A framework for evaluating the effects of observational type and quality on vector-borne disease forecast.

•Accuracy of operational disease forecasts depends on the quality of observations available for system optimization.•The potential benefits of observational data types on forecast accuracy can be assessed using a synthetic testing framework.•In our synthetic tests, we find that forecasts improve as observational error decreases.•The assimilation of vector infection rates improves predictive accuracy over human observations alone.•Reducing uncertainty in model parameter values can improve forecast skill, particularly for 1- to 4-week ahead targets.