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Epidemics. 2019 Mar;26:43-57. doi: 10.1016/j.epidem.2018.08.004. Epub 2018 Aug 29.

Dengue modeling in rural Cambodia: Statistical performance versus epidemiological relevance.

Author information

1
Institut de biologie de l'Ecole normale supérieure (IBENS), Ecole normale supérieure, CNRS, INSERM, PSL Research University, 75005 Paris, France; CREST, ENSAE, Université Paris Saclay, 5 avenue Henry Le Chatelier, 91120 Palaiseau, France. Electronic address: champagn@biologie.ens.fr.
2
Institut Pasteur, Unité de Génétique Fonctionnelle des Maladies Infectieuses, Department of Genomes and Genetics, F-75724 Paris cedex 15, France; Centre National de la Recherche Scientifique (CNRS), Génomique évolutive, modélisation et santé, UMR 2000, 75724 Paris cedex 15, France.
3
Institut Pasteur in Cambodia, Epidemiology and Public Health Unit, Phnom Penh, Cambodia.
4
Institut Pasteur in Cambodia, Virology Unit, Phnom Penh, Cambodia.
5
National Dengue Control Program, Cambodia.
6
Institut de biologie de l'Ecole normale supérieure (IBENS), Ecole normale supérieure, CNRS, INSERM, PSL Research University, 75005 Paris, France; Centre National de la Recherche Scientifique (CNRS), Génomique évolutive, modélisation et santé, UMR 2000, 75724 Paris cedex 15, France; International Center for Mathematical and Computational Modeling of Complex Systems (UMMISCO), UMI 209 UPMC/IRD, Bondy cedex, France. Electronic address: cazelles@biologie.ens.fr.

Abstract

Dengue dynamics are shaped by the complex interplay between several factors, including vector seasonality, interaction between four virus serotypes, and inapparent infections. However, paucity or quality of data do not allow for all of these to be taken into account in mathematical models. In order to explore separately the importance of these factors in models, we combined surveillance data with a local-scale cluster study in the rural province of Kampong Cham (Cambodia), in which serotypes and asymptomatic infections were documented. We formulate several mechanistic models, each one relying on a different set of hypotheses, such as explicit vector dynamics, transmission via asymptomatic infections and coexistence of several virus serotypes. Models are confronted with the observed time series using Bayesian inference, through Markov chain Monte Carlo. Model selection is then performed using statistical information criteria, and the coherence of epidemiological characteristics (reproduction numbers, incidence proportion, dynamics of the susceptible classes) is assessed in each model. Our analyses on transmission dynamics in a rural endemic setting highlight that two-strain models with interacting effects better reproduce the long term data, but they are difficult to parameterize when relying on incidence cases only. On the other hand, considering the available data, incorporating vector and asymptomatic components seems of limited added-value when seasonality and underreporting are already accounted for.

KEYWORDS:

Cambodia; Dengue; Mathematical model; Model selection; Vector borne disease

PMID:
30206040
DOI:
10.1016/j.epidem.2018.08.004
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