Attributable Fraction and Forecasting for COVID-19 Confirmed Cases in Nigeria Using Facebook- Prophet Machine Learning Model
Bibliographic record
Abstract
Aims: The motivation is to know the attributable fraction among Nigerians who tested positive for covid-19 and forecast the covid-19 cases. Place and Duration of Study: We extracted data from (https://covid19.ncdc.gov.ng/) on 8th September,2021 and covid.19analytics package on 7th September, 2021, from Data Repository by Johns Hopkins University Center for Systems Science and Engineering , Status of Cases in Toronto – City of Toronto , COVID-19: Open Data Toronto ,COVID-19: Health Canada , Severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1, COViD-19 Vaccination and Testing records from “Our World In Data” and Pandemics historical records from Visual Capitalist. Data in Nigeria contained the number of samples tested, confirmed cases, active cases, discharged cases and deaths. Methodology: Attributable fraction was used to compute the proportion of patients who tested positive to Covid-19. By using the time for regressor, Prophet model will fit many non-linear and linear functions of time components. Prophet uses the Fourier series to get flexible model to forecast and fit the seasonality effects. A fast solution for L-BFGS which stands for Limited memory Broyden-Fletcher-Goldfarb-Shannon algorithm, is used with Stan backend for the prediction problem. Results: As at Saturday 11th September 2021,7:18am Nigeria local time, a total of 2884034 samples have been tested for covid-19, with 198239 confirmed cases,9871 active cases,185780 discharged cases and 2588 deaths. The attributable fraction for covid-19 in Nigeria was 0.0687. The r square is very high (0.999), and the p value is very low (2.2e-16). Conclusion: The attributable fraction gives the percentage of the patients who tested positive to covid-19, among the 2884034 samples tested. It implies that the remaining percentage of patients who tested negative to covid-19 only exhibit covid-19 symptoms or were exposed to the virus. The confirmed cases were found to be highest on Saturdays with the lowest on Tuesdays.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".