A Literature Review of Mathematical Models of Hepatitis B Virus Transmission Applied to Immunization Strategies From 1994 to 2015.

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hepatitis B 537 is an important theoretical epidemiology method, which has been used to simulate the prevalence of hepatitis B and evaluate different immunization strategies. However, differences lie in the mathematical processes
hepatitis B 1769 shortcomings may lead to unreliable results. When the mathematical model closely reflects the fact of hepatitis B spread, the results of the model fit will provide valuable information for controlling the transmission
hepatitis B 1888 the results of the model fit will provide valuable information for controlling the transmission of hepatitis B .INTRODUCTIONInfection with hepatitis B virus (HBV) is a challenge to global health. There are more than
hepatitis B 1927 provide valuable information for controlling the transmission of hepatitis B.INTRODUCTIONInfection with hepatitis B virus (HBV) is a challenge to global health. There are more than 350 million chronic carriers of HBV
hepatitis B 2422 safe and effective vaccines is the most attractive and most economical way to reduce the incidence of hepatitis B , in terms of both cost-effectiveness and cost-benefit ratios.[4]–[9] Despite the success of immunization,
hepatitis B 3714 the early 1980s, the transmission dynamics model was first used to study the transmission dynamics of hepatitis B and the effectiveness of control. With the introduction of available hepatitis B vaccine, how to use
hepatitis B 3795 transmission dynamics of hepatitis B and the effectiveness of control. With the introduction of available hepatitis B vaccine, how to use a mathematical model to predict the long-term effects of vaccination on hepatitis
hepatitis B 3899 hepatitis B vaccine, how to use a mathematical model to predict the long-term effects of vaccination on hepatitis B control became the main focus. McLean and Blumberg first proposed a differential equation model of HBV
hepatitis B 4128 to address questions concerning the impact of a mass vaccination program on the prevalence status of hepatitis B in 1994.[14] Since then, many researchers have studied the transmission dynamics of HBV and assessed
hepatitis B 4884 results.This study conducted a literature review of the existing research on mathematical models of hepatitis B transmission under different vaccine strategies. The main aims of this study are: (i) to describe the
hepatitis B 5953 studies that were found using Google Scholar. The research MeSH, or keywords, were defined as: (“ hepatitis B ” OR “HBV”) AND (“modeling” OR “mathematics model”) AND (“vaccine” OR “vaccination”
hepatitis B 16811 environment, in which the birth and death rates of a population are equal during the epidemic period of hepatitis B , and that there is no HBV related death. Six studies[15],[16],[19],[20],[24],[26] considered that the
hepatitis B 20880 0.115Pang, et al(2010)[18]Time-dependent. λ = β(y + αc),β: 0.85, estimated from the reported acute hepatitis B data by Ministry of Health of China.0.16 per year4 per year0.005–0.025 per yearCarrier: 0.7–0.9O’Leary,
hepatitis B 21304 yearCarrier: 0.11Zou, et al(2010)[19]Time-dependent, λ = β(y + αc)β: 1, estimated from the reported acute hepatitis B data by Ministry of Health of China0.8856 per year4 per year0.025 per yearCarrier: 0.11Zou, et al(2010)[20]Age-time-dependent,
hepatitis B 23461 individuals in the population (n = 9). Transmission coefficients were estimated from reported acute hepatitis B data (n = 4) or determined by basic reproduction number (n = 1). The others were assumed or followed
hepatitis B 25728 parameter values (Table 1), these papers explored the impact of different vaccination strategies on the hepatitis B infection in different areas. As summarized in Table 2, the forecast period of the modeling was set
hepatitis B 26339 HBV prevalence, and immunization of susceptible adults or highly risk groups or screening for chronic hepatitis B have a moderate additional effect on controlling HBV infection. However, in low HBV endemic areas, immunization
hepatitis B 29025 continue to provide a route of infection to those still susceptible.Mann (2011)[26]DISCUSSIONThe spread of hepatitis B is a very complicated process. It is restricted and influenced by many interacting factors, including
hepatitis B 29369 population size, and the presence of medical interventions. To accurately predict the prevalence of hepatitis B and successfully apply predictions to the design of optimal, feasible public health policy for HBV prevention
hepatitis B 29722 considered, including the population characteristics, natural history, and transmission pattern of hepatitis B .The compartmental model is an effective tool to assess theoretical and practical contributions affecting
hepatitis B 31062 compartment via epidemic survey data. Meanwhile, in comparison with the long-term natural history of chronic hepatitis B , the acute and latent infection with HBV is transient. So, this simplified model is feasible.Based on
hepatitis B 32401 features, a difference equation model with age structure is recommended for use in the prediction of hepatitis B prevalence.The compartmental model is also associated with population characteristics. The complex process
hepatitis B 35005 incidence data reported through the national epidemic surveillance system; however, the incidence data of hepatitis B were only the symptomatic clinical cases. In fact, only 1% of neonates, 10% of children aged 1–5 years
hepatitis B 35497 infection. In the recruited studies, only 7 studies take into account the differences in the force of hepatitis B infection in each age group. All of the studies take into account the changes in this parameter over
hepatitis B 35922 infection and transmission coefficient will cause predictions to deviate from the true prevalence of hepatitis B .[37]Vertical transmission, including the intrauterine, intrapartum, and postpartum transmission, is
hepatitis B 36805 to HBV at birth. At present, the intrapartum and postpartum infection with HBV can be blocked by the hepatitis B vaccine and immune globulin, but there is no effective method to prevent intrauterine transmission.
hepatitis B 37255 the accumulated evidence indicates that HBV genotype is closely related to the clinical outcome of hepatitis B .[46]–[49] HBV genotype distribution has certain regional and ethnic characteristics, so the dynamics
hepatitis B 39057 shortcomings may lead to unreliable results. When the mathematical model closely reflects the fact of hepatitis B spread, the results of the model fit will provide valuable information to controlling the transmission
hepatitis B 39175 spread, the results of the model fit will provide valuable information to controlling the transmission of hepatitis B
infectious disease 419 12/2017Publication date (collection): /2018AbstractA mathematical model of the transmission dynamics of infectious disease is an important theoretical epidemiology method, which has been used to simulate the prevalence of hepatitis
infectious disease 2750 useful information for public health decision-making. One feasible method to predict the prevalence of infectious disease is to use a mathematical model.The transmission dynamics model, also known as the compartmental model,
infectious disease 2963 compartmental model, is an important theoretical epidemiology method used to study the transmission dynamics of infectious disease . The transmission dynamics models is based on the population characteristics, the infection characteristics
infectious disease 3097 transmission dynamics models is based on the population characteristics, the infection characteristics of the infectious disease , and related social factors, and is used to analyze the dynamic behavior of infectious disease and to
infectious disease 3192 the infectious disease, and related social factors, and is used to analyze the dynamic behavior of infectious disease and to do some mathematical simulations. The resulting model is conducive to predicting the transmission
infectious disease 13552 constant.Considering that age is one of the most important characteristics in the modeling of populations and infectious disease s. Some researchers[15],[16],[20],[24],[25] developed age-dependent mathematical models for studying
infectious disease 29859 is an effective tool to assess theoretical and practical contributions affecting the transmission of infectious disease .[30],[31] The remarkable features of these models are that the populations were stratified into different
infectious disease 34494 data regarding individuals at different ages are analyzed using the catalysis model, while assuming infectious disease in the steady state.[40] However, with the introduction of vaccination and other health interventions,

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