for improving the forecasts.

4 What are some examples of forecast verification for health?

Forecast verification for health can be applied to various domains and topics, such as infectious diseases, chronic conditions, mortality, morbidity, and health care utilization. For instance, one can verify the forecasts of COVID-19 cases, deaths, and hospitalizations by comparing them with the reported data and using metrics such as error, bias, and coverage. Alternatively, one can verify the forecasts of life expectancy, disability, and health expenditure by comparing them with the observed trends and using metrics such as correlation, skill, and value. In both cases, one can also use graphical methods, such as plots, maps, and histograms, to visualize and compare the forecasts and the data.

5 What are the benefits of forecast verification for health?

Forecast verification for health can have several benefits for researchers, practitioners, and policymakers. First, it can enhance the credibility and confidence of the forecasts, as it can demonstrate their accuracy, skill, and value. Second, it can improve the quality and utility of the forecasts, as it can reveal their strengths and weaknesses, and suggest ways to refine and update them. Third, it can facilitate the learning and innovation of the forecasting models, as it can provide feedback and insights for developing and testing new methods and approaches. Fourth, it can support the decision-making and planning for health, as it can inform and guide the allocation of resources, the design of interventions, and the evaluation of outcomes.