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

Abstract

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.

Real-time projections of Ebola outbreak size and duration with and without vaccine use in Équateur, Democratic Republic of Congo, as of May 27, 2018

“…While real-time modeling projections historically overestimate outbreak size and duration, 47,48 this study exemplifies how simple mathematical models can be useful for advising real-time decision making because they provide rapid projections and similar estimates of R as compared to complex models. 49 Vaccine use, regardless of 62% or 44% coverage levels, is projected to provide a limited preventive benefit, which may be partially due to timing of implementation (after predicted peak of the epidemic curve). As such, the massive mobilization of resources to roll it out should not detract from the prioritization of patient and survivor care, lest we recapitulate the control-overcare paradigm that marred the response to the 2013-2016 outbreak in West Africa.…”

Automated monitoring of tweets for early detection of the 2014 Ebola epidemic

“…Some studies did not specify their methods or details relating to data extraction and mining and extent of human moderation [8,10,13]. No literature to our knowledge has yet analysed the utility of symptom data from tweets for the detection of EVD [14]. Thus, the aim of our study is to evaluate the value of symptomatic tweets for rapid infectious disease surveillance of the 2014 Ebola epidemic in West Africa, using automated monitoring.…”

The risk of selective investment in downstream pandemic planning

“…The epidemic threshold is defined as R equals 1, and any infection with an R0>1 has epidemic potential. Ebola, for example, was estimated to have a R0 of about 2 during the 2014 West African epidemic (8). Diseases such as smallpox and Ebola are epidemic in nature and a delay in response of even weeks can be critical because of the exponential growth in case numbers.…”