Products of Compartmental Models in Epidemiology.

We show that many structured epidemic models may be described using a straightforward product structure in this paper. Such products, derived from products of directed graphs, may represent useful refinements including geographic and demographic structure, age structure, gender, risk groups, or immunity status. Extension to multistrain dynamics, that is, pathogen heterogeneity, is also shown to be feasible in this framework. Systematic use of such products may aid in model development and exploration, can yield insight, and could form the basis of a systematic approach to numerical structural sensitivity analysis

Determination of Annual Net Single Premium Health Insurance for Epidemic Cases with Model Susceptible-Infected-Recovered

The calculation of a single annual net premium for health insurance from the epidemic disease SIR model has two models, including a single annual net premium with an inpatient benefit model and a single annual net premium with a lump sum benefit model. This study used Bank Indonesia’s annual interest rate of 6,5% and the calculation used the equivalence principle. The calculation results from one of the original data obtained an annual net single premium with inpatient benefits is IDR 418,359 for a benefit IDR 1,000,000 and the single annual net premium with lump sum benefits is IDR 15,949 for a benefit of IDR 1,000,000

Toward a comprehensive system for constructing compartmental epidemic models

Compartmental models are valuable tools for investigating infectious diseases. Researchers building such models typically begin with a simple structure where compartments correspond to individuals with different epidemiological statuses, e.g., the classic SIR model which splits the population into susceptible, infected, and recovered compartments. However, as more information about a specific pathogen is discovered, or as a means to investigate the effects of heterogeneities, it becomes useful to stratify models further -- for example by age, geographic location, or pathogen strain. The operation of constructing stratified compartmental models from a pair of simpler models resembles the Cartesian product used in graph theory, but several key differences complicate matters. In this article we give explicit mathematical definitions for several so-called ``model products'' and provide examples where each is suitable. We also provide examples of model stratification where no existing model product will generate the desired result

Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods

Addressing the challenge of scaling-up epidemiological inference to complex and heterogeneous models, we introduce Poisson Approximate Likelihood (PAL) methods. In contrast to the popular ODE approach to compartmental modelling, in which a large population limit is used to motivate a deterministic model, PALs are derived from approximate filtering equations for finite-population, stochastic compartmental models, and the large population limit drives consistency of maximum PAL estimators. Our theoretical results appear to be the first likelihood-based parameter estimation consistency results which apply to a broad class of partially observed stochastic compartmental models and address the large population limit. PALs are simple to implement, involving only elementary arithmetic operations and no tuning parameters, and fast to evaluate, requiring no simulation from the model and having computational cost independent of population size. Through examples we demonstrate how PALs can be used to: fit an age-structured model of influenza, taking advantage of automatic differentiation in Stan; compare over-dispersion mechanisms in a model of rotavirus by embedding PALs within sequential Monte Carlo; and evaluate the role of unit-specific parameters in a meta-population model of measles

Sur l’application de la structure de graphes pour le calcul automatique de nombres de reproduction dans les modèles à compartiments déterministes

En basant l'analyse des modèles épidémiologiques sur leur représentation graphique plutôt que sur leurs équations différentielles, il est possible de mettre en évidence plusieurs concepts importants à l'aide des composantes d'un hypergraphe. On décrit une manière formelle de créer automatiquement un système d'équations différentielles à partir de ces composantes et on adapte ensuite la définition du produit cartésien pour les hypergraphes décrits, ce qui permet la fusion de modèles. À l'aide d'un algorithme qui ajoute automatiquement de nouvelles composantes à l'hypergraphe, il est possible d'isoler virtuellement certains individus, afin d'expliciter le calcul de nombres de reproduction. On montre ensuite que la forme des équations différentielles créées admettent une solution unique et que l'algorithme d'ajout aux hypergraphes est stable au niveau de la structure et de la dynamique des hypergraphes. On trouve que la méthode décrite pour le calcul des nombres de reproduction permet une meilleure prédiction de la croissance de l'épidémie que le calcul standard $\mathcal{R}_t = \mathcal{R}_0 \cdot S / N$ et que le calcul de $\mathcal{R}_0$ est très similaire aux résultats trouvés à l'aide de la matrice de prochaine génération, en plus d'être plus simple à mettre en place et d'offrir une justification plus robuste. On conclue ce mémoire en décrivant sommairement un processus d'apprentissage automatique des paramètres dans les modèles à compartiments, afin de permettre une calibration de modèles plus rapide. L'apprentissage machine peut être intégré en faisant appel à la librarie torchdiffeq, qui implémente les équations différentielles ordinaires neuronales en utilisant Pytorch.By basing the analysis of epidemiological models on their graphical representation rather than on their differential equations, it is possible to highlight a few key concepts by using the components of a hypergraph. We give a formal way to automatically create a system of differential equations by using these components and we then adapt the definition of the cartesian product for the defined hypergraphs, which permits the merging of models. Using an algorithm which automatically adds new components to the graph, we can virtually isolate a few individuals to explicitly compute the reproduction numbers. We then show that the resulting differential equations allow for a unique solution and that the modification algorithm is stable for the structure and dynamics of the hypergraphs. We find that the described method for the computation of reproduction numbers gives a more accurate prediction of the growth of the epidemic than the standard computation $\mathcal{R}_t = \mathcal{R}_0 \cdot S/N$ and that the computation of $\mathcal{R}_0$ is very similar to the results found using the next generation matrix method, as well as being simpler to integrate into models and offering a more robust justification. We conclude this thesis with a brief outline of an automatic learning process for the parameters in compartmental models, which allows a faster calibration of epidemiological models. The implementation of machine learning can be done through the torchdiffeq library, which applies the theory of neural ordinary differential equations using Pytorch

Heterogeneity in Agent-based models

Agent-based models are an incredibly flexible tool that among other things, allow modellers to capture heterogeneity in agent attributes, characteristics, and behaviours. This study defines heterogeneity in agent-based models as agent granularity: the level of description used to define the agent population. Consequently, this increased complexity can make the already challenging tasks of calibration and parameter identification, even more difficult. Although modellers recognise the significance of model calibration, the process of uniquely determining model input from any given model output is overlooked. This thesis proposes an impact of heterogeneity in agent-based models is parameter non-identification. To this end, this research conducts a thorough examination of agent heterogeneity by the comparative study of homogeneous and heterogeneous scenarios in agent-based models. Using an emotional contagion case study model and approximate Bayesian computation calibration, it finds that the introduction of heterogeneity results in inaccurate parameter calibration compared to the homogeneous case. This study proposes the inaccurate results as the consequence of a failure to uniquely distinguish the effect of additional parameters in the model. Furthermore, failing to identify model parameters limits the predictive or forecasting power of the agent-based model. A simple case study is used to demonstrate that the use of unidentifiable models to inform real-world governmental and social policies can lead to erroneous conclusions and potentially unsound interventions

Products of Compartmental Models in Epidemiology.

We show that many structured epidemic models may be described using a straightforward product structure in this paper. Such products, derived from products of directed graphs, may represent useful refinements including geographic and demographic structure, age structure, gender, risk groups, or immunity status. Extension to multistrain dynamics, that is, pathogen heterogeneity, is also shown to be feasible in this framework. Systematic use of such products may aid in model development and exploration, can yield insight, and could form the basis of a systematic approach to numerical structural sensitivity analysis