Mathematical Analysis of The Role of Quarantine and Isolation in Epidemiology
Bibliographic record
Abstract
The quarantine of people suspected of being exposed to a disease, and the isolation of those with clinical symptoms of the disease, constitute what is probably the oldest infection control mechanism since the beginning of recorded human history. The thesis is based on using mathematical modelling and analysis to gain qualitative insight into the transmission dynamics of a disease that is controllable using quarantine and isolation. A basic model, which takes the form of an autonomous deterministic system of non-linear differential equations with standard incidence, is formulated first of all. Rigorous analysis of the basic model shows that its disease-free equilibrium is globally-asymptotically stable whenever a certain epidemiological threshold (denoted by Rc) is less than unity. The epidemiological implication of this result is that the disease will be eliminated from the community if the use of quarantine and isolation could result in making Rc < 1. The model has a unique endemic equilibrium whenever Rc > 1. Using a Lyapunov function of Goh-Volterra type, it is shown that the unique endemic equilibrium is globally-asymptotically stable for a special case. The basic model is extended to incorporate various epidemiological and biological aspects relating to the transmission dynamics and control of a communicable disease, such as the use of time delay to model the latency period, effect of periodicity (seasonality), the use of an imperfect vaccine and the use of multiple latent and infectious stages (coupled with gamma-distributed average waiting times in these stages). One of the main mathematical findings of this thesis is that adding time delay, periodicity and multiple latent and infectious stages to the basic quarantine/isolation model does not alter the essential qualitative features of the basic model (pertaining to the persistence or elimination of the disease). On the other hand, the use of an imperfect vaccine induces the phenomenon of backward bifurcation (a dynamical feature not present in the basic model), the consequence of which is that disease elimination becomes more difficult using quarantine and isolation (since, in this case, the epidemiological requirement Rc < 1 is, although necessary, no longer sufficient for disease elimination). Numerous numerical simulations are carried out, using parameter values relevant to the 2003 SARS outbreaks in the Greater Toronto Area of Canada, to illustrate some of the theoretical findings as well as to evaluate the population-level impact of quarantine/isolation and an imperfect vaccine. In particular, threshold conditions for which the aforementioned control measures could have a positive or negative population-level impact are determined.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".