scholarly journals Modelling Malaria Incidence in the Limpopo Province, South Africa: Comparison of Classical and Bayesian Methods of Estimation

2020 ◽  
Vol 17 (14) ◽  
pp. 5016
Author(s):  
Makwelantle Asnath Sehlabana ◽  
Daniel Maposa ◽  
Alexander Boateng
Keyword(s):  
South Africa ◽  
Malaria Control ◽  
Bayesian Method ◽  
Negative Binomial ◽  
Climatic Factors ◽  
Classical Method ◽  
Malaria Incidence ◽  
Limpopo Province ◽  
Malaria Control Programme ◽  
Normalised Difference Vegetation Index

Malaria infects and kills millions of people in Africa, predominantly in hot regions where temperatures during the day and night are typically high. In South Africa, Limpopo Province is the hottest province in the country and therefore prone to malaria incidence. The districts of Vhembe, Mopani and Sekhukhune are the hottest districts in the province. Malaria cases in these districts are common and malaria is among the leading causes of illness and deaths in these districts. Factors contributing to malaria incidence in Limpopo Province have not been deeply investigated, aside from the general knowledge that the province is the hottest in South Africa. Bayesian and classical methods of estimation have been applied and compared on the effect of climatic factors on malaria incidence. Credible and confidence intervals from a negative binomial model estimated via Bayesian estimation and maximum likelihood estimation, respectively, were utilized in the comparison process. Overall assumptions underpinning each method were given. The Bayesian method appeared more robust than the classical method in analysing malaria incidence in Limpopo Province. The classical method identified rainfall and temperature during the night to be significant predictors of malaria incidence in Mopani, Vhembe and Waterberg districts. However, the Bayesian method found rainfall, normalised difference vegetation index, elevation, temperatures during the day and night to be the significant predictors of malaria incidence in Mopani, Sekhukhune and Vhembe districts of Limpopo Province. Both methods affirmed that Vhembe district is more susceptible to malaria incidence, followed by Mopani district. We recommend that the Department of Health and Malaria Control Programme of South Africa allocate more resources for malaria control, prevention and elimination to Vhembe and Mopani districts of Limpopo Province.

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

2019 ◽  
Vol 16 (11) ◽  
pp. 2000 ◽  
Author(s):  
Gbenga J. Abiodun ◽  
Olusola S. Makinde ◽  
Abiodun M. Adeola ◽  
Kevin Y. Njabo ◽  
Peter J. Witbooi ◽  
...  
Keyword(s):  
South Africa ◽  
Time Series ◽  
Regression Model ◽  
Negative Binomial ◽  
Malaria Incidence ◽  
Negative Binomial Regression ◽  
Limpopo Province ◽  
Negative Binomial Regression Model ◽  
Climate Variables ◽  
Binomial Regression

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.

scholarly journals Perceptions of Climate Change and Drivers of Insect Pest Outbreaks in Vegetable Crops in Limpopo Province of South Africa

Climate ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 27 ◽  
Author(s):  
Mutondwa Masindi Phophi ◽  
Paramu Mafongoya ◽  
Shenelle Lottering
Keyword(s):  
Climate Change ◽  
South Africa ◽  
Insect Pests ◽  
Climatic Factors ◽  
Smallholder Farmers ◽  
Limpopo Province ◽  
Bivariate Analysis ◽  
Vegetable Production ◽  
Dry Spells ◽  
Pest Outbreaks

Vegetable production is a source of income for smallholder farmers in Limpopo Province, South Africa. Vegetable production is constrained by the negative impacts of climate change and pests. This study assessed farmers’ awareness of climate change, farmers’ knowledge of insect pests and factors that influence insect pests’ prevalence. The data were collected using quantitative and qualitative methods. The data were subjected to descriptive and bivariate analysis. About 84.5% of smallholder farmers were aware of climate change. Late rainfall (24.4%), long dry spells (15%) and increased drought frequency (19.4%) were highlighted as dominant indicators of climate change by farmers. Aphids (22.2%), Bagrada hilaris (12.5%) and Spodoptera frugiperda (10.2%) were the most prevalent insect pests within the Vhembe District. Warmer winters, dry spells and high temperatures were perceived by farmers to influence insect pests’ prevalence within the district. It can be concluded that farmers are aware of climate change and climatic factors influencing pest prevalence within the district. Pest risk maps are needed to improve the preparedness of the government and farmers in controlling insect pests under changing climates.

scholarly journals Seasonally lagged effects of climatic factors on malaria incidence in South Africa

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Takayoshi Ikeda ◽  
Swadhin K. Behera ◽  
Yushi Morioka ◽  
Noboru Minakawa ◽  
Masahiro Hashizume ◽  
...  
Keyword(s):  
South Africa ◽  
Climatic Factors ◽  
Malaria Incidence ◽  
Lagged Effects

scholarly journals Influence of Climate on the Spatiotemporal Distribution of Malaria in Thulamela Municipality, Limpopo Province, South Africa

2020 ◽  
Vol 13 (1) ◽  
pp. 246-256
Author(s):  
Lungile Makondo ◽  
Abiodun Adeola ◽  
Thabo Makgoale ◽  
Joel Botai ◽  
Omolola Adisa ◽  
...  
Keyword(s):  
South Africa ◽  
Time Series ◽  
Spatial Distribution ◽  
Climatic Factors ◽  
Ordinary Least Squares ◽  
Limpopo Province ◽  
Spatiotemporal Distribution ◽  
Maximum Temperature ◽  
Spearman Correlation ◽  
Significance Level

Background: Malaria, though curable, continues to be a major health and socioeconomic challenge. Malaria cases have been on the rise for the last two years in the malaria-endemic region of South Africa. Thulamela Municipality in Limpopo, South Africa, which falls within several municipalities at Vhembe district that are affected by malaria. About 33,448 malaria cases were reported over a period of 20 years (1998 January-2018 December). Objective: The study aims to determine the influence of climate on the spatiotemporal distribution of malaria cases in Thulamela Municipality for the last two decades (1998 January-2018 December). Methods: The analysis is divided into two sections, including temporal and spatial distribution of malaria cases, and the correlating climatic and environmental factors. Time series analysis is conducted to determine the variations of malaria and climate. Malaria and climatic factors (rainfall, maximum temperature, minimum temperature) were globally correlated using matrix scatterplot spearman correlation with a certain significance level. The Ordinary Least Squares (OLS) regression was performed to determine the significant climate factors that locally affect the spatial distribution of malaria cases. The local environmental factor (rivers) was analyzed using buffering and terrain analysis. Results: A positive spearman correlation of the time series was found with the significance level of 0.01. The climate variables were not strongly significant to the spatial distribution of malaria at the village level. The villages which continued to record high malaria cases were in proximity to rivers by 2km. The Thulamela municipality falls within 20-30°C, which is essential for the incubation of mosquitoes and transmission of malaria. The areas receiving about 125 to 135 mm of total monthly rainfall record high malaria cases. The temperature, rainfall, and rivers are important factors for malaria transmission. Conclusion: Knowledge of the drivers of the spatiotemporal distribution of malaria is essential for a predicting system to enhance effective malaria control in communities such as the Thulamela municipality.

scholarly journals Malaria incidence in Limpopo Province, South Africa, 1998–2007

2008 ◽  
Vol 7 (1) ◽  
Author(s):  
Annette AM Gerritsen ◽  
Philip Kruger ◽  
Maarten F Schim van der Loeff ◽  
Martin P Grobusch
Keyword(s):  
South Africa ◽  
Malaria Incidence ◽  
Limpopo Province

scholarly journals The Relation Between Climatic Factors and Malaria Incidence in Sistan and Baluchestan, Iran

SAGE Open ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 215824401986420
Author(s):  
Minoo Mohammadkhani ◽  
Narges Khanjani ◽  
Bahram Bakhtiari ◽  
Seyed Mehdi Tabatabai ◽  
Khodadad Sheikhzadeh
Keyword(s):  
Negative Binomial ◽  
Positive Impact ◽  
Climatic Factors ◽  
Malaria Incidence ◽  
Negative Binomial Regression ◽  
Monthly Basis ◽  
Major Health Problem ◽  
Incidence Data ◽  
Low Humidity ◽  
Sistan And Baluchestan

Malaria is a major health problem in many tropical and subtropical countries and in the south of Iran. In this study, due to the importance of the disease in Sistan and Baluchestan province, the influence of temperature, humidity, and rainfall on malaria has been evaluated in areas with a high incidence of malaria. Malaria incidence data were inquired from the Province Health Authority, and climatic variables were inquired from the Bureau of Meteorology from 2000 to 2012 and were analyzed on a monthly basis. Changes in incidence of malaria with climatic factors were analyzed by negative binomial regression by Stata 11, and the correlations were calculated with Minitab15 for determining the potential impact of meteorological variables with and without lags on malaria transmission. The incidence of malaria had a significant positive correlation with the average, minimum, and maximum monthly temperatures and a negative correlation with rainfall and low humidity (<60%). However, humidity >60% had a positive impact on incidence; as in the town of Chabahar after adjusting variables such as rainfall and temperature; every one percent increase in humidity caused a 4% increase in malaria incidence in the same month and a significant 6% increase in the next month. Temperature and humidity over 60% are effective climate parameters in the incidence of malaria. These factors should be considered in planning for controlling and preventing malaria.

scholarly journals Lack of community awareness on malaria and its vectors can impede malaria control: A case study in Great Nicobar Islands

2017 ◽  
Vol 12 ◽  
pp. 1-13
Author(s):  
Vidhya Pachalil Thiruvoth ◽  
◽  
Sunish Ittoop Pulikotil ◽  
Keyword(s):  
Malaria Transmission ◽  
Malaria Control ◽  
Malaria Incidence ◽  
Survey Methods ◽  
Control Programme ◽  
Malaria Control Programme ◽  
Nicobar Island ◽  
Cross Sectional Survey ◽  
Community Knowledge ◽  
Nicobar Islands

Andaman and Nicobar Islands has historically been known for its high malaria transmission in the past. The aftermath of tsunami (2004), increased its risk and vulnerability, due to stagnant water bodies. Anopheles sundaicus is the predominant vector responsible for the perennial transmission of malaria in these islands. The Great Nicobar Island being one of the Tehsil of Nicobar District, is an important tourism centre attracting both national and international visitors in large numbers throughout the year. Community knowledge on malaria and its vectors is a pre-requisite for any successful malaria control programme. In order to determine the community knowledge regarding malaria transmission and control, a cross sectional survey was carried out in three villages of Great Nicobar Island, having high malaria incidence. A total of 170 individuals, viz., 70 Nicobarese (tribal) and 100 non-Nicobarese (non-tribal) were enquired. Both quantitative (KAP) and qualitative (FGD) survey methods were employed to collect the data. Among the Nicobarese, 98.6% were aware of the disease, 94.2% had knowledge of its symptoms. Similar observation was found among the non-Nicobarese community. However, knowledge on the bionomics of malaria vector and transmission was observed to be low in both the groups (17-23%). In the community based awareness campaign, field visits to the vector breeding sites is to be emphasized. Involvement of Self Help Groups and school children would facilitate easy dissemination of knowledge on vectors to the community. This improved awareness could help in reducing vector proliferation and form a basis for effective implementation of malaria control programme.

scholarly journals A Bayesian Spatio-Temporal Analysis of Malaria in the Greater Accra Region of Ghana from 2015 to 2019

2021 ◽  
Vol 18 (11) ◽  
pp. 6080
Author(s):  
Elorm Donkor ◽  
Matthew Kelly ◽  
Cecilia Eliason ◽  
Charles Amotoh ◽  
Darren J. Gray ◽  
...  
Keyword(s):  
Malaria Transmission ◽  
Malaria Control ◽  
Negative Binomial ◽  
Malaria Incidence ◽  
Temporal Analysis ◽  
Climatic Variables ◽  
Negative Binomial Regression Model ◽  
District Health ◽  
South Central ◽  
Greater Accra Region

The Greater Accra Region is the smallest of the 16 administrative regions in Ghana. It is highly populated and characterized by tropical climatic conditions. Although efforts towards malaria control in Ghana have had positive impacts, malaria remains in the top five diseases reported at healthcare facilities within the Greater Accra Region. To further accelerate progress, analysis of regionally generated data is needed to inform control and management measures at this level. This study aimed to examine the climatic drivers of malaria transmission in the Greater Accra Region and identify inter-district variation in malaria burden. Monthly malaria cases for the Greater Accra Region were obtained from the Ghanaian District Health Information and Management System. Malaria cases were decomposed using seasonal-trend decomposition, based on locally weighted regression to analyze seasonality. A negative binomial regression model with a conditional autoregressive prior structure was used to quantify associations between climatic variables and malaria risk and spatial dependence. Posterior parameters were estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. A total of 1,105,370 malaria cases were recorded in the region from 2015 to 2019. The overall malaria incidence for the region was approximately 47 per 1000 population. Malaria transmission was highly seasonal with an irregular inter-annual pattern. Monthly malaria case incidence was found to decrease by 2.3% (95% credible interval: 0.7–4.2%) for each 1 °C increase in monthly minimum temperature. Only five districts located in the south-central part of the region had a malaria incidence rate lower than the regional average at >95% probability level. The distribution of malaria cases was heterogeneous, seasonal, and significantly associated with climatic variables. Targeted malaria control and prevention in high-risk districts at the appropriate time points could result in a significant reduction in malaria transmission in the Greater Accra Region.

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