Factors that trigger human infection with animal influenza virus progressing into a pandemic are poorly understood. Within a project developing an evidence-based risk assessment framework for influenza viruses in animals, we conducted a review of the literature for evidence of human infection with animal influenza viruses by diagnostic methods used. The review covering Medline, Embase, SciSearch and CabAbstracts yielded 6,955 articles, of which we retained 89; for influenza A(H5N1) and A(H7N9), the official case counts of the World Health Organization were used. An additional 30 studies were included by scanning the reference lists. Here, we present the findings for confirmed infections with virological evidence. We found reports of 1,419 naturally infected human cases, of which 648 were associated with avian influenza virus (AIV) A(H5N1), 375 with other AIV subtypes, and 396 with swine influenza virus (SIV). Human cases naturally infected with AIV spanned haemagglutinin subtypes H5, H6, H7, H9 and H10. SIV cases were associated with endemic SIV of H1 and H3 subtype descending from North American and Eurasian SIV lineages and various reassortants thereof. Direct exposure to birds or swine was the most likely source of infection for the cases with available information on exposure.

BackgroundMiddle East respiratory syndrome coronavirus (MERS-CoV) is endemic in dromedary camels in the Arabian Peninsula, and zoonotic transmission to people is a sporadic event. In the absence of epidemiological data on the reservoir species, patterns of zoonotic transmission have largely been approximated from primary human cases. This study aimed to identify meteorological factors that may increase the risk of primary MERS infections in humans.MethodsA case-crossover design was used to identify associations between primary MERS cases and preceding weather conditions within the 2-week incubation period in Saudi Arabia using univariable conditional logistic regression. Cases with symptom onset between January 2015 – December 2017 were obtained from a publicly available line list of human MERS cases maintained by the World Health Organization. The complete case dataset (N = 1191) was reduced to approximate the cases most likely to represent spillover transmission from camels (N = 446). Data from meteorological stations closest to the largest city in each province were used to calculate the daily mean, minimum, and maximum temperature (οC), relative humidity (%), wind speed (m/s), and visibility (m). Weather variables were categorized according to strata; temperature and humidity into tertiles, and visibility and wind speed into halves.ResultsLowest temperature (Odds Ratio = 1.27; 95% Confidence Interval = 1.04–1.56) and humidity (OR = 1.35; 95% CI = 1.10–1.65) were associated with increased cases 8–10 days later. High visibility was associated with an increased number of cases 7 days later (OR = 1.26; 95% CI = 1.01–1.57), while wind speed also showed statistically significant associations with cases 5–6 days later.ConclusionsResults suggest that primary MERS human cases in Saudi Arabia are more likely to occur when conditions are relatively cold and dry. This is similar to seasonal patterns that have been described for other respiratory diseases in temperate climates. It was hypothesized that low visibility would be positively associated with primary cases of MERS, however the opposite relationship was seen. This may reflect behavioural changes in different weather conditions. This analysis provides key initial evidence of an environmental component contributing to the development of primary MERS-CoV infections.

Global disease suitability models are essential tools to inform surveillance systems and enable early detection. We present the first global suitability model of highly pathogenic avian influenza (HPAI) H5N1 and demonstrate that reliable predictions can be obtained at global scale. Best predictions are obtained using spatial predictor variables describing host distributions, rather than land use or eco-climatic spatial predictor variables, with a strong association with domestic duck and extensively raised chicken densities. Our results also support a more systematic use of spatial cross-validation in large-scale disease suitability modelling compared to standard random cross-validation that can lead to unreliable measure of extrapolation accuracy. A global suitability model of the H5 clade 2.3.4.4 viruses, a group of viruses that recently spread extensively in Asia and the US, shows in comparison a lower spatial extrapolation capacity than the HPAI H5N1 models, with a stronger association with intensively raised chicken densities and anthropogenic factors.DOI: http://dx.doi.org/10.7554/eLife.19571.001

Over the years, the emergence of novel H5 and H7 highly pathogenic avian influenza viruses (HPAI) has been taking place through two main mechanisms: first, the conversion of a low pathogenic into a highly pathogenic virus, and second, the reassortment between different genetic segments of low and highly pathogenic viruses already in circulation. We investigated and summarized the literature on emerging HPAI H5 and H7 viruses with the aim of building a spatio-temporal database of all these recorded conversions and reassortments events. We subsequently mapped the spatio-temporal distribution of known emergence events, as well as the species and production systems that they were associated with, the aim being to establish their main characteristics. From 1959 onwards, we identified a total of 39 independent H7 and H5 LPAI to HPAI conversion events. All but two of these events were reported in commercial poultry production systems, and a majority of these events took place in high-income countries. In contrast, a total of 127 reassortments have been reported from 1983 to 2015, which predominantly took place in countries with poultry production systems transitioning from backyard to intensive production systems. Those systems are characterized by several co-circulating viruses, multiple host species, regular contact points in live bird markets, limited biosecurity within value chains, and frequent vaccination campaigns that impose selection pressures for emergence of novel reassortants. We conclude that novel HPAI emergences by these two mechanisms occur in different ecological niches, with different viral, environmental and host associated factors, which has implications in early detection and management and mitigation of the risk of emergence of novel HPAI viruses.