Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence

Abstract

Background: The modifiable areal unit problem (MAUP) arises when the support size of a spatial variable affects the relationship between prevalence and environmental risk factors. Its effect on schistosomiasis modelling studies could lead to unreliable parameter estimates. The present research aims to quantify MAUP effects on environmental drivers of Schistosoma japonicum infection by (i) bringing all covariates to the same spatial support, (ii) estimating individuallevel regression parameters at 30 m, 90 m, 250 m, 500 m and 1 km spatial supports, and (iii) quantifying the differences between parameter estimates using five models. Methods: We modelled the prevalence of Schistosoma japonicum using sub-provinces health outcome data and pixel-level environmental data. We estimated and compared regression coefficients from convolution models using Bayesian statistics. Results: Increasing the spatial support to 500 m gradually increased the parameter estimates and their associated uncertainties. Abrupt changes in the parameter estimates occur at 1 km spatial support, resulting in loss of significance of almost all the covariates. No significant differences were found between the predicted values and their uncertainties from the five models. We provide suggestions to define an appropriate spatial data structure for modelling that gives more reliable parameter estimates and a clear relationship between risk factors and the disease. Conclusions: Inclusion of quantified MAUP effects was important in this study on schistosomiasis. This will support helminth control programmes by providing reliable parameter estimates at the same spatial support and suggesting the use of an adequate spatial data structure, to generate reliable maps that could guide efficient mass drug administration campaigns.

Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln

“…Such an estimated density of points at one location can be derived by applying kernel smoothing functions [17] that include the concentration of points within the neighbouring areas, allow for the depiction of smoothed surface maps, and aid in the identification of areas with high or low densities of events, such as identified COVID-19 cases. When it comes to disease mapping, various epidemiological and health science studies have addressed the MAUP throughout the years [18][19][20][21][22][23]. However, only a few studies describe the spatio-temporal pattern of COVID-19 at the point level.…”