Primary blood-hosts of mosquitoes are influenced by social and ecological conditions in a complex urban landscape.

Background: Temperate urban landscapes support persistent and growing populations of Culex and Aedes mosquito vectors. Large urban mosquito populations can represent a significant risk for transmission of emergent arboviral infection. However, even large mosquito populations are only a risk to the animals they bite.

A tale of two city blocks: differences in immature and adult mosquito abundances between socioeconomically different urban blocks in Baltimore (Maryland, USA).

Infrastructure degradation in many post-industrial cities has increased the availability of potential mosquito habitats, including container habitats that support infestations of invasive disease-vectors. This study is unique in examining both immature and adult mosquito abundance across the fine-scale variability in socio-economic condition that occurs block-to-block in many cities. We hypothesized that abundant garbage associated with infrastructure degradation would support greater mosquito production but instead, found more mosquito larvae and host-seeking adults (86%) in parcels across the higher socio-economic, low-decay block.

Higher mosquito production in low-income neighborhoods of Baltimore and Washington, DC: understanding ecological drivers and mosquito-borne disease risk in temperate cities.

Mosquito-vectored pathogens are responsible for devastating human diseases and are (re)emerging in many urban environments. Effective mosquito control in urban landscapes relies on improved understanding of the complex interactions between the ecological and social factors that define where mosquito populations can grow. We compared the density of mosquito habitat and pupae production across economically varying neighborhoods in two temperate U.

Data-model fusion to better understand emerging pathogens and improve infectious disease forecasting.

Ecologists worldwide are challenged to contribute solutions to urgent and pressing environmental problems by forecasting how populations, communities, and ecosystems will respond to global change. Rising to this challenge requires organizing ecological information derived from diverse sources and formally assimilating data with models of ecological processes. The study of infectious disease has depended on strategies for integrating patterns of observed disease incidence with mechanistic process models since John Snow first mapped cholera cases around a London water pump in 1854.