Predicting the extinction of Ebola spreading in Liberia due to mitigation strategies

Valdez, Lucas D.; Rego, Henio H. A.; Stanley, H. Eugene; Braunstein, Lidia A.

doi:10.1038/srep12172

Cited by 36 publications

(31 citation statements)

References 27 publications

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“…Based on local information, a susceptible individual interrupts the contact with an infected individual with a certain probability and restores it after a fixed period of time. In a similar way [36], extended the model and was able to successfully predict the date of extinction of the Ebolaʼs outbreak in Liberia in 2014. Ebola outbreaks have been studied by the scientific community due to the high impact of this epidemic on certain regions of southwest Africa, mainly in Guinea, Sierra Leona and Liberia.…”

Section: Introductionmentioning

confidence: 98%

“…Ebola outbreaks have been studied by the scientific community due to the high impact of this epidemic on certain regions of southwest Africa, mainly in Guinea, Sierra Leona and Liberia. Fortunately, there exists available data for the scientific community enabling to study more accurately the behavior and propagation of this disease [36,37].…”

Section: Introductionmentioning

confidence: 99%

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Multiple outbreaks in epidemic spreading with local vaccination and limited vaccines

et al. 2018

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How to prevent the spread of human diseases is a great challenge for the scientific community and so far there are many studies in which immunization strategies have been developed. However, these kind of strategies usually do not consider that medical institutes may have limited vaccine resources available. In this manuscript, we explore the susceptible-infected-recovered model with local dynamic vaccination, and considering limited vaccines. In this model, susceptibles in contact with an infected individual, are vaccinated -with probability ωand then get infected -with probability β. However, when the fraction of immunized individuals reaches a threshold V L , the vaccination stops, after which only the infection is possible. In the steady state, besides the critical points β c and ω c that separate a non-epidemic from an epidemic phase, we find for a range of V L another transition points, β * >β c and ω * <ω c , which correspond to a novel discontinuous phase transition. This critical value separates a phase where the amount of vaccines is sufficient, from a phase where the disease is strong enough to exhaust all the vaccination units. For a disease with fixed β, the vaccination probability ω can be controlled in order to drastically reduce the number of infected individuals, using efficiently the available vaccines. Furthermore, the temporal evolution of the system close to β * or ω * , shows that after a peak of infection the system enters into a quasi-stationary state, with only a few infected cases. But if there are no more vaccines, these few infected individuals could originate a second outbreak, represented by a second peak of infection. This state of apparent calm, could be dangerous since it may lead to misleading conclusions and to an abandon of the strategies to control the disease.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Multiple outbreaks in epidemic spreading with local vaccination and limited vaccines

et al. 2018

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“…However, recent results have shown that these estimates are much larger than the true extent of the disease outcome [24]. This overestimate of late dynamics when using early dynamics has been raised by many authors in general cases, as well as specifically for the Ebola virus [25,26]. We build upon these known observations to show that even in fully mixed models, * louzouy@math.biu.ac.il the early dynamics cannot be used to estimate the outcome of the late dynamics, and we propose an alternative approach.…”

Section: Introductionmentioning

confidence: 99%

Effects of distribution of infection rate on epidemic models

Lachiany

Louzoun

2016

Phys. Rev. E

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A goal of many epidemic models is to compute the outcome of the epidemics from the observed infected early dynamics. However, often, the total number of infected individuals at the end of the epidemics is much lower than predicted from the early dynamics. This discrepancy is argued to result from human intervention or nonlinear dynamics not incorporated in standard models. We show that when variability in infection rates is included in standard susciptible-infected-susceptible (SIS) and susceptible-infected-recovered (SIR) models the total number of infected individuals in the late dynamics can be orders lower than predicted from the early dynamics. This discrepancy holds for SIS and SIR models, where the assumption that all individuals have the same sensitivity is eliminated. In contrast with network models, fixed partnerships are not assumed. We derive a moment closure scheme capturing the distribution of sensitivities. We find that the shape of the sensitivity distribution does not affect R_{0} or the number of infected individuals in the early phases of the epidemics. However, a wide distribution of sensitivities reduces the total number of removed individuals in the SIR model and the steady-state infected fraction in the SIS model. The difference between the early and late dynamics implies that in order to extrapolate the expected effect of the epidemics from the initial phase of the epidemics, the rate of change in the average infectivity should be computed. These results are supported by a comparison of the theoretical model to the Ebola epidemics and by numerical simulation.

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“…In addition to comparing the modeling results with those derived from the WHO criteria, we examined how influential relatively unknown parameters are to determining the week in which the end of an epidemic can be declared. While the relevance of sexual transmission to the overall transmission dynamics has been explored elsewhere ( Abbate et al, 2016;Vinson et al, 2016 ), as has the extinction time of Ebola using a mathematical model ( Abbate et al, 2016;Valdez et al, 2015 ), the present study is the first to model the end of Ebola, explicitly accounting for sexual transmission.…”

Section: Discussionmentioning

confidence: 99%

Sexual transmission and the probability of an end of the Ebola virus disease epidemic

Lee

Nishiura

2019

Journal of Theoretical Biology

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a b s t r a c tThe criteria of zero Ebola cases defined by the World Health Organization did not explicitly account for the sexual transmission and led to multiple recrudescent events in West Africa from 2015 to 2016, partly indeed caused by sexual transmission from survivors. We devised a statistical model to compute the probability of the end of an Ebola virus disease epidemic, accounting for sexual transmission and underascertainment of cases. Analyzing the empirical data in Guinea, Liberia and Sierra Leone, the performance of the proposed model was compared with the existing criteria comprising a fixed waiting time of 42 days since the last case testing negative or burial. We showed that the waiting time can vary depending on the sexual behaviors of survivors and their adherence to refraining from unprotected sex is likely one of the key factors in determining the absence of additional cases after declaration. If the proportional weight of sexual transmission among all secondary transmission events was substantial, ascertaining the end could even require waiting 1 year from the purported last case. While our proposed method offers an objectively interpretable probability of the end of an epidemic, it highlights that the computation requires a good knowledge of sexual contact.

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Predicting the extinction of Ebola spreading in Liberia due to mitigation strategies

Cited by 36 publications

References 27 publications

Multiple outbreaks in epidemic spreading with local vaccination and limited vaccines

Multiple outbreaks in epidemic spreading with local vaccination and limited vaccines

Effects of distribution of infection rate on epidemic models

Sexual transmission and the probability of an end of the Ebola virus disease epidemic

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