Modelling Malaria Incidence in the Limpopo Province, South Africa
Comparison of Classical and Bayesian Methods of Estimation
Bibliographic Data
| ID | 15486334 |
|---|---|
| Authors | Makwelantle Asnath Sehlabana (0000-0001-9052-7938, University of Limpopo), Daniel Maposa (0000-0003-2641-0502, University of Limpopo, corresponding author), Alexander Boateng (0000-0002-1034-5008, University of Limpopo) |
| Year | 2020 |
| Volume | 17 |
| Issue | 14 |
| Pages | 5016-5016 |
| Publication date | 2020-07-13 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph17145016 |
| PMID | 32668606 |
| OpenAlex | W3041716168 |
| Language | EN |
| Citations received | 2 |
| References cited | 18 |
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
Environmental protection · Estimation · Geography · Incidence (geometry · Malaria · Socioeconomics · Demography · Malaria Research and Control · Mathematics · Medicine
Monte Carlo sampling methods using Markov chains and their applications
Bayesian Estimation and Inference
The economic and social burden of malaria
Long-Run Relative Importance of Temperature as the Main Driver to Malaria Transmission in Limpopo Province, South Africa
A Dynamical and Zero-Inflated Negative Binomial Regression Modelling of Malaria Incidence in Limpopo Province, South Africa
Climatic Variables and Malaria Morbidity in Mutale Local Municipality, South Africa
Effect of meteorological factors on clinical malaria risk among children
| Citations per year | 0.67 |
|---|---|
| Citation span | 2023 - 2023 (1) |
| Highly cited | No |