Vector-borne disease risk assessment is crucial to optimize surveillance, preventative measures (vector control), and resource allocation (medical supplies). High arthropod abundance and host interaction strongly correlate to vector-borne pathogen transmission. Increasing host density and movement increases the possibility of local and long-distance pathogen transmission. Therefore, we developed a risk-assessment framework using climate (average temperature and rainfall) and host demographic (host density and movement) data, particularly suitable for regions with unreported or underreported incidence data. This framework consisted of a spatiotemporal network-based approach coupled with a compartmental disease model and nonhomogeneous Gillespie algorithm. The correlation of climate data with vector abundance and host-vector interactions is expressed as vectorial capacity-a parameter that governs the spreading of infection from an infected host to a susceptible one via vectors. As an example, the framework is applied for dengue in Bangladesh. Vectorial capacity is inferred for each week throughout a year using average monthly temperature and rainfall data. Long-distance pathogen transmission is expressed with human movement data in the spatiotemporal network. We have identified the spatiotemporal suitability of dengue spreading in Bangladesh as well as the significant-incidence window and peak-incidence period. Analysis of yearly dengue data variation suggests the possibility of a significant outbreak with a new serotype introduction. The outcome of the framework comprised spatiotemporal suitability maps and probabilistic risk maps for spatial infection spreading. This framework is capable of vector-borne disease risk assessment without historical incidence data and can be a useful tool for preparedness with accurate human movement data.