Reliable and real-time municipality-level predictive modeling and forecasts of infectious disease activity have the potential to transform the way public health decision-makers design interventions such as information campaigns, preemptive/reactive vaccinations, and vector control, in the presence of health threats across the world. While the links between disease activity and factors such as: human mobility, climate and environmental factors, socio-economic determinants, and social media activity have long been known in the epidemic literature, few efforts have focused on the evident need of developing an open-source platform capable of leveraging multiple data sources, factors, and disparate modeling methodologies, across a large and heterogeneous nation to monitor and forecast disease transmission, over four geographic scales (nation, state, city, and municipal). The overall goal of this project is to develop such a platform. Our long-term goal is to investigate effective ways to incorporate the findings from multiple disparate studies on disease dynamics around the globe with local and global factors such as weather conditions, socio- economic status, satellite imagery and online human behavior, to develop an operational, robust, and real- time data-driven disease forecasting platform. The objective of this grant is to leverage the expertise of three complementary scientific research teams and a wealth of information from a diverse array of data sources to build a modeling platform capable of combining information to produce real-time short term disease forecasts at the local level. As part of this, we will evaluate the predictive power of disparate data streams and modeling approaches to monitor and forecast disease at multiple geographic scales--nation, state, city, and municipality--using Brazil as a test case. Additionally, we will use machine learning and mechanistic models to understand disease dynamics at multiple spatial scales, across a heterogeneous country such as Brazil. Our specific aims will (1) Assess the utility of individual data streams and modeling techniques for disease forecasting; (2) Fuse modeling techniques and data streams to improve accuracy and robustness at the four spatial scales; (3) Characterize the basic computational infrastructure necessary to build an operational disease forecasting platform; and (4) Validate our approach in a real-world setting. This contribution is significant because It will advance our scientific knowledge on the accuracy and limitations of disparate data streams and multiple modeling approaches when used to forecast disease transmission. Our efforts will help produce operational and systematic disease forecasts at a local level (city- and municipality-level). Moreover, we aim at building a new open-source computational platform for the epidemiological community to use as a knowledge discovery tool. Finally, we aim at developing this platform under the guidance of a Subject Matter Expert (SME) panel comprising of WHO, CDC, academics, and local and federal stakeholders within Brazil. The proposed approach is innovative because few efforts have focused on developing an open-source computational platform capable of combining disparate data sources and drivers, across a heterogeneous and large nation, into multiple modeling approaches to monitor and forecast disease transmission, over multiple geographic scales.. In addition, we propose to investigate how to best combine modeling approaches that have, to this date, been developed and interpreted independently, namely, traditional epidemiological mechanistic models and novel machine-learning predictive models, in order to produce accurate and robust real-time disease activity estimates and forecasts.