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Modeling the impact of early case detection on dengue transmission: deterministic vs. stochastic
A.K. Srivastav, P.K. Tiwari,
Published in Bellwether Publishing, Ltd.
Volume: 39
Issue: 3
Pages: 434 - 455
On the global platform of emerging infectious diseases, dengue fever added a serious health concern, especially in the tropical and subtropical countries with poor health services. Due to unavailability of proper vaccination, primary prevention of dengue is possible at social and personal levels by controlling the propagation of vectors as well as avoiding the bites of infected ones, respectively. Proper diagnosis of infection and quarantine/hospitalization is very important to treat the patients in the early stage of infection which can effectively combat the disease by preventing the cases of secondary infection. In view of this, we propose a compartmental model for dengue which consists of the classes of moderately infected human population, infected but undetected human population, severely infected and detected human population, and quarantined/hospitalized human population with two contemporary classes for humans and vectors. Sensitivity analysis employing normalized forward sensitivity indices and partial rank correlation coefficients are used for ranking the importance of each parameter on basic reproduction number and prevalence of dengue in humans/vectors, respectively. Our results recommend that by adequate management of quarantine/hospitalization, the disease prevalence can be terminated. Moreover, the deterministic model is converted into stochastic one, and results of stochastic and deterministic models are compared using numerical simulations. © 2020 Taylor & Francis Group, LLC.
About the journal
JournalStochastic Analysis and Applications
PublisherBellwether Publishing, Ltd.