Exploring local and global regression models to estimate the spatial variability of Zika and Chikungunya cases in Recife, Brazil

Anjos, Rafael Silva dos; Nóbrega, Ranyére Silva; Ferreira, Henrique dos Santos; Lacerda, António Pais de; Neves, Nuno de Sousa

doi:10.1590/0037-8682-0027-2020

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…Twenty papers used a geographically weighted regression (GWR) model [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65] which fits local regression models to each observation or region rather than a single global model [66]. Each local model has different coefficients, estimated using information from connected observations that are weighted by a function of distance, such as the one shown in figure 3c.…”

Section: Local Regression Modelsmentioning

confidence: 99%

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

Lee

Jarvis

Edmunds

et al. 2021

J. R. Soc. Interface.

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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.

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Section: Local Regression Modelsmentioning

confidence: 99%

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

Lee

Jarvis

Edmunds

et al. 2021

J. R. Soc. Interface.

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Zika, chikungunya and co-occurrence in Brazil: space-time clusters and associated environmental–socioeconomic factors

Gardini Sanches Palasio,

Marques Moralejo Bermudi,

Luiz de Lima Macedo

et al. 2023

Sci Rep

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Chikungunya and Zika have been neglected as emerging diseases. This study aimed to analyze the space-time patterns of their occurrence and co-occurrence and their associated environmental and socioeconomic factors. Univariate (individually) and multivariate (co-occurrence) scans were analyzed for 608,388 and 162,992 cases of chikungunya and Zika, respectively. These occurred more frequently in the summer and autumn. The clusters with the highest risk were initially located in the northeast, dispersed to the central-west and coastal areas of São Paulo and Rio de Janeiro (2018–2021), and then increased in the northeast (2019–2021). Chikungunya and Zika demonstrated decreasing trends of 13% and 40%, respectively, whereas clusters showed an increasing trend of 85% and 57%, respectively. Clusters with a high co-occurrence risk have been identified in some regions of Brazil. High temperatures are associated with areas at a greater risk of these diseases. Chikungunya was associated with low precipitation levels, more urbanized environments, and places with greater social inequalities, whereas Zika was associated with high precipitation levels and low sewage network coverage. In conclusion, to optimize the surveillance and control of chikungunya and Zika, this study’s results revealed high-risk areas with increasing trends and priority months and the role of socioeconomic and environmental factors.

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The influence of the way of regression on the results obtained by the receptorial responsiveness method (RRM), a procedure to estimate a change in the concentration of a pharmacological agonist near the receptor

Ováry

Viczjan

Erdei

et al. 2023

Preprint

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The receptorial responsiveness method (RRM) enables the estimation of a change in the concentration of a degradable agonist, near its receptor, by fitting its model to (at least) two concentration-effect (E/c) curves of a stable agonist of this receptor. One curve should be generated before this change in concentration, while the other one after this change, in the same (or in identical) biological system(s). It follows that RRM yields a surrogate parameter ('cx'), the concentration of the stable agonist that is equieffective with the change in the concentration of the degradable agonist. However, the curve fitting can be implemented several ways, which can affect accuracy, precision and convenience of use. This study utilized data of previous ex vivo investigations. Known concentrations of stable agonists were estimated with RRM by performing individual (local) or global fitting (with one or two model(s)), combined with the use of a logarithmic (logcx) or non-logarithmic parameter (cx), and with ordinary least-squares or robust regression. We found that the individual regression, the most complicated option, was the most accurate, followed closely by the moderately complicated two-model global regression and then by the easy-to-perform one-model global regression. The two-model global fitting was the most precise, followed by the individual fitting (closely) and by the one-model global fitting (from afar). The use of cx and robust regression did not, whereas pairwise fitting (i.e. fitting only two E/c curves at once) did improve the quality of estimation. Thus, the two-model global fitting, performed pairwise, is recommended for RRM, but the individual fitting is a good alternative.

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Exploring local and global regression models to estimate the spatial variability of Zika and Chikungunya cases in Recife, Brazil

Cited by 5 publications

References 18 publications

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

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

Zika, chikungunya and co-occurrence in Brazil: space-time clusters and associated environmental–socioeconomic factors

The influence of the way of regression on the results obtained by the receptorial responsiveness method (RRM), a procedure to estimate a change in the concentration of a pharmacological agonist near the receptor

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