Spatiotemporal and risk analysis of H5 highly pathogenic avian influenza in Vietnam, 2014-2017

Avian Influenza: Lessons from Past Outbreaks and an Inventory of Data Sources, Mathematical and AI Models, and Early Warning Systems for Forecasting and Hotspot Detection to Tackle Ongoing Outbreaks

BACKGROUND/OBJECTIVES

The ongoing avian influenza (H5N1) outbreak, one of the most widespread and persistent in recent history, has significantly impacted public health and the poultry and dairy cattle industries. This review covers lessons from past outbreaks, risk factors for transmission, molecular epidemiology, clinical features, surveillance strategies, and socioeconomic impacts. Since 1997, H5N1 has infected over 900 individuals globally, with a fatality rate exceeding 50%. Key factors influencing infection rates include demographic, socioeconomic, environmental, and ecological variables. The virus's potential for sustained human-to-human transmission remains a concern. The current outbreak, marked by new viral clades, has complicated containment efforts.

METHODS

This review discusses how to integrate technological advances, such as mathematical modeling and artificial intelligence (AI), to improve forecasting, hotspot detection, and early warning systems.

RESULTS

We provide inventories of data sources, covering both conventional and unconventional data streams, as well as those of mathematical and AI models, which can be vital for comprehensive surveillance and outbreak responses.

CONCLUSION

In conclusion, integrating AI, mathematical models, and technological innovations into a One-Health approach is essential for improving surveillance, forecasting, and response strategies to mitigate the impacts of the ongoing avian influenza outbreak. Strengthening international collaboration and biosecurity measures will be pivotal in controlling future outbreaks and protecting both human and animal populations from this evolving global threat.

Risk factors for avian influenza in Danish poultry and wild birds during the epidemic from June 2020 to May 2021

Exploring the risk factors of avian influenza (AI) occurrence helps us to monitor and control the disease. Since late 2020, the number of avian influenza outbreaks in domestic and wild birds has increased in most European countries, including Denmark. This study was conducted to identify potential risk factors for wild birds and poultry during the epidemic in 2020/2021 in Denmark. Using Danish AI surveillance data of actively surveyed poultry and passively surveyed wild birds from June 2020 to May 2021, we calculated geographical attributes for bird locations and assessed the potential risk factors of AI detections using logistic regression analyses. 4% of actively surveyed poultry and 39% of passively surveyed wild birds were detected with AI circulating or ongoing at the time. Of these, 10 and 99% tested positive for the H5/H7 AI subtypes, respectively. Our analyses did not find any statistically significant risk factors for actively surveyed poultry within the dataset. For passively surveyed wild birds, bird species belonging to the Anseriformes order had a higher risk of being AI virus positive than five other taxonomic bird orders, and Galliformes were of higher risk than two other taxonomic bird orders. Besides, every 1 km increase in the distance to wetlands was associated with a 5.18% decrease in the risk of being AI positive (OR (odds ratio) 0.95, 95% CI 0.91, 0.99), when all other variables were kept constant. Overall, bird orders and distance to wetlands were associated with the occurrence of AI. The findings may provide targets for surveillance strategies using limited resources and assist in risk-based surveillance during epidemics.

Anti-rheumatic colchicine phytochemical exhibits potent antiviral activities against avian and seasonal Influenza A viruses (IAVs) via targeting different stages of IAV replication cycle

BACKGROUND

The continuous evolution of drug-resistant influenza viruses highlights the necessity for repurposing naturally-derived and safe phytochemicals with anti-influenza activity as novel broad-spectrum anti-influenza medications.

METHODS

In this study, nitrogenous alkaloids were tested for their viral inhibitory activity against influenza A/H1N1 and A/H5N1 viruses. The cytotoxicity of tested alkaloids on MDCK showed a high safety range (CC50 > 200 µg/ml), permitting the screening for their anti-influenza potential.

RESULTS

Herein, atropine sulphate, pilocarpine hydrochloride and colchicine displayed anti-H5N1 activities with IC50 values of 2.300, 0.210 and 0.111 µg/ml, respectively. Validation of the IC50 values was further depicted by testing the three highly effective alkaloids, based on their potent IC50 values against seasonal influenza A/H1N1 virus, showing comparable IC50 values of 0.204, 0.637 and 0.326 µg/ml, respectively. Further investigation suggests that colchicine could suppress viral infection by primarily interfering with IAV replication and inhibiting viral adsorption, while atropine sulphate and pilocarpine hydrochloride could directly affect the virus in a cell-free virucidal effect. Interestingly, the in silico molecular docking studies suggest the abilities of atropine, pilocarpine, and colchicine to bind correctly inside the active sites of the neuraminidases of both influenza A/H1N1 and A/H5N1 viruses. The three alkaloids exhibited good binding energies as well as excellent binding modes that were similar to the co-crystallized ligands. On the other hand, consistent with in vitro results, only colchicine could bind correctly against the M2-proton channel of influenza A viruses (IAVs). This might explicate the in vitro antiviral activity of colchicine at the replication stage of the virus replication cycle.

CONCLUSION

This study highlighted the anti-influenza efficacy of biologically active alkaloids including colchicine. Therefore, these alkaloids should be further characterized in vivo (preclinical and clinical studies) to be developed as anti-IAV agents.

Avian influenza A(H5) virus circulation in live bird markets in Vietnam, 2017-2022

BACKGROUND

Highly pathogenic avian influenza A(H5) human infections are a global concern, with many A(H5) human cases detected in Vietnam, including a case in October 2022. Using avian influenza virus surveillance from March 2017-September 2022, we described the percent of pooled samples that were positive for avian influenza A, A(H5), A(H5N1), A(H5N6), and A(H5N8) viruses in live bird markets (LBMs) in Vietnam.

METHODS

Monthly at each LBM, 30 poultry oropharyngeal swab specimens and five environmental samples were collected. Samples were pooled in groups of five and tested for influenza A, A(H5), A(H5N1), A(H5N6), and A(H5N8) viruses by real-time reverse-transcription polymerase chain reaction. Trends in the percent of pooled samples that were positive for avian influenza were summarized by LBM characteristics and time and compared with the number of passively detected avian influenza outbreaks using Spearman's rank correlation.

RESULTS

A total of 25,774 pooled samples were collected through active surveillance at 167 LBMs in 24 provinces; 36.9% of pooled samples were positive for influenza A, 3.6% A(H5), 1.9% A(H5N1), 1.1% A(H5N6), and 0.2% A(H5N8). Influenza A(H5) viruses were identified January-December and at least once in 91.7% of sampled provinces. In 246 A(H5) outbreaks in poultry; 20.3% were influenza A(H5N1), 60.2% A(H5N6), and 19.5% A(H5N8); outbreaks did not correlate with active surveillance.

CONCLUSIONS

In Vietnam, influenza A(H5) viruses were detected by active surveillance in LBMs year-round and in most provinces sampled. In addition to outbreak reporting, active surveillance for A(H5) viruses in settings with high potential for animal-to-human spillover can provide situational awareness.

Diseases spectrum in the field of spatiotemporal patterns mining of infectious diseases epidemics: A bibliometric and content analysis

Numerous investigations of the spatiotemporal patterns of infectious disease epidemics, their potential influences, and their driving mechanisms have greatly contributed to effective interventions in the recent years of increasing pandemic situations. However, systematic reviews of the spatiotemporal patterns of communicable diseases are rare. Using bibliometric analysis, combined with content analysis, this study aimed to summarize the number of publications and trends, the spectrum of infectious diseases, major research directions and data-methodological-theoretical characteristics, and academic communities in this field. Based on 851 relevant publications from the Web of Science core database, from January 1991 to September 2021, the study found that the increasing number of publications and the changes in the disease spectrum have been accompanied by serious outbreaks and pandemics over the past 30 years. Owing to the current pandemic of new, infectious diseases (e.g., COVID-19) and the ravages of old infectious diseases (e.g., dengue and influenza), illustrated by the disease spectrum, the number of publications in this field would continue to rise. Three logically rigorous research directions-the detection of spatiotemporal patterns, identification of potential influencing factors, and risk prediction and simulation-support the research paradigm framework in this field. The role of human mobility in the transmission of insect-borne infectious diseases (e.g., dengue) and scale effects must be extensively studied in the future. Developed countries, such as the USA and England, have stronger leadership in the field. Therefore, much more effort must be made by developing countries, such as China, to improve their contribution and role in international academic collaborations.

Spatial epidemiology of highly pathogenic avian influenza subtype H5N6 in Gyeonggi Province, South Korea, 2016-2017

Over four months in the winter of 2016-2017, 343 poultry farms in South Korea reported highly pathogenic avian influenza (HPAI) H5N6 occurrences, leading to the culling of 40 million poultry. Our study aimed to describe the spatial epidemiology of the 2016-2017 HPAI H5N6 outbreak in Gyeonggi Province, the most affected area in South Korea, comprising 35.9% (123) of the HPAI-infected poultry farms, to identify spatial risk factors for the increased probability of HPAI H5N6 occurrence, and to delineate areas with the highest likelihood of infection among different target poultry species. Although the poultry density was risk factor for the all species, rice paddy was only identified as risk factor for chicken and duck farms, not other species farms suggesting different biosecurity measures are required depending on the species. Although spatial effects of HPAI occurrence tended to be clustered within 16 km, the cluster range was reduced to 7 km when considering the identified risk factors, indicating a more geographically focused outbreak response when taking risk factors into account. The areas identified with the highest likelihood of infection can provide evidence, with accessibility to policymakers, to improve risk-based surveillance for HPAI. Our findings provide epidemiological understanding helpful in improving surveillance activity and assisting in the design of more cost-effective intervention policies related to future HPAI outbreaks in South Korea. This article is protected by copyright. All rights reserved.

Risk profile of low pathogenicity avian influenza virus infections in farms in southern Vietnam

The impact of low pathogenicity avian influenza (LPAI) has been confirmed mainly in farms. Unlike apparent losses caused by the high pathogenicity avian influenza (HPAI), the LPAI impact has been hardly evaluated due to underestimating its spread and damage. In 2019, a questionnaire study was conducted in southern Vietnam to identify the specific risk factors of LPAI virus (LPAIV) circulation and to find associations between husbandry activities and LPAI prevalence. A multilevel regression analysis indicated that keeping Muscovy ducks during farming contributed to LPAIV positivity [Odds ratio=208.2 (95% confidence interval: 13.4–1.1 × 10⁴)]. In cluster analysis, farmers willing to report avian influenza (AI) events and who agreed with the local AI control policy had a slightly lower risk for LPAIV infection although there was no significance in the correlation between farmer characteristics and LPAI occurrence. These findings indicated that keeping Muscovy ducks without appropriate countermeasures might increase the risk of LPAIV infection. Furthermore, specific control measures at the local level are effective for LPAIV circulation, and the improvement of knowledge about biosecurity and attitude contributes to reducing LPAI damage.

Machine learning-driven dynamic risk prediction for highly pathogenic avian influenza at poultry farms in Republic of Korea: Daily risk estimation for individual premises

Highly pathogenic avian influenza (HPAI) is a fatal zoonotic disease that damages the poultry industry and endangers human lives via exposure to the pathogen. A risk assessment model that precisely predicts high-risk groups and occurrence of HPAI infection is essential for effective biosecurity measures that minimize the socio-economic losses of massive outbreaks. However, the conventional risk prediction approaches have difficulty incorporating the broad range of factors associated with HPAI infections at poultry holdings. Therefore, it is difficult to accommodate the complexity of the dynamic transmission mechanisms and generate risk estimation on a real-time basis. We proposed a continuous risk prediction framework for HPAI occurrences that used machine learning algorithms (MLAs). This integrated environmental, on-farm biosecurity, meteorological, vehicle movement tracking, and HPAI wild bird surveillance data to improve accuracy and timeliness. This framework consisted of (i) the generation of 1788 predictors from six types of data and reconstructed them with an outcome variable into a data mart based on a temporal assumption (i.e. infected period and day-ahead forecasting); (ii) training of the predictors with the temporally rearranged outcome variable that corresponded to HPAI H5N6 infected state at each individual farm on daily basis during the 2016-2017 HPAI epidemic using three different MLAs [Random Forest, Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting]; (iii) predicting the daily risk of HPAI infection during the 2017-2018 HPAI epidemic using the pre-trained MLA models for each farm across the country. The models predicted the high risk to 8-10 out of 19 infected premises during the infected period in advance. The GBM MLAs outperformed the 7-day forecasting of HPAI prediction at individual poultry holdings, with an area under the curve (AUC) of receiver operating characteristic of 0.88. Therefore, this approach enhances the flexibility and timing of interventions against HPAI outbreaks at poultry farms.

A systematic approach to illuminate a new hot spot of avian influenza virus circulation in South Vietnam, 2016-2017

In South Vietnam, live bird markets (LBMs) are key in the value chain of poultry products and spread of avian influenza virus (AIV) although they may not be the sole determinant of AIV prevalence. For this reason, a risk analysis of AIV prevalence was conducted accounting for all value chain factors. A cross-sectional study of poultry flock managers and poultry on backyard farms, commercial (high biosecurity) farms, LBMs and poultry delivery stations (PDSs) in four districts of Vinh Long province was conducted between December 2016 and August 2017. A total of 3597 swab samples were collected from birds from 101 backyard farms, 50 commercial farms, 58 sellers in LBMs and 19 traders in PDSs. Swab samples were submitted for AIV isolation. At the same time a questionnaire was administered to flock managers asking them to provide details of their knowledge, attitude and practices related to avian influenza. Multiple correspondence analysis and a mixed-effects multivariable logistic regression model were developed to identify enterprise and flock manager characteristics that increased the risk of AIV positivity. A total of 274 birds were positive for AIV isolation, returning an estimated true prevalence of 7.6% [95% confidence interval (CI): 6.8%-8.5%]. The odds of a bird being AIV positive if it was from an LBM or PDS were 45 (95% CI: 3.4-590) and 25 (95% CI: 1.4-460), respectively, times higher to the odds of a bird from a commercial poultry farm being AIV positive. The odds of birds being AIV positive for respondents with a mixed (uncertain or inconsistent) level and a low level of knowledge about AI were 5.0 (95% CI: 0.20-130) and 3.5 (95% CI: 0.2-62), respectively, times higher to the odd of birds being positive for respondents with a good knowledge of AI. LBMs and PDSs should receive specific emphasis in AI control programs in Vietnam. Our findings provide evidence to support the hypothesis that incomplete respondent knowledge of AI and AIV spread mechanism were associated with an increased risk of AIV positivity. Delivery of education programs specifically designed for those in each enterprise will assist in this regard. The timing and frequency of delivery of education programs are likely to be important if the turnover of those working in LBMs and PDSs is high.

Reassortant Highly Pathogenic H5N6 Avian Influenza Virus Containing Low Pathogenic Viral Genes in a Local Live Poultry Market, Vietnam

Sites of live poultry trade and marketing are hot spots for avian influenza virus (AIV) transmission. We conducted active surveillance at a local live poultry market (LPM) in northern Vietnamese provinces in December 2016. Feces samples from the market were collected and tested for AIV. A new reassorted AIV strain was isolated from female chickens, named A/chicken/Vietnam/AI-1606/2016 (H5N6), and was found to belong to group C of clade 2.3.4.4 H5N6 highly pathogenic (HP) AIVs. The neuraminidase gene belongs to the reassortant B type. The viral genome also contained polymerase basic 2 and polymerase acidic, which were most closely related to domestic-duck-origin low pathogenic AIVs in Japan (H3N8) and Mongolia (H4N6). The other six genes were most closely related to poultry-origin H5N6 HP AIVs in Vietnam and had over 97% sequence identity with human AIV isolate A/Guangzhou/39715/2014 (H5N6). The new reassorted AIV isolate A/chicken/Vietnam/AI-1606/2016 (H5N6) identified in this study exemplifies AIVs reassortment and evolution through contact among wild birds, poultry farms, and LPMs. Therefore, active surveillance of AIVs is necessary to prevent potential threats to human and animal health.