FITTING A MODEL OF DENGUE TRANSMISSION TO EMPIRICAL DATA- EXAMPLE OF RAYONG PROVINCE IN THAILAND
Author(s)
Kharitonova E1, Aballea S1, Knerer G2, Kelly C3
1Creativ-Ceutical, Paris, France, 2Takeda Pharmaceuticals International, Zurich, Switzerland, 3Takeda Pharmaceuticals International, Boston, MA, USA
OBJECTIVES: A mathematical model of dengue transmission was developed to estimate the impact of dengue vaccination. It was first fitted to the empirical dengue seroprevalence data from Rayong province of Thailand in order to reproduce the epidemiology observed without vaccination. METHODS: The deterministic age-structured compartmental model included explicit host and vector populations, seasonality, all four dengue serotypes and interactions between them. To fit the model to the empirical data, twenty-five calibration scenarios were determined. Each scenario was a combination of cross-protection duration and an increase in susceptibility following the primary infection. For each combination, the probability of virus transmission was estimated by fitting the model to the seroprevalence collected in children aged 6 to 18 in Rayong. Incidence curves predicted with each combination were visually assessed and compared to the reported incidence corrected for under-reporting. RESULTS: Scenarios with good fit to seroprevalence data and realistic incidence curves were characterised by high values of susceptibility enhancement (4- to 9-fold increases in susceptibility following primary infection) and cross-protection duration. A high susceptibility enhancement factor was required for the model to predict comparable prevalence of multiple dengue infections. The modelled prevalence was close to observed values for almost all age groups, although lower for children aged 6 to 7 by up to 10 percentage points. Scenarios with higher susceptibility enhancement and cross-protection predicted greater fluctuations in annual dengue incidence, in line with observed data (corrected for under-reporting). CONCLUSIONS: Several scenarios allowed fitting the model to the empirical data from Rayong province. The inclusion of susceptibility enhancement considerably improved the fit to seroprevalence data. Higher enhancement factors in combination with longer cross-protection duration also led to more realistic incidence patterns. The validated scenarios reproduced empirical dengue incidence in the Rayong province, which is a preliminary condition for analyses of vaccination impact scenarios.
Conference/Value in Health Info
2018-09, ISPOR Asia Pacific 2018, Tokyo, Japan
Value in Health, Vol. 21, S2 (September 2018)
Code
PRM24
Topic
Methodological & Statistical Research
Topic Subcategory
Modeling and simulation
Disease
Infectious Disease (non-vaccine)