Assessing the correlation between malaria case mortality rates and access to health facilities in the malaria region of Vhembe District, South Africa

BACKGROUND : Local villages in the Vhembe district of South Africa have experienced high malaria infection rates and a high variability of malaria case mortality rates over the past 20 years. *is research project sets out to determine if specific socioeconomic factors have influence on the varying malaria case mortality rates. METHODS : The study used existing malaria records of all reported malaria cases in the Vhembe district between 1998 and 2017. The data set was sampled using maximum variation sampling combined with a stratified sampling approach to select the source locations with the highest reported variations in malaria case mortality. The number of medical facilities used, distances to the medical facilities, and proximity to significant water sources were subsequently spatially and statistically analysed for potential correlations between these factors and the malaria case fatality rates of the source locations. RESULTS : Within the period of study, a total of 57,974 malaria infections were reported from 850 source locations across the villages and neighbourhoods. The result of the sampling methods gave 30 source locations with highest reported variations in malaria case mortality. The statistical analysis indicated a significant negative correlation between the case mortality rates and the number of medical facilities used, the number of infections reported, and the maximum and mean distances travelled to the medical facilities used. In addition, the analysis indicated a positive correlation between the minimum distances travelled to the medical facilities used and the case mortality rates. The spatial analysis supported the majority of the findings from the statistical analysis. Proximity to significant water bodies was not found to have any significant impact on case mortality rates. CONCLUSION : The results suggested that malaria patients from larger communities, those who had financial or other means to consult more advanced facilities, or those with a larger variety of services had a significantly lower risk of mortality. The findings of this study could assist societies and authorities in mitigating the negative effects of malaria infections on human life expectancies through improved socioeconomic development.http://www.hindawi.com/journals/jepham2021Geography, Geoinformatics and MeteorologySchool of Health Systems and Public Health (SHSPH)UP Centre for Sustainable Malaria Control (UP CSMC

Spatial distribution of temporal precipitation contrasts in South Africa

The focus of the present study was to investigate the spatial-temporal variability and trends of precipitation concentration across South Africa using the Tropical Rainfall Measuring Mission (TRMM) 3B42 version 7 satellite precipitation data sets spanning 1998–2015. In the analysis, the precipitation concentration index (PCI) was used to infer the variability of temporal precipitation contrasts and the spatial distributions at annual, seasonal and supra-seasonal timescales. The results indicate that precipitation concentration across South Africa exhibits noticeable spatial-temporal variability. In terms of PCI classification criteria, the precipitation distribution ranges from relatively uniform (mainly in the central and southern interior of South Africa) to highly irregular (especially to the northeastern and western parts of South Africa) at annual timescales. At seasonal timescales, the precipitation distribution is uniform during December–February season, moderate during March–May and September–November seasons while during the June–August season, the precipitation distribution is highly irregular. Furthermore, during the 1998–2015 period, there exists a spatial and temporal pattern of PCI trends which are generally statistically insignificant. The PCI analysis results reported in this study are essential because they provide valuable information on the longterm total variability in the precipitation records across South Africa. In particular, this study contributes towards evaluating the spatial contrasts or concentration of the different accumulated amounts of the received precipitation. Results from this study have important scientific and practical applications in hydrological hazard risks (floods and droughts) and soil erosion monitoring. SIGNIFICANCE : • Precipitation concentration exhibits spatial-temporal variability. • At an annual timescale, precipitation concentration is highly irregular in most parts of the country. • Precipitation concentration distribution varies across seasons.http://www.sajs.co.zaam2019Geography, Geoinformatics and Meteorolog

The hydrometeorology of the Kariba catchment area based on the probability distributions

In this paper, monthly, maximum seasonal, and maximum annual hydrometeorological (i.e., evaporation, lake water levels, and rainfall) data series from the Kariba catchment area of the Zambezi River basin, Zimbabwe, have been analyzed in order to determine appropriate probability distribution models of the underlying climatology from which the data were generated. In total, 16 probability distributions were considered and the Kolmogorov–Sminorv (KS), Anderson–Darling (AD), and chi-square (x2) goodness-of-fit (GoF) tests were used to evaluate the best-fit probability distribution model for each hydrometeorological data series. A ranking metric that uses the test statistic from the three GoF tests was formulated and used to select the most appropriate probability distribution model capable of reproducing the statistics of the hydrometeorological data series. Results showed that, for each hydrometeorological data series, the best-fit probability distribution models were different for the different time scales, corroborating those reported in the literature. The evaporation data series was best fit by the Pearson system, the Lake Kariba water levels series was best fit by theWeibull family of probability distributions, and the rainfall series was best fit by the Weibull and the generalized Pareto probability distributions. This contribution has potential applications in such areas as simulation of precipitation concentration and distribution and water resources management, particularly in the Kariba catchment area and the larger Zambezi River basin, which is characterized by (i) nonuniform distribution of a network of hydrometeorological stations, (ii) significant data gaps in the existing observations, and (iii) apparent inherent impacts caused by climatic extreme events and their corresponding variability.http://www2.ametsoc.org/ams/index.cfm/publications/journals/earth-interactions/2015-10-31hb201

Observed trends and projections of temperature and precipitation in the Olifants River Catchment in South Africa

Among the projected effects of climate change, water resources are at the center of the matrix. Certainly, the southern African climate is changing, consequently, localized studies are needed to determine the magnitude of anticipated changes for effective adaptation. Utilizing historical observation data over the Olifants River Catchment, we examined trends in temperature and rainfall for the period 1976–2019. In addition, future climate change projections under the RCP 4.5 and RCP 8.5 scenarios for two time periods of 2036–2065 (near future) and 2066–2095 (far future) were analysed using an ensemble of eight regional climate model (RCA4) simulations of the CORDEX Africa initiative. A modified Mann-Kendall test was used to determine trends and the statistical significance of annual and seasonal rainfall and temperature. The characteristics of extreme dry conditions were assessed by computing the Standardized Precipitation Index (SPI). The results suggest that the catchment has witnessed an increase in temperatures and an overall decline in rainfall, although no significant changes have been detected in the distribution of rainfall over time. Furthermore, the surface temperature is expected to rise significantly, continuing a trend already evident in historical developments. The results further indicate that the minimum temperatures over the Catchment are getting warmer than the maximum temperatures. Seasonally, the minimum temperature warms more frequently in the summer season from December to February (DJF) and the spring season from September to November (SON) than in the winter season from June to August (JJA) and in the autumn season from March to May (MAM). The results of the SPI affirm the persistent drought conditions over the Catchment. In the context of the current global warming, this study provides an insight into the changing characteristics of temperatures and rainfall in a local context. The information in this study can provide policymakers with useful information to help them make informed decisions regarding the Olifants River Catchment and its resources.DATA AVAILABILITY STATEMENT : The data process is done using R Software which includes several packages for mapping NetCDF data. Sample data and scripts used for this study are made available on Open Science Framework at https://osf.io/8rhn2 and https://osf.io/3d9j4.The South32 mining company.http://www.plosone.orgdm2022Geography, Geoinformatics and MeteorologySchool of Health Systems and Public Health (SHSPH

The impacts of land use and land cover dynamics on natural resources and rural livelihoods in Dedza District, Malawi

The sustainable management of natural resources requires critical understanding of land use and land cover changes and how these changes impact natural resources and rural livelihoods. This study examined the impacts of LULC changes on natural resources and rural livelihoods of Central Malawi. The study used an integrated approach combining remote sensing, household surveys consisting of structured and semi-structured questionnaires, focus group discussions and key informant interviews. Local communities perceived that LULC changes have resulted in the decline of agricultural land (57.3%), crop production (82.8%) and forest cover (87.4%) In response to observed LULC changes, respondents deployed short-term coping strategies such as seeking piecework opportunities and the use of savings and credits. The study has provided evidence that LULC changes have led to significant losses in natural resources, with serious consequences for rural livelihoods in Dedza. The study has contributed to better understanding of the complicated human-environment interaction in Malawi.https://www.tandfonline.com/loi/tgei202021-07-21hj2020Geography, Geoinformatics and Meteorolog

Comparison of predictive models and impact assessment of lockdown for COVID-19 over the United States

The novel Coronavirus Disease 2019 (COVID-19) remains a worldwide threat to community health, social stability, and economic development. Since the first case was recorded on December 29, 2019, in Wuhan of China, the disease has rapidly extended to other nations of the world to claim many lives, especially in the USA, the United Kingdom, and Western Europe. To stay ahead of the curve consequent of the continued increase in case and mortality, predictive tools are needed to guide adequate response. Therefore, this study aims to determine the best predictive models and investigate the impact of lockdown policy on the USA’ COVID-19 incidence and mortality. This study focuses on the statistical modelling of the USA daily COVID-19 incidence and mortality cases based on some intuitive properties of the data such as overdispersion and autoregressive conditional heteroscedasticity. The impact of the lockdown policy on cases and mortality was assessed by comparing the USA incidence case with that of Sweden where there is no strict lockdown. Stochastic models based on negative binomial autoregressive conditional heteroscedasticity [NB INGARCH (p,q)], the negative binomial regression, the autoregressive integrated moving average model with exogenous variables (ARIMAX) and without exogenous variables (ARIMA) models of several orders are presented, to identify the best fitting model for the USA daily incidence cases. The performance of the optimal NB INGARCH model on daily incidence cases was compared with the optimal ARIMA model in terms of their Akaike Information Criteria (AIC). Also, the NB model, ARIMA model and without exogenous variables are formulated for USA daily COVID-19 death cases. It was observed that the incidence and mortality cases show statistically significant increasing trends over the study period. The USA daily COVID-19 incidence is autocorrelated, linear and contains a structural break but exhibits autoregressive conditional heteroscedasticity. Observed data are compared with the fitted data from the optimal models. The results further indicate that the NB INGARCH fits the observed incidence better than ARIMA while the NB models perform better than the optimal ARIMA and ARIMAX models for death counts in terms of AIC and root mean square error (RMSE). The results show a statistically significant relationship between the lockdown policy in the USA and incidence and death counts. This suggests the efficacy of the lockdown policy in the USA.https://www.atlantis-press.com/journals/jegham2022Geography, Geoinformatics and Meteorolog

Spatial and temporal analysis of the mid-summer dry spells for the summer rainfall region of South Africa

South Africa is frequently subjected to severe droughts and dry spells during the rainy season. As such, rainfall is one of the most significant factors limiting dryland crop production in South Africa. The mid-summer period is particularly important for agriculture since a lack of rain during this period negatively affects crop yields. Dry spell frequency analyses are used to investigate the impacts of sub-seasonal rainfall variability on crop yield, since seasonal rainfall totals alone do not explain the relationship between rainfall and crop yields. This study investigated the spatial and temporal occurrences of the mid-summer dry spells based on magnitude, length and time of occurrence in the major maize growing areas of the summer rainfall region of South Africa. Three thresholds of 5 mm, 10 mm, and 15 mm total rainfall for a pentad were used for the analysis of dry spells. Dry spell analysis showed that dry pentads occur during mid-summer with differing intensity, duration and frequency across the summer rainfall region. Annual frequency of dry pentads for the mid-summer period ranged between 0 and 4 pentads for the 5 mm threshold and 1 to 7 for the 10 mm and 15 mm thresholds. The non-parametric Mann-Kendall trend analysis of the dry pentads indicates that there is no significant trend in the frequency of dry spells at a 95% confidence level. The initial and conditional probabilities of getting a dry spell using the Markov chain model also showed that there is a 32% to 80% probability that a single pentad will be dry using the 15 mm threshold. There is a 5% to 48% probability of experiencing two consecutive dry pentads and 1% to 29% probability of getting three consecutive dry pentads. The duration and intensity of dry spells, as well as the Markov chain probabilities, showed a decrease in dry spells from west to east of the maize-growing areas of the summer rainfall region of South Africa.The Water Research Commission and the South African Weather Service.https://watersa.netpm2022Geography, Geoinformatics and MeteorologySchool of Health Systems and Public Health (SHSPH)UP Centre for Sustainable Malaria Control (UP CSMC

Rainfall trends and malaria occurrences in Limpopo Province, South Africa

This contribution aims to investigate the influence of monthly total rainfall variations on malaria transmission in the Limpopo Province. For this purpose, monthly total rainfall was interpolated from daily rainfall data from weather stations. Annual and seasonal trends, as well as cross-correlation analyses, were performed on time series of monthly total rainfall and monthly malaria cases in five districts of Limpopo Province for the period of 1998 to 2017. The time series analysis indicated that an average of 629.5 mm of rainfall was received over the period of study. The rainfall has an annual variation of about 0.46%. Rainfall amount varied within the five districts, with the northeastern part receiving more rainfall. Spearman’s correlation analysis indicated that the total monthly rainfall with one to two months lagged e ect is significant in malaria transmission across all the districts. The strongest correlation was noticed in Vhembe (r = 0.54; p-value = <0.001), Mopani (r = 0.53; p-value = <0.001), Waterberg (r = 0.40; p-value =< 0.001), Capricorn (r = 0.37; p-value = <0.001) and lowest in Sekhukhune (r = 0.36; p-value = <0.001). Seasonally, the results indicated that about 68% variation in malaria cases in summer—December, January, and February (DJF)—can be explained by spring—September, October, and November (SON)—rainfall in Vhembe district. Both annual and seasonal analyses indicated that there is variation in the e ect of rainfall on malaria across the districts and it is seasonally dependent. Understanding the dynamics of climatic variables annually and seasonally is essential in providing answers to malaria transmission among other factors, particularly with respect to the abrupt spikes of the disease in the province.http://www.mdpi.com/journal/ijerpham2020Geography, Geoinformatics and MeteorologySchool of Health Systems and Public Health (SHSPH

Variability of rainfall over Lake Kariba catchment area in the Zambezi river basin, Zimbabwe

In this study, average monthly and annual rainfall totals recorded for the period 1970 to 2010 from a network of 13 stations across the Lake Kariba catchment area of the Zambezi river basin were analyzed in order to characterize the spatial-temporal variability of rainfall across the catchment area. In the analysis, the data were subjected to intervention and homogeneity analysis using the Cumulative Summation (CUSUM) technique and step change analysis using rank-sum test. Furthermore, rainfall variability was characterized by trend analysis using the non-parametric Mann-Kendall statistic. Additionally, the rainfall series were decomposed and the spectral characteristics derived using Cross Wavelet Transform (CWT) and Wavelet Coherence (WC) analysis. The advantage of using the wavelet-based parameters is that they vary in time and can therefore be used to quantitatively detect time-scale-dependent correlations and phase shifts between rainfall time series at various localized time-frequency scales. The annual and seasonal rainfall series were homogeneous and demonstrated no apparent significant shifts. According to the inhomogeneity classification, the rainfall series recorded across the Lake Kariba catchment area belonged to category A (useful) and B (doubtful), i.e., there were zero to one and two absolute tests rejecting the null hypothesis (at 5 % significance level), respectively. Lastly, the long-term variability of the rainfall series across the Lake Kariba catchment area exhibited non-significant positive and negative trends with coherent oscillatory modes that are constantly locked in phase in the Morlet wavelet space.http://link.springer.com/journal/7042017-04-12hb201