Exploring the Seasonality of Reported Treated Malaria Cases in Mpumalanga, South Africa

Silal, Sheetal P; Barnes, Karen I.; Kok, Gerdalize; Mabuza, Aaron; Little, Francesca

doi:10.1371/journal.pone.0076640

Cited by 25 publications

(42 citation statements)

References 15 publications

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“…Statistical models help to determine the relative contribution of environmental drivers to temporal variations in disease manifestation, support a wide variety of data types, and may offer a platform for developing early-warning systems based on disease and host surveillance [127][128][129]. Models may differ in their choice of health outcome measure, definitions of exposure or methods of quantifying associations and how different combinations of these components lead to different uncertainties in model estimates.…”

Section: (I) Statistical Modelsmentioning

confidence: 99%

“…Seasonality is characterized by timing ( position of extrema on the seasonal curve), magnitude (difference between maxima and minima) and duration. Seasonal patterns of health events measured by their frequency or observed counts per time unit may vary by type of health condition, location and population of interest [128]. In order to account for seasonality, studies may be stratified by summer/winter, warm/cold, dry/wet season or by considering periodic fluctuations.…”

Section: (I) Statistical Modelsmentioning

confidence: 99%

“…R. Soc. B 370: 20130551 exposure and disease incidence is informative and enables predictions of the potential impacts of environmental drivers on disease manifestation [128].…”

Section: (I) Statistical Modelsmentioning

confidence: 99%

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Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission

Parham

Waldock

Christophides

et al. 2015

Phil. Trans. R. Soc. B

242

224

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Arguably one of the most important effects of climate change is the potential impact on human health. While this is likely to take many forms, the implications for future transmission of vector-borne diseases (VBDs), given their ongoing contribution to global disease burden, are both extremely important and highly uncertain. In part, this is owing not only to data limitations and methodological challenges when integrating climate-driven VBD models and climate change projections, but also, perhaps most crucially, to the multitude of epidemiological, ecological and socio-economic factors that drive VBD transmission, and this complexity has generated considerable debate over the past 10–15 years. In this review, we seek to elucidate current knowledge around this topic, identify key themes and uncertainties, evaluate ongoing challenges and open research questions and, crucially, offer some solutions for the field. Although many of these challenges are ubiquitous across multiple VBDs, more specific issues also arise in different vector–pathogen systems.

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Section: (I) Statistical Modelsmentioning

confidence: 99%

Section: (I) Statistical Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission

Parham

Waldock

Christophides

et al. 2015

Phil. Trans. R. Soc. B

242

224

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“…We focus on KNP because it is a hotspot of mosquito diversity [15], it is in a region of southern Africa with regular outbreaks of mosquitoborne disease (e.g. [34,35]), and it includes a sentinel site for mosquitoes and pathogen surveillance [36]). Sampling sites within KNP were chosen to cover the geographical extent of the park and to capture the variability in rainfall, geology and vegetation types within KNP [37].…”

Section: Study Locationmentioning

confidence: 99%

A comparative assessment of adult mosquito trapping methods to estimate spatial patterns of abundance and community composition in southern Africa

Gorsich

Beechler

Bodegom

et al. 2019

Preprint

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Background: Assessing adult mosquito populations is an important component of disease surveillance programs and ecosystem health assessments. Inference from adult trapping datasets involves comparing populations across space and time, but comparisons based on different trapping methods may be biased if traps have different efficiencies or sample different subsets of the mosquito community. Methods:We compared four widely-used trapping methods for adult mosquito data collection in Kruger National Park (KNP), South Africa: Centers for Disease Control miniature light trap (CDC), Biogents Sentinel trap (BG), Biogents gravid Aedes trap (GAT) and a net trap. We quantified how trap choice and sampling effort influence inferences on the regional distribution of mosquito abundance, richness and community composition. Results:The CDC and net traps together collected 96% (47% and 49% individually) of the 955 female mosquitoes sampled and 100% (85% and 78% individually) of the 40 species or species complexes identified. The CDC and net trap also identified similar regional patterns of community composition. However, inference on the regional patterns of abundance differed between these traps because mosquito abundance in the net trap was influenced by variation in weather conditions. The BG and GAT traps collected significantly fewer mosquitoes, limiting regional comparisons of abundance and community composition. Conclusions:This study represents the first systematic assessment of trapping methods in natural savanna ecosystems in southern Africa. We recommend the CDC trap or the net trap for future monitoring and surveillance programs.

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“…Mpumalanga Province has maintained a successful control programme encompassing rapid detection and treatment of confirmed malaria cases at primary health care facilities and vector control through indoor residual spraying with insecticides and focal larviciding (Govere et al, 2000). However, the province still contributes to 44% of the country's notified malaria cases (Silal et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa

Kapwata¹,

Gebreslasie²

2016

Geospat Health

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Malaria is an environmentally driven disease. In order to quantify the spatial variability of malaria transmission, it is imperative to understand the interactions between environmental variables and malaria epidemiology at a micro-geographic level using a novel statistical approach. The random forest (RF) statistical learning method, a relatively new variable-importance ranking method, measures the variable importance of potentially influential parameters through the percent increase of the mean squared error. As this value increases, so does the relative importance of the associated variable. The principal aim of this study was to create predictive malaria maps generated using the selected variables based on the RF algorithm in the Ehlanzeni District of Mpumalanga Province, South Africa. From the seven environmental variables used [temperature, lag temperature, rainfall, lag rainfall, humidity, altitude, and the normalized difference vegetation index (NDVI)], altitude was identified as the most influential predictor variable due its high selection frequency. It was selected as the top predictor for 4 out of 12 months of the year, followed by NDVI, temperature and lag rainfall, which were each selected twice. The combination of climatic variables that produced the highest prediction accuracy was altitude, NDVI, and temperature. This suggests that these three variables have high predictive capabilities in relation to malaria transmission. Furthermore, it is anticipated that the predictive maps generated from predictions made by the RF algorithm could be used to monitor the progression of malaria and assist in intervention and prevention efforts with respect to malaria.

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Exploring the Seasonality of Reported Treated Malaria Cases in Mpumalanga, South Africa

Cited by 25 publications

References 15 publications

Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission

Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission

A comparative assessment of adult mosquito trapping methods to estimate spatial patterns of abundance and community composition in southern Africa

Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa

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