Evaluating survey designs for targeting preventive chemotherapy against Schistosoma haematobium and Schistosoma mansoni across Sub-Saharan Africa: a geostatistical analysis and modelling study

Abstract Background Schistosomiasis control programmes primarily use school-based surveys to identify areas for mass drug administration of preventive chemotherapy. However, as the spatial distribution of schistosomiasis can be highly focal, transmission may not be detected by surveys implemented at districts or larger spatial units. Improved mapping strategies are required to accurately and cost effectively target preventive chemotherapy to remaining foci across all possible spatial distributions of schistosomiasis. Methods Here, we use geostatistical models to quantify the spatial heterogeneity of Schistosoma haematobium and S. mansoni across Sub-Saharan Africa using the most comprehensive dataset available on school-based surveys. Applying this information to parameterise simulations, we assess the accuracy and cost of targeting alternative implementation unit sizes across the range of plausible schistosomiasis distributions. We evaluate the consequences of decisions based on survey designs implemented at district and subdistrict levels sampling different numbers of schools. Cost data was obtained from field surveys conducted across multiple countries and years, with cost effectiveness evaluated as the cost per correctly identified school. Results Models identified marked differences in prevalence and spatial distributions between countries and species; however, results suggest implementing surveys at subdistrict level increase the accuracy of treatment classifications across most scenarios. While intensively at subdistrict level resulted in the highest classification accuracy, this sampling strategy resulted in the highest costs. Alternatively, sampling the same numbers of schools currently recommended at district levels but stratifying by subdistrict increased cost effectiveness. Conclusions This provides a new tool to evaluate schistosomiasis survey designs across a range of transmission settings. Results highlight the importance of considering spatial structure when designing sampling strategies, illustrating that a substantial proportion of children may be undertreated even when an implementation unit is correctly classified. Control programmes need to weigh the increased accuracy of more detailed mapping strategies against the survey costs and treatment priorities

Model building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict

This paper provides statistical guidance on the development and application of model-based geostatistical methods for disease prevalence mapping. We illustrate the different stages of the analysis, from exploratory analysis to spatial prediction of prevalence, through a case study on malaria mapping in Tanzania. Throughout the paper, we distinguish between predictive modelling, whose main focus is on maximizing the predictive accuracy of the model, and explanatory modelling, where greater emphasis is placed on understanding the relationships between the health outcome and risk factors. We demonstrate that these two paradigms can result in different modelling choices. We also propose a simple approach for detecting over-fitting based on inspection of the correlation matrix of the estimators of the regression coefficients. To enhance the interpretability of geostatistical models, we introduce the concept of domain effects in order to assist variable selection and model validation. The statistical ideas and principles illustrated here in the specific context of disease prevalence mapping are more widely applicable to any regression model for the analysis of epidemiological outcomes but are particularly relevant to geostatistical models, for which the separation between fixed and random effects can be ambiguous

Heat metering for central thermal energy installation

What is described is a virtual heat metering system (10), comprising: a plurality of sensors (12, 14, 16, 18, 20), adapted to be associated with a supply circuit of a central thermal installation (I) and arranged for supplying main signals (Qman., Tman., Trit., Pman., Prit., s) indicative of physical quantities representing the operation of the supply circuit (C) in a predetermined period of time (ΔtTOT); a control apparatus (22), comprising: - a memory module (23) arranged for storing a thermal and fluid dynamic model (M) defined initially and representing the central thermal installation (I), identified on the basis of physical quantities representing the operation of the supply circuit (C) and the heat exchanger devices (H1,1,..., H1,n1; H2,1,..., H2,n2;...; Hm,1,..., Hm,nm), detected in specified conditions of operation and stimulation of the installation (I); and data representing the variation of said main signals (Qman., Tman., Trit., Pman., Prit., s) in the period of time (ΔTOT); and - a processing unit (24), arranged for receiving at its input the data representing the variation of the main signals (Qman., Tman., Trit., Pman., Prit., s) in the period of time (ΔtTOT), and configured to process these data according to the thermal and fluid dynamic model (M) and to supply at its output the data (Ê1,1,..., Ê1,n1:Ê2,1,..., Ê2,n2;...;Êm,1,..., Êm,nm) which represent the estimate of the thermal energy (E1,1,..., E1,n1:E2,1,..., E2,n2;...;Em,1,..., Em,nm) individually exchanged between each heat exchanger device (H1,1,..., H1,n1:H2,1,..., H2,n2;...;Hm,1,..., Hm,nm) and the corresponding thermal user (U1,..., Um)

Spatial distribution and risk factors for human cysticercosis in Colombia

Background: Cysticercosis is a zoonotic neglected tropical disease (NTD) that affects humans and pigs following the ingestion of Taenia solium eggs. Human cysticercosis poses a substantial public health burden in endemic countries. The World Health Organization (WHO) aims to target high-endemicity settings with enhanced interventions in 17 countries by 2030. Between 2008 and 2010, Colombia undertook a national baseline serosurvey of unprecedented scale, which led to an estimated seroprevalence of T. solium cysticercus antibodies among the general population of 8.6%. Here, we use contemporary geostatistical approaches to analyse this unique dataset with the aim of understanding the spatial distribution and risk factors associated with human cysticercosis in Colombia to inform how best to target intervention strategies. Methods: We used a geostatistical model to estimate individual and household risk factors associated with seropositivity to T. solium cysticercus antibodies from 29,253 people from 133 municipalities in Colombia. We used both independent and spatially structured random effects at neighbourhood/village and municipality levels to account for potential clustering of exposure to T. solium. We present estimates of the distribution and residual correlation of seropositivity at the municipality level. Results: High seroprevalence was identified in municipalities located in the north and south of Colombia, with spatial correlation in seropositivity estimated up to approximately 140 km. Statistically significant risk factors associated with seropositivity to T. solium cysticercus were related to age, sex, educational level, socioeconomic status, use of rainwater, consumption of partially cooked/raw pork meat and possession of dogs. Conclusions: In Colombia, the distribution of human cysticercosis is influenced by socioeconomic considerations, education and environmental factors related to the spread of T. solium eggs. This information can be used to tailor national intervention strategies, such as targeting spatial hotspots and more highly exposed groups, including displaced people and women. Large-scale seroprevalence surveys accompanied by geospatial mapping are an essential step towards reaching the WHO’s 2021‒2030 NTD roadmap targets. Graphical Abstract: [Figure not available: see fulltext.]