Spatial assessment of heavy metals contamination in household garden soils in rural Limpopo Province, South Africa.

Heavy metal pollution in soil poses a serious health threat to humans living in close proximity and in contact with contaminated soil. Exposure to heavy metals can result in a range of adverse health effects, including skin lesions, cardiovascular effects, lowering of IQ scores and cancers. The main objectives of this study were to (1) use a portable XRF spectrophotometer to measure concentrations of lead (Pb), arsenic (As), mercury (Hg) and cadmium (Cd) in residential soils in rural Giyani in the Limpopo province of South Africa; (2) to assess the spatial distribution of soil metal concentrations; and (3) to assess pollution levels in residential soils. There were elevated levels of As at one of the sites where 54% of soil samples exceeded the Canadian reference levels for As of 20 mg/kg. Using the geoaccumulation index (Igeo) to determine contamination levels of As, 57% of soil samples from the most polluted site were found to be moderately to heavily and extremely contaminated with As (Igeo class 2-5). The site is located near the Giyani Greenstone Belt, which is characterized by abandoned mines and artisanal mining activities. Gold ores are closely associated with sulphide minerals such as arsenopyrite, and these have been found to contain high amounts of As. This study highlighted the potential for soil contamination and the importance of site-specific risk assessment in the context of environment and health impact assessments prior to major developments, including human settlement developments.

Defining the South African Acute Respiratory Infectious Disease Season.

The acute respiratory infectious disease season, or colloquially the "flu season", is defined as the annually recurring period characterized by the prevalence of an outbreak of acute respiratory infectious diseases. It has been widely agreed that this season spans the winter period globally, but the precise timing or intensity of the season onset in South Africa is not well defined. This limits the efficacy of the public health sector to vaccinate for influenza timeously and for health facilities to synchronize efficiently for an increase in cases. This study explores the statistical intensity thresholds in defining this season to determine the start and finish date of the acute respiratory infectious disease season in South Africa. Two sets of data were utilized: public-sector hospitalization data that included laboratory-tested RSV and influenza cases and private-sector medical insurance claims under ICD 10 codes J111, J118, J110, and J00. Using the intensity threshold methodology proposed by the US CDC in 2017, various thresholds were tested for alignment with the nineteen-week flu season as proposed by the South African NICD. This resulted in varying thresholds for each province. The respiratory disease season commences in May and ends in September. These findings were seen in hospitalization cases and medical insurance claim cases, particularly with influenza-positive cases in Baragwanath hospital for the year 2019. These statistically determined intensity thresholds and timing of the acute respiratory infectious disease season allow for improved surveillance and preparedness among the public and private healthcare.

Health Risks of Temperature Variability on Hospital Admissions in Cape Town, 2011-2016.

Epidemiological studies have provided compelling evidence of associations between temperature variability (TV) and health outcomes. However, such studies are limited in developing countries. This study aimed to investigate the relationship between TV and hospital admissions for cause-specific diseases in South Africa. Hospital admission data for cardiovascular diseases (CVD) and respiratory diseases (RD) were obtained from seven private hospitals in Cape Town from 1 January 2011 to 31 October 2016. Meteorological data were obtained from the South African Weather Service (SAWS). A quasi-Poisson regression model was used to investigate the association between TV and health outcomes after controlling for potential effect modifiers. A positive and statistically significant association between TV and hospital admissions for both diseases was observed, even after controlling for the non-linear and delayed effects of daily mean temperature and relative humidity. TV showed the greatest effect on the entire study group when using short lags, 0-2 days for CVD and 0-1 days for RD hospitalisations. However, the elderly were more sensitive to RD hospitalisation and the 15-64 year age group was more sensitive to CVD hospitalisations. Men were more susceptible to hospitalisation than females. The results indicate that more attention should be paid to the effects of temperature variability and change on human health. Furthermore, different weather and climate metrics, such as TV, should be considered in understanding the climate component of the epidemiology of these (and other diseases), especially in light of climate change, where a wider range and extreme climate events are expected to occur in future.

Exploring the Association between Ambient Temperature and Daily Hospital Admissions for Diarrhea in Mopani District, Limpopo Province, South Africa.

Diarrhea contributes significantly to global morbidity and mortality. There is evidence that diarrhea prevalence is associated with ambient temperature. This study aimed to determine if there was an association between ambient temperature and diarrhea at a rural site in South Africa. Daily diarrheal hospital admissions (2007 to 2016) at two large district hospitals in Mopani district, Limpopo province were compared to average daily temperature and apparent temperature (Tapp, 'real-feel' temperature that combined temperature, relative humidity, and wind speed). Linear regression and threshold regression, age-stratified to participants ≤5 years and >5 years old, considered changes in daily admissions by unit °C increase in Tapp. Daily ranges in ambient temperature and Tapp were 2-42 °C and -5-34 °C, respectively. For every 1 °C increase in average daily temperature, there was a 6% increase in hospital admissions for diarrhea for individuals of all ages (95% CI: 0.04-0.08; p < 0.001) and a 4% increase in admissions for individuals older than 5 years (95% CI: 0.02-0.05; p < 0.001). A positive linear relationship between average daily Tapp and all daily diarrheal admissions for children ≤5 years old was not statistically significant (95% CI: -0.00-0.03; p = 0.107). Diarrhea is common in children ≤5 years old, however, is more likely triggered by factors other than temperature/Tapp, while it is likely associated with increased temperature in individuals >5 years old. We are limited by lack of data on confounders and effect modifiers, thus, our findings are exploratory. To fully quantify how temperature affects hospital admission counts for diarrhea, future studies should include socio-economic-demographic factors as well as WASH-related data such as personal hygiene practices and access to clean water.

Understanding diarrhoeal diseases in response to climate variability and drought in Cape Town, South Africa: a mixed methods approach.

BACKGROUND: The climate of southern Africa is expected to become hotter and drier with more frequent severe droughts and the incidence of diarrhoea to increase. From 2015 to 2018, Cape Town, South Africa, experienced a severe drought which resulted in extreme water conservation efforts. We aimed to gain a more holistic understanding of the relationship between diarrhoea in young children and climate variability in a system stressed by water scarcity. METHODS: Using a mixed-methods approach, we explored diarrhoeal disease incidence in children under 5 years between 2010 to 2019 in Cape Town, primarily in the public health system through routinely collected diarrhoeal incidence and weather station data. We developed a negative binomial regression model to understand the relationship between temperature, precipitation, and relative humidity on incidence of diarrhoea with dehydration. We conducted in-depth interviews with stakeholders in the fields of health, environment, and human development on perceptions around diarrhoea and health-related interventions both prior to and over the drought, and analysed them through the framework method. RESULTS: From diarrhoeal incidence data, the diarrhoea with dehydration incidence decreased over the decade studied, e.g. reduction of 64.7% in 2019 [95% confidence interval (CI): 5.5-7.2%] compared to 2010, with no increase during the severe drought period. Over the hot dry diarrhoeal season (November to May), the monthly diarrhoea with dehydration incidence increased by 7.4% (95% CI: 4.5-10.3%) per 1 °C increase in temperature and 2.6% (95% CI: 1.7-3.5%) per 1% increase in relative humidity in the unlagged model. Stakeholder interviews found that extensive and sustained diarrhoeal interventions were perceived to be responsible for the overall reduction in diarrhoeal incidence and mortality over the prior decade. During the drought, as diarrhoeal interventions were maintained, the expected increase in incidence in the public health sector did not occur. CONCLUSIONS: We found that that diarrhoeal incidence has decreased over the last decade and that incidence is strongly influenced by local temperature and humidity, particularly over the hot dry season. While climate change and extreme weather events especially stress systems supporting vulnerable populations such as young children, maintaining strong and consistent public health interventions helps to reduce negative health impacts.

Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model.

Climatic factors influence malaria transmission via the effect on the Anopheles vector and Plasmodium parasite. Modelling and understanding the complex effects that climate has on malaria incidence can enable important early warning capabilities. Deep learning applications across fields are proving valuable, however the field of epidemiological forecasting is still in its infancy with a lack of applied deep learning studies for malaria in southern Africa which leverage quality datasets. Using a novel high resolution malaria incidence dataset containing 23 years of daily data from 1998 to 2021, a statistical model and XGBOOST machine learning model were compared to a deep learning Transformer model by assessing the accuracy of their numerical predictions. A novel loss function, used to account for the variable nature of the data yielded performance around + 20% compared to the standard MSE loss. When numerical predictions were converted to alert thresholds to mimic use in a real-world setting, the Transformer's performance of 80% according to AUROC was 20-40% higher than the statistical and XGBOOST models and it had the highest overall accuracy of 98%. The Transformer performed consistently with increased accuracy as more climate variables were used, indicating further potential for this prediction framework to predict malaria incidence at a daily level using climate data for southern Africa.

Predicting diarrhoea outbreaks with climate change.

BACKGROUND: 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. METHODS: We applied two deep Learning DL techniques, Convolutional Neural Networks (CNNs) and Long-Short term Memory Networks (LSTMs); and a Support Vector Machine (SVM) 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 data-set. Furthermore, Relevance Estimation and Value Calibration (REVAC) was used to tune the parameters of the ML methods 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 method. RESULTS: Our results 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. However, the level of accuracy for each method varied across different experiments, with the deep learning methods outperforming the SVM method. Among the deep learning techniques, the CNN method performed best when only real-world data-set was used, while the LSTM method outperformed the other methods when the real-world data-set was augmented with synthetic data. Across the provinces, the accuracy of all three ML methods improved by at least 30 percent when data augmentation was implemented. In addition, REVAC improved the accuracy of the CNN method by about 2.5% in each province. Our parameter sensitivity analysis revealed that the most influential climate variables to be considered when predicting outbreak of diarrhoea in South Africa were precipitation, humidity, evaporation and temperature conditions. CONCLUSIONS: Overall, experiments indicated that the prediction capacity of our DL methods (Convolutional Neural Networks) was found to be superior (with statistical significance) in terms of prediction accuracy across most provinces. This study's results have important implications for the development of automated early warning systems for diarrhoea (and related disease) outbreaks across the globe.

The 2020 WMO Symposium on Climatological, Meteorological and Environmental factors in the COVID-19 pandemic: A special issue from symposium presentations.

The COVID-19 pandemic has become one of the great historical events of the modern era, presenting a generational challenge to the world. Questions about the role of weather on SARS-CoV-2 transmission led to the gathering of scientists at an online event, the "International Virtual Symposium on Climatological, Meteorological and Environmental factors in the COVID-19 pandemic," convened on 4-6 August 2020 under the auspices of the World Meteorological Organization. This collection of papers arise from the Symposium.

Tracking Progress Towards the Sustainable Development Goals in Four Rural Villages in Limpopo, South Africa.

Background: Measuring national progress towards the Sustainable Development Goals (SDGs) enables the identification of gaps which need to be filled to end poverty, protect the planet and improve lives. Progress is typically calculated using indicators stemming from published methodologies. South Africa tracks progress towards the SDGs at a national scale, but aggregated data may mask progress, or lack thereof, at local levels. Objective: To assess the progress towards achievement of the SDGs in four low-income, rural villages (Giyani) in South Africa and to relate the findings to national SDG indicators. Methods: Using data from a cross-sectional environmental health study, the global indicator framework for the SDGs was applied to calculate indicators for Giyani. Local progress towards SDG achievement was compared with national progress, to contextualize and supplement national scale tracking. Findings: Village scores were mostly in line with country scores for those indices which were computable, given the available data. Low data availability prevented a complete local progress assessment. Higher levels of poverty prevail in the study villages compared to South Africa as a whole (17.7% compared to 7.4%), high unemployment (49.0% compared to 27.3%) and lack of access to information via the Internet (only 4.2% compared to 61.8%) were indicators in the villages identified as falling far short of the South African averages. Conclusions: Understanding progress towards the SDGs at a local scale is important when trying to unpack national progress. It shines a light upon issues that are not picked up by national composite assessments yet require most urgent attention. Gaps in data required to measure progress towards targets represents a serious stumbling block, preventing the creation of a true reflection of local and national scale progress.

Exploring rural hospital admissions for diarrhoeal disease, malaria, pneumonia, and asthma in relation to temperature, rainfall and air pollution using wavelet transform analysis.

BACKGROUND: Climate variables impact human health and in an era of climate change, there is a pressing need to understand these relationships to best inform how such impacts are likely to change. OBJECTIVES: This study sought to investigate time series of daily admissions from two public hospitals in Limpopo province in South Africa with climate variability and air quality. METHODS: We used wavelet transform cross-correlation analysis to monitor coincidences in changes of meteorological (temperature and rainfall) and air quality (concentrations of PM2.5 and NO2) variables with admissions to hospitals for gastrointestinal illnesses including diarrhoea, pneumonia-related diagnosis, malaria and asthma cases. We were interested to disentangle meteorological or environmental variables that might be associated with underlying temporal variations of disease prevalence measured through visits to hospitals. RESULTS: We found preconditioning of prevalence of pneumonia by changes in air quality and showed that malaria in South Africa is a multivariate event, initiated by co-occurrence of heat and rainfall. We provided new statistical estimates of time delays between the change of weather or air pollution and increase of hospital admissions for pneumonia and malaria that are addition to already known seasonal variations. We found that increase of prevalence of pneumonia follows changes in air quality after a time period of 10 to 15 days, while the increase of incidence of malaria follows the co-occurrence of high temperature and rainfall after a 30-day interval. DISCUSSION: Our findings have relevance for early warning system development and climate change adaptation planning to protect human health and well-being.

Winter Is Coming: A Southern Hemisphere Perspective of the Environmental Drivers of SARS-CoV-2 and the Potential Seasonality of COVID-19.

SARS-CoV-2 virus infections in humans were first reported in December 2019, the boreal winter. The resulting COVID-19 pandemic was declared by the WHO in March 2020. By July 2020, COVID-19 was present in 213 countries and territories, with over 12 million confirmed cases and over half a million attributed deaths. Knowledge of other viral respiratory diseases suggests that the transmission of SARS-CoV-2 could be modulated by seasonally varying environmental factors such as temperature and humidity. Many studies on the environmental sensitivity of COVID-19 are appearing online, and some have been published in peer-reviewed journals. Initially, these studies raised the hypothesis that climatic conditions would subdue the viral transmission rate in places entering the boreal summer, and that southern hemisphere countries would experience enhanced disease spread. For the latter, the COVID-19 peak would coincide with the peak of the influenza season, increasing misdiagnosis and placing an additional burden on health systems. In this review, we assess the evidence that environmental drivers are a significant factor in the trajectory of the COVID-19 pandemic, globally and regionally. We critically assessed 42 peer-reviewed and 80 preprint publications that met qualifying criteria. Since the disease has been prevalent for only half a year in the northern, and one-quarter of a year in the southern hemisphere, datasets capturing a full seasonal cycle in one locality are not yet available. Analyses based on space-for-time substitutions, i.e., using data from climatically distinct locations as a surrogate for seasonal progression, have been inconclusive. The reported studies present a strong northern bias. Socio-economic conditions peculiar to the 'Global South' have been omitted as confounding variables, thereby weakening evidence of environmental signals. We explore why research to date has failed to show convincing evidence for environmental modulation of COVID-19, and discuss directions for future research. We conclude that the evidence thus far suggests a weak modulation effect, currently overwhelmed by the scale and rate of the spread of COVID-19. Seasonally modulated transmission, if it exists, will be more evident in 2021 and subsequent years.

Publisher Correction: Malaria predictions based on seasonal climate forecasts in South Africa: A time series distributed lag nonlinear model.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Malaria predictions based on seasonal climate forecasts in South Africa: A time series distributed lag nonlinear model.

Although there have been enormous demands and efforts to develop an early warning system for malaria, no sustainable system has remained. Well-organized malaria surveillance and high-quality climate forecasts are required to sustain a malaria early warning system in conjunction with an effective malaria prediction model. We aimed to develop a weather-based malaria prediction model using a weekly time-series data including temperature, precipitation, and malaria cases from 1998 to 2015 in Vhembe, Limpopo, South Africa and apply it to seasonal climate forecasts. The malaria prediction model performed well for short-term predictions (correlation coefficient, r > 0.8 for 1- and 2-week ahead forecasts). The prediction accuracy decreased as the lead time increased but retained fairly good performance (r > 0.7) up to the 16-week ahead prediction. The demonstration of the malaria prediction process based on the seasonal climate forecasts showed the short-term predictions coincided closely with the observed malaria cases. The weather-based malaria prediction model we developed could be applicable in practice together with skillful seasonal climate forecasts and existing malaria surveillance data. Establishing an automated operating system based on real-time data inputs will be beneficial for the malaria early warning system, and can be an instructive example for other malaria-endemic areas.

Major threats of pollution and climate change to global coastal ecosystems and enhanced management for sustainability.

Coastal zone is of great importance in the provision of various valuable ecosystem services. However, it is also sensitive and vulnerable to environmental changes due to high human populations and interactions between the land and ocean. Major threats of pollution from over enrichment of nutrients, increasing metals and persistent organic pollutants (POPs), and climate change have led to severe ecological degradation in the coastal zone, while few studies have focused on the combined impacts of pollution and climate change on the coastal ecosystems at the global level. A global overview of nutrients, metals, POPs, and major environmental changes due to climate change and their impacts on coastal ecosystems was carried out in this study. Coasts of the Eastern Atlantic and Western Pacific were hotspots of concentrations of several pollutants, and mostly affected by warming climate. These hotspots shared the same features of large populations, heavy industry and (semi-) closed sea. Estimation of coastal ocean capital, integrated management of land-ocean interaction in the coastal zone, enhancement of integrated global observation system, and coastal ecosystem-based management can play effective roles in promoting sustainable management of coastal marine ecosystems. Enhanced management from the perspective of mitigating pollution and climate change was proposed.

Development of a monoclonal antibody detection assay for species-specific identification of abalone.

Species identification based on biochemical and molecular techniques has a broad range of applications. These include compliance enforcement, the management and conservation of marine organisms, and commercial quality control. Abalone poaching worldwide and illegal trade in abalone products have increased mainly because of the attractive prices obtained and caused a sharp decline in stocks. Alleged poachers have been acquitted because of lack of evidence to correctly identify species. Therefore, a robust method is required that would identify tissue of abalone origin to species level. The aim of this study was to develop immunologic techniques, using monoclonal and polyclonal antibodies, to identify 10 different abalone species and subspecies from South Africa, the United States, Australia, and Japan. The combination of 3 developed monoclonal antibodies to South African abalone (Haliotis midae) enabled differentiation between most of the 10 species including the subspecies H. diversicolor supertexta and H. diversicolor diversicolor. In a novel approach, using antibodies of patients with allergy to abalone, the differentiation of additional subspecies, H. discus discus and H. discus hannai, was possible. A field-based immunoassay was developed to identify confiscated tissue of abalone origin.