Determination of epidemic threshold parameters in communicable disease with compartmental model by applying branching process and Bayesian methods and compare them with existing epidemic threshold parameters
Bibliographic record
Abstract
Background & Objective: The basic reproduction number (R 0 ) has a key role in epidemics and can be utilized for preventing epidemics.R 0 is the expected number of cases generated by a single infectious individual in a fully susceptible population.In fact, R 0 is compared with 1 to determine how fast the disease is spreading.If R 0 is larger than 1, the disease will spread between individuals and an epidemic will happen.The incidence of disease will fade to zero if R 0 <1, because the number of infected individuals will reduce over time.The value of R 0 , helps determine the vaccination coverage and vaccination strategies to overcome the speared of the disease.In this study, different methods are used for estimating R 0 's and their vaccination coverage to find the formula with the best performance for influenza (H1N1) using appropriate indices.Methods: This methodological study is extension of the models from the theoretical point of view and its application in the field of influenza is a secondary study.In this study, R 0 and corresponding vaccination coverage (VC) were computed for Iran (Kerman city) (2015-16), Canada (2009), Canada (2017-18) and USA (Idaho) (2017-2018) using attack rate (AR), exponential growth rate (EG), maximum likelihood (ML), time-dependent reproduction number (TD), Gamma GT, final size of epidemic (FS), Sequential Bayes (SB), Bayesian model 1 (M(I)), and Bayesian model 2 (M(II)).The gamma distribution is considered as the distribution and the generation of time.Also, simulation study was performed and the best method was determined using MSE, Bias and Relative Bias indices.Results: The generation time obey the Gamma distribution with mean and standard deviation of 3.6 and 1.6, respectively, was utilized for the generation time.The maximum of R 0 (95% CI) for Kerman equaled 2.03 (1.85, 2.21) with vaccination coverage of 63.37%.For Canada influenza data (2009), the maximum value of R 0 (95%CI) was related to M(II) method (4.99 (5.50, 6.06)) with 79.95% vaccination coverage.The maximum of R 0 (95% CI) for Canada influenza data (2017-18) was 1.52 (1.26, 1.77) by SB method, and vaccination coverage was estimated 34.21%.Finally, the maximum value of R 0 (95%CI) and its vaccination coverage equaled 2.66 (2.18, 3.14) and 62.41% respectively for Idaho which were derived from TD method.In addition, M(II) method had minimum value of MSE, Bias and Relative Bias.After M(II) method, the minimum value of performance indices were related to TD method.Conclusion: The R 0 estimations were greater than one for Kerman, Canada and Idaho using different methods, indicating that an epidemic has occurred in these areas (R 0 >1).The order of performance of the models in this study is as follows:1. M(II) 2. TD 3. Gamma GT 4.SB 5. EG 6.ML 7. M(I) 8.AR 9.FS According to the methods performance order, the best performance is related to M(II) Bayesian method and also TD method had the second best performance.In the other words, the TD as a classic method was superior to the Bayesian methods SB and M(I).Therefore, due to the lower complexity and higher operating speeds, it can be concluded that use of the classic methods such as TD and Gamma GT for researcher are likely easier.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".