Transmission Dynamics and Final Epidemic Size of Ebola Virus Disease Outbreaks with Varying Interventions

Barbarossa, Maria Vittoria; Dénes, Attila; Kiss, Gábor Dávid; Nakata, Yukihiko; Röst, Gergely; Vizi, Zsolt

doi:10.1371/journal.pone.0131398

Cited by 64 publications

(66 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, past modeling efforts have incorporated mechanisms to account for slower than exponential growth patterns including models that gradually mitigate the transmission rate over time [20-23] or incorporate phenomenological parameters to capture non-homogeneous population mixing [24,25] and models with particular spatial network structures [26-34]. Designing mathematical models and statistical approaches that more flexibly capture the profile of epidemic growth could lead to enhanced model fit, improved estimates of key transmission parameters, and more realistic epidemic forecasts [12].…”

Section: Description Of Early Epidemic Growth Profiles Using Phenomentioning

confidence: 99%

Mathematical models to characterize early epidemic growth: A review

Chowell

Sattenspiel

Bansal

et al. 2016

Physics of Life Reviews

403

393

View full text Add to dashboard Cite

There is a long tradition of using mathematical models to generate insights into the transmission dynamics of infectious diseases and assess the potential impact of different intervention strategies. The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing reliable models that capture the baseline transmission characteristics of specific pathogens and social contexts. More refined models are needed however, in particular to account for variation in the early growth dynamics of real epidemics and to gain a better understanding of the mechanisms at play. Here, we review recent progress on modeling and characterizing early epidemic growth patterns from infectious disease outbreak data, and survey the types of mathematical formulations that are most useful for capturing a diversity of early epidemic growth profiles, ranging from sub-exponential to exponential growth dynamics. Specifically, we review mathematical models that incorporate spatial details or realistic population mixing structures, including meta-population models, individual-based network models, and simple SIR-type models that incorporate the effects of reactive behavior changes or inhomogeneous mixing. In this process, we also analyze simulation data stemming from detailed large-scale agent-based models previously designed and calibrated to study how realistic social networks and disease transmission characteristics shape early epidemic growth patterns, general transmission dynamics, and control of international disease emergencies such as the 2009 A/H1N1 influenza pandemic and the 2014-15 Ebola epidemic in West Africa.

show abstract

Section: Description Of Early Epidemic Growth Profiles Using Phenomentioning

confidence: 99%

Mathematical models to characterize early epidemic growth: A review

Chowell

Sattenspiel

Bansal

et al. 2016

Physics of Life Reviews

403

393

View full text Add to dashboard Cite

show abstract

“…parameters defining the transition between infection stages) will be required and calibrated. For example, Barbarossa et al [28] created a model with seven compartments (including hospitalised and buried) and estimated state parameters based on the outcomes of a range of earlier EVD modelling studies [24,[29][30][31]. It is noted that model parameters should be estimated with caution as they are prone to biases, and the intended prediction outcomes can be The nature of disease spread relationship is compartmentalised by observed biological processes that are thought to have given rise to the data.…”

Section: Overview Of Evd Modelling Studiesmentioning

confidence: 99%

A systematic review of early modelling studies of Ebola virus disease in West Africa

et al. 2017

View full text Add to dashboard Cite

SUMMARYPhenomenological and mechanistic models are widely used to assist resource planning for pandemics and emerging infections. We conducted a systematic review, to compare methods and outputs of published phenomenological and mechanistic modelling studies pertaining to the 2013-2016 Ebola virus disease (EVD) epidemics in four West African countries -Sierra Leone, Liberia, Guinea and Nigeria. We searched Pubmed, Embase and Scopus databases for relevant English language publications up to December 2015. Of the 874 articles identified, 41 met our inclusion criteria. We evaluated these selected studies based on: the sources of the case data used, and modelling approaches, compartments used, population mixing assumptions, model fitting and calibration approaches, sensitivity analysis used and data bias considerations. We synthesised results of the estimated epidemiological parameters: basic reproductive number (R 0 ), serial interval, latent period, infectious period and case fatality rate, and examined their relationships. The median of the estimated mean R 0 values were between 1·30 and 1·84 in Sierra Leone, Liberia and Guinea. Much higher R 0 value of 9·01 was described for Nigeria. We investigated several issues with uncertainty around EVD modes of transmission, and unknown observation biases from early reported case data. We found that epidemic models offered R 0 mean estimates which are country-specific, but these estimates are not associating with the use of several key disease parameters within the plausible ranges. We find simple models generally yielded similar estimates of R 0 compared with more complex models. Models that accounted for data uncertainty issues have offered a higher case forecast compared with actual case observation. Simple model which offers transparency to public health policy makers could play a critical role for advising rapid policy decisions under an epidemic emergency.

show abstract

“…This portion of the dynamics inclused features of particular interest, such as the early turnover and re-ignition of the epidemic in Guinea, which non-spatial compartmental models were previously unable to explain. However, further work to consider the late dynamics after intervention would be of interest—such work would also need to consider the significant changes in parameters over time due to the massive scale up of intervention efforts and ongoing behavior change [32, 46–48]. …”

Section: Discussionmentioning

confidence: 99%

Modeling spatial invasion of Ebola in West Africa

D'Silva

Eisenberg

2017

Journal of Theoretical Biology

View full text Add to dashboard Cite

The 2014–2015 Ebola Virus Disease (EVD) epidemic in West Africa was the largest ever recorded, representing a fundamental shift in Ebola epidemiology with unprecedented spatiotemporal complexity. To understand the spatiotemporal dynamics of EVD in West Africa, we developed spatial transmission models using a gravity-model framework at both the national and district-level scales, which we used to compare effectiveness of local interventions (e.g. local quarantine) and long-range interventions (e.g. border-closures). The country-level gravity model captures the epidemic data, including multiple waves of initial epidemic growth observed in Guinea. We found that local-transmission reductions were most effective in Liberia, while long-range transmission was dominant in Sierra Leone. Both models illustrated that interventions in one region result in an amplified protective effect on other regions by preventing spatial transmission. In the district-level model, interventions in the strongest of these amplifying regions reduced total cases in all three countries by over 20%, in spite of the region itself generating only ~ 0.1% of total cases. This model structure and associated intervention analysis provide information that can be used by public health policymakers to assist planning and response efforts for future epidemics.

show abstract

Transmission Dynamics and Final Epidemic Size of Ebola Virus Disease Outbreaks with Varying Interventions

Cited by 64 publications

References 24 publications

Mathematical models to characterize early epidemic growth: A review

Mathematical models to characterize early epidemic growth: A review

A systematic review of early modelling studies of Ebola virus disease in West Africa

Modeling spatial invasion of Ebola in West Africa

Contact Info

Product

Resources

About