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Chinese medicine (CM) has been used to control infectious diseases for thousands of years. In 2003 outbreaks of severe acute respiratory syndrome (SARS) occurred in China, Hong Kong and Taiwan. In view of the possible beneficial effect of CM on SARS, we conducted this study to examine whether CM is of any benefit as a supplementary treatment of SARS. Four severe laboratory-confirmed SARS patients received routine western-medicine treatment plus different supplementary treatment: CM A, CM B and CM C (placebo control). We reported the course of the cases in terms of changes in chest radiographic scores. Case 1 treated as a placebo control passed away on the 9th day after onset of disease. The other three cases treated with CM A or CM B survived. The initial findings seemed to indicate a favorable effect of CM on management of SARS. The findings need to be verified with a larger sample. Using CM as a supplementary treatment of severe SARS seems to indicate that natural herbal medicine can be used against avian influenza. Hence, such related experience or clinical trials should be taken into consideration when facing the possible outbreak of avian influenza in the future.

The rapidly changing influenza virus has remained a consistent threat to the well-being of a variety of species on the planet. Influenza virus’ high mutation rate has allowed the virus to rapidly and continuously evolve, as well as generate new strains that are resistant to the current commercially available antivirals. Thus, the increased resistance has compelled the scientific community to explore alternative compounds that have antiviral effects against influenza virus. In this paper, the authors systematically review numerous herbal extracts that were shown to have antiviral effects against the virus. Specifically, the herbal antiviral targets mainly include hemagglutinin, neuraminidase and matrix 2 proteins. In some instances, herbal extracts inhibited the replication of oseltamivir-resistant strains and certain pentacyclic triterpenes exhibited higher antiviral activity than oseltamivir. This paper also explores the possibility of targeting various host-cell signaling pathways that are utilized by the virus during its replication process. Infected cell pathways are hijacked by intracellular signaling cascades such as NF-kB signaling, PI3K/Akt pathway, MAPK pathway and PKC/PKR signaling cascades. Herbal antivirals have been shown to target these pathways by suppressing nuclear export of influenza vRNP and thus inhibiting the phosphorylation signaling cascade. In conclusion, copious amounts of herbal antivirals have been shown to inhibit influenza virus, however further studies are needed for these new compounds to be up to modern pharmacological standards.

The number of cases of H5N1 avian influenza in birds and humans exhibit seasonality which peaks during the winter months. What causes the seasonality in H5N1 cases is still being investigated. This article addresses the question of modeling the periodicity in cumulative number of human cases of H5N1. Three potential drivers of influenza seasonality are investigated: (1) seasonality in bird-to-bird transmission; (2) seasonality caused by wild bird migration or seasonal fluctuation of avian influenza in wild birds; (3) seasonality caused by environmental transmission. A framework of seven models is composed. The seven models involve these three mechanisms and combinations of the mechanisms. Each of the models in the framework is fitted to the cumulative number of humans cases of H5N1. The corrected akaike information criterion (AICc) is used to compare the models and it is found that the model with periodic bird-to-bird transmission rate best explains the data. The best fitted model with the best fitted parameters gives a reproduction number of highly pathogenic avian influenza . The best fitted model is a simple SI epidemic model with periodic transmission rate and disease-induced mortality, however, this model is capable of very complex dynamical behavior such as period doubling and chaos.

At present, H5N1 avian influenza (AI) is a zoonotic disease where the transmission to humans occurs from infected domestic birds. Since 2003, more than 500 people have been infected and nearly 60% of them have died. If the H5N1 virus becomes efficiently human-to-human transmittable, a pandemic will occur with potentially high mortality. A mathematical model of AI, which involves human influenza, is introduced to better understand the complex epidemiology of AI and the emergence of a pandemic strain. Demographic and epidemiological data on birds and humans are used for the parameterization of the model. The differential equation system faithfully projects the cumulative number of H5N1 human cases and captures the dynamics of the yearly cases. The model is used to rank the efficacy of the current control measures used to prevent the emergence of a pandemic strain. We find that culling without re-population and vaccination are the two most efficient control measures each giving 22% decrease in the number of H5N1 infected humans for each 1% change in the affected parameters (μ_b, ν_b for culling and β_b, ν_b for vaccination). Control measures applied to humans, such as wearing protective gear, are not very efficient, giving less than 1% decrease in the number of H5N1 infected humans for each 1% decrease in β_Y, the bird-to-human transmission coefficient of H5N1. Furthermore, we find that should a pandemic strain emerge, it will invade, possibly displacing the human influenza virus in circulation at that time. Moreover, higher prevalence levels of human influenza will obstruct the invasion capabilities of the pandemic H5N1 strain. This effect is not very pronounced, as we find that 1% increase in human influenza prevalence will decrease the invasion capabilities of the pandemic strain with 0.006%.

Tackling the Virus: Patent Analysis Unravels Promising Technologies to Combat Avian Influenza.

The Impact of Sars on the Philippines Economy.

South Korean Scientists Develop Tailored Stem Cells to Treat Individuals.

New Antiviral Therapy for Avian Influenza.

MicroRNAs as Oncogenes?

Sharp's Plasmacluster Ions Effectively Deactivate H5N1 Avian Influenza Virus.

While there has been no pandemic outbreak of influenza evolving from the H5N1 strain yet, the virus has already killed people. This suggests that without any significant mutations the influenza virus can live within the human body for days in which its life cycles can continue. The first step for infection is the host cell surface binding which is the function of the glycoprotein hemagglutinin (HA). In this investigation, quantum chemical calculations were performed on the systems comprising four structures coming from parts of the HA, with its cell receptor-analog substrate, determined from X-ray structures of the 1934 Spanish flu and avian influenza antigens. The calculations are aimed at partitioning the system into several parts, thus obtaining global and partial contributions of binding energy from each of them. As a result, it was possible to propose quantitatively the main contributions of key amino residues of the avian influenza virus glycoprotein around the binding pocket relevant to the binding process.

The main binding energy contributions of the Spanish flu HA were from Tyr95, Val135, Thr136, Ala137, Glu190, Asp225, and Gln226, while the main contributions of the avian flu HA were from Ser129, Val131, Ser132, and Ser133. It was also found that the effect from the HA with an avian characteristic, Gln226 and Gly228, was not relatively high according to the contributed binding energy, whereas the effect from nearby water molecules was significant. Thus, it was concluded that both human and avian virus HA could recognize human cell receptors better than the avian cell receptors according to the binding energy. Therefore, the preference to any particular cell receptor types might involve some other factors rather than being determined solely by the HA binding process.

The time series prediction of avian influenza epidemics is a complex issue, because avian influenza has latent seasonality which is difficult to identify. Although researchers have applied a neural network (NN) model and the Box-Jenkins model for the seasonal epidemic series research area, the results are limited. In this study, we develop a new prediction seasonal auto-regressive-based support vector regression (SAR-SVR) model which combines the seasonal auto-regressive (SAR) model with a support vector regression (SVR) model to address this prediction problem to overcome existing limitations. Fast Fourier transformation is also merged into this method to identify the latent seasonality inside the time series. The experiments demonstrate that the developed SAR-SVR method out-performs SVR, Box-Jenkins models and two layer feed forward NN model-both in accuracy and stability in the avian influenza epidemic disease time series prediction.

H5N1 avian influenza outbreak detection is a significant issue for early warning of epidemics. This paper proposes domain knowledge-based joint one class classification model for avian influenza outbreak. Instead of focusing on manipulations of the one class classification model, we delve into the one class avian influenza dataset, divide it into sub-classes by domain knowledge, train the sub-class classifiers and unify the result of each classifier. The proposed joint method solves the one class classification and features selection problems together. The experiment results demonstrate that the proposed joint model definitely outperforms the normal one class classification model on the animal avian influenza dataset.

Avian influenza (AI) is a highly contagious and zoonotic viral disease that affects several animal species. It causes heavy economic losses in the domestic poultry. A quick response is always desired in the event of any disease outbreak. The principal approach to control a contagious disease involves the killing of diseased animals along with the bio containment of infectious agent. Mass depopulation of the infected birds plays an important role in the eradication of the disease. The possible strategies for mass depopulation include maceration, electrocution, cervical dislocation, gassing and foaming. All of these procedures are much intensive and time consuming because it involves a lot of man power, biosecurity risks, applicability for all house types and suitability for large-scale emergency implementation. The basic objectives of these strategies include (1) To reduce pain and suffering to the birds, (2) To minimize disease spread and (3) To ensure of protection to human operators from potential biohazards. A suitable depopulation technique can only be suggested keeping in view the species and type of bird involved, and differences in husbandry practices like management, housing and stocking density. Mass depopulation is an important tool to control the spread of any disease but the selection of procedure depends upon the prevailing circumstances. In this paper, various mass depopulation strategies and their selection in different conditions is reviewed and discussed.

Reverse transcription polymerase chain reaction (RT-PCR) was used routinely to detect the avian influenza virus (AIV) nucleoprotein (NP) gene. The purpose of the present study was to compare the correctness of a nested RT-PCR (nRT-PCR), one conventional RT-PCR with its outer primer (oRT-PCR) and the other conventional RT-PCR with its inner primer (iRT-PCR) to detect AIV NP gene. A total of 365 AI-free fecal swabs (73 pools), 7 tracheal swabs and anllantoic fluid from 25 chicken embryos were used to determine the analytic specificities of those tests. Compared with the iRT-PCR, the nRT-PCR was more sensitive for AIV detection. However, the specificities of nRT-PCR, oRT-PCR and iRT-PCR were 48.6% (35/72), 100% (67/67) and 91.3% (84/92), respectively. The amplifying band was sequenced and confirmed to be the AIV NP gene as the positive control. The specificity of this nRT-PCR is too low to be used for the AIV screening test.

The ability of accurate epidemic prediction facilitates early preparation for the disease and minimizes losses due to any strikes. We devised a platform on the Web for users to exchange their information/opinions on the possible avian flu outbreaks in Taiwan. The likelihood of the first human infection from bird flu in Taiwan in, say, December 2005 is securitized in the form of a futures contract. Incentives are introduced via a tournament: users trade the futures in the market on our Web server in order to win the awards at the end of the tournament. We ran such a tournament during the period between December 2005 and February 2006. The results of the futures' prices correctly predicted no outbreaks of bird flu among the residents in Taiwan during the 3-month period, suggesting that the design of the futures exchange on the Web be a potentially useful tool for event forecasting. Another crucial aspect of the experiment is that, associated with the price convergence, the transaction volume also quickly converges to zero, which is closely related to the famous no-trade theorem in theoretical economics.

Avian influenza, caused by influenza A viruses, has received worldwide attention over recent years. In this study, we formulate a mathematical model for avian influenza that includes human–human transmission and incorporates the effects of infection latency and treatments. We investigate the essential dynamics of the model through an equilibrium analysis. Meanwhile, we explore effective treatment strategies to control avian influenza outbreaks using optimal control theory. Our results show that strategically deployed medical treatments can significantly reduce the numbers of exposed and infection persons.

This paper presents the mathematical analysis of the dynamical system for avian influenza. The proposed model considers a nonlinear dynamical model of birds and human. The half-saturated incidence rate is used for the transmission of avian influenza infection. Rigorous mathematical results are presented for the proposed models. The local and global dynamics of each model are presented and proven that when ${{ℛ}}_0<1$, then the disease-free equilibrium of each model is stable both locally and globally, and when ${{ℛ}}_0>1$, then the endemic equilibrium is stable both locally and globally. The numerical results obtained for the proposed model shows that influenza could be eliminated from the community if the threshold is not greater than unity.