Containing Ebola at the Source with Ring Vaccination

Merler, Stefano; Ajelli, Marco; Fumanelli, Laura; Parlamento, Stefano; Piontti, Ana Pastore y; Dean, Natalie E.; Putoto, Giovanni; Carraro, Dante; Longini, Ira M.; Halloran, M. Elizabeth; Vespignani, Alessandro

doi:10.1371/journal.pntd.0005093

Cited by 64 publications

(68 citation statements)

References 21 publications

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“…The ring vaccination strategy identifies and vaccinates contacts of infectious individuals, as well as contacts of contacts. Recent studies have used a network-based transmission agent-based model [14] and spatially explicit agentbased models [1,46] to examine the impact of ring vaccination of EVD outbreaks using data from the 2014 West Africa Outbreak. The explicit inclusion of space within the model follows the modeling approach of ring vaccination models for foot and mouth disease [47,37].…”

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confidence: 99%

The potential impact of a prophylactic vaccine for Ebola in Sierra Leone

Bodine

Cook

Shorten

2017

MBE

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The 2014 outbreak of Ebola virus disease (EVD) in West Africa was multinational and of an unprecedented scale primarily affecting the countries of Guinea, Liberia, and Sierra Leone. One of the qualities that makes EVD of high public concern is its potential for extremely high mortality rates (up to 90%). A prophylactic vaccine for ebolavirus (rVSV-ZEBOV) has been developed, and clinical trials show near-perfect efficacy. We have developed an ordinary differential equations model that simulates an EVD epidemic and takes into account (1) transmission through contact with infectious EVD individuals and deceased EVD bodies, (2) the heterogeneity of the risk of becoming infected with EVD, and (3) the increased survival rate of infected EVD patients due to greater access to trained healthcare providers. Using fitted parameter values that closely simulate the dynamics of the 2014 outbreak in Sierra Leone, we utilize our model to predict the potential impact of a prophylactic vaccine for the ebolavirus using various vaccination strategies including ring vaccination. Our results show that an rVSV-ZEBOV vaccination coverage as low as 40% in the general population and 95% in healthcare workers will prevent another catastrophic outbreak like the 2014 outbreak from occurring.

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confidence: 99%

The potential impact of a prophylactic vaccine for Ebola in Sierra Leone

Bodine

Cook

Shorten

2017

MBE

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“…and how can the outbreak be mitigated with certain intervention approaches [58,7]. Answering those questions requires the use of assumptive parameters as well as actual outbreak data [7,58,26,15]. Our results showed that using information on within-host infection dynamics allows the identification of those key characteristics in the disease transmission.…”

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confidence: 89%

“…http://dx.doi.org/10.1101/133421 doi: bioRxiv preprint first posted online Nguyen et al The interplays between within-host infection and between hosts transmission led to arising attempts connecting the two levels [14,21,22,12,23,24,25,4], but the approach is still at its infancy [13]. On one hand, most of these models were conceptual and theoretical [13] or rely on assumptive and previously obtained parameter estimates [26,15,27]. We propose that this limitation can be overcome by using explicitly within-host infection model.…”

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High-Resolution Epidemic Simulation Using Within-Host Infection and Contact Data

Nguyen

Mikolajczyk

Hernandez‐Vargas

2017

Preprint

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Background: Transmission in epidemics of infectious diseases is characterized by a high level of subject-specific elements. These include heterogeneous infection conditions, time-dependent transmission potential, and age-dependent contact structure. These insights are often lost in epidemic models using population data. Here we submit an approach that can capture these details, paving the way for studying epidemics in a more mechanistic and realistic way. Methods: Using experimental data, we formulated mathematical models of a pathogen infection dynamics from which we can simulate its transmission potential mechanistically. The models were then embedded in our implement of an age-specific contact network structure that allows to express all elements relevant to the transmission process. This approach is illustrated here with an example of Ebola virus (EBOV). Results:The results showed that within-host infection dynamics can capture EBOV's transmission parameters as good as approaches using population data. Population age-structure, contact distribution and patterns can also be captured with our network generating algorithm. This framework opens vast opportunities for the investigations of each element involved in the epidemic process. Here, estimating EBOV's reproduction number revealed a heterogeneous pattern among age-groups, prompting questions on current estimates which are not adjusted for this factor. Assessments of mass vaccination strategies showed that a time window from five months before to one week after the start of an epidemic appeared to be effective. Noticeably, compared to a non-intervention scenario, a low vaccination coverage of 33% could reduce number of cases by ten to hundred times as well as lessen the case-fatality rate. Conclusions: This is the first effort coupling directly within-host infection model into an age-structured epidemic network model, adding more realistic elements in simulating epidemic processes. Experimental data at the within-host infection are shown able to capture upfront key parameters of a pathogen; the applications of this approach will give us more time to prepare for potential epidemics. Population of interest in epidemic assessments could be modeled with an age-specific contact network without exhaustive amount of data. Further assessments and adaptations for different pathogens and scenarios are underway to explore multilevel aspects in infectious diseases epidemics.Keywords: high-resolution; epidemic; simulation; within-host infection; age-structure; contact network CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/133421 doi: bioRxiv preprint first posted online Nguyen et al. Page 2 of 22 BackgroundEpidemics of infectious diseases are listed among the potential catastrophes and can be potentially misused as mass destruction weapons [1]. Overwhelming research efforts have been developed to early predict the danger ...

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“…The quantity in equation (1), and particularly the version in which I(0) = 1, is used extensively in the epidemiological modelling literature [8,9,21,[26][27][28][29][30][31][32][33][34][35]. It is increasingly used in real-time during emerging outbreaks.…”

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confidence: 99%

Will an outbreak exceed available resources for control? Estimating the risk from invading pathogens using practical definitions of a severe epidemic

Thompson

Gilligan

Cunniffe

2019

Preprint

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14Forecasting whether or not initial reports of disease will be followed by a major epidemic 15 is an important component of disease management. Most estimates of the probability of 16 a major epidemic involve assuming that infections occur according to a branching 17 process. Surprisingly, however, these calculations can be carried out without the factors 18 that differentiate a major epidemic from a minor outbreak ever being defined precisely. 19We assess potential implications of this lack of explicitness by considering the differences 20 between three practically relevant possible definitions of a major epidemic. Specifically, 21we consider a major epidemic as an outbreak in which: i) a large number of hosts are 22 infected simultaneously; ii) a large number of infections occur in total; and iii) disease 23 persists in the population for a long time. We calculate the probability of a major epidemic 24 under each of these definitions, initially considering the commonly used stochastic 25Susceptible-Infected-Susceptible model as a tractable case study allowing analytical 26 progress. We show how the probability of a major epidemic can either be similar to, or 27 very different from, the branching process estimate, depending on which definition of a 28 major epidemic is used. We also show, using other models, that this ambiguity continues 29 to hold in different epidemiological settings. In particular, we consider two additional 30 models: the stochastic Susceptible-Infected-Removed model, and a more complex host-31 vector model parameterised for Zika virus. Our work highlights how the precise definition 32 of a major epidemic must be considered carefully when estimating the risk posed by a 33 new outbreak. It also demonstrates that the precise definition of a major epidemic must 34 be tailored to the epidemiology of the host-pathogen system under consideration. 35 36 KEYWORDS 37 mathematical modelling; infectious disease epidemiology; major epidemic; forecasting 38 39 SHORT TITLE 40 Assessing the practically relevant risk of a major epidemic 41 42 AUTHOR SUMMARY 43When cases of disease are first reported in a new region or country, policy-makers must 44 decide if a disease control programme should be rolled out, as well as how to deploy 45 limited resources. Central to this assessment is understanding the risk of initial cases 46 generating a major epidemic as opposed to fading out as a minor outbreak in which only 47

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Containing Ebola at the Source with Ring Vaccination

Cited by 64 publications

References 21 publications

The potential impact of a prophylactic vaccine for Ebola in Sierra Leone

The potential impact of a prophylactic vaccine for Ebola in Sierra Leone

High-Resolution Epidemic Simulation Using Within-Host Infection and Contact Data

Will an outbreak exceed available resources for control? Estimating the risk from invading pathogens using practical definitions of a severe epidemic

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