Emerging and re-emerging infectious diseases of zoonotic origin like highly pathogenic avian influenza pose a significant threat to human and animal health due to their elevated transmissibility. Identifying the drivers of such viruses is challenging, and estimation of spatial diffusion is complicated by the fact that the variability of viral spread from locations could be caused by a complex array of unknown factors. Several techniques exist to help identify these drivers, including bioinformatics, phylogeography, and spatial epidemiology, but these methods are generally evaluated separately and do not consider the complementary nature of each other. Here, we studied an approach that integrates these techniques and identifies the most important drivers of viral spread by focusing on H5N1 influenza A virus in Egypt because of its recent emergence as an epicenter for the disease. We used a Bayesian phylogeographic generalized linear model (GLM) to reconstruct spatiotemporal patterns of viral diffusion while simultaneously assessing the impact of factors contributing to transmission. We also calculated the cross-species transmission rates among hosts in order to identify the species driving transmission. The densities of both human and avian species were supported contributors, along with latitude, longitude, elevation, and several meteorological variables. Also supported was the presence of a genetic motif found near the hemagglutinin cleavage site. Various genetic, geographic, demographic, and environmental predictors each play a role in H1N1 diffusion. Further development and expansion of phylogeographic GLMs such as this will enable health agencies to identify variables that can curb virus diffusion and reduce morbidity and mortality.

Background: Time series models can play an important role in disease prediction. Incidence data can be used to predict the future occurrence of disease events. Developments in modeling approaches provide an opportunity to compare different time series models for predictive power.

Results: We applied ARIMA and Random Forest time series models to incidence data of outbreaks of highly pathogenic avian influenza (H5N1) in Egypt, available through the online EMPRES-I system. We found that the Random Forest model outperformed the ARIMA model in predictive ability. Furthermore, we found that the Random Forest model is effective for predicting outbreaks of H5N1 in Egypt.

Conclusions: Random Forest time series modeling provides enhanced predictive ability over existing time series models for the prediction of infectious disease outbreaks. This result, along with those showing the concordance between bird and human outbreaks (Rabinowitz et al. 2012), provides a new approach to predicting these dangerous outbreaks in bird populations based on existing, freely available data. Our analysis uncovers the time-series structure of outbreak severity for highly pathogenic avian influenza (H5N1) in Egypt.

Background: Influenza A H5N1 has killed millions of birds and raises serious public health concern because of its potential to spread to humans and cause a global pandemic. While the early focus was in Asia, recent evidence suggests that Egypt is a new epicenter for the disease. This includes characterization of a variant clade 2.2.1.1, which has been found almost exclusively in Egypt.
We analyzed 226 HA and 92 NA sequences with an emphasis on the H5N1 2.2.1.1 strains in Egypt using a Bayesian discrete phylogeography approach. This allowed modeling of virus dispersion between Egyptian governorates including the most likely origin.

Results: Phylogeography models of hemagglutinin (HA) and neuraminidase (NA) suggest Ash Sharqiyah as the origin of virus spread, however the support is weak based on Kullback–Leibler values of 0.09 for HA and 0.01 for NA. Association Index (AI) values and Parsimony Scores (PS) were significant (p-value < 0.05), indicating that dispersion of H5N1 in Egypt was geographically structured. In addition, the Ash Sharqiyah to Al Gharbiyah and Al Fayyum to Al Qalyubiyah routes had the strongest statistical support.

Conclusion: We found that the majority of routes with strong statistical support were in the heavily populated Delta region. In particular, the Al Qalyubiyah governorate appears to represent a popular location for virus transition as it represented a large portion of branches in both trees. However, there remains uncertainty about virus dispersion to and from this location and thus more research needs to be conducted in order to examine this. Phylogeography can highlight the drivers of H5N1 emergence and spread. This knowledge can be used to target public health efforts to reduce morbidity and mortality. For Egypt, future work should focus on using data about vaccination and live bird markets in phylogeography models to study their impact on H5N1 diffusion within the country.

Background: The majority of emerging infectious diseases are zoonotic (transmissible between animals and humans) in origin, and therefore integrated surveillance of disease events in humans and animals has been recommended to support effective global response to disease emergence. While in the past decade there has been extensive global surveillance for highly pathogenic avian influenza (HPAI) infection in both animals and humans, there have been few attempts to compare these data streams and evaluate the utility of such integration.

Methodology: We compared reports of bird outbreaks of HPAI H5N1 in Egypt for 2006–2011 compiled by the World Organization for Animal Health (OIE) and the UN Food and Agriculture Organization (FAO) EMPRESi reporting system with confirmed human H5N1 cases reported to the World Health Organization (WHO) for Egypt during the same time period.

Principal Findings: Both human cases and bird outbreaks showed a cyclic pattern for the country as a whole, and there was a statistically significant temporal correlation between the data streams. At the governorate level, the first outbreak in birds in a season usually but not always preceded the first human case, and the time lag between events varied widely, suggesting regional differences in zoonotic risk and/or surveillance effectiveness. In a multivariate risk model, lower temperature, lower urbanization, higher poultry density, and the recent occurrence of a bird outbreak were associated with increased risk of a human case of HPAI in the same governorate, although the positive predictive value of a bird outbreak was low.

Conclusions: Integrating data streams of surveillance for human and animal cases of zoonotic disease holds promise for better prediction of disease risk and identification of environmental and regional factors that can affect risk. Such efforts can also point out gaps in human and animal surveillance systems and generate hypotheses regarding disease transmission.

Ancestral state reconstructions in Bayesian phylogeography of virus pandemics have been improved by utilizing a Bayesian stochastic search variable selection (BSSVS) framework. Recently, this framework has been extended to model the transition rate matrix between discrete states as a generalized linear model (GLM) of genetic, geographic, demographic, and environmental predictors of interest to the virus and incorporating BSSVS to estimate the posterior inclusion probabilities of each predictor. Although the latter appears to enhance the biological validity of ancestral state reconstruction, there has yet to be a comparison of phylogenies created by the two methods.

In this paper, we compare these two methods, while also using a primitive method without BSSVS, and highlight the differences in phylogenies created by each. We test six coalescent priors and six random sequence samples of H3N2 influenza during the 2014–15 flu season in the U.S. We show that the GLMs yield significantly greater root state posterior probabilities than the two alternative methods under five of the six priors, and significantly greater Kullback-Leibler divergence values than the two alternative methods under all priors. Furthermore, the GLMs strongly implicate temperature and precipitation as driving forces of this flu season and nearly unanimously identified a single root state, which exhibits the most tropical climate during a typical flu season in the U.S.

The GLM, however, appears to be highly susceptible to sampling bias compared with the other methods, which casts doubt on whether its reconstructions should be favored over those created by alternate methods. We report that a BSSVS approach with a Poisson prior demonstrates less bias toward sample size under certain conditions than the GLMs or primitive models, and believe that the connection between reconstruction method and sampling bias warrants further investigation.

A key factor in the effectiveness of the seasonal influenza vaccine is its immunological compatibility with the circulating viruses during the season. Here we propose a new bioinformatics approach for analysis of influenza viruses which could be used as an efficient tool for selection of vaccine viruses, assessment of the effectiveness of seasonal influenza vaccines, and prediction of the epidemic/pandemic potential of novel influenza viruses.

The worst Ebola virus (EV) outbreak in history has hit Liberia, Sierra Leone and Guinea hardest and the trend lines in this crisis are grave, and now represents a global public health threat concern. Limited therapeutic and/or prophylactic options are available for people suffering from Ebola virus disease (EVD) and further complicate the situation. Previous studies suggested that the EV glycoprotein (GP) is the main determinant causing structural damage of endothelial cells that triggers the hemorrhagic diathesis, but molecular mechanisms underlying this phenomenon remains elusive. Using the informational spectrum method (ISM), a virtual spectroscopy method for analysis of the protein-protein interactions, the interaction of GP with endothelial extracellular matrix (ECM) was investigated. Presented results of this in silico study suggest that Elastin Microfibril Interface Located Proteins (EMILINs) are involved in interaction between GP and ECM. This finding could contribute to a better understanding of EV/endothelium interaction and its role in pathogenesis, prevention and therapy of EVD.