Forecasting Malaria Morbidity to 2036 Based on Geo-Climatic Factors in the Democratic Republic of Congo
Bibliographic record
Abstract
Background: Malaria is a global burden in terms of morbidity and mortality. In the Democratic Republic of Congo, malaria prevalence is increasing due to strong climatic variations. Reductions in malaria morbidity and mortality, the fight against climate change, good health and well-being constitute key development aims as set by the United Nations Sustainable Development Goals (SDGs). This study aims to predict malaria morbidity to 2036 in relation to climate variations between 2001 and 2019, which may serve as a basis to develop an early warning system that integrates monitoring of rainfall and temperature trends and early detection of anomalies in weather patterns. Methods: Meteorological data were collected at the Mettelsat and the database of the Epidemiological Surveillance Directorate including all malaria cases registered in the surveillance system based on positive blood test results, either by microscopy or by a rapid diagnostic test for malaria, was used to estimate malaria morbidity and mortality by province of the DRC from 2001 to 2019. Malaria prevalence and mortality rates by year and province using direct standardization and mean annual percentage change were calculated using DRC mid-year populations. Time series combining several predictive models were used to forecast malaria epidemic episodes to 2036. Finally, the impact of climatic factors on malaria morbidity was modeled using multivariate time series analysis. Results: The geographical distribution of malaria prevalence from 2001 and 2019 shows strong disparities between provinces with the highest of 7700 cases per 100,000 people at risk for South Kivu. In the northwest, malaria prevalence ranges from 4980 to 7700 cases per 100,000 people at risk. Malaria has been most deadly in Sankuru with a case-fatality rate of 0.526%, followed by Kasai (0.430%), Kwango (0.415%), Bas-Uélé, (0.366%) and Kwilu (0.346%), respectively. However, the stochastic trend model predicts an average annual increase of 6024.07 malaria cases per facility with exponential growth in epidemic waves over the next 200 months of the study. This represents an increase of 99.2%. There was overwhelming evidence of associations between geographic location (western, central and northeastern region of the country), total evaporation under shelter, maximum daily temperature at two meters altitude and malaria morbidity (p < 0.0001). Conclusions: The stochastic trends in our time series observed in this study suggest an exponential increase in epidemic waves over the next 200 months of the study. The increase in new malaria cases is statistically related to population density, average number of rainy days, average wind speed, and unstable and intermediate epidemiological facies. Therefore, the results of this research should provide relevant information for the Congolese government to respond to malaria in real time by setting up a warning system integrating the monitoring of rainfall and temperature trends and early detection of anomalies in weather patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".