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.

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.

Nutritional, Texture, and Sensory Properties of composite biscuits produced from breadfruit and wheat flours enriched with edible fish meal.

This study aimed to develop biscuits with improved nutritional contents using edible fish meal from catfish as the source of macro- and micronutrient enrichment while trying to reduce the input of wheat flour in biscuit-making process. The biscuit was produced using edible fish meal (EFM: 0%-40%) from catfish, improved quality breadfruit (IQBF: 0%-60%), and wheat flours (WF: 0%-40%). Macro (crude protein, fat, fiber, ash, and carbohydrate)- and micro (calcium, magnesium, potassium, phosphorus, sodium, and iron)-nutrient contents of the biscuit were determined. The color (lightness-L*, redness-a*, and yellowness-b*), texture (hardness, springiness, and adhesiveness), and sensory (taste, texture, and overall acceptability) attributes of the biscuits were assessed using standard methods. Model characteristics of the responses were profiled, and numerical optimization technique was used to predict combination/blends that produce biscuits with desired nutritional contents. Moisture, crude protein, fat, fiber, and ash values were in the range of 3.50%-5.57%, 3.06%-15.52%, 13.62%-26.00%, 0.31%-1.40%, and 1.98%-5.32%, respectively. The iron, calcium, and phosphorus contents of the biscuit ranged from 103.85 to 201.30 mg/100 g, 100 to 754 mg/100 g, and 8 mg/100 g to 304 mg/100 g, respectively. Interaction between the models for WF and EFM was significant and this significantly affected the L* (36.37-51.90) and adhesiveness (0.01-0.29) values for color and texture, respectively. Similar observations were also noticed for most of the nutrients. The quadratic models selected for the nutrients were all significant (p < .05) and the adjusted R 2 ranged from 0.61 to 0.84 and 0.59 to 0.97 for the macro- and micronutrients, respectively. In conclusion, a biscuit from IQBF, WF, and EFM of 61.33, 0.07, and 38.60 with protein, fat, ash, iron, and calcium contents of 10.41%, 17.59%, 2.05%, 120.52 mg/100 g, and 500.00 mg/100 g, respectively, was produced.

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. This 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.

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 effect 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 effect 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.

A Dynamical and Zero-Inflated Negative Binomial Regression Modelling of Malaria Incidence in Limpopo Province, South Africa.

Recent studies have considered the connections between malaria incidence and climate variables using mathematical and statistical models. Some of the statistical models focused on time series approach based on Box-Jenkins methodology or on dynamic model. The latter approach allows for covariates different from its original lagged values, while the Box-Jenkins does not. In real situations, malaria incidence counts may turn up with many zero terms in the time series. Fitting time series model based on the Box-Jenkins approach and ARIMA may be spurious. In this study, a zero-inflated negative binomial regression model was formulated for fitting malaria incidence in Mopani and Vhembe-two of the epidemic district municipalities in Limpopo, South Africa. In particular, a zero-inflated negative binomial regression model was formulated for daily malaria counts as a function of some climate variables, with the aim of identifying the model that best predicts reported malaria cases. Results from this study show that daily rainfall amount and the average temperature at various lags have a significant influence on malaria incidence in the study areas. The significance of zero inflation on the malaria count was examined using the Vuong test and the result shows that zero-inflated negative binomial regression model fits the data better. A dynamical climate-based model was further used to investigate the population dynamics of mosquitoes over the two regions. Findings highlight the significant roles of Anopheles arabiensis on malaria transmission over the regions and suggest that vector control activities should be intense to eradicate malaria in Mopani and Vhembe districts. Although An. arabiensis has been identified as the major vector over these regions, our findings further suggest the presence of additional vectors transmitting malaria in the study regions. The findings from this study offer insight into climate-malaria incidence linkages over Limpopo province of South Africa.

Predicting malaria cases using remotely sensed environmental variables in Nkomazi, South Africa.

There has been a conspicuous increase in malaria cases since 2016/2017 over the three malaria-endemic provinces of South Africa. This increase has been linked to climatic and environmental factors. In the absence of adequate traditional environmental/climatic data covering ideal spatial and temporal extent for a reliable warning system, remotely sensed data are useful for the investigation of the relationship with, and the prediction of, malaria cases. Monthly environmental variables such as the normalised difference vegetation index (NDVI), the enhanced vegetation index (EVI), the normalised difference water index (NDWI), the land surface temperature for night (LSTN) and day (LSTD), and rainfall were derived and evaluated using seasonal autoregressive integrated moving average (SARIMA) models with different lag periods. Predictions were made for the last 56 months of the time series and were compared to the observed malaria cases from January 2013 to August 2017. All these factors were found to be statistically significant in predicting malaria transmission at a 2-months lag period except for LSTD which impact the number of malaria cases negatively. Rainfall showed the highest association at the two-month lag time (r=0.74; P<0.001), followed by EVI (r=0.69; P<0.001), NDVI (r=0.65; P<0.001), NDWI (r=0.63; P<0.001) and LSTN (r=0.60; P<0.001). SARIMA without environmental variables had an adjusted R2 of 0.41, while SARIMA with total monthly rainfall, EVI, NDVI, NDWI and LSTN were able to explain about 65% of the variation in malaria cases. The prediction indicated a general increase in malaria cases, predicting about 711 against 648 observed malaria cases. The development of a predictive early warning system is imperative for effective malaria control, prevention of outbreaks and its subsequent elimination in the region.

Exploring the Influence of Daily Climate Variables on Malaria Transmission and Abundance of Anopheles arabiensis over Nkomazi Local Municipality, Mpumalanga Province, South Africa.

The recent resurgence of malaria incidence across epidemic regions in South Africa has been linked to climatic and environmental factors. An in-depth investigation of the impact of climate variability and mosquito abundance on malaria parasite incidence may therefore offer useful insight towards the control of this life-threatening disease. In this study, we investigate the influence of climatic factors on malaria transmission over Nkomazi Municipality. The variability and interconnectedness between the variables were analyzed using wavelet coherence analysis. Time-series analyses revealed that malaria cases significantly declined after the outbreak in early 2000, but with a slight increase from 2015. Furthermore, the wavelet coherence and time-lagged correlation analyses identified rainfall and abundance of Anopheles arabiensis as the major variables responsible for malaria transmission over the study region. The analysis further highlights a high malaria intensity with the variables from 1998-2002, 2004-2006, and 2010-2013 and a noticeable periodicity value of 256-512 days. Also, malaria transmission shows a time lag between one month and three months with respect to mosquito abundance and the different climatic variables. The findings from this study offer a better understanding of the importance of climatic factors on the transmission of malaria. The study further highlights the significant roles of An. arabiensis on malaria occurrence over Nkomazi. Implementing the mosquito model to predict mosquito abundance could provide more insight into malaria elimination or control in Africa.

Physical, chemical, and sensory properties of biscuits prepared from flour blends of unripe cooking banana, pigeon pea, and sweet potato.

Biscuits were produced from 14 flour blends of cooking banana (UBF), pigeon pea (PPF), and sweet potato (SPF). The physical properties, nutrient composition, and sensory characteristics of the biscuits were evaluated using standard methods. Data obtained were subjected to analysis of variance, and mean values were separated using Duncan's multiple range test. The hardness of the biscuit samples decreased as PPF increased, while the fracturability decreased with increase in UBF. Biscuits were significantly (p < .05) different in their nutrient composition, with the crude protein, crude fiber, ash contents, and dietary fiber content increasing as the PPF level increased. Cookies were rich in magnesium (576.54-735.06 mg/100 g) with favorable Na/K ratio (<1.0). The antinutritional factors in the biscuit samples were within permissible levels. Biscuits prepared from flour blend of 21.67% unripe cooking banana, 21.67% pigeon pea, and 56.67% sweet potato were the most preferred in terms of shape, mouthfeel, taste, crunchiness, and overall acceptability. Flour blends of unripe cooking banana, pigeon pea, and sweet potato could therefore be used as raw materials for the production of biscuits, with high protein, total dietary, and energy content.

Climatic Variables and Malaria Morbidity in Mutale Local Municipality, South Africa: A 19-Year Data Analysis.

The north-eastern parts of South Africa, comprising the Limpopo Province, have recorded a sudden rise in the rate of malaria morbidity and mortality in the 2017 malaria season. The epidemiological profiles of malaria, as well as other vector-borne diseases, are strongly associated with climate and environmental conditions. A retrospective understanding of the relationship between climate and the occurrence of malaria may provide insight into the dynamics of the disease's transmission and its persistence in the north-eastern region. In this paper, the association between climatic variables and the occurrence of malaria was studied in the Mutale local municipality in South Africa over a period of 19-year. Time series analysis was conducted on monthly climatic variables and monthly malaria cases in the Mutale municipality for the period of 1998-2017. Spearman correlation analysis was performed and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed. Microsoft Excel was used for data cleaning, and statistical software R was used to analyse the data and develop the model. Results show that both climatic variables' and malaria cases' time series exhibited seasonal patterns, showing a number of peaks and fluctuations. Spearman correlation analysis indicated that monthly total rainfall, mean minimum temperature, mean maximum temperature, mean average temperature, and mean relative humidity were significantly and positively correlated with monthly malaria cases in the study area. Regression analysis showed that monthly total rainfall and monthly mean minimum temperature (R² = 0.65), at a two-month lagged effect, are the most significant climatic predictors of malaria transmission in Mutale local municipality. A SARIMA (2,1,2) (1,1,1) model fitted with only malaria cases has a prediction performance of about 51%, and the SARIMAX (2,1,2) (1,1,1) model with climatic variables as exogenous factors has a prediction performance of about 72% in malaria cases. The model gives a close comparison between the predicted and observed number of malaria cases, hence indicating that the model provides an acceptable fit to predict the number of malaria cases in the municipality. To sum up, the association between the climatic variables and malaria cases provides clues to better understand the dynamics of malaria transmission. The lagged effect detected in this study can help in adequate planning for malaria intervention.

Nutrient composition, functional, and pasting properties of unripe cooking banana, pigeon pea, and sweetpotato flour blends.

This study investigated some quality attributes of unripe cooking banana (UBF), pigeon pea (PPF), and sweetpotato (SPF) flour blends. Simplex centroid mixture design was used to obtain 17 blends from the flours. The nutrient composition, color, and functional properties of the blends were evaluated using standard methods. Data were subjected to analysis of variance and treatment means were compared using Duncan's multiple range test at 5% probability level. There were significant (p < .05) differences in the nutrient composition, and functional and pasting properties of the blends. The crude protein, crude fiber, ash, foaming capacity, emulsion capacity, and least gelation capacity of the blends increased as the PPF level increased. The blends had Na/K ratio of <1.0. The dispersibility, bulk density, water, and oil absorption capacities of the blends increased as SPF and UBF increased. The peak, setback, and final viscosities increased as UBF and SPF inclusion increased,whereas pasting temperature and time increased as the PPF level increased. The L*, a*, and b* values of the flour blends which were significantly (p < .05) different ranged from 79.58 to 102.71, -0.15 to 2.79, and 13.82 to 23.69, respectively. Cooking banana-pigeon pea-sweetpotato flour blends are desirable for alleviating malnutrition in Nigeria and developing new food formulations.

Effects of sodium benzoate on storage stability of previously improved beverage from tamarind (Tamarindus indica L.).

The effect of sodium benzoate on the quality attributes of improved tamarind beverage during storage was investigated. Tamarind beverages were produced according to a previously reported improved method, with or without chemical preservatives (100 mg/100 mL sodium benzoate). Tamarind beverage produced according to traditional processing method served as the control. The tamarind beverages were stored for 4 months at room (29 ± 2°C) and refrigerated (4-10°C) temperatures. Samples were analyzed, at regular intervals, for chemical, sensory, and microbiological qualities. Appearance of coliforms or overall acceptability score of 5.9 was used as deterioration index. The control beverages deteriorated by 2nd and 10th days at room and refrigerated temperatures, respectively. Improved tamarind beverage produced without the inclusion of sodium benzoate was stable for 3 and 5 weeks at room and refrigerated temperatures, respectively. Sodium benzoate extended the shelf life of the improved tamarind beverage to 6 and 13 weeks, respectively, at room and refrigerated temperatures.