Dengue is one of the most rapidly spreading vector-borne diseases, which is considered to be a major health concern in tropical and sub-tropical countries. It is strongly believed that the spread and abundance of vectors are related to climate. Construction of climate-based mathematical model that integrates meteorological factors into disease infection model becomes compelling challenge since the climate is positively associated with both incidence and vector existence. A host-vector model is constructed to simulate the dynamic of transmission. The infection rate parameter is replaced with the time-dependent coefficient obtained by optimization to approximate the daily dengue data. Further, the optimized infection rate is denoted as a function of climate variables using the Autoregressive Distributed Lag (ARDL) model. The infection parameter can be extended when updated daily climates are known, and it can be useful to forecast dengue incidence. This approach provides proper prediction, even when tested in increasing or decreasing prediction windows. In addition, associations between climate and dengue are presented as a reversed slide-shaped curve for dengue-humidity and a reversed U-shaped curves for dengue-temperature and dengue-precipitation. The range of optimal temperature for infection is 24.3–30.5 °C. Humidity and precipitation are positively associated with dengue upper the threshold 70% at lag 38 days and below 50 mm at lag 50 days, respectively. Identification of association between climate and dengue is potentially useful to counter the high risk of dengue and strengthen the public health system and reduce the increase of the dengue burden.

Dengue is a re-emergent vector-borne disease affecting large portions of the world's population living in the tropics and subtropics. The virus is transmitted through the bites of female Aedes aegypti mosquitoes, and it is widely believed that these bites occur primarily in the daytime. The transmission of dengue is a complicated process, and one of the main sources of this complexity is due to the movement of people, e.g. between home and their places of work. Hence, the mechanics of disease progression may also differ between day and night. A discrete-time multi-patch dengue transmission model which takes into account the mobility of people as well as processes of infection, recovery, recruitment, mortality, and outbound and return movements is considered here. One patch (the city) is connected to all other patches (the villages) in a spoke-like network. We obtain here the basic reproductive ratio (ℛ₀) of the transmission model which represents a threshold for an epidemic to occur. Dynamical analysis for vector control, human treatment and vaccination, and different kinds of mobility are performed. It is shown that changes in human movement patterns can, in some situations, affect the ability of the disease to persist in a predictable manner. We conclude with biological implications for the prevention and control of dengue virus transmission.