Predicting diarrhoea outbreak with climate change

Climate change is expected to exacerbate diarrhoea outbreak in South Africa, a leading cause of morbidity and mortality in the region. In this study, we modelled the impacts of climate change on diarrhoea with machine learning methods. We applied two deep learning techniques, convolutional neural networks (CNNs) and long-short term memory networks (LSTMs); and a support vector machine to predict daily diarrhoea cases over the different South African provinces by incorporating climate information. Generative Adversarial Networks (GANs) was used to generate synthetic data which was used to augment the available dataset. Furthermore, relevance estimation and value calibration (REVAC) was used to tune the parameters of the machine learning algorithms to optimize the accuracy of their predictions. Sensitivity analysis was also performed to investigate the contribution of the different climate factors to the diarrhoea prediction model. The results of the study showed that all three ML methods were appropriate for predicting daily diarrhoea cases with respect to the selected climate variables in each South African province. The ML methods were all able to yield low and similar RMSE. However, the level of accuracy for each model varied across different experiments, with the deep learning models outperforming the SVM model. Among the deep learning techniques, the CNN model performed best when only real-world dataset was used, while the LSTM model outperformed the other models when the real dataset was augmented with synthetic data. Across the provinces, the accuracy of all three ML algorithms improved by at least 30% when data augmentation was implemented. In addition, REVAC improved the accuracy of the CNN model by more than 12% in KwaZulu Natal province. However, the percentage increase in accuracy of the LSTM model was less than 4% in Western Cape province when REVAC was used. Our sensitivity analysis revealed that the most influential climate variables to be considered when predicting outbreak of diarrhoea in South Africa are precipitation, humidity, evaporation and temperature conditions. The result of this study is important for the development of an early warning system for diarrhoea outbreak over South Africa

Predicting diarrhoea outbreaks with climate change

Climate change is expected to exacerbate diarrhoea outbreaks across the developing world, most notably in Sub-Saharan countries such as South Africa. In South Africa, diseases related to diarrhoea outbreak is a leading cause of morbidity and mortality. In this study, we modelled the impacts of climate change on diarrhoea with various machine learning (ML) methods to predict daily outbreak of diarrhoea cases in nine South African provinces

Intelligent Noninvasive Diagnosis of Aneuploidy:Raw Values and Highly Imbalanced Dataset

The objective of this paper is to introduce a noninvasive diagnosis procedure for aneuploidy and to minimize the social and financial cost of prenatal diagnosis tests that are performed for fetal aneuploidies in an early stage of pregnancy. We propose a method by using artificial neural networks trained with data from singleton pregnancy cases, while undergoing first trimester screening. Three different datasets' with a total of 122 362 euploid and 967 aneuploid cases were used in this study. The data for each case contained markers collected from the mother and the fetus. This study, unlike previous studies published by the authors for a similar problem differs in three basic principles: 1) the training of the artificial neural networks is done by using the markers' values in their raw form (unprocessed), 2) a balanced training dataset is created and used by selecting only a representative number of euploids for the training phase, and 3) emphasis is given to the financials and suggest hierarchy and necessity of the available tests. The proposed artificial neural networks models were optimized in the sense of reaching a minimum false positive rate and at the same time securing a 100% detection rate for Trisomy 21. These systems correctly identify other aneuploidies (Trisomies 13&18, Turner, and Triploid syndromes) at a detection rate greater than 80%. In conclusion, we demonstrate that artificial neural network systems can contribute in providing noninvasive, effective early screening for fetal aneuploidies with results that compare favorably to other existing methods

Recurrent neural network based approach for estimating the dynamic evolution of grinding process variables

170 p.El proceso de rectificado es ampliamente utilizado para la fabricación de componentes de precisión por arranque de viruta por sus buenos acabados y excelentes tolerancias. Así, el modelado y el control del proceso de rectificado es altamente importante para alcanzar los requisitos económicos y de precisión de los clientes. Sin embargo, los modelos analíticos desarrollados hasta ahora están lejos de poder ser implementados en la industria. Es por ello que varias investigaciones han propuesto la utilización de técnicas inteligentes para el modelado del proceso de rectificado. Sin embargo, estas propuestas a) no generalizan para nuevas muelas y b) no tienen en cuenta el desgaste de la muela, efecto esencial para un buen modelo del proceso de rectificado. Es por ello que se propone la utilización de las redes neuronales recurrentes para estimar variables del proceso de rectificado que a) sean capaces de generalizar para muelas nuevas y b) que tenga en cuenta el desgaste de la muela, es decir, que sea capaz de estimar variables del proceso de rectificado mientras la muela se va desgastando. Así, tomando como base la metodología general, se han desarrollado sensores virtuales para la medida del desgaste de la muela y la rugosidad de la pieza, dos variables esenciales del proceso de rectificado. Por otro lado, también se plantea la utilización la metodología general para estimar fuera de máquina la energía específica de rectificado que puede ayudar a seleccionar la muela y los parámetros de rectificado por adelantado. Sin embargo, una única red no es suficiente para abarcar todas las muelas y condiciones de rectificado existentes. Así, también se propone una metodología para generar redes ad-hoc seleccionando unos datos específicos de toda la base de datos. Para ello, se ha hecho uso de los algoritmos Fuzzy c-Means. Finalmente, hay que decir que los resultados obtenidos mejoran los existentes hasta ahora. Sin embargo, estos resultados no son suficientemente buenos para poder controlar el proceso. Así, se propone la utilización de las redes neuronales de impulsos. Al trabajar con impulsos, estas redes tienen inherentemente la capacidad de trabajar con datos temporales, lo que las hace adecuados para estimar valores que evolucionan con el tiempo. Sin embargo, estas redes solamente se usan para clasificación y no predicción de evoluciones temporales por la falta de métodos de codificación/decodificación de datos temporales. Así, en este trabajo se plantea una metodología para poder codificar en trenes de impulsos señales secuenciales y poder reconstruir señales secuenciales a partir de trenes de impulsos. Esto puede llevar a en un futuro poder utilizar las redes neuronales de impulsos para la predicción de secuenciales y/o temporales

The use of satellite data, meteorology and land use data to define high resolution temperature exposure for the estimation of health effects in Italy

Introduction. Despite the mounting evidence on heat-related health risks, there is limited evidence in suburban and rural areas. The limited spatial resolution of temperature data also hinders the evidence of the differential heat effect within cities due to individual and area-based characteristics. Methods. Satellite land surface temperature (LST), observed meteorological and spatial and spatio-temporal land use data were combined in mixed-effects regression models to estimate daily mean air temperature with a 1x1km resolution for the period 2000-2010. For each day, random intercepts and slopes for LST were estimated to capture the day-to-day temporal variability of the Ta–LST relationship. The models were also nested by climate zones to better capture local climates and daily weather patterns across Italy. The daily exposure data was used to estimate the effects and impacts of heat on cause-specific mortality and hospital admissions in the Lazio region at municipal level in a time series framework. Furthermore, to address the differential effect of heat within an urban area and account for potential effect modifiers a case cross-over study was conducted in Rome. Mean temperature was attributed at the individual level to the Rome Population Cohort and the urban heat island (UHI) intensity using air temperature data was calculated for Rome. Results. Exposure model performance was very good: in the stage 1 model (only on grid cells with both LST and observed data) a mean R2 value of 0.96 and RMSPE of 1.1°C and R2 of 0.89 and 0.97 for the spatial and temporal domains respectively. The model was also validated with regional weather forecasting model data and gave excellent results (R2=0.95 RMSPE=1.8°C. The time series study showed significant effects and impacts on cause-specific mortality in suburban and rural areas of the Lazio region, with risk estimates comparable to those found in urban areas. High temperatures also had an effect on respiratory hospital admissions. Age, gender, pre-existing cardiovascular disease, marital status, education and occupation were found to be effect modifiers of the temperature-mortality association. No risk gradient was found by socio-economic position (SEP) in Rome. Considering the urban heat island (UHI) and SEP combined, differential effects of heat were observed by UHI among same SEP groupings. Impervious surfaces and high urban development were also effect modifiers of the heat-related mortality risk. Finally, the study found that high resolution gridded data provided more accurate effect estimates especially for extreme temperature intervals. Conclusions. Results will help improve heat adaptation and response measures and can be used predict the future heat-related burden under different climate change scenarios.Open Acces

Climate change and childhood diarrhoea in Kathmandu, Nepal: a health risk assessment and exploration of surveillance capacity

There is substantial evidence that the onset and transmission of infectious diseases, particularly vector-borne diseases and diarrheal diseases, are influenced by many factors including climate change. Improving the understanding of the impacts of climate change on infectious diseases is important to inform policy decision making on disease control and prevention, as well as predicting the trends in the infectious diseases burden. Epidemiological analysis of long-term surveillance data on infectious diseases and meteorological factors are instrumental in establishing the association between infectious disease incidence and climate change. Advanced epidemiological techniques are now available to precisely estimate the nature of association (linear, non-linear) as well as the delayed effect: this means that it is possible to plan and design climate-based early warning systems to predict conditions that are likely to be favourable for an outbreak of climate-sensitive infectious disease. However, the association between infectious diseases and climate change varies, depending upon the pathogens responsible for infection. Similarly, the ability of infectious disease surveillance systems or disease control divisions to generate this evidence and utilise the knowledge to cope or adapt to the impacts of climate change is contingent upon the social, economic, political and other contextual problems. In the Nepalese context, the impacts of climate change on infectious diseases, in particular diarrheal disease, remains unknown: similarly, there has been no exploration of the contextual factors associated with the integration of climate change-related risk in Nepalese infectious diseases surveillance systems. Given this background, the first aim of this PhD thesis is to characterize the association between diarrhoea among children below five years of age and climate variables in Kathmandu, Nepal and then project the future burden of diarrhoea due to climate change. The second aim is to understand the association between rotavirus infection among children below five years of age and temperature variability in Kathmandu and compute the fraction of rotavirus infection that is attributable to temperature. The third aim is to explore the extant research on climate change and infectious diseases in Nepal and to identify the reasons behind sparse evidence on the topic. The final aim is to explore social, economic and cultural factors associated with infectious diseases surveillance in Nepal in the context of climate change. A mixed method study design was employed to achieve the goals of this project. There are four analytical chapters in this thesis: two quantitative studies; a study that reviews evidence of the impacts of climate change on infectious disease and policy documents related to infectious disease control and prevention in Nepal; and a qualitative study. Two quantitative studies were carried out to estimate the association between climate variability and childhood diarrhoea, and childhood rotavirus infection in Kathmandu. Study 1 and study 2 utilised time series design involving Poisson regression equations fitted with distributed lag models to characterise exposure-response and possible lagged association between climate variables and diarrhoea, and rotavirus infection. A qualitative research study was undertaken to explore the social, economic, cultural and political factors associated with infectious diseases surveillance in the context of climate change in Nepal. In study 4, semi-structured interviews were conducted with key informants and stakeholders from the Department of Health Services Nepal, World Health Organization Nepal, the Department of Hydrology and Meteorology Nepal and infectious disease experts working in both public and private sectors in Nepal. The interviews and subsequent thematic analysis of data were conducted from a critical realist perspective. Study 1 established a significant positive association between childhood diarrhoea and temperature, and rainfall. A 1°C increase in maximum temperature above the monthly average was found to be associated with 8.1% (RR: 1.081; 95% CI: 1.02-1.14) increase in the monthly count of diarrhoea among children below five years of age living in Kathmandu, Nepal. Similarly, a 10mm increase in monthly cumulative rainfall above the mean value was associated with 0.09% (RR: 1.009; 95% CI: 1.004-1.015) increase in childhood diarrhoea. It was further projected that 1357 (UI: 410–2274) additional cases of childhood diarrhoea could be experienced by 2050 given the projected change in climate under low-risk scenario (0.9°C increase in maximum temperature). Study 2 established a nonlinear negative association between temperature (maximum, mean and minimum) and weekly rotavirus infection cases among children below five years of age in Kathmandu. Compared to the median value of mean temperature, an increased risk (RR: 1.52; 95% CI: 1.08–2.15) of rotavirus infection was detected at the lower quantile (10th percentile) and a decreased risk (RR: 0.64; 95% CI: 0.43–0.95) was detected at the higher quantile (75th percentile). Similarly, an increased risk [(RR: 1.93; 95% CI: 1.40–2.65) and (RR: 1.42; 95% CI: 1.04–1.95)] of infection was detected for both maximum and minimum temperature at their lower quantile (10th percentile). It was further estimated that 47.01% of the rotavirus infection cases reported between 2013 and 2016 in Kathmandu could be attributed to minimum temperature. Study 3 identified that there was little evidence describing the impacts of climate change on infectious diseases and no evidence describing the projected burden under climate change scenarios. I explored the reasons behind paucity in the evidence and challenges faced by epidemiologists in Nepal. The challenges identified included poor quality infectious disease datasets, shortage of trained human resources, inadequate funding and political instability. As such, it was recommended that an integrated digital network of interdisciplinary experts be established and increased collaboration among different stakeholders be promoted to advance the evidence base on the impacts of climate change on infectious diseases in Nepal. The fourth and final study outlined that climate change and its impacts on infectious disease surveillance is treated as a less serious issue than other more ‘salient’ public health risks in the context of Nepal. The study further illustrates how climate change is variably constructed as a contingent risk for infectious diseases transmission and public health systems. The analysis exposes a weaker alliance among different stakeholders, particularly policymakers and evidence generators that leads to the continuation of traditional practices of infectious diseases surveillance without consideration of the impacts of climate change. In summary, this thesis brings to prominence important progress in understanding the link between climate change and infectious diseases, in particular childhood diarrhoea, in a subtropical highland climate from a low and middle income South Asian country. So far, we have not found any other study that explores the contextual factors (social, economic, cultural and political) that impede the integration of climate change-related risk in the disease surveillance systems. Therefore, this thesis illustrates a novel facet of infectious disease surveillance and climate change. This thesis makes an important contribution to address the gap on information related to climate change and infectious diseases in Nepal and can have significant implications towards building a climate-resilient public health system in Nepal.Thesis (Ph.D.) -- University of Adelaide, School of Public Health, 202

Water Quality Assessments for Urban Water Environment

This special issue entitled “Water Quality Assessments for Urban Water Environment,” strives to highlights the status quo of water environment, opportunities and challenges for their sustainable management in lieu of rapid global changes (land us eland cover changes, climate change, population growth, change in socio-economic dimension, urbanization etc.), in the urban space particularly in developing nations around the world. It also highlights the effect of COVID19 pandemic on water resources and way forward to minimize the risk of spreading health risk associated with wastewater management. Considering the complex nature of the urban water security, it highlights the importance of emerging approaches like socio-hydrology, landscape ecology, regional-circular-ecological sphere etc., which presents a perfect combination of hard (infrastructure) and soft (numerical simulations, spatial technologies, participatory approaches, indigenous knowledge) measures, as the potential solutions to manage this precious water resource in coming future. Finally, what is the way forward to enhance science-policy interface in a better way to achieve global goals e.g., SDGs at local level in a timely manner. It provides valuable information about sustainable water resource management at the urban landscape, which is very much useful for policy-makers, decision-makers, local communities, and other relevant stakeholders

Recurrent neural network based approach for estimating the dynamic evolution of grinding process variables

170 p.El proceso de rectificado es ampliamente utilizado para la fabricación de componentes de precisión por arranque de viruta por sus buenos acabados y excelentes tolerancias. Así, el modelado y el control del proceso de rectificado es altamente importante para alcanzar los requisitos económicos y de precisión de los clientes. Sin embargo, los modelos analíticos desarrollados hasta ahora están lejos de poder ser implementados en la industria. Es por ello que varias investigaciones han propuesto la utilización de técnicas inteligentes para el modelado del proceso de rectificado. Sin embargo, estas propuestas a) no generalizan para nuevas muelas y b) no tienen en cuenta el desgaste de la muela, efecto esencial para un buen modelo del proceso de rectificado. Es por ello que se propone la utilización de las redes neuronales recurrentes para estimar variables del proceso de rectificado que a) sean capaces de generalizar para muelas nuevas y b) que tenga en cuenta el desgaste de la muela, es decir, que sea capaz de estimar variables del proceso de rectificado mientras la muela se va desgastando. Así, tomando como base la metodología general, se han desarrollado sensores virtuales para la medida del desgaste de la muela y la rugosidad de la pieza, dos variables esenciales del proceso de rectificado. Por otro lado, también se plantea la utilización la metodología general para estimar fuera de máquina la energía específica de rectificado que puede ayudar a seleccionar la muela y los parámetros de rectificado por adelantado. Sin embargo, una única red no es suficiente para abarcar todas las muelas y condiciones de rectificado existentes. Así, también se propone una metodología para generar redes ad-hoc seleccionando unos datos específicos de toda la base de datos. Para ello, se ha hecho uso de los algoritmos Fuzzy c-Means. Finalmente, hay que decir que los resultados obtenidos mejoran los existentes hasta ahora. Sin embargo, estos resultados no son suficientemente buenos para poder controlar el proceso. Así, se propone la utilización de las redes neuronales de impulsos. Al trabajar con impulsos, estas redes tienen inherentemente la capacidad de trabajar con datos temporales, lo que las hace adecuados para estimar valores que evolucionan con el tiempo. Sin embargo, estas redes solamente se usan para clasificación y no predicción de evoluciones temporales por la falta de métodos de codificación/decodificación de datos temporales. Así, en este trabajo se plantea una metodología para poder codificar en trenes de impulsos señales secuenciales y poder reconstruir señales secuenciales a partir de trenes de impulsos. Esto puede llevar a en un futuro poder utilizar las redes neuronales de impulsos para la predicción de secuenciales y/o temporales

Bibliometric Studies and Worldwide Research Trends on Global Health

Global health, conceived as a discipline, aims to train, research and respond to problems of a transboundary nature, in order to improve health and health equity at the global level. The current worldwide situation is ruled by globalization, and therefore the concept of global health involves not only health-related issues, but also those related to the environment and climate change. Therefore, in this Special Issue, the problems related to global health have been addressed from a bibliometric approach in four main areas: environmental issues, diseases, health, education and society