A mathematical model for multiple COVID-19 waves applied to Kenya
Bibliographic record
Abstract
Abstract The COVID-19 pandemic, which began in December 2019, prompted governments to implement non-pharmaceutical interventions (NPIs) to curb its spread. Despite these efforts and the discovery of vaccines and treatments, the disease continued to circulate globally, evolving into multiple waves, largely driven by emerging COVID-19 variants. Mathematical models have been very useful in understanding the dynamics of the pandemic. Mainly, their focus has been limited to individual waves without easy adaptability to multiple waves. In this study, we propose a compartmental model that can accommodate multiple waves, built on three fundamental concepts. Firstly, we consider the collective impact of all factors affecting COVID-19 and express their influence on the transmission rate through piecewise exponential-cum-constant functions of time. Secondly, we introduce techniques to model the fore sections of observed waves, that change infection curves with negative gradients to those with positive gradients, hence, generating new waves. Lastly, we implement a jump mechanism in the susceptible fraction, enabling further adjustments to align the model with observed infection curve. By applying this model to the Kenyan context, we successfully replicate all COVID-19 waves from March 2020 to January 2023. The identified change points align closely with the emergence of dominant COVID-19 variants, affirming their pivotal role in driving the waves. Furthermore, this adaptable approach can be extended to investigate any new COVID-19 variant or any other periodic infectious diseases, including influenza.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".