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 6 publications

(5 citation statements)

References 18 publications

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“…The geographically weighted regression method has found application in studies regarding various infectious diseases: COVID-19 [107][108][109], AIDS [110], the Zika virus [111,112], tuberculosis [113], malaria [114], and others [115,116]. The GWR method can be used to estimate the effects of explanatory variables (in the case of this study, a number of social, demographic, and environmental factors) on the dependent variable (number of cases of SARS-CoV-2 in Polish counties) and also to identify the counties in which the influence of the variables differs and to explore and interpret the spatial non-stationarity.…”

Section: Methodsmentioning

confidence: 99%

Influence of the Demographic, Social, and Environmental Factors on the COVID-19 Pandemic—Analysis of the Local Variations Using Geographically Weighted Regression

Rząsa

Ciski

2022

IJERPH

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As the COVID-19 pandemic continues, an increasing number of different research studies focusing on various aspects of the pandemic are emerging. Most of the studies focus on the medical aspects of the pandemic, as well as on the impact of COVID-19 on various areas of life; less emphasis is put on analyzing the influence of socio-environmental factors on the spread of the pandemic. In this paper, using the geographically weighted regression method, the extent to which demographic, social, and environmental factors explain the number of cases of SARS-CoV-2 is explored. The research was performed for the case-study area of Poland, considering the administrative division of the country into counties. The results showed that the demographic factors best explained the number of cases of SARS-CoV-2; the social factors explained it to a medium degree; and the environmental factors explained it to the lowest degree. Urban population and the associated higher amount and intensity of human contact are the most influential factors in the development of the COVID-19 pandemic. The analysis of the factors related to the areas burdened by social problems resulting primarily from the economic exclusion revealed that poverty-burdened areas are highly vulnerable to the development of the COVID-19 pandemic. Using maps of the local R2 it was possible to visualize how the relationships between the explanatory variables (for this research—demographic, social, and environmental factors) and the dependent variable (number of cases of SARS-CoV-2) vary across the study area. Through the GWR method, counties were identified as particularly vulnerable to the pandemic because of the problem of economic exclusion. Considering that the COVID-19 pandemic is still ongoing, the results obtained may be useful for local authorities in developing strategies to counter the pandemic.

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Section: Methodsmentioning

confidence: 99%

Influence of the Demographic, Social, and Environmental Factors on the COVID-19 Pandemic—Analysis of the Local Variations Using Geographically Weighted Regression

Rząsa

Ciski

2022

IJERPH

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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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“…In this paper (similarly to the recent ones: Szabo et al, 2019a ; Viczjan et al, 2021 ; 2022 ), this procedure will be referred to as individual regression. (Alternatively, this type of fitting can be called local regression, as opposed to global regression: Anjos et al, 2020 ; Spatial Data Science, 2024 ). On the other hand, the intact and distorted E/c curves can be fitted at once, via global regression.…”

Section: Introductionmentioning

confidence: 99%

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

Ovari,

Viczjan,

Erdei

et al. 2024

Front. Pharmacol.

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The receptorial responsiveness method (RRM) enables the estimation of a change in concentration of an (even degradable) agonist, near its receptor, via curve fitting to (at least) two concentration-effect (E/c) curves of a stable agonist. One curve should be generated before this change, and the other afterwards, in the same system. It follows that RRM yields a surrogate parameter (“cx”) as the concentration of the stable agonist being equieffective with the change in concentration of the other agonist. However, regression can be conducted several ways, which can affect the accuracy, precision and ease-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, this latter with one or two model(s), using a logarithmic (logcx) or a nonlogarithmic (cx) parameter (the latter in a complex or in a simplified equation), with ordinary least-squares or robust regression, and with an “all-at-once” or “pairwise” fitting manner. We found that the simplified model containing logcx was superior to all alternative models. The most complicated individual regression 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). Pairwise fitting (two E/c curves at once) improved the estimation. Thus, the two-model global fitting, performed pairwise, and the individual fitting are recommended for RRM, using the simplified model containing logcx.

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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 6 publications

References 18 publications

Influence of the Demographic, Social, and Environmental Factors on the COVID-19 Pandemic—Analysis of the Local Variations Using Geographically Weighted Regression

Influence of the Demographic, Social, and Environmental Factors on the COVID-19 Pandemic—Analysis of the Local Variations Using Geographically Weighted Regression

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

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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