A systematic review of early modelling studies of Ebola virus disease in West Africa
| Autor: | Zoie Shui-Yee Wong, Chandini Raina MacIntyre, Abrar Ahmad Chughtai, Chau Minh Bui |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
medicine.medical_specialty Ebola virus Epidemiology Scopus medicine.disease_cause Sierra leone 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Infectious Diseases Geography Pandemic Case fatality rate medicine Econometrics 030212 general & internal medicine Basic reproduction number Serial interval |
| Zdroj: | Epidemiology and Infection. 145:1069-1094 |
| ISSN: | 1469-4409 0950-2688 |
| DOI: | 10.1017/s0950268817000164 |
| Popis: | 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 (R0), serial interval, latent period, infectious period and case fatality rate, and examined their relationships. The median of the estimated mean R0 values were between 1·30 and 1·84 in Sierra Leone, Liberia and Guinea. Much higher R0 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 R0 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 R0 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. |
| Databáze: | OpenAIRE |
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