Mathematical models of Ebola—Consequences of underlying assumptions

Feng, Zhilan; Zheng, Yu; Hernandez-Ceron, Nancy; Zhao, Henry; Glasser, John W.; Hill, Andrew

doi:10.1016/j.mbs.2016.04.002

Cited by 30 publications

(43 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although age changes continuously and can be more generally described using a continuous-age model with, for example, partial differential equations (Feng et al, 2016;Martcheva, 2015), we examine an age-structured model with two discrete age classes so that our population is sub-divided into children and adults. Such a model would be relevant for infections that spread much faster among children than adults, whether due to higher susceptibility or greater contact rates.…”

Section: Infectious Populationmentioning

confidence: 99%

An introduction to compartmental modeling for the budding infectious disease modeler

Blackwood

Childs

2018

Letters in Biomathematics

112

146

View full text Add to dashboard Cite

Section: Infectious Populationmentioning

confidence: 99%

An introduction to compartmental modeling for the budding infectious disease modeler

Blackwood

Childs

2018

Letters in Biomathematics

112

146

View full text Add to dashboard Cite

“…The parameters , , and are assumed to be independent. Other assumptions on the dependence of these parameters might also be considered, but they would not affect the qualitative conclusions of this study (see Feng et al [21], for more detailed discussions about various underlying biological assumptions on this topic in the context of Ebola models). A disease transmission diagram for each sub-group is depicted in Fig.…”

Section: =1mentioning

confidence: 99%

Staggered release policies for COVID-19 control: Costs and benefits of relaxing restrictions by age and risk

Zhao

Feng

2020

Mathematical Biosciences

Self Cite

View full text Add to dashboard Cite

Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

show abstract

“…This would lead to the development of more effective control and prevention measures for future outbreaks. Different mathematical models have been studied to provide valuable information for better understanding of EVD outbreaks, for example the SEIR model (Susceptible, Exposed, Infective, Removed) [20], the SEIHFR model (Susceptible, Exposed, Infective, Hospitalized, Funeral, Removed) [9,10,21] and many others [12,13,17–19,22]. These models use Ebola data to identify the parameters involved in transmission rates, to estimate the average number of secondary infections generated by a typical infected case, in its entire period of infectiousness, in a completely susceptible population, and this quantity is called the basic reproduction number which is denoted ℜ 0 .…”

Section: Mathematical Models and Stability Analysismentioning

confidence: 99%

“…In this paper, the SEIHFR epidemic model [9,10] was modified to study the spread of Ebola by intervention (vaccination) and incorporating contact tracing [11,12]. Different from the ring vaccination model [6], the effectiveness of vaccination is modeled to directly reduce the numbers of susceptible people in a population.…”

Section: Introductionmentioning

confidence: 99%

Data Fitting and Scenario Analysis of Vaccination in the 2014 Ebola Outbreak in Liberia

Xie¹

2019

Osong Public Health Res Perspect

View full text Add to dashboard Cite

Objectives This study aimed to extend an epidemiological model (SEIHFR) to analyze epidemic trends, and evaluate intervention efficacy. Methods SEIHFR was modified to examine disease transmission dynamics after vaccination for the Ebola outbreak. Using existing data from Liberia, sensitivity analysis of various epidemic scenarios was used to inform the model structure, estimate the basic reproduction number ℜ 0 and investigate how the vaccination could effectively change the course of the epidemic. Results If a randomized mass vaccination strategy was adopted, vaccines would be administered prophylactically or as early as possible (depending on the availability of vaccines). An effective vaccination rate threshold for Liberia was estimated as 48.74% among susceptible individuals. If a ring vaccination strategy was adopted to control the spread of the Ebola virus, vaccines would be given to reduce the transmission rate improving the tracing rate of the contact persons of an infected individual. Conclusion The extended SEIHFR model predicted the total number of infected cases, number of deaths, number of recoveries, and duration of outbreaks among others with different levels of interventions such as vaccination rate. This model may be used to better understand the spread of Ebola and develop strategies that may achieve a disease-free state.

show abstract

Mathematical models of Ebola—Consequences of underlying assumptions

Cited by 30 publications

References 13 publications

An introduction to compartmental modeling for the budding infectious disease modeler

An introduction to compartmental modeling for the budding infectious disease modeler

Staggered release policies for COVID-19 control: Costs and benefits of relaxing restrictions by age and risk

Data Fitting and Scenario Analysis of Vaccination in the 2014 Ebola Outbreak in Liberia

Contact Info

Product

Resources

About