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Dynamics of HSV-2 infection with a therapeutic vaccine.
Venturino, Ezio; Shoukat, Affan; Moghadas, Seyed M.
Affiliation
  • Venturino E; Dipartimento di Matematica "Giuseppe Peano", Università di Torino, Torino, Italy.
  • Shoukat A; Center for Infectious Disease Modeling and Analysis, School of Public Health, Yale University, CT, USA.
  • Moghadas SM; Agent-Based Modelling Laboratory, York University, Toronto, ON, Canada.
Heliyon ; 6(7): e04368, 2020 Jul.
Article in En | MEDLINE | ID: mdl-32695902
Herpes-Simplex Virus type 2 (HSV-2) is a lifelong infection, which has infected over 400 million individuals aged 15-49 years, worldwide. While the disease can be treated with episodic and suppressive antiviral drugs to reduce the rate of recurrence (i.e., symptomatic disease) and viral shedding, none of the currently available therapies can clear the virus from the body of an infected person. A number of therapeutic vaccine platforms are currently in development in order to achieve similar effects to treatment. Due to the inadequate data from clinical trials of therapeutic vaccines, modeling efforts to quantify the impact of vaccination have been limited. In this study, we propose a compartmental deterministic model for the dynamics of HSV-2 to evaluate the effect of a potential vaccine candidate with the inclusion of a booster dose. Despite its simplicity that may not address the complexity of HSV-2 disease, the model shows that targeting symptomatic infection for vaccination is the most effective strategy in the long-term. This conclusion is based on the assumption of an optimal vaccine efficacy, conferring immunity levels that prevent viral shedding and recurrence transiently. Our model provides a framework for developing a computational system to include more heterogeneous characteristics of the disease and individuals, and investigate effectiveness and cost-effectiveness of vaccination scenarios when clinical data become available.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Heliyon Year: 2020 Document type: Article Affiliation country: Italy Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Heliyon Year: 2020 Document type: Article Affiliation country: Italy Country of publication: United kingdom