Abstract In this paper, we develop and analyze a malaria model with seasonality of mosquito life‐history traits: periodic‐mosquitoes per capita birth rate, ‐mosquitoes death rate, ‐probability of mosquito to human disease transmission, ‐probability of human to mosquito disease transmission, and ‐mosquitoes biting rate. All these parameters are assumed to be time dependent leading to a nonautonomous differential equation system. We provide a global analysis of the model depending on two threshold parameters and (with ). When , then the disease‐free stationary state is locally asymptotically stable. In the presence of the human disease‐induced mortality, the global stability of the disease‐free stationary state is guarantied when . On the contrary, if , the disease persists in the host population in the long term and the model admits at least one positive periodic solution. Moreover, by a numerical simulation, we show that a sub‐critical (backward) bifurcation is possible at . Finally, the simulation results are in accordance with the seasonal variation of the reported cases of a malaria‐epidemic region in Mpumalanga province in South Africa.
The use of crop modeling as a decision tool by farmers and other decision-makers in the agricultural sector to improve production efficiency has been on the increase. In this study, artificial neural network (ANN) models were used for predicting maize in the major maize producing provinces of South Africa. The maize production prediction and projection analysis were carried out using the following climate variables: precipitation (PRE), maximum temperature (TMX), minimum temperature (TMN), potential evapotranspiration (PET), soil moisture (SM) and land cultivated (Land) for maize. The analyzed datasets spanned from 1990 to 2017 and were divided into two segments with 80% used for model training and the remaining 20% for testing. The results indicated that PET, PRE, TMN, TMX, Land, and SM with two hidden neurons of vector (5,8) were the best combination to predict maize production in the Free State province, whereas the TMN, TMX, PET, PRE, SM and Land with vector (7,8) were the best combination for predicting maize in KwaZulu-Natal province. In addition, the TMN, SM and Land and TMN, TMX, SM and Land with vector (3,4) were the best combination for maize predicting in the North West and Mpumalanga provinces, respectively. The comparison between the actual and predicted maize production using the testing data indicated performance accuracy adjusted R2 of 0.75 for Free State, 0.67 for North West, 0.86 for Mpumalanga and 0.82 for KwaZulu-Natal. Furthermore, a decline in the projected maize production was observed across all the selected provinces (except the Free State province) from 2018 to 2019. Thus, the developed model can help to enhance the decision making process of the farmers and policymakers.
This paper presents a review of numerous items of published literature on the use of spatial technology for malaria epidemiology in South Africa between 1930 and 2013. In particular, focus is on the use of statistical and mathematical models as well as geographic information science (GIS) and remote sensing (RS) technology for malaria research. First, the review takes cognisance of the use of predictive models to determine the association between climatic factors and malaria epidemics only in KwaZulu-Natal province. Similar studies in other endemic regions such as Limpopo and Mpumalanga provinces have not been reported in the literature. While the integration of GIS with remote sensing has the potential of identifying, characterising, and monitoring breeding habitats and mapping malaria risk areas in South Africa, studies on the application of spatial technology in malaria research and control in South Africa are inexhaustive and have not been reported in the literature. As a result, a critical robust malaria warning system, which uses GIS and RS in South Africa, is yet to be realised. It is recommended that the wide range of datasets available from different sources including RS and global positioning systems (GPS) ought to be integrated into a GIS system, which is a core spatial technology vital for understanding the epidemiological processes of malaria and hence support in decision-making in malaria control.
27th Annual Conference of the South African Society for Atmospheric Sciences: the Interdependent Atmosphere, Land and Ocean, Hartbeespoort, 22-23 September 2011
Challenges emanating from rapid urbanisation require innovative strategies to transform cities into global climate action and adaptation centres. We provide an analysis of the impacts of rapid urbanisation in the Gauteng City-Region, South Africa, highlighting major challenges related to (i) land use management, (ii) service delivery (water, energy, food, and waste and sanitation), and (iii) social cohesion. Geospatial techniques were used to assess spatio-temporal changes in the urban landscapes, including variations in land surface temperatures. Massive impervious surfaces, rising temperatures, flooding and heatwaves are exacerbating the challenges associated with rapid urbanisation. An outline of the response pathways towards sustainable and resilient cities is given as a lens to formulate informed and coherent adaptation urban planning strategies. The assessment facilitated developing a contextualised conceptual framework, focusing on demographic, climatic, and environmental changes, and the risks associated with rapid urbanisation. If not well managed in an integrated manner, rapid urbanisation poses a huge environmental and human health risk and could retard progress towards sustainable cities by 2030. Nexus planning provides the lens and basis to achieve urban resilience, by integrating complex, but interlinked sectors, by considering both ecological and built infrastructures, in a balanced manner, as key to resilience and adaptation strategies.
Early studies of weather, seasonality, and environmental influences on COVID-19 have yielded inconsistent and confusing results. To provide policy-makers and the public with meaningful and actionable environmentally-informed COVID-19 risk estimates, the research community must meet robust methodological and communication standards.
Globally, a growing body of research has shown that ambient air pollution is one of the most critical environmental issues, especially in relation to human health. Exposure to ambient air pollution leads to serious health conditions such as lower respiratory infections, cancers, diabetes mellitus type 2, ischaemic heart disease, stroke and chronic obstructive pulmonary disease.To estimate the burden of disease attributable to ambient air pollution in South Africa (SA) for the years 2000, 2006 and 2012.Comparative risk assessment method was used to determine the burden of disease due to two pollutants (particulate matter (PM2.5) and ambient ozone). Regionally optimised fully coupled climate chemistry models and surface air pollution observations were used to generate concentrations of PM2.5 and ozone for each SA Census Small Area Level, for the year 2012. For 2000 and 2006, population-weighted PM2.5and ozone were estimated, based on the 2012 results. Following the identification of disease outcomes associated with particulate matter with aerodynamic diameter <2.5 μm (PM2.5) and ozone exposure, the attributable burden of disease was estimated for 2000, 2006 and 2012. Furthermore, for the year 2012, the burden of disease attributable to ambient air pollution exposure was computed at provincial levels.In 2012, approximately 97.6% of people in SA were exposed to PM2.5 at levels above the 2005 World Health Organization guideline: 10 μg/m3 annual mean. From 2000 to 2012, population-weighted annual average PM2.5 increased from 26.6 μg/m3 to 29.7 μg/m3, and ozone 6-month high 8-hour daily maximum increased from 64.4 parts per billion (ppb) to 72.1 ppb. At a national scale, in the year 2000, 15 619 (95% uncertainty interval (UI) 8 958 - 21 849) deaths were attributed to PM2.5 exposure, while 1 326 (95% UI 534 - 1 885) deaths were attributed to ozone. In 2006, an estimated 19 672 deaths (95% UI 11 526 - 27 086) were attributed to PM2.5, and a further 1 591 deaths (95% UI 651 - 2 236) to ozone exposure. In 2012, deaths attributed to PM2.5 were 19 507 (95% UI 11 318 - 27 111), and to ozone 1 734 (95% UI 727 - 2 399). Additionally, population-weighted provincial scale analysis showed that Gauteng Province had the highest number of attributable deaths due to both PM2.5 and ozone in 2012.The study showed that ambient air pollution exposure is an important health risk in SA, requiring both short- and long-term intervention. In the short term, the SA Ambient Air Quality Standards and industrial minimum emissions standards need to be enforced. In the longer term, to reduce air pollution and the associated disease burden, the combustion of fossil fuels as a source of energy for power generation and transportation, as well as industrial and domestic uses, needs to be replaced with clean renewable energy sources. In addition to local measures, when the southern African prevalent anticyclonic air dynamics that transport regionally emitted pollutants into SA (especially from biomass burning) are considered, it is also advisable to establish long-term regional co-operation in reducing air pollution.
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