Utilizing the Susceptible Infectious Susceptible (sis) Model to Evaluate the Risk for Waterborne Parasites to Spread through Household Water Sources in Bamenda, Cameroon
Bibliographic record
Abstract
This research presents a distributed continuous-time epidemic model, called a SUSCEPTIBLE INFECTIOUS SUSCEPTIBLE- SIS model to denote a waterborne disease spreading over a network of different domestic waters sources such as tap water, well water and spring water in the understanding of infectious pathogens in the Municipality of Bamenda. It involves multiple groups of individuals sharing a water source which comprises the population in the communities or quarter of Upstation mile 1, Nkwen, Mankon and Nsongwa in and around this urban area. A sufficient condition is obtained for the healthy state, at which all individuals are not infected and the water is not contaminated, to be globally asymptotically stable. The effects of the shared water source on the disease spreading were analyzed through the comparison of the basic reproduction number with the networked SIS model without water and demonstrated via simulations. It was realized that human activities and generally anthropogenic influences contaminate these water sources making individuals of this localities to become susceptible to waterborne infections. The characterization and identification of intestinal parasites reveal that, those who consume these contaminated water were infected and these infected individuals intend re-contaminate the environment including water bodies and or food thereby by making the population and uninfected individuals susceptible to waterborne diseases in the community.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".