Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

Abstract

Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.

Spatio-temporal dynamics of dengue-related deaths and associated factors

“…To avoid this, we considered the existence of spatial and temporal dependence in the phenomenon studied, which has not been the rule in spatio-temporal studies on dengue 38 . To this end, spatially and temporally structured random effects were included in our models, in addition to the interactions between them, which allowed the self-correlation found in the data to be incorporated into the structure of the models 22 , 39 . Additionally, the hierarchical Bayesian models used in the present study are recognized as suitable for investigating spatiotemporal disease patterns 40 .…”

The impact of climate suitability, urbanisation, and connectivity on the expansion of dengue in 21st century Brazil

“…Previous studies have found that imported cases driven by human movement are responsible for dengue outbreaks in temperate cities [ 24 , 25 ]. The choice of spatial connectivity assumption and data can lead to very different results and the use of the REGIC levels of influence as a spatial covariate rather than including the direct links may miss some important patterns [ 27 ]. Future work will aim to incorporate the complex urban network from the REGIC studies into a statistical framework to account for direct and indirect links between metropoles and regional capitals, and smaller urban centres in their hinterland.…”

“…The expansion of Aedes aegypti and the arboviruses they transmit into rural parts of the Amazon has been linked to connections to and within the area by air, road or boat [ 13 , 26 ]. Despite this, the investigation of spatial connections created by human movement is little explored in the literature and the vast majority of spatial modelling studies of mosquito-borne diseases assume connectivity is based on distance alone [ 27 ]. Brazilian cities are connected to one another within a complex urban network, described within the Regions of influence of cities ("Regiões de Influência das Cidades”, REGIC) studies carried out by the Brazilian Institute of Geography and Statistics [ 28 , 29 ].…”

Machine learning in handling disease outbreaks: a comprehensive review

“…Basu et al [18] discussed the application Bulletin of Electr Eng & Inf ISSN: 2302-9285  of machine learning for diabetes clinical epidemiology to improve risk stratification and prediction. Special issues of review studies have been conducted for particular diseases, such as cancer risk assessment [19], mosquito-borne disease transmission [20], COVID-19 diagnosis [21], and others. From the analysis of previous studies, we capture that the existing research discusses the use of ML for specific disease problems or certain purposes such as disease detection, risk prediction, and spread estimation.…”