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
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».