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A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease
Published on: April 28, 2019
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Comparison of stochastic and deterministic frameworks in dengue modelling.
Clara Champagne1, Bernard Cazelles2
1Institut de Biologie de l'Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, CNRS UMR 8197,46 rue d'Ulm, Paris 75005, France; CREST, ENSAE, Université Paris Saclay, 5, avenue Henry Le Chatelier, Palaiseau cedex 91764, France.
Mathematical Biosciences
|February 9, 2019
Summary
This study estimated dengue transmission models in Cambodia, incorporating stochasticity and complex structures. Stochastic models better captured uncertainty than deterministic ones, despite higher computational costs.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Infectious Disease Dynamics
Background:
- Dengue fever poses a significant public health challenge, particularly in tropical regions like Cambodia.
- Understanding dengue transmission dynamics is crucial for effective control strategies.
- Compartment models are widely used to study disease spread, but incorporating realistic complexities remains a challenge.
Purpose of the Study:
- To estimate and compare compartment models of dengue transmission in rural Cambodia.
- To investigate the impact of increasing model complexity, including stochasticity and structural elements.
- To evaluate the performance of deterministic versus stochastic modeling approaches for dengue.
Main Methods:
- Estimation of compartment models with increasing complexity in model structure and stochasticity.
- Inclusion of three sources of stochasticity: observation noise, demographic variability, and environmental hazard.
- Introduction of vector-borne transmission, asymptomatic infections, and interacting virus serotypes.
- Bayesian framework estimation using Markov Chain Monte Carlo (MCMC) and Particle Markov Chain Monte Carlo (PMCMC).
- Utilized case data from dengue epidemics in Kampong Cham, Cambodia.
Main Results:
- Deterministic models approximated mean trajectories efficiently but did not fully capture uncertainty.
- Stochastic frameworks provided a more accurate reflection of parameter and simulation uncertainty.
- Model complexity, particularly the inclusion of stochastic elements, improved the representation of dengue transmission dynamics.
- Comparison of different model formulations highlighted practical advantages and disadvantages.
Conclusions:
- Stochastic compartment models are superior to deterministic ones for accurately representing dengue transmission uncertainty in real-world settings.
- While deterministic models offer computational efficiency for mean trajectories, stochastic approaches are essential for robust risk assessment and intervention planning.
- The study provides valuable insights into the practical application of complex epidemiological models for dengue control in resource-limited areas.
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