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

Phenomenological 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.

Quantified degree of poultry exposure differs for human cases of avian influenza H5N1 and H7N9.

Preliminary evidence suggests that direct poultry contact may play a lesser role in transmission of avian influenza A(H7N9) than A(H5N1) to humans. To better understand differences in risk factors, we quantified the degree of poultry contact reported by H5N1 and H7N9 World Health Organization-confirmed cases. We used publicly available data to classify cases by their degree of poultry contact, including direct and indirect. To account for potential data limitations, we used two methods: (1) case population method in which all cases were classified using a range of sources; and (2) case subset method in which only cases with detailed contact information from published research literature were classified. In the case population, detailed exposure information was unavailable for a large proportion of cases (H5N1, 54%; H7N9, 86%). In the case subset, direct contact proportions were higher in H5N1 cases (70·3%) than H7N9 cases (40·0%) (χ 2 = 18·5, P < 0·001), and indirect contact proportions were higher in H7N9 cases (44·6%) than H5N1 cases (19·4%) (χ 2 = 15·5, P < 0·001). Together with emerging evidence, our descriptive analysis suggests direct poultry contact is a clearer risk factor for H5N1 than for H7N9, and that other risk factors should also be considered for H7N9.

Phylogeography of H5N1 avian influenza virus in Indonesia.

Highly pathogenic avian influenza (HPAI) viruses of the H5N1 subtype are a major concern to human and animal health in Indonesia. This study aimed to characterize transmission dynamics of H5N1 over time using novel Bayesian phylogeography methods to identify factors which have influenced the spread of H5N1 in Indonesia. We used publicly available hemagglutinin sequence data sampled between 2003 and 2016 to model ancestral state reconstruction of HPAI H5N1 evolution. We found strong support for H5N1 transmission routes between provinces in Java Island and inter-island transmissions, such as between Nusa Tenggara and Kalimantan Islands, not previously described. The spread is consistent with wild bird flyways and poultry trading routes. H5N1 migration was associated with the regions of high chicken densities and low human development indices. These results can be used to inform more targeted planning of H5N1 control and prevention activities in Indonesia.