AbstractLong term surveillance of vectors and arboviruses is an integral aspect of disease prevention and control systems in countries affected by increasing risk. Yet, little effort has been made to adjust space-time risk estimation by integrating disease case counts with vector surveillance data, which may result in inaccurate risk projection when several vector species are present, and little is known about their likely role in local transmission. Here, we integrate 13 years of dengue case surveillance and associatedAedesoccurrence data across 462 localities in 63 districts to estimate the risk of infection in the Republic of Panama. Our space-time modelling approach detected the presence of five clusters, which varied by duration, relative risk, and spatial extent after incorporating vector species as covariates. Dengue prevalence (n = 49,910) was predicted by the presence of residentAedes aegyptialone, while all other covariates exhibited insignificant statistical relationships with it, including the presence and absence of invasiveAedes albopictus. Furthermore, theAe. aegyptimodel contained the highest number of districts with more dengue cases than would be expected given baseline population levels. This implies that arbovirus case surveillance coupled with entomological surveillance can affect cluster detection and risk estimation, improving efforts to understand outbreak dynamics at national scales.Author SummaryDengue cases have increased in tropical regions worldwide owing to climate change, urbanization, and globalization facilitating the spread ofAedesmosquito vectors. National surveillance programs monitor trends in dengue fever and inform the public about epidemiological scenarios where outbreak preventive actions are most needed. Yet, most estimations of dengue risk so far derive only from disease case data, ignoringAedesoccurrence as a key aspect of dengue transmission dynamic. Here we illustrate how incorporating vector presence and absence as a model covariate can considerably alter the characteristics of space-time cluster estimations of dengue cases. We further show thatAe. aegyptihas likely been a greater driver of dengue infection in high risk districts of Panama thanAe. albopictus, and provide a discussion of possible public health implications of both spatial and non-spatial model outcomes.
A Sparse Representation of Social Media, Internet Query, and Surveillance Data to Forecast Dengue Case Number using Hybrid DecompositionBidirectional LSTM
