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Covariate regression coefficients density plots for the convolution and ecological models. (a) Intercept, (b) Normalized Difference Water Index, (c) Day Land Surface Temperature, (d) Night Land Surface Temperature, (e) Elevation, and (f) Nearest Distance to Water Bodies.
Source publication
Keywords: schistosomiasis; pure specification bias; uncertainty; Bayesian statistics; convolution model
Citations
... Incorporating covariates with known or hypothesized biological links to disease transmission into geospatial modeling improves model predictions. 26 We downloaded climate data related to temperature and precipitation from the WorldClim v. 2.1 database, 14 which provides interpolated long-term averages of climate and global weather data obtained from $15,000 weather stations distributed across the world. Also, data on population density, elevation, slope of terrain, nighttime light emissivity, and Euclidean distance to surface water bodies and the edge of nature reserves were all downloaded from the WorldPop repository. ...
A comprehensive understanding of the spatial distribution and correlates of infection are key for the planning of disease control programs and assessing the feasibility of elimination and/or eradication. In this work, we used species distribution modeling to predict the environmental suitability of the Guinea worm ( Dracunculus medinensis ) and identify important climatic and sociodemographic risk factors. Using Guinea worm surveillance data collected by the Chad Guinea Worm Eradication Program (CGWEP) from 2010 to 2022 in combination with remotely sensed climate and sociodemographic correlates of infection within an ensemble machine learning framework, we mapped the environmental suitability of Guinea worm infection in Chad. The same analytical framework was also used to ascertain the contribution and influence of the identified climatic risk factors. Spatial distribution maps showed predominant clustering around the southern regions and along the Chari River. We also identified areas predicted to be environmentally suitable for infection. Of note are districts near the western border with Cameroon and southeastern border with Central African Republic. Key environmental correlates of infection as identified by the model were proximity to permanent rivers and inland lakes, farmlands, land surface temperature, and precipitation. This work provides a comprehensive model of the spatial distribution of Guinea worm infections in Chad 2010–2022 and sheds light on potential environmental correlates of infection. As the CGWEP moves toward elimination, the methods and results in this study will inform surveillance activities and help optimize the allocation of intervention resources.
... This alternative could enrich the selection of global determinants and allow predictions for a given Spatio-temporal window. Another approach might consider the hierarchical Bayesian framework due to its flexibility in modelling space interaction in epidemiological studies (Navas et al., 2019). In this context, the Bayesian models assume that the number of cases (deaths) is Poisson distributed and express the relative risk as a linear combination of variables (environmental, socioeconomic, demographic, and biological) and spatial random effects. ...
Bogota, the capital and largest city of Colombia, constantly fights against easily transmitted and endemic–epidemic diseases that lead to enormous public health problems. Pneumonia is currently the leading cause of mortality attributable respiratory infections in the city. Its recurrence and impact have been partially explained by biological, medical, and behavioural factors. Against this background, this study investigates Pneumonia mortality rates in Bogota from 2004 and 2014. We identified a set of environmental, socioeconomic, behavioural, and medical care factors whose interaction in space could explain the occurrence and impact of the disease in the Iberoamerican city. We adopted a spatial autoregressive models framework to study the spatial dependence and heterogeneity of Pneumonia mortality rates associated with well-known risk factors. The results highlight the different types of spatial processes governing Pneumonia mortality. Furthermore, they demonstrate and quantify the driving factors that stimulate the spatial spread and clustering of mortality rates. Our study stresses the importance of spatial modelling of context-dependent diseases such as Pneumonia. Likewise, we emphasize the need to develop comprehensive public health policies that consider the space and contextual factors.
... In addition, the residues associated with the binding of vertebrate MIF to its well-described receptors, CD 74, which mediates MIF proinflammatory functions, and CXCR2 and CXCR4, which are G protein-coupled membrane receptors of chemokines (11), are not conserved in the EsMIF sequence. Furthermore, orthologs of the genes encoding these MIF receptors were not detected in analysis of the E. scolopes genome (41), suggesting that EsMIF binds to different receptors. ...
Significance
Daily biological rhythms are fundamental, evolutionarily conserved features of many symbioses. Here we use the binary squid–vibrio association, the simplicity of which has offered the resolution to explore strategies of symbiosis that are shared with more complex systems, such as those of mammals. We demonstrate a pivotal role for an evolutionarily conserved cytokine, macrophage migration inhibitory factor, or MIF, which is abundant in epithelia supporting the symbionts of both the squid light organ and the mammalian gut. In the mature squid association, MIF regulates the cyclic metabolic dialogue that underlies oscillations in host-provided symbiont nutrients. As such, this study integrates the role of MIF across two distinct time scales: the host’s life history and its daily rhythms.
... As a consequence, localized areas of high endemicity may not be addressed properly. In a recent study [27], we quantified the effect of pure specification bias, that originates when using group-level (i.e. aggregated) survey data at an administrative level for individual-level inferences. ...
... We modelled human S. japonicum infection at the five increasing SSAs using a convolution model that accounts for pure specification bias [27]. Pure specification is a source of uncertainty [11,49] that produces loss of information on the real relationship between the disease and the environmental covariate data, when using aggregated survey data in a non-linear model, for example, for individual-level inferences [50]. ...
... It is called 'pure' because it specifically addresses model specification bias [51], and it biases the estimates because any direct link between exposure and health outcomes is imperfectly measured [52]. This is because the regression function does not approximate the real relationship between the affected population and their exposure [27]. Pure specification bias can be reduced as the within area exposure is more homogenous [50]. ...
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 individual-level 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.
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 individual-level 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 programs 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. Keywords: schistosomiasis modelling; modifiable areal unit problem; uncertainty; Bayesian statistics; convolution model.
