SummaryThe aim of this work was to explore the comparative epidemiology of influenza viruses, H5N1 and H7N9, in both bird and human populations. Specifically, the article examines similarities and differences between the two viruses in their genetic characteristics, distribution patterns in human and bird populations and postulated mechanisms of global spread. In summary, H5N1 is pathogenic in birds, while H7N9 is not. Yet both have caused sporadic human cases, without evidence of sustained, human-to-human spread. The number of H7N9 human cases in the first year following its emergence far exceeded that of H5N1 over the same time frame. Despite the higher incidence of H7N9, the spatial distribution of H5N1 within a comparable time frame is considerably greater than that of H7N9, both within China and globally. The pattern of spread of H5N1 in humans and birds around the world is consistent with spread through wild bird migration and poultry trade activities. In contrast, human cases of H7N9 and isolations of H7N9 in birds and the environment have largely occurred in a number of contiguous provinces in south-eastern China. Although rates of contact with birds appear to be similar in H5N1 and H7N9 cases, there is a predominance of incidental contact reported for H7N9 as opposed to close, high-risk contact for H5N1. Despite the high number of human cases of H7N9 and the assumed transmission being from birds, the corresponding level of H7N9 virus in birds in surveillance studies has been low, particularly in poultry farms. H7N9 viruses are also diversifying at a much greater rate than H5N1 viruses. Analyses of certain H7N9 strains demonstrate similarities with engineered transmissible H5N1 viruses which make it more adaptable to the human respiratory tract. These differences in the human and bird epidemiology of H5N1 and H7N9 raise unanswered questions as to how H7N9 has spread, which should be investigated further.

In recent years multiple novel influenza A strains have emerged in humans. We reviewed publically available data to summarise epidemiological characteristics of distinct avian influenza viruses known to cause human infection and describe changes over time. Most recently identified zoonotic strains have emerged in China (H7N9, H5N6, H10N8) – these strains have occurred mostly in association with visiting a live bird market. Most zoonotic AIVs and swine influenza variants typically cause mild infections in humans however severe illness and fatalities are associated with zoonotic H5N6, H10N8, H7N9 and H5N1 serotypes, and the H1N1 1918 Spanish Influenza. The changing landscape of avian influenza globally indicates a need to reassess the risk of a pandemic influenza outbreak of zoonotic origin.Electronic supplementary materialThe online version of this article (doi:10.1186/s13690-017-0182-z) contains supplementary material, which is available to authorized users.

Background In 2017, the New South Wales Cancer Registry (NSWCR) participated in a project, supported by Cancer Australia, aiming to provide national stage data for melanoma, prostate, colorectal, breast, and lung cancers diagnosed in 2011. Simplified business rules based on the American Joint Committee for Cancer (AJCC) Tumour-Node-Metastasis (TNM) stage were applied to obtain Registry-Derived (RD) stage, defined as the best estimate of TNM stage at diagnosis using routine notifications available within cancer registries. RD-stage was compared with Degree of Spread (DoS), which has been recorded for all applicable cancers in NSWCR at a population-based level since 1972, and a summary AJCC-TNM stage group, which has been collected variably since 2006. For each of the five high incidence cancers, we compared the level of improvements RD-staging provided in terms of completeness and accuracy (alignment to more clinically relevant AJCC-TNM) over DoS. Methods For each of the five cancers, stage data were extracted from NSWCR pre- and post- RD-staging to compare data completeness across all three staging systems. The alignment between DoS/RD-stage and AJCC-TNM was compared, as were the expected and observed cross-tabulated frequency distributions using a subset of NSWCR data. To determine differences between use of DoS, RD-stage, and AJCC-TNM in an epidemiological analysis, we compared survival models developed from each of the three stage variables. Results We found RD-staging provided greatest stage data completeness and alignment to AJCC-TNM for prostate cancers, followed by breast, then melanoma and lung cancers. For colorectal cancer, summary stage from DoS was confirmed as an equivalent surrogate staging system to both AJCC-TNM and RD-stage. Conclusions This analysis provides an evidence-based approach that can be used to inform decision-making for resource planning and potential implementation of a new stage data field in population-based cancer registries. Electronic supplementary material The online version of this article (10.1186/s12885-019-6062-x) contains supplementary material, which is available to authorized users.

Public health messaging about antimicrobial resistance (AMR) sometimes conveys the problem as an epidemic. We outline why AMR is a serious endemic problem manifested in hospital and community-acquired infections.AMR is not an epidemic condition, but may complicate epidemics, which are characterised by sudden societal impact due to rapid rise in cases over a short timescale. Influenza, which causes direct viral effects, or secondary bacterial complications is the most likely cause of an epidemic or pandemic where AMR may be a problem. We discuss other possible causes of a pandemic with AMR, and present a risk assessment formula to estimate the impact of AMR during a pandemic. Finally, we flag the potential impact of genetic engineering of pathogens on global risk and how this could radically change the epidemiology of AMR as we know it.Understanding the epidemiology of AMR is key to successfully addressing the problem. AMR is an endemic condition but can play a role in epidemics or pandemics, and we present a risk analysis method for assessing the impact of AMR in a pandemic.

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.