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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.

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.

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.

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.

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.

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.

When COVID-19 began to spread, environmental scientists recognized that the world faced a dangerous upper respiratory viral disease that might exhibit sensitivity to seasonal weather conditions. Many of these scientists have sought to aid COVID-19 response by studying the potential to monitor, forecast, or project disease transmission rates or symptom severity as a function of climate zone, season, meteorological variability, air quality, and other environmental parameters1,2,3,4. The rapid pace of COVID-19 research has meant that studies on this topic appeared on pre-print servers and then on news and social media outlets faster than the information could be cross-checked and peer-reviewed. As many such studies accumulated, it became clear that reported evidence was often contradictory, and in some cases studies were being selected subjectively in a manner that seemed intended to support political agendas. Carlson et al.5 recently provided a cogent assessment of the policy-relevant challenges associated with studies that have attempted to quantify meteorological sensitivities of the virus and the disease. We appreciate this perspective. Here we argue that the research community must act to ensure that work on this topic meets its potential to contribute to pandemic understanding and response, and that fears of inappropriate data analysis or miscommunication do not dampen innovation or the effective use of research results. Por pares

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

Millions of individuals are at risk of malaria infection in sub-Saharan Africa. Compared to other highly affected countries on the continent, South Africa has an excellent record of malaria control. Nevertheless, the northeastern districts of the country, neighboring some of the worst malaria affected regions in southern Africa, still experience seasonal malaria outbreaks particularly during the summer months of September-February. The year to year variations of the malaria outbreaks in southern Africa, as in many other parts of the world, are often linked to interannual variations in rainfall and temperature. These meteorological factors in turn are seen to be associated with large-scale climate phenomena such as El Nino/La Nina. Here, we present evidence of a new mode of climate variation in the Indian Ocean that could explain the interannual variation of malaria incidences in South Africa. This phenomenon appeared as a dipolar association in the sea surface temperature (SST) anomalies of southwestern Indian Ocean. Both poles of the dipole strongly correlated with the malaria incidence index of Vhembe district, one of South Africa's highest malaria-affected districts. The identified correlations were stronger than those found with other climate phenomena such as El Nino/La Nina and Indian Ocean Dipole. A decadal shift in the SST dipole pattern was also observed, and the associated decrease in seasonal rainfall could partly explain the recent reduction in malaria cases.

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.

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., Publisher Correction: A supplementary file containing Fig S1 was omitted from the original version of this Article. This has been corrected in the HTML version of the Article; the PDF version was correct at time of publication. https://doi.org/10.1038/s41598-020-58890-y, Scientific reports, 9(1), art.no.17882; 2019

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.

BackgroundDiarrheal disease is one of the leading causes of morbidity and mortality globally, particularly in children under 5 years of age. Factors related to diarrheal disease incidence include infection, malnutrition, and exposure to contaminated water and food. Climate factors also contribute to diarrheal disease.ObjectivesWe aimed to explore the relationship between temperature, precipitation and diarrhea case counts of hospital admissions among vulnerable communities living in a rural setting in South Africa.MethodsWe applied a novel approach of ‘contour analysis’ to visually examine simultaneous observations in frequencies of anomalously high and low diarrhea case counts occurring in a season and assigning colors to differences that were statistically significant based on chi-squared test results.ResultsThere was a significantly positive difference between high and low ‘groups’ when there was a lack of rain (0 mm of cumulative rain) for 1 to 2 weeks in winter for children under 5.Diarrhea prevalence was greater among children under 5 years when conditions were hotter than usual during winter and spring.DiscussionDry conditions may lead to increased water storage raising the risks of water contamination. Reduced use of water for personal hygiene and cleaning of outdoor pit latrines affect sanitation quality. Rural communities require adequate and uninterrupted water provision and healthcare providers should raise awareness about potential diarrheal risks especially during the dry season.

Metrics of the spatiotemporal variability of the physical and biological properties of Large Marine Ecosystems (LMEs) are critical indices of the trends and changes in these coastal systems. Given that the world's oceans, including coastal systems, are undergoing substantial changes as reported by a range of global assessments, the variability of these changes in space and time is important to understand, if the impacts are to be mitigated and adaptation measures required are to be appropriately applied. We have assessed the trends in Sea Surface Temperature (SST) (1982–2019) and chlorophyll-a concentrations (1997–2019) for all seven of the African LMEs. Our analysis revealed that as a whole almost 99% of all the combined area of African LMEs has warmed. We found rates of SST warming of between 0.11 °C/dec (Agulhas LME) and 0.39 °C/dec (Mediterranean Sea LME) on average for entire LMEs, and regions with rates of as high as 0.58 °C/dec in the Canary Current LME. We also found that 1.1% of the area of the LMEs had cooling trends in association with upwelling regions found in four of the seven LMEs. In this study, we have carefully considered the spatial distribution of warming and cooling in the respective LMEs and identified warming ‘hotspots’ and re-catgorized the LMEs sub-regions into ‘slow’, ‘moderate’, ‘fast’ and ‘superfast’ warming areas, as defined in the Transboundary Waters Assessment Project of the IOC-UNECO-UNEP (2016). In terms of chlorophyll-a, we report that concentration estimates are highly heterogeneous in space and time and that robust trends are, however, difficult to extract from available data. Nevertheless, we do report an overall range in chlorophyll-a concentrations of 0.06–10.9 mg/m3 and the highest median concentrations in the two Eastern Boundary Upwelling Systems, that is, the Benguela Current LME and the Canary Current LME (which also contain the regions with the highest estimates of Net Primary Production (NPP) of 7063 and 7070 mg C/m2/day, respectively). The lowest concentrations are in the oligotrophic Agulhas Current LME, which also has the lowest estimate of NPP of 342 mg C/m2/day. General declines in chlorophyll-a concentrations are observed with statistically significant rates apparent in the Agulhas LME and contrasting areas of local and patchy increases in chlorophyll-a concentrations over time. Decadal trends in NPP are assessed as generally negative in the eastern LMEs and positive in the Western LMEs. The main conclusion from this study is that while the trends themselves are a further iteration of the manifestation of climate change impacts on African LMEs, it is the high degree of heterogeneity in these properties within the LMEs, along the trans-LME distribution in some instances, that must be taken into consideration for assessment of ecosystem impacts and effective management responses.

Seasonal Climate Forecasting (SCF) in South Africa has a history spanning several decades. During this period a number of SCF systems have been developed for the prediction of seasonal-to-interannual variability of rainfall and surface temperatures. Areas of highest predictability, albeit relatively modest, have also been identified. The north-eastern parts of South Africa that includes the Limpopo province has been demonstrated to be one of the areas of highest SCF skill in the country. Statistical post-processing techniques applied to global climate model output were part of this forecast system development, and were subsequently successfully used in the construction of forecasts systems for applications in sectors which are associated with ENSO-driven climate variability, such as dry-land crop yields and river flows. Here we follow a similar post-model processing approach to test SCF systems for application to the incidence of seasonal malaria in Limpopo. The malaria forecast system introduced here makes use of the seasonal rainfall output fields of one of the North American Multi-Model Ensemble (NMME) climate models, which is then linked statistically through multiple linear regression to observed malaria incidence. The verification results as calculated over a 20-year hindcast period show that the season of highest malaria incidence forecast skill is during the austral mid-summer time of December to February. Moreover, the hindcasts based on the NMME model outscore those of statistical forecast models that separately use Indian and Pacific Ocean sea-surface temperatures as predictors, thus justifying the use of physical global climate models for this kind of application. Additional results indicate that model skill levels may include quasi-decadal variability, that the periods over which forecast verification is performed strongly influences forecast skill, and that poorly predicted malaria seasons may have serious financial implications on public health operations.

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 is 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. 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 a 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.

Concern and general awareness about the impacts of climate change in all sectors of the social-ecological-economic system is growing as a result of improved climate science products and information, as well as increased media coverage of the apparent manifestations of the phenomenon in our society. However, scales of climate variability and change, in space and time, are often confused and so attribution of impacts on various sectors, including the health sector, can be misunderstood and misrepresented. In this review, we assess the mechanistic links between climate and infectious diseases in particular, and consider how this relationship varies, and may vary according to different time scales, especially for aetiologically climate-linked diseases. While climate varies in the medium (inter-annual) time frame, this variability itself may be oscillating and/or trending on cyclical and long-term (climate change) scales because of regional and global scale climate phenomena such as the El-Nino southern oscillation coupled with global-warming drivers of climate change. As several studies have shown, quantifying and modelling these linkages and associations at appropriate time and space scales is both necessary and increasingly feasible with improved climate science products and better epidemiological data. The application of this approach is considered for South Africa, and the need for a more concerted effort in this regard is supported., South African Medical Journal, 105(12), pp.1018-1023; 2015

Diarrheal disease is one of the leading causes of morbidity and mortality globally, particularly in children under 5 years of age. Factors related to diarrheal disease incidence include infection, malnutrition, and exposure to contaminated water and food. Climate factors also contribute to diarrheal disease. We aimed to explore the relationship between temperature, precipitation and diarrhoea case counts of hospital admissions among vulnerable communities living in a rural setting in South Africa. We applied &lsquo, contour analysis&rsquo, to visually examine simultaneous observations in frequencies of anomalously high and low diarrhoea case counts occurring in a season, and assigning colours to differences that were statistically significant based on chi-squared test results. Children under 5 years of age were especially vulnerable to diarrhoea during very dry, hot conditions as well as when conditions were wetter than usual. We saw an anomalously higher number of diarrhoea cases during &lsquo, warmer than usual&rsquo, conditions in the dry winter season, with average winter temperatures in Limpopo being from about 5 to 10 &deg, C. As for &lsquo, wetter than usual&rsquo, conditions, we saw an anomalously higher number of diarrhoea cases during &lsquo, drier than usual&rsquo, conditions for the winter and spring. The lagged association seen in cumulative rainfall could not be distinguished in the same way for temperature-related variables (indicating rainfall had a larger impact on higher cases of diarrhoea), nor for the older age group of 5 years and older. Dry conditions were associated with diarrhoea in children under 5 years of age, such conditions may lead to increased water storage, raising the risks of water contamination. Reduced use of water for personal hygiene and cleaning of outdoor pit latrines also affect sanitation quality. Rural communities require adequate and uninterrupted water provision, and healthcare providers should raise awareness about potential diarrhoeal risks, especially during the dry season as well as during wintertime when conditions are warmer than usual.