Influenza A H5N1 and H7N9 in China: A spatial risk analysis

Designing a multi-epitope vaccine to provoke the robust immune response against influenza A H7N9

A new strain of Influenza A Virus (IAV), so-called "H7N9 Avian Influenza", is the first strain of this virus in which a human is infected by transmitting the N9 of influenza virus. Although continuous human-to-human transmission has not been reported, the occurrence of various H7N9-associated epidemics and the lack of production of strong antibodies against H7N9 in humans warn of the potential for H7N9 to become a new pandemic. Therefore, the need for effective vaccination against H7N9 as a life-threatening viral pathogen has become a major concern. The current study reports the design of a multi-epitope vaccine against Hemagglutinin (HA) and Neuraminidase (NA) proteins of H7N9 Influenza A virus by prediction of Cytotoxic T lymphocyte (CTL), Helper T lymphocyte (HTL), IFN-γ and B-cell epitopes. Human β-defensin-3 (HβD-3) and pan HLA DR-binding epitope (PADRE) sequence were considered as adjuvant. EAAAK, AAY, GPGPG, HEYGAEALERAG, KK and RVRR linkers were used as a connector for epitopes. The final construct contained 777 amino acids that are expected to be a recombinant protein of about ~ 86.38 kDa with antigenic and non-allergenic properties after expression. Modeled protein analysis based on the tertiary structure validation, docking studies, and molecular dynamics simulations results like Root-mean-square deviation (RMSD), Gyration, Root-mean-square fluctuation (RMSF) and Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) showed that this protein has a stable construct and capable of being in interaction with Toll-like receptor 7 (TLR7), TLR8 and m826 antibody. Analysis of the obtained data the demonstrates that suggested vaccine has the potential to induce the immune response by stimulating T and Bcells, and may be utilizable for prevention purposes against Avian Influenza A (H7N9).

A framework for the risk prediction of avian influenza occurrence: An Indonesian case study

Avian influenza viruses can cause economically devastating diseases in poultry and have the potential for zoonotic transmission. To mitigate the consequences of avian influenza, disease prediction systems have become increasingly important. In this study, we have proposed a framework for the prediction of the occurrence and spread of avian influenza events in a geographical area. The application of the proposed framework was examined in an Indonesian case study. An extensive list of historical data sources containing disease predictors and target variables was used to build spatiotemporal and transactional datasets. To combine disparate sources, data rows were scaled to a temporal scale of 1-week and a spatial scale of 1-degree × 1-degree cells. Given the constructed datasets, underlying patterns in the form of rules explaining the risk of occurrence and spread of avian influenza were discovered. The created rules were combined and ordered based on their importance and then stored in a knowledge base. The results suggested that the proposed framework could act as a tool to gain a broad understanding of the drivers of avian influenza epidemics and may facilitate the prediction of future disease events.

Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)

OBJECTIVES

Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries with more than 432,902 recorded deaths and 7,898,442 confirmed cases worldwide so far (on June 14, 2020). It is crucial to investigate the spatial drivers to prevent and control the epidemic of COVID-19.

METHODS

This is the first comprehensive study of COVID-19 in Iran; and it carries out spatial modeling, risk mapping, change detection, and outbreak trend analysis of the disease spread. Four main steps were taken: comparison of Iranian coronavirus data with the global trends, prediction of mortality trends using regression modeling, spatial modeling, risk mapping, and change detection using the random forest (RF) machine learning technique (MLT), and validation of the modeled risk map.

RESULTS

The results show that from February 19 to June 14, 2020, the average growth rates (GR) of COVID-19 deaths and the total number of COVID-19 cases in Iran were 1.08 and 1.10, respectively. Based on the World Health Organisation (WHO) data, Iran's fatality rate (deaths/0.1M pop) is 10.53. Other countries' fatality rates were, for comparison, Belgium - 83.32, UK - 61.39, Spain - 58.04, Italy - 56.73, Sweden - 48.28, France - 45.04, USA - 35.52, Canada - 21.49, Brazil - 20.10, Peru - 19.70, Chile - 16.20, Mexico- 12.80, and Germany - 10.58. The fatality rate for China is 0.32 (deaths/0.1M pop). Over time, the heatmap of the infected areas identified two critical time intervals for the COVID-19 outbreak in Iran. The provinces were classified in terms of disease and death rates into a large primary group and three provinces that had critical outbreaks were separate from the others. The heatmap of countries of the world shows that China and Italy were distinguished from other countries in terms of nine viral infection-related parameters. The regression models for death cases showed an increasing trend but with some evidence of turning. A polynomial relationship was identified between the coronavirus infection rate and the province population density. Also, a third-degree polynomial regression model for deaths showed an increasing trend recently, indicating that subsequent measures taken to cope with the outbreak have been insufficient and ineffective. The general trend of deaths in Iran is similar to the world's, but Iran's shows lower volatility. Change detection of COVID-19 risk maps with a random forest model for the period from March 11 to March 18 showed an increasing trend of COVID-19 in Iran's provinces. It is worth noting that using the LASSO MLT to evaluate variables' importance, indicated that the most important variables were the distance from bus stations, bakeries, hospitals, mosques, ATMs (automated teller machines), banks, and the minimum temperature of the coldest month.

CONCLUSIONS

We believe that this study's risk maps are the primary, fundamental step to take for managing and controlling COVID-19 in Iran and its provinces.

Emergence of human avian influenza A(H7N9) virus infections in Wenshan City in Southwest China, 2017

BACKGROUND

The emergence of human infection with avian influenza A(H7N9) virus was reported in Wenshan City, southwestern China in 2017. The study describes the epidemiological and virological features of the outbreak and discusses the origin of the infection.

METHODS

Poultry exposure and timelines of key events for each patient were collected. Samples derived from the patients, their close contacts, and environments were tested for influenza A(H7N9) virus by real-time reverse transcription polymerase chain reaction. Genetic sequencing and phylogenetic analysis were also conducted.

RESULTS

Five patients were reported in the outbreak. An epidemiological investigation showed that all patients had been exposed at live poultry markets. The A(H7N9) isolates from these patients had low pathogenicity in avian species. Both epidemiological investigations of chicken sources and phylogenetic analysis of viral gene sequences indicated that the source of infection was from Guangxi Province, which lies 100 km to the east of Wenshan City.

CONCLUSIONS

In the study, a sudden emergence of human cases of H7N9 was documented in urban area of Wenshan City. Chickens were an important carrier in the H7N9 virus spreading from Guangxi to Wenshan. Hygienic management of live poultry markets and virological screening of chickens transported across regions should be reinforced to limit the spread of H7N9 virus.

Characterization of thermostable Newcastle disease virus recombinants expressing the hemagglutinin of H5N1 avian influenza virus as bivalent vaccine candidates

Newcastle disease virus (NDV) has been used as a vector in the development of vaccines and gene delivery. In the present study, we generated the thermostable recombinant NDV (rNDV) expressing the different forms of hemagglutinin (HA) of highly pathogenic avian influenza virus (HPAIV) H5N1 based on the full-length cDNA clone of thermostable TS09-C strain. The recombinant thermostable Newcastle disease viruses, rTS-HA, rTS-HA1 and rTS-tPAs/HA1, expressed the HA, HA1 or modified HA1 protein with the tissue plasminogen activator signal sequence (tPAs), respectively. The rNDVs displayed similar thermostability, growth kinetics and pathogenicity compared with the parental TS09-C virus. The tPAs facilitated the expression and secretion of HA1 protein in cells infected with rNDV. Animal studies demonstrated that immunization with rNDVs elicited effective H5N1- and NDV-specific antibody responses and conferred immune protection against lethal H5N1 and NDV challenges in chickens and mice. Importantly, vaccination of rTS-tPAs/HA1 resulted in enhanced protective immunity in chickens and mice. Our study thus provides a novel thermostable NDV-vectored vaccine candidate expressing a soluble form of a heterologous viral protein, which will greatly aid the poultry industry in developing countries.

Identifying Risk Factors Of A(H7N9) Outbreak by Wavelet Analysis and Generalized Estimating Equation

Five epidemic waves of A(H7N9) occurred between March 2013 and May 2017 in China. However, the potential risk factors associated with disease transmission remain unclear. To address the spatial–temporal distribution of the reported A(H7N9) human cases (hereafter referred to as “cases”), statistical description and geographic information systems were employed. Based on long-term observation data, we found that males predominated the majority of A(H7N9)-infected individuals and that most males were middle-aged or elderly. Further, wavelet analysis was used to detect the variation in time-frequency between A(H7N9) cases and meteorological factors. Moreover, we formulated a Poisson regression model to explore the relationship among A(H7N9) cases and meteorological factors, the number of live poultry markets (LPMs), population density and media coverage. The main results revealed that the impact factors of A(H7N9) prevalence are manifold, and the number of LPMs has a significantly positive effect on reported A(H7N9) cases, while the effect of weekly average temperature is significantly negative. This confirms that the interaction of multiple factors could result in a serious A(H7N9) outbreak. Therefore, public health departments adopting the corresponding management measures based on both the number of LPMs and the forecast of meteorological conditions are crucial for mitigating A(H7N9) prevalence.

Characterising routes of H5N1 and H7N9 spread in China using Bayesian phylogeographical analysis

Avian influenza H5N1 subtype has caused a global public health concern due to its high pathogenicity in poultry and high case fatality rates in humans. The recently emerged H7N9 is a growing pandemic risk due to its sustained high rates of human infections, and recently acquired high pathogenicity in poultry. Here, we used Bayesian phylogeography on 265 H5N1 and 371 H7N9 haemagglutinin sequences isolated from humans, animals and the environment, to identify and compare migration patterns and factors predictive of H5N1 and H7N9 diffusion rates in China. H7N9 diffusion dynamics and predictor contributions differ from H5N1. Key determinants of spatial diffusion included: proximity between locations (for H5N1 and H7N9), and lower rural population densities (H5N1 only). For H7N9, additional predictors included low avian influenza vaccination rates, low percentage of nature reserves and high humidity levels. For both H5N1 and H7N9, we found viral migration rates from Guangdong to Guangxi and Guangdong to Hunan were highly supported transmission routes (Bayes Factor > 30). We show fundamental differences in wide-scale transmission dynamics between H5N1 and H7N9. Importantly, this indicates that avian influenza initiatives designed to control H5N1 may not be sufficient for controlling the H7N9 epidemic. We suggest control and prevention activities to specifically target poultry transportation networks between Central, Pan-Pearl River Delta and South-West regions.

Risk factors associated with highly pathogenic avian influenza subtype H5N8 outbreaks on broiler duck farms in South Korea

Highly Pathogenic Avian Influenza (HPAI) subtype H5N8 outbreaks occurred in poultry farms in South Korea in 2014 resulting in significant damage to the poultry industry. Between 2014 and 2016, the pandemic disease caused significant economic loss and social disruption. To evaluate the risk factors for HPAI infection in broiler duck farms, we conducted a retrospective case-control study on broiler duck farms. Forty-three farms with confirmed laboratories on premises were selected as the case group, and 43 HPAI-negative farms were designated as the control group. Control farms were matched based on farm location and were within a 3-km radius from the case premises. Spatial and environmental factors were characterized by site visit and plotted through a geographic information system (GIS). Univariable and multivariable logistic regression models were developed to assess possible risk factors associated with HPAI broiler duck farm infection. Four final variables were identified as risk factors in a final multivariable logistic model: "Farms with ≥7 flocks" (odds ratio [OR] = 6.99, 95% confidence interval [CI] 1.34-37.04), "Farm owner with ≥15 years of raising poultry career" (OR = 7.91, 95% CI 1.69-37.14), "Presence of any poultry farms located within 500 m of the farm" (OR = 6.30, 95% CI 1.08-36.93) and "Not using a faecal removal service" (OR = 27.78, 95% CI 3.89-198.80). This highlights that the HPAI H5N8 outbreaks in South Korea were associated with farm owner education, number of flocks and facilities and farm biosecurity. Awareness of these factors may help to reduce the spread of HPAI H5N8 across broiler duck farms in Korea during epidemics. Greater understanding of the risk factors for H5N8 may improve farm vulnerability to HPAI and other subtypes and help to establish policies to prevent re-occurrence. These findings are relevant to global prevention recommendations and intervention protocols.

The temporal distribution of new H7N9 avian influenza infections based on laboratory-confirmed cases in Mainland China, 2013-2017

In this study, estimates of the growth rate of new infections, based on the growth rate of new laboratory-confirmed cases, were used to provide a statistical basis for in-depth research into the epidemiological patterns of H7N9 epidemics. The incubation period, interval from onset to laboratory confirmation, and confirmation time for all laboratory-confirmed cases of H7N9 avian influenza in Mainland China, occurring between January 2013 and June 2017, were used as the statistical data. Stochastic processes theory and maximum likelihood were used to calculate the growth rate of new infections. Time-series analysis was then performed to assess correlations between the time series of new infections and new laboratory-confirmed cases. The rate of new infections showed significant seasonal fluctuation. Laboratory confirmation was delayed by a period of time longer than that of the infection (average delay, 13 days; standard deviation, 6.8 days). At the lags of −7.5 and −15 days, respectively, the time-series of new infections and new confirmed cases were significantly correlated; the cross correlation coefficients (CCFs) were 0.61 and 0.16, respectively. The temporal distribution characteristics of new infections and new laboratory-confirmed cases were similar and strongly correlated.

Avian influenza overview October 2016-August 2017

The A(H5N8) highly pathogenic avian influenza (HPAI) epidemic occurred in 29 European countries in 2016/2017 and has been the largest ever recorded in the EU in terms of number of poultry outbreaks, geographical extent and number of dead wild birds. Multiple primary incursions temporally related with all major poultry sectors affected but secondary spread was most commonly associated with domestic waterfowl species. A massive effort of all the affected EU Member States (MSs) allowed a descriptive epidemiological overview of the cases in poultry, captive birds and wild birds, providing also information on measures applied at the individual MS level. Data on poultry population structure are required to facilitate data and risk factor analysis, hence to strengthen science-based advice to risk managers. It is suggested to promote common understanding and application of definitions related to control activities and their reporting across MSs. Despite a large number of human exposures to infected poultry occurred during the ongoing outbreaks, no transmission to humans has been identified. Monitoring the avian influenza (AI) situation in other continents indicated a potential risk of long-distance spread of HPAI virus (HPAIV) A(H5N6) from Asia to wintering grounds towards Western Europe, similarly to what happened with HPAIV A(H5N8) and HPAIV A(H5N1) in previous years. Furthermore, the HPAI situation in Africa with A(H5N8) and A(H5N1) is rapidly evolving. Strengthening collaborations at National, EU and Global levels would allow close monitoring of the AI situation, ultimately helping to increase preparedness. No human case was reported in the EU due to AIVs subtypes A(H5N1), A(H5N6), A(H7N9) and A(H9N2). Direct transmission of these viruses to humans has only been reported in areas, mainly in Asia and Egypt, with a substantial involvement of wild bird and/or poultry populations. It is suggested to improve the collection and reporting of exposure events of people to AI.