Abstract

BackgroundCurrent recommendations about dengue vaccination by the World Health Organization depend on seroprevalence levels and serological status in populations and individuals. However, seroprevalence estimation may be difficult due to a diversity of factors. Thus, estimation through models using data from epidemiological surveillance systems could be an alternative procedure to achieve this goal. ObjectiveTo estimate the expected dengue seroprevalence in children of selected areas in Argentina, using a simple model based on data from passive epidemiological surveillance systems. MethodsA Markov model using a simulated cohort of individuals from age 0 to 9 years was developed. Parameters regarding the reported annual incidence of dengue, proportion of inapparent cases, and expansion factors for outpatient and hospitalized cases were considered as transition probabilities. The proportion of immune population at 9 years of age was taken as a proxy of the expected seroprevalence, considering this age as targeted for vaccination. The model was used to evaluate the expected seroprevalence in Misiones and Salta provinces and in Buenos Aires city, three settings showing different climatic favorability for dengue. ResultsThe estimates of the seroprevalence for the group of 9-year-old children for Misiones was 79% (95%CI:46–100%), and for Salta 22% (95%CI:14–30%), both located in northeastern and northwestern Argentina, respectively. Buenos Aires city, from central Argentina, showed a likely seroprevalence of 7% (95%CI: 3–11%). According to the deterministic sensitivity analyses, the parameter showing the highest influence on these results was the probability of inapparent cases. ConclusionsThis model allowed the estimation of dengue seroprevalence in settings where this information is not available. Particularly for Misiones, the expected seroprevalence was higher than 70% in a wide range of scenarios, thus in this province a vaccination strategy directed to seropositive children of >9 years should be analyzed, including further considerations as safety, cost-effectiveness, and budget impact.

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