137 - Advances and challenges in vector-borne forecasting and modeling

Abstract: Vector-borne diseases present unique challenges due to the complex interactions of pathogens, multiple vectors and hosts, and the environment. We never have all the information on these components in real-time and face challenging decisions with limited resources and diverse mitigation options. In the last decade, infectious disease modeling and forecasting has gone from research aims to real-time public health tools that can help fill in these gaps. Ensemble forecasts have proven effective for short-term situational awareness and collaborative modeling of future scenarios has provided invaluable insight for longer term planning. Nonetheless many challenges remain for vector-borne diseases. Despite integrating information about vector biology and the environment, most forecasts for vector-borne diseases offer little improvement over baseline models informed only by historical disease data. Models have also been used extensively to assess interventions such as the potential impacts of vector control, Wolbachia, and vaccines on dengue. However, validation of these scenario-based models is even more difficult, making it hard to assess their reliability and integrate them into routine use. One key challenge is the limited availability of real-time case data and data on vectors and other hosts. Moreover, while models can represent complex transmission ecology that are critical to the spatiotemporal dynamics of vector-borne diseases, the interactions between varied environments, vectors, and hosts are difficult to measure and easy to overestimate. Improving real-time data availability and assessing the value of detailed vector and non-human host population data could drive advances in model reliability and utility. Models will be most useful if they are validated, generalizable, accessible, scalable, and directly integrated into vector control and public health practice. They will be critical for difficult and important challenges such as optimizing the spatiotemporal application of vector control, assessing the relative effectiveness of intervention strategies, and preparing for an uncertain future.