Exploring Climate-sensitive Patterns of Vector-Borne Diseases in South Africa using Big Data analytics
1
Environmental Health, University of Johannesburg, Johannesburg, South Africa
2
Electrical Engineering, University of Johannesburg, Johannesburg, South Africa
3
Human Anatomy and Physiology, University of Johannesburg, Johannesburg, South Africa
Popul. Med. 2026;8(Supplement Supplement 1):A469
ABSTRACT
INTRODUCTION:
Vector-borne diseases (VBD) remain a major public health challenge globally, accounting for approximately 17% of all infectious diseases.¹ Climate variability, including changes in temperature, rainfall, and humidity, influence vector ecology and disease transmission.²˒³ In South Africa (SA), VBD outbreaks continue to place pressure on public health systems, highlighting the need for climate-informed surveillance approaches that support timely preparedness and response.⁴ This study aims to apply advanced modelling techniques to identify key climatic drivers of VBD and forecast future outbreaks.
METHODS:
A retrospective ecological study design was applied using laboratory-confirmed VBD cases from the National Health Laboratory Service and meteorological data from the South African Weather Service for the period 2012 to 2024. Datasets were cleaned and aggregated at monthly and district levels. Descriptive analyses were conducted to characterise spatial and temporal patterns in VBD incidence and climate variables. Associations were assessed using correlation analysis and poisson–inverse gaussian regression.⁵ Spatial clustering was evaluated using Moran’s I and Getis-ord Gi*, while temporal trends were assessed using the Mann–Kendall test.⁶˒⁷ Predictive models including random forest, extreme gradient boosting, autoregressive integrated moving average with exogenous variables, and long short-term memory networks were applied.
RESULTS:
A total of 266955 confirmed cases of VBDs were reported in SA between 2012 and 2024. Schistosoma haematobium accounted for the largest proportion of cases (47.40%), followed by Schistosoma haematobium ova observed (24.17%) and Malaria (28.12%). Preliminary analyses indicate significant spatial and temporal heterogeneity in VBD incidence, with emerging climate-sensitive patterns across selected districts. These findings demonstrate the feasibility of integrating climate and health datasets for risk modelling and early warning applications.
CONCLUSIONS:
Integrating climate and disease surveillance data using big data analytics has the potential to strengthen climate-sensitive early warning systems and support public health preparedness in SA and similar low- and middle-income countries.
Vector-borne diseases (VBD) remain a major public health challenge globally, accounting for approximately 17% of all infectious diseases.¹ Climate variability, including changes in temperature, rainfall, and humidity, influence vector ecology and disease transmission.²˒³ In South Africa (SA), VBD outbreaks continue to place pressure on public health systems, highlighting the need for climate-informed surveillance approaches that support timely preparedness and response.⁴ This study aims to apply advanced modelling techniques to identify key climatic drivers of VBD and forecast future outbreaks.
METHODS:
A retrospective ecological study design was applied using laboratory-confirmed VBD cases from the National Health Laboratory Service and meteorological data from the South African Weather Service for the period 2012 to 2024. Datasets were cleaned and aggregated at monthly and district levels. Descriptive analyses were conducted to characterise spatial and temporal patterns in VBD incidence and climate variables. Associations were assessed using correlation analysis and poisson–inverse gaussian regression.⁵ Spatial clustering was evaluated using Moran’s I and Getis-ord Gi*, while temporal trends were assessed using the Mann–Kendall test.⁶˒⁷ Predictive models including random forest, extreme gradient boosting, autoregressive integrated moving average with exogenous variables, and long short-term memory networks were applied.
RESULTS:
A total of 266955 confirmed cases of VBDs were reported in SA between 2012 and 2024. Schistosoma haematobium accounted for the largest proportion of cases (47.40%), followed by Schistosoma haematobium ova observed (24.17%) and Malaria (28.12%). Preliminary analyses indicate significant spatial and temporal heterogeneity in VBD incidence, with emerging climate-sensitive patterns across selected districts. These findings demonstrate the feasibility of integrating climate and health datasets for risk modelling and early warning applications.
CONCLUSIONS:
Integrating climate and disease surveillance data using big data analytics has the potential to strengthen climate-sensitive early warning systems and support public health preparedness in SA and similar low- and middle-income countries.
| eISSN: | 2654-1459 |
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