Modeling the transmission dynamics of Ebola virus disease in Liberia

Xia, Zhiqiang; Wang, Shifu; Li, Shenlong; Huang, Liuyu; Zhang, Wenyi; Sun, Gui‐Quan; Gai, Zhongtao; Jin, Zhen

doi:10.1038/srep13857

Cited by 42 publications

(34 citation statements)

References 32 publications

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“…The transmission coefficients β 1 , β 2 , β 3 and γ are unknown. On the basis of the actual reported confirmed cases, we use the least-squares method to estimate these parameter values such that the sum of squared errors between the actual data and the solution of Eq (2) is minimum [28][29][30]. The accumulated confirmed incidence (I total ) in the Republic of Korea is fitted to estimate four parameters (β 1 , β 2 , β 3 and γ):…”

Section: Estimation Of Basic Reproduction Number R 0 In the Republic mentioning

confidence: 99%

“…The basic reproduction number is the threshold whether disease will outbreak and be prevalent in the crowd. When R 0 > 1, the disease will spread in the population; when R 0 < 1, the disease will gradually disappear [18,29,31]. So it is important to study the sensitivity and uncertainty analysis of basic reproduction number R 0 on crucial parameters.…”

Section: Sensitivity and Uncertainty Analysis Of Basic Reproduction Nmentioning

confidence: 99%

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Modeling the Transmission of Middle East Respirator Syndrome Corona Virus in the Republic of Korea

et al. 2015

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The 2015 epidemic of Middle East respiratory syndrome (MERS) in the Republic of Korea has been the largest outbreak outside Middle East. This epidemic had caused 185 laboratory-confirmed cases and 36 deaths in the Republic of Korea until September 2, 2015, which attracted public’s attention. Based on the detailed data of patients released by World Health Organization (WHO) and actual propagation of the epidemic, we construct two dynamical models to simulate the propagation processes from May 20 to June 8 and from June 9 to July 10, 2015, respectively and find that the basic reproduction number R 0 reaches up to 4.422. The numerical analysis shows that the reasons of the outbreak spread quickly are lack of self-protection sense and targeted control measures. Through partial correction analysis, the parameters β 1 and γ have strong correlations with R 0, i.e., the infectivity and proportion of the asymptomatic infected cases have much influence on the spread of disease. By sensitivity analysis, strengthening self-protection ability of susceptible and quickly isolating or monitoring close contacts are effective measures to control the disease.

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Section: Estimation Of Basic Reproduction Number R 0 In the Republic mentioning

confidence: 99%

Section: Sensitivity and Uncertainty Analysis Of Basic Reproduction Nmentioning

confidence: 99%

Modeling the Transmission of Middle East Respirator Syndrome Corona Virus in the Republic of Korea

et al. 2015

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“…In 2013, a new type of avian influenza, H7N9, appeared in Mainland China and subsequently caused considerable economic losses alongside public health risks [2]. In 2014, Ebola virus disease emerged in West Africa and infected more than 10,000 people [3,4]. Another new virus-Middle East Respiratory Syndrome coronavirus (MERS-CoV)-had emerged in 2012 and by mid 2015 caused 1,379 human infections with a death toll of 531 people from 26 countries [5].…”

Section: Introductionmentioning

confidence: 99%

Pattern transitions in spatial epidemics: Mechanisms and emergent properties

Sun

Jusup

Jin

et al. 2016

Physics of Life Reviews

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Infectious diseases are a threat to human health and a hindrance to societal development. Consequently, the spread of diseases in both time and space has been widely studied, revealing the different types of spatial patterns. Transitions between patterns are an emergent property in spatial epidemics that can serve as a potential trend indicator of disease spread. Despite the usefulness of such an indicator, attempts to systematize the topic of pattern transitions have been few and far between. We present a mini-review on pattern transitions in spatial epidemics, describing the types of transitions and their underlying mechanisms. We show that pattern transitions relate to the complexity of spatial epidemics by, for example, being accompanied with phenomena such as coherence resonance and cyclic evolution. The results presented herein provide valuable insights into disease prevention and control, and may even be applicable outside epidemiology, including other branches of medical science, ecology, quantitative finance, and elsewhere.

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“…At national level, our approach was not comparable to results of other methods. Several mechanistic [6,10,14,[16][17][18][19][50][51][52] and phenomenological [15,34,53] The ecological analysis showed associations between R 0 and population-level factors linked to urbanisation and crowding. Analysis at the population-level is appropriate when the mechanism of action involves interactions between individuals, for example the potential for spread of an infection in densely populated areas [54].…”

Section: Discussionmentioning

confidence: 99%

Heterogeneity in District-Level Transmission of Ebola Virus Disease during the 2013-2015 Epidemic in West Africa

et al. 2016

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The Ebola virus disease (EVD) epidemic in West Africa in 2013–2015 spread heterogeneously across the three hardest-hit countries Guinea, Liberia and Sierra Leone and the estimation of national transmission of EVD provides little information about local dynamics. To investigate district-level transmissibility of EVD, we applied a statistical modelling approach to estimate the basic reproduction number (R0) for each affected district and each country using weekly incident case numbers. We estimated growth rates during the early exponential phase of the outbreak using exponential regression of the case counts on the first eight weeks since onset. To take into account the heterogeneity between and within countries, we fitted a mixed effects model and calculated R0 based on the predicted individual growth rates and the reported serial interval distribution. At district level, R0 ranged from 0.36 (Dubréka) to 1.72 (Beyla) in Guinea, from 0.53 (Maryland) to 3.37 (Margibi) in Liberia and from 1.14 (Koinadugu) to 2.73 (Western Rural) in Sierra Leone. At national level, we estimated an R0 of 0.97 (95% CI 0.77–1.18) for Guinea, 1.26 (95% CI 0.98–1.55) for Liberia and 1.66 (95% CI 1.32–2.00) for Sierra Leone. Socio-demographic variables related to urbanisation such as high population density and high wealth index were found positively associated with R0 suggesting that the consequences of fast urban growth in West Africa may have contributed to the increased spread of EVD.

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Modeling the transmission dynamics of Ebola virus disease in Liberia

Cited by 42 publications

References 32 publications

Modeling the Transmission of Middle East Respirator Syndrome Corona Virus in the Republic of Korea

Modeling the Transmission of Middle East Respirator Syndrome Corona Virus in the Republic of Korea

Pattern transitions in spatial epidemics: Mechanisms and emergent properties

Heterogeneity in District-Level Transmission of Ebola Virus Disease during the 2013-2015 Epidemic in West Africa

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