The economic impact of schistosomiasis

BACKGROUND: The economic impact of schistosomiasis and the underlying tradeoffs between water resources development and public health concerns have yet to be quantified. Schistosomiasis exerts large health, social and financial burdens on infected individuals and households. While irrigation schemes are one of the most important policy responses designed to reduce poverty, particularly in sub-Saharan Africa, they facilitate the propagation of schistosomiasis and other diseases. METHODS: We estimate the economic impact of schistosomiasis in Burkina Faso via its effect on agricultural production. We create an original dataset that combines detailed household and agricultural surveys with high-resolution geo-statistical disease maps. We develop new methods that use the densities of the intermediate host snails of schistosomiasis as instrumental variables together with panel, spatial and machine learning techniques. RESULTS: We estimate that the elimination of schistosomiasis in Burkina Faso would increase average crop yields by around 7%, rising to 32% for high infection clusters. Keeping schistosomiasis unchecked, in turn, would correspond to a loss of gross domestic product of approximately 0.8%. We identify the disease burden as a shock to the agricultural productivity of farmers. The poorest households engaged in subsistence agriculture bear a far heavier disease burden than their wealthier counterparts, experiencing an average yield loss due to schistosomiasis of between 32 and 45%. We show that the returns to water resources development are substantially reduced once its health effects are taken into account: villages in proximity of large-scale dams suffer an average yield loss of around 20%, and this burden decreases as distance between dams and villages increases. CONCLUSIONS: This study provides a rigorous estimation of how schistosomiasis affects agricultural production and how it is both a driver and a consequence of poverty. It further quantifies the tradeoff between the economics of water infrastructures and their impact on public health. Although we focus on Burkina Faso, our approach can be applied to any country in which schistosomiasis is endemic

Kurcuma: a kitchen utensil recognition collection for unsupervised domain adaptation

The use of deep learning makes it possible to achieve extraordinary results in all kinds of tasks related to computer vision. However, this performance is strongly related to the availability of training data and its relationship with the distribution in the eventual application scenario. This question is of vital importance in areas such as robotics, where the targeted environment data are barely available in advance. In this context, domain adaptation (DA) techniques are especially important to building models that deal with new data for which the corresponding label is not available. To promote further research in DA techniques applied to robotics, this work presents Kurcuma (Kitchen Utensil Recognition Collection for Unsupervised doMain Adaptation), an assortment of seven datasets for the classification of kitchen utensils—a task of relevance in home-assistance robotics and a suitable showcase for DA. Along with the data, we provide a broad description of the main characteristics of the dataset, as well as a baseline using the well-known domain-adversarial training of neural networks approach. The results show the challenge posed by DA on these types of tasks, pointing to the need for new approaches in future work.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by the I+D+i project TED2021-132103A-I00 (DOREMI), funded by MCIN/AEI/10.13039/501100011033. Some of the computing resources were provided by the Generalitat Valenciana and the European Union through the FEDER funding program (IDIFEDER/2020/003). The second author is supported by grant APOSTD/2020/256 from “Programa I+D+i de la Generalitat Valenciana”

Finite-element-model updating of civil engineering structures using a hybrid UKF-HS algorithm

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this recordFinite-element-model updating allows reducing the discrepancies between the numerical and the experimental dynamic behaviour of civil engineering structures. Among the different methods to tackle the updating problem, the maximum likelihood method has been widely used for practical engineering applications. In this method, the updating problem is transformed into an optimization problem where the relative differences between the numerical and experimental modal properties of the structure are reduced via the modification of the most relevant physical parameters of the model. However, this method often presents the drawback of requiring high simulation times in order to perform the updating process when dealing with complex structures. To overcome this limitation, in this paper a novel hybrid Unscented Kalman Filter – Harmony Search (UKF-HS) algorithm is proposed and its implementation details are discussed. In order to validate such hybrid algorithm and further illustrate its performance, the finite-element-model updating of a benchmark footbridge is performed using two different approaches (single-objective and multi-objective) and three different computational algorithms, namely: (i) genetic algorithms; (ii) harmony search; and (iii) the novel UKF-HS hybrid algorithm. The obtained results reveal that the proposed hybrid algorithm may be considered as an adequate alternative tool to efficiently perform the finite-element-mMinisterio de Economía y Competitividad of SpainEuropean Regional Development Fund (ERDF)Universidad de Sevill

Partial order label decomposition approaches for melanoma diagnosis

Melanoma is a type of cancer that develops from the pigment-containing cells known as melanocytes. Usually occurring on the skin, early detection and diagnosis is strongly related to survival rates. Melanoma recognition is a challenging task that nowadays is performed by well trained dermatologists who may produce varying diagnosis due to the task complexity. This motivates the development of automated diagnosis tools, in spite of the inherent difficulties (intra-class variation, visual similarity between melanoma and non-melanoma lesions, among others). In the present work, we propose a system combining image analysis and machine learning to detect melanoma presence and severity. The severity is assessed in terms of melanoma thickness, which is measured by the Breslow index. Previous works mainly focus on the binary problem of detecting the presence of the melanoma. However, the system proposed in this paper goes a step further by also considering the stage of the lesion in the classification task. To do so, we extract 100 features that consider the shape, colour, pigment network and texture of the benign and malignant lesions. The problem is tackled as a five-class classification problem, where the first class represents benign lesions, and the remaining four classes represent the different stages of the melanoma (via the Breslow index). Based on the problem definition, we identify the learning setting as a partial order problem, in which the patterns belonging to the different melanoma stages present an order relationship, but where there is no order arrangement with respect to the benign lesions. Under this assumption about the class topology, we design several proposals to exploit this structure and improve data preprocessing. In this sense, we experimentally demonstrate that those proposals exploiting the partial order assumption achieve better performance than 12 baseline nominal and ordinal classifiers (including a deep learning model) which do not consider this partial order. To deal with class imbalance, we additionally propose specific over-sampling techniques that consider the structure of the problem for the creation of synthetic patterns. The experimental study is carried out with clinician-curated images from the Interactive Atlas of Dermoscopy, which eases reproducibility of experiments. Concerning the results obtained, in spite of having augmented the complexity of the classification problem with more classes, the performance of our proposals in the binary problem is similar to the one reported in the literature

Efficacy of the Combination of Pinaverium Bromide 100mg Plus Simethicone 300mg in Abdominal Pain and Bloating in Irritable Bowel Syndrome: A Randomized, Placebo-controlled Trial

Goals: We aimed to evaluate the efficacy and safety of PB+S (pinaverium bromide 100 mg plus simethicone 300 mg) in patients with irritable bowel syndrome (IBS). Background: IBS is a multifactorial disorder; thus, combination therapy with different mechanisms of action is expected to be useful. PB+S has shown effectiveness in an open-label clinical study in IBS. However, there are no placebo-controlled trials. Materials and Methods: IBS-Rome III patients with abdominal pain/discomfort for at least 2 days within the week prior to baseline assessment were included in this 12-week, randomized, doubleblind, placebo-controlled study of PB+S versus placebo, bid. The primary endpoint was overall symptom improvement, evaluated weekly by the patient (Likert Scale). Secondary endpoints included the weekly improvement in the severity of abdominal pain and bloating assessed both by patients (10-cm Visual Analogue Scale) and investigators (Likert Scale); frequency of Bristol Scale stool types (consistency) evaluated by patients and the IBS Quality of Life scores. Results: A total of 285 patients (female: 83%; 36.5±8.9 y old) received at least 1 dose of PB+S (n=140) or placebo (n=145). No difference was observed in overall symptom improvement between the groups (P=0.13). However, PB+S was superior in abdominal pain (effect size: 31%, P=0.038) and bloating (33%, P=0.019). Patients with IBS-C and IBS-M showed the best improvement in the frequency of stool types with PB+S. No differences were observed in IBS Quality of Life scores and adverse events

Spatial and temporal patterns of Holocene precipitation change in the Iberian Peninsula

Precipitation is a key climate parameter of vegetation and ecosystems in the Iberian Peninsula. Here, we use a regional pollen-climate calibration model and fossil pollen data from eight sites from the Atlantic coast to southern Spain to provide quantitative reconstructions of annual precipitation trends and excursions and their regional patterns for the last 11 700 years. The Early Holocene (11 700 to 11 000 cal. a BP) was characterized by high precipitation values followed by a slowly declining trend until about 9000 cal. a BP in the south and about 8000 cal. a BP in the north. From 8000 to 6000 cal. a BP the reconstructed precipitation values are the highest in most records, especially in those located in the Mediterranean climatic region in the southern part of the peninsula, with maximum values nearly 100% higher than the modern reconstructed values. The results suggest a declining precipitation during the Late Holocene in the south, with a positive excursion at around 2500 cal. a BP, while in the north precipitation remained high until 500 cal. a BP. However, the Late Holocene climate reconstructions in the Iberian Peninsula are biased by intensifying human impact on vegetation. The statistical time series analyses using SiZer technique do not indicate any statistically significant high-frequency drought events in the region. In general, our results suggest regional differences in the precipitation patterns between the northern and southern parts of the peninsula, with a more distinct Middle Holocene period of high humidity in the south.Peer reviewe

Spatial and temporal patterns of Holocene precipitation change in the Iberian Peninsula

Precipitation is a key climate parameter of vegetation and ecosystems in the Iberian Peninsula. Here, we use a regional pollen-climate calibration model and fossil pollen data from eight sites from the Atlantic coast to southern Spain to provide quantitative reconstructions of annual precipitation trends and excursions and their regional patterns for the last 11 700 years. The Early Holocene (11 700 to 11 000 cal. a BP) was characterized by high precipitation values followed by a slowly declining trend until about 9000 cal. a BP in the south and about 8000 cal. a BP in the north. From 8000 to 6000 cal. a BP the reconstructed precipitation values are the highest in most records, especially in those located in the Mediterranean climatic region in the southern part of the peninsula, with maximum values nearly 100% higher than the modern reconstructed values. The results suggest a declining precipitation during the Late Holocene in the south, with a positive excursion at around 2500 cal. a BP, while in the north precipitation remained high until 500 cal. a BP. However, the Late Holocene climate reconstructions in the Iberian Peninsula are biased by intensifying human impact on vegetation. The statistical time series analyses using SiZer technique do not indicate any statistically significant high-frequency drought events in the region. In general, our results suggest regional differences in the precipitation patterns between the northern and southern parts of the peninsula, with a more distinct Middle Holocene period of high humidity in the south.Peer reviewe

Achieving coordinated national immunity and cholera elimination in Haiti through vaccination: a modelling study

Background: Cholera was introduced into Haiti in 2010. Since then, more than 820 000 cases and nearly 10 000 deaths have been reported. Oral cholera vaccine (OCV) is safe and effective, but has not been seen as a primary tool for cholera elimination due to a limited period of protection and constrained supplies. Regionally, epidemic cholera is contained to the island of Hispaniola, and the lowest numbers of cases since the epidemic began were reported in 2019. Hence, Haiti may represent a unique opportunity to eliminate cholera with OCV. Methods: In this modelling study, we assessed the probability of elimination, time to elimination, and percentage of cases averted with OCV campaign scenarios in Haiti through simulations from four modelling teams. For a 10-year period from January 19, 2019, to Jan 13, 2029, we compared a no vaccination scenario with five OCV campaign scenarios that differed in geographical scope, coverage, and rollout duration. Teams used weekly department-level reports of suspected cholera cases from the Haiti Ministry of Public Health and Population to calibrate the models and used common vaccine-related assumptions, but other model features were determined independently. Findings: Among campaigns with the same vaccination coverage (70% fully vaccinated), the median probability of elimination after 5 years was 0–18% for no vaccination, 0–33% for 2-year campaigns focused in the two departments with the highest historical incidence, 0–72% for three-department campaigns, and 35–100% for nationwide campaigns. Two-department campaigns averted a median of 12–58% of infections, three-department campaigns averted 29–80% of infections, and national campaigns averted 58–95% of infections. Extending the national campaign to a 5-year rollout (compared to a 2-year rollout), reduced the probability of elimination to 0–95% and the proportion of cases averted to 37–86%. Interpretation: Models suggest that the probability of achieving zero transmission of Vibrio cholerae in Haiti with current methods of control is low, and that bolder action is needed to promote elimination of cholera from the region. Large-scale cholera vaccination campaigns in Haiti would offer the opportunity to synchronise nationwide immunity, providing near-term population protection while improvements to water and sanitation promote long-term cholera elimination. Funding: Bill & Melinda Gates Foundation, Global Good Fund, Institute for Disease Modeling, Swiss National Science Foundation, and US National Institutes of Health

The seasonality of cholera in sub-Saharan Africa: a statistical modelling study

Background: Cholera remains a major threat in sub-Saharan Africa (SSA), where some of the highest case-fatality rates are reported. Knowing in what months and where cholera tends to occur across the continent could aid in improving efforts to eliminate cholera as a public health concern. However, largely due to the absence of unified large-scale datasets, no continent-wide estimates exist. In this study, we aimed to estimate cholera seasonality across SSA and explore the correlation between hydroclimatic variables and cholera seasonality. Methods: Using the global cholera database of the Global Task Force on Cholera Control, we developed statistical models to synthesise data across spatial and temporal scales to infer the seasonality of excess (defined as incidence higher than the 2010–16 mean incidence rate) suspected cholera occurrence in SSA. We developed a Bayesian statistical model to infer the monthly risk of excess cholera at the first and second administrative levels. Seasonality patterns were then grouped into spatial clusters. Finally, we studied the association between seasonality estimates and hydroclimatic variables (mean monthly fraction of area flooded, mean monthly air temperature, and cumulative monthly precipitation). Findings: 24 (71%) of the 34 countries studied had seasonal patterns of excess cholera risk, corresponding to approximately 86% of the SSA population. 12 (50%) of these 24 countries also had subnational differences in seasonality patterns, with strong differences in seasonality strength between regions. Seasonality patterns clustered into two macroregions (west Africa and the Sahel vs eastern and southern Africa), which were composed of subregional clusters with varying degrees of seasonality. Exploratory association analysis found most consistent and positive correlations between cholera seasonality and precipitation and, to a lesser extent, between cholera seasonality and temperature and flooding. Interpretation: Widespread cholera seasonality in SSA offers opportunities for intervention planning. Further studies are needed to study the association between cholera and climate. Funding: US National Aeronautics and Space Administration Applied Sciences Program and the Bill & Melinda Gates Foundation