Machine Learning Approach for the Prediction of COVID-19 Spread in Nigeria Using SIR Model
Bibliographic record
Abstract
The recent evolution of the novel COVID-19 pandemic posed unprecedented challenges for the global community and researchers alike without reference literature or prior prediction approaches applied to that context, leading to its great impact on the economies of different countries. This work aims to address these challenges by predicting the spread of COVID- 19 in Nigeria. In order to carry out this research, raw data on COVID-19 pandemic in Nigeria was gotten from John Hopkins University repository through Github as submitted by the Nigeria Center for Disease Control (NCDC) through the World Health Organization (WHO). Python, along with its packages such as Numpy, SciPy, Pandas, Matplotlib, and Plotly, was used to process and visualize the data for this model. Google Colab; a Jupyter notebook environment that run on the cloud was employed too. The SIR (Susceptible, Infected, Recovered or Removed) population model, which is commonly employed for modelling epidemics, was used to predict the spread of the pandemic in Nigeria. The machine learning Python packages used in the analysis included Pandas for data analysis and manipulation, Numpy for executing mathematical and logical operations, and SciPy for operations such as regression, minimization, and Fourier series. The model developed in this study predicted a peak of the pandemic between June and August, 2020, and a flattening of the curve in January 2021; the predicted dates were accurate when compared to real-world data. The outcome of the study provides better insight (for instance; rate of spread in response to certain conditions and corresponding mortality or survival rate) on the future of the COVID-19 pandemic; yielding more information that will help in planning for the future through evidenced correctness and reliability. The work present the recovery coefficient rate (β), the transmission coefficient (γ) and, the basic reproduction ratio (R0) values for the data under consideration that indicates a reliable model for predictions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".