Predicting and Evaluating the Epidemic Trend of Ebola Virus Disease in the 2014-2015 Outbreak and the Effects of Intervention Measures

Guo, Zuiyuan; Xiao, Dingquan; Li, Dongli; Wang, Xiuhong; Wang, Yayu; Yan, Tiecheng; Wang, Zhiqi

doi:10.1371/journal.pone.0152438

Cited by 22 publications

(14 citation statements)

References 28 publications

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“…Mathematical models of infectious diseases can help deepen our understanding of the epidemiological distribution of infectious diseases. Currently, the most commonly used model is the Susceptible-Exposed-Infectious-Recovered (SEIR) model, from which many models have been derived and widely adopted to analyse infectious outbreaks of Ebola, tuberculosis, and influenza, among other diseases [16][17][18]. Indeed, the SEIR model has proven to be critical for revealing the epidemiological characteristics of infectious diseases.…”

Section: Introductionmentioning

confidence: 99%

An artificially simulated outbreak of a respiratory infectious disease

Guo

Tong

et al. 2020

BMC Public Health

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Background: Outbreaks of respiratory infectious diseases often occur in crowded places. To understand the pattern of spread of an outbreak of a respiratory infectious disease and provide a theoretical basis for targeted implementation of scientific prevention and control, we attempted to establish a stochastic model to simulate an outbreak of a respiratory infectious disease at a military camp. This model fits the general pattern of disease transmission and further enriches theories on the transmission dynamics of infectious diseases. Methods: We established an enclosed system of 500 people exposed to adenovirus type 7 (ADV 7) in a military camp. During the infection period, the patients transmitted the virus randomly to susceptible people. The spread of the epidemic under militarized management mode was simulated using a computer model named "the random collision model", and the effects of factors such as the basic reproductive number (R 0), time of isolation of the patients (TOI), interval between onset and isolation (IOI), and immunization rates (IR) on the developmental trend of the epidemic were quantitatively analysed. Results: Once the R 0 exceeded 1.5, the median attack rate increased sharply; when R 0 = 3, with a delay in the TOI, the attack rate increased gradually and eventually remained stable. When the IOI exceeded 2.3 days, the median attack rate also increased dramatically. When the IR exceeded 0.5, the median attack rate approached zero. The median generation time was 8.26 days, (95% confidence interval [CI]: 7.84-8.69 days). The partial rank correlation coefficients between the attack rate of the epidemic and R 0 , TOI, IOI, and IR were 0.61, 0.17, 0.45, and − 0.27, respectively. Conclusions: The random collision model not only simulates how an epidemic spreads with superior precision but also allows greater flexibility in setting the activities of the exposure population and different types of infectious diseases, which is conducive to furthering exploration of the epidemiological characteristics of epidemic outbreaks.

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Section: Introductionmentioning

confidence: 99%

An artificially simulated outbreak of a respiratory infectious disease

Guo

Tong

et al. 2020

BMC Public Health

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“…For example, Ebola virus (EBOV) is remarkable for its extreme virulence in humans. Mortality rates due to EBOV are approximately 50% . Given that EBOV is spread through direct contact with infected individuals, EBOV's excessive virulence curtails the duration of infectivity and person‐to‐person contact rates, thus reducing the transmission potential of the virus.…”

Section: Virus Evolutionmentioning

confidence: 99%

Evolutionary ecology of virus emergence

Dennehy

2016

Annals of the New York Academy of Sciences

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The cross-species transmission of viruses into new host populations, termed virus emergence, is a significant issue in public health, agriculture, wildlife management, and related fields. Virus emergence requires overlap between host populations, alterations in virus genetics to permit infection of new hosts, and adaptation to novel hosts such that between-host transmission is sustainable, all of which are the purview of the fields of ecology and evolution. A firm understanding of the ecology of viruses and how they evolve is required for understanding how and why viruses emerge. In this paper, I address the evolutionary mechanisms of virus emergence and how they relate to virus ecology. I argue that, while virus acquisition of the ability to infect new hosts is not difficult, limited evolutionary trajectories to sustained virus between-host transmission and the combined effects of mutational meltdown, bottlenecking, demographic stochasticity, density dependence, and genetic erosion in ecological sinks limit most emergence events to dead-end spillover infections. Despite the relative rarity of pandemic emerging viruses, the potential of viruses to search evolutionary space and find means to spread epidemically and the consequences of pandemic viruses that do emerge necessitate sustained attention to virus research, surveillance, prophylaxis, and treatment.

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“…Work done by Berge et al [29] was an extension of their previous work done in 2015 [26], in which a simple mathematical model was developed, which incorporated both the direct and the indirect Ebola virus transmission in such a way that there is a provision of Ebola viruses. Models have also been developed to try and understand various intervention strategies in trying to curtail the spread of EVD [31][32][33][34][35][36][37][38][39][40]. The impact of vaccination and vaccines was investigated in [35,36,39,40] and the issue of quarantining analysed in [35,37] through mathematical models.…”

Section: Introductionmentioning

confidence: 99%

Dynamical analysis and control strategies in modelling Ebola virus disease

Mhlanga

2019

Adv Differ Equ

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Ebola virus disease (EVD) is a severe infection with an extremely high fatality rate spread through direct and indirect contacts. Recently, an outbreak of EVD in West Africa brought public attention to this deadly disease. We study the spread of EVD through a two-patch model. We determine the basic reproduction number, the disease-free equilibrium, two boundary equilibria and the endemic equilibrium when the disease persists in the two sub-populations for specific conditions. Further, we introduce time-dependent controls into our proposed model. We analyse the optimal control problem where the control system is a mathematical model for EVD that incorporates educational campaigns. The control functions represent educational campaigns in their respective patches, with one patch having more effective controls than the other. We aim to study how these control measures would be implemented for a certain time period, in order to reduce or eliminate EVD in the respective communities, while minimising the intervention implementation costs. Numerical simulations results are provided to illustrate the dynamics of the disease in the presence of controls.

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Predicting and Evaluating the Epidemic Trend of Ebola Virus Disease in the 2014-2015 Outbreak and the Effects of Intervention Measures

Cited by 22 publications

References 28 publications

An artificially simulated outbreak of a respiratory infectious disease

An artificially simulated outbreak of a respiratory infectious disease

Evolutionary ecology of virus emergence

Dynamical analysis and control strategies in modelling Ebola virus disease

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