Geographically weighted Poisson regression for disease association mapping

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Spatially Weighted Interaction Models (SWIM) (2016, methodology): “The Poisson origin- focused model then is formulated as follows: λuij D exp k uif gCa uif glnvi C g uif glnwj Cb uif glndij ; ð11Þ where λuij denotes the flow between an origin i and a destination j weighted according to the distance origin i is from u. Equation 11 can be calibrated using the geogra”
Estimation and inference in spatially varying coefficient models (2018, unknown): “, 2017), epidemiology (Nakaya et al., 2005), meteorology (Lu et al.”
A Moran coefficient-based mixed effects approach to investigate spatially varying relationships (2016, methodology): “GWR has been widely applied in socioeconomic studies 6 (e.g., Bitter et al., 2007; Huang et al., 2010), ecological studies (e.g., Wang et al., 2005; 7 Austin, 2007), health studies (e.g., Nakaya et al., 2005; Hu et al., 2012), and many others.”
Semiparametric geographically weighted generalisedlinear modelling in GWR 4.0 (2009, methodology): “Combining geographically local scoring and back-fitting algorithms, we can compute the estimates of coefficients and indicators for model diagnostics including standard errors of coefficients, degree-of-freedom and information criterion such as AICc (corrected AIC) to decide an optimal bandwidth siz”