Mapping H5N1 highly pathogenic avian influenza risk in Southeast Asia

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.

Modeling transmission of avian influenza viruses at the human-animal-environment interface in Cuba

Introduction

The increasing geographical spread of highly pathogenic avian influenza viruses (HPAIVs) is of global concern due to the underlying zoonotic and pandemic potential of the virus and its economic impact. An integrated One Health model was developed to estimate the likelihood of Avian Influenza (AI) introduction and transmission in Cuba, which will help inform and strengthen risk-based surveillance activities.

Materials and methods

The spatial resolution used for the model was the smallest administrative district ("Consejo Popular"). The model was parameterised for transmission from wild birds to poultry and pigs (commercial and backyard) and then to humans. The model includes parameters such as risk factors for the introduction and transmission of AI into Cuba, animal and human population densities; contact intensity and a transmission parameter (β).

Results

Areas with a higher risk of AI transmission were identified for each species and type of production system. Some variability was observed in the distribution of areas estimated to have a higher probability of AI introduction and transmission. In particular, the south-western and eastern regions of Cuba were highlighted as areas with the highest risk of transmission.

Discussion

These results are potentially useful for refining existing criteria for the selection of farms for active surveillance, which could improve the ability to detect positive cases. The model results could contribute to the design of an integrated One Health risk-based surveillance system for AI in Cuba. In addition, the model identified geographical regions of particular importance where resources could be targeted to strengthen biosecurity and early warning surveillance.

Multifaceted analysis of temporal and spatial distribution and risk factors of global poultry HPAI-H5N1, 2005-2023

The purpose of this study was to analyze the characteristics of occurrence and spread of highly pathogenic avian influenza H5N1 (HPAI-H5N1) globally, understand its spatiotemporal characteristics, investigate the risk factors influencing outbreaks, and identify high-risk areas for disease occurrence. We collected the data on global poultry HPAI-H5N1 outbreaks from January 2005 to April 2023, and conducted a thorough analysis of the spatial and temporal characteristics of the disease through time series decomposition and directional distribution analysis. Additionally, an ecological niche model was established to explore the major factors influencing the occurrence of HPAI-H5N1 and to pinpoint high-risk areas. Our findings revealed that HPAI-H5N1 outbreaks were cyclical, and seasonal, exhibiting a rising trend, with a predominant northwest-southeast transmission direction. The ecological niche model highlighted that species factors and economic trade factors are critical in influencing the outbreak of HPAI-H5N1. Variables such as chicken and duck density, population density, isothermality, and road density, contributed to importantly risk of outbreaks. High-risk areas for HPAI-H5N1 occurrence were primarily identified in Europe, West Africa, Southeast Asia, and Southeast China. This study provided valuable insights into the spatial and temporal distribution characteristics and risk factors of global poultry HPAI-H5N1 outbreaks. The identification of high-risk areas provides essential information that can be used to develop more effective prevention and control policies.

A case-control study of the infection risk of H5N8 highly pathogenic avian influenza in Japan during the winter of 2020-2021

In Japan, outbreaks of H5N8 highly pathogenic avian influenza (HPAI) were reported between November 2020 and March 2021 in 52 poultry farms. Understanding HPAI epidemiology would help poultry industries improve their awareness of the disease and enhance the immediate implementation of biosecurity measures. This study was a simulation-based matched case-control study to elucidate the risk factors associated with HPAI outbreaks in chicken farms in Japan. Data were collected from 42 HPAI-affected farms and 463 control farms that were within a 5-km radius of each case farm but remained uninfected. When infected farms were detected as clusters, one farm was randomly selected from each cluster, considering the possibility that the cluster was formed by farm-to-farm transmission within an epidemic area. For each case farm, up to three control farms were selected within a 5-km radius. Overall, 26 case farms (16 layer and 10 broiler farms) and 75 control farms (45 layer and 30 broiler farms) were resampled 1000 times for the conditional logistic regression model with explanatory variables comprising geographical factors and farm flock size. A larger flock size and shorter distance to water bodies from the farm were found to increase infection risk in layer farms. Similarly, in broiler farms, a shorter distance to water bodies increased infection risk. On larger farms, frequent access of farm staff and instrument carriages to premises could lead to increased infection risk. Waterfowl visiting water bodies around farms may also be associated with infection risk.

Seasonal changes in bird communities on poultry farms and house sparrow-wild bird contacts revealed by camera trapping

Introduction

Wild birds are considered reservoirs of poultry pathogens although transmission routes have not been conclusively established. Here we use camera trapping to study wild bird communities on commercial layer and red-legged partridge farms over a one-year timeframe. We also analyze direct and indirect interactions of other bird species with the house sparrow (Passer domesticus), a potential bridge host.

Methods

We conducted camera trapping events between January 2018 and October 2019, in two caged layer farms, one free-range layer farm, and two red-legged partridge farms in South-Central Spain.

Results and Discussion

We observed wild bird visits on all types of farms, with the significantly highest occurrence on red-legged partridge farms where food and water are more easily accessible, followed by commercial caged layer farms, and free-range chicken farms. The house sparrow (Passer domesticus) followed by spotless starlings (Sturnus unicolor) was the most encountered species on all farms, with the highest frequency in caged layer farms. On partridge farms, the house sparrow accounted for 58% of the wild bird detections, while on the free-range chicken farm, it made up 11% of the detections. Notably, the breeding season, when food and water are scarce in Mediterranean climates, saw the highest number of wild bird visits to the farms. Our findings confirm that the house sparrow, is in direct and indirect contact with layers and red-legged partridges and other wild birds independent of the type of farm. Contacts between house sparrows and other bird species were most frequent during the breeding season followed by the spring migration period. The species most frequently involved in interactions with the house sparrow belonged to the order Passeriformes. The study provides a comparative description of the composition and seasonal variations of bird communities in different types of layer/ poultry farms in Southern Spain i.e. a Mediterranean climate. It confirms the effectiveness of biosecurity measures that restrict access to feed and water. Additionally, it underscores the importance of synanthropic species, particularly the house sparrow, as potential bridge vector of avian pathogens.

Mapping wetlands in Northeast China by using knowledge-based algorithms and microwave (PALSAR-2, Sentinel-1), optical (Sentinel-2, Landsat), and thermal (MODIS) images

Wetlands are rich in biodiversity, provide habitats for many wildlife species, and play a vital role in the transmission of bird-borne infectious diseases (e.g., highly pathogenic avian influenza). However, wetlands worldwide have been degraded or even disappeared due to natural and anthropogenic activities over the past two centuries. At present, major data products of wetlands have large uncertainties, low to moderate accuracies, and lack regular updates. Therefore, accurate and updated wetlands maps are needed for the sustainable management and conservation of wetlands. Here, we consider the remote sensing capability and define wetland types in terms of plant growth form (tree, shrub, grass), life cycle (perennial, annual), leaf seasonality (evergreen, deciduous), and canopy type (open, closed). We identify unique and stable features of individual wetland types and develop knowledge-based algorithms to map them in Northeast China at 10 m spatial resolution by using microwave (PALSAR-2, Sentinel-1), optical (Landsat (ETM+/OLI), Sentinel-2), and thermal (MODIS land surface temperature, LST) imagery in 2020. The resultant wetland map has a high overall accuracy of >95%. There were a total 154,254 km2 of wetlands in Northeast China in 2020, which included 27,219 km2 of seasonal open-canopy marsh, 69,158 km2 of yearlong closed-canopy marsh, and 57,878 km2 of deciduous forest swamp. Our results demonstrate the potential of knowledge-based algorithms and integrated multi-source image data for wetlands mapping and monitoring, which could provide improved data for the planning of wetland conservation and restoration.

Ecological drivers for poultry farms predisposed to highly pathogenic avian influenza virus infection during the initial phase of the six outbreaks between 2010-2021: a nationwide study in South Korea

Background

Highly pathogenic avian influenza (HPAI) has caused substantial economic losses worldwide. An understanding of the environmental drivers that contribute to spillover transmission from wild birds to poultry farms is important for predicting areas at risk of introduction and developing risk-based surveillance strategies. We conducted an epidemiological study using data from six HPAI outbreak events in South Korea.

Materials and methods

An aggregate-level study design was implemented using third-level administrative units in South Korea. Only regions with high natural reservoir suitability were included. The incidence of HPAI at chicken and duck farms during the initial phase (30 and 45 days after the first case) of each outbreak event was used as the outcome variable, assuming that cross-species transmission from wild birds was the dominant exposure leading to infection. Candidate environmental drivers were meteorological factors, including temperature, precipitation, humidity, and altitude, as well as the proportion of protected area, farm density, deforestation level, and predator species richness. Logistic regression models were implemented; conditional autoregression models were used in cases of spatial autocorrelation of residuals.

Results

Lower temperature, higher farm density, and lower predator species richness were significantly associated with a higher risk of HPAI infection on chicken farms. Lower temperature, higher proportion of protected area, and lower predator species richness were significantly associated with a higher risk of HPAI infection on duck farms.

Conclusion

The predicted dominant transmission routes on chicken and duck farms were horizontal and spillover, respectively. These results reveal a potential protective effect of predator species richness against HPAI outbreaks. Further studies are required to confirm a causal relationship.

The role of vaccination and environmental factors on outbreaks of high pathogenicity avian influenza H5N1 in Bangladesh

High Pathogenicity Avian Influenza (HPAI) H5N1 outbreaks continue to wreak havoc on the global poultry industry and threaten the health of wild bird populations, with sporadic spillover in humans and other mammals, resulting in widespread calls to vaccinate poultry. Bangladesh has been vaccinating poultry since 2012, presenting a prime opportunity to study the effects of vaccination on HPAI H5N1circulation in both poultry and wild birds. We investigated the efficacy of vaccinating commercial poultry against HPAI H5N1 along with climatic and socio-economic factors considered potential drivers of HPAI H5N1 outbreak risk in Bangladesh. Using a multivariate modeling approach, we estimated that the rate of outbreaks was 18 times higher before compared to after vaccination, with winter months having a three times higher chance of outbreaks than summer months. Variables resulting in small but significant increases in outbreak rate were relatively low ambient temperatures for the time of year, literacy rate, chicken and duck density, crop density, and presence of highways; this may be attributable to low temperatures supporting viral survival outside the host, higher literacy driving reporting rate, density of the host reservoir, and spread of the virus through increased connectivity. Despite the substantial impact of vaccination on outbreaks, we note that HPAI H5N1 is still enzootic in Bangladesh; vaccinated poultry flocks have high rates of H5N1 prevalence, and spillover to wild birds has increased. Vaccination in Bangladesh thus bears the risk of supporting "silent spread," where the vaccine only provides protection against disease and not also infection. Our findings underscore that poultry vaccination can be part of holistic HPAI mitigation strategies when accompanied by monitoring to avoid silent spread.

Habitat loss exacerbates pathogen spread: An Agent-based model of avian influenza infection in migratory waterfowl

Habitat availability determines the distribution of migratory waterfowl along their flyway, which further influences the transmission and spatial spread of avian influenza viruses (AIVs). The extensive habitat loss in the East Asian-Australasian Flyway (EAAF) may have potentially altered the virus spread and transmission, but those consequences are rarely studied. We constructed 6 fall migration networks that differed in their level of habitat loss, wherein an increase in habitat loss resulted in smaller networks with fewer sites and links. We integrated an agent-based model and a susceptible-infected-recovered model to simulate waterfowl migration and AIV transmission. We found that extensive habitat loss in the EAAF can 1) relocate the outbreaks northwards, responding to the distribution changes of wintering waterfowl geese, 2) increase the outbreak risk in remaining sites due to larger goose congregations, and 3) facilitate AIV transmission in the migratory population. In addition, our modeling output was in line with the predictions from the concept of "migratory escape", i.e., the migration allows the geese to "escape" from the location where infection risk is high, affecting the pattern of infection prevalence in the waterfowl population. Our modeling shed light on the potential consequences of habitat loss in spreading and transmitting AIV at the flyway scale and suggested the driving mechanisms behind these effects, indicating the importance of conservation in changing spatial and temporal patterns of AIV outbreaks.

Surveillance of the spread of avian influenza virus type A in live bird markets in Tripoli, Libya, and determination of the associated risk factors

Background and Aim: Studies on avian influenza virus (AIV) in Libya are few and limited. This study aimed to determine the presence of AIV in live bird markets (LBMs) in Tripoli and determine the risk factors associated with AIV spread. Materials and Methods: In total, 269 cloacal swabs were randomly collected from different bird species in 9 LBMs located in Tripoli and its surrounding regions. The target species were ducks, geese, local chickens, Australian chickens, Brahma chickens, turkeys, pigeons, quails, peacock broiler chicks, and pet birds. Total RNA was extracted from the swab samples and used for real-time polymerase chain reaction to detect AIV type A. Results: Of the 269 samples, 28 (10.41% of total samples) were positive for AIV type A. The LBMs with positive samples were Souq Aljumaa, Souq Alkhamees, Souq Althulatha, and Souq Tajoura. The highest percentage (35.71%) of AIV was recorded in Souq Aljumaa. Positive results for AIV type A were obtained primarily in three species of birds: Ducks (14/65; highest percentage: 21.5%), local chickens (12/98; 12.24%), and geese (2/28; 7.14%). Furthermore, the following three risk factors associated with the spread of AIV type A were identified: Time spent by breeders/vendors at the market (odds ratio [OR] = 11.181; 95% confidence interval [CI] = 3.827–32.669), methods used for disposing dead birds (OR = 2.356; 95% CI = 1.005–5.521), and last visited LBM (OR = 0.740; 95% CI = 0.580–0.944). Restricting the movement of poultry vendors from one market to another may protect against AIV spread. Conclusion: The findings of this study indicate the high risk of AIV spread in LBMs and highlight the need for continuous surveillance of LBMs across the country.

Evaluating Effects of Medium-Resolution Optical Data Availability on Phenology-Based Rice Mapping in China

The phenology-based approach has proven effective for paddy rice mapping due to the unique flooding and transplanting features of rice during the early growing season. However, the method may be greatly affected if no valid observations are available during the flooding and rice transplanting phase. Here, we compare the effects of data availability of different sensors in the critical phenology phase, thereby supporting paddy rice mapping based on phenology-based approaches. Importantly, our study further analyzed the effects of the spatial pattern of the valid observations related to certain factors (i.e., sideslips, clouds, and temporal window lengths of flooding and rice transplanting), which supply the applicable area of the phenology-based approach indications. We first determined the flooding and rice transplanting phase using in situ observational data from agrometeorological stations and remote sensing data, then evaluated the effects of data availability in this phase of 2020 in China using all Landsat-7 and 8 and Sentinel-2 data. The results show that on the country level, the number of average valid observations during the flooding and rice transplanting phase was more than ten for the integration of Landsat and Sentinel images. On the sub-country level, the number of average valid observations was high in the cold temperate zone (17.4 observations), while it was relatively lower in southern China (6.4 observations), especially in Yunnan–Guizhou Plateau, which only had three valid observations on average. Based on the multicollinearity test, the three factors are significantly correlated with the absence of valid observations: (R2 = 0.481) and Std.Coef. (Std. Err.) are 0.306 (0.094), −0.453 (0.003) and −0.547 (0.019), respectively. Overall, these results highlight the substantial spatial heterogeneity of valid observations in China, confirming the reliability of the integration of Landsat-7 and 8 and Sentinel-2 imagery for paddy rice mapping based on phenology-based approaches. This can pave the way for a national-scale effort of rice mapping in China while further indicating potential omission errors in certain cloud-prone regions without sufficient optical observation data, i.e., the Sichuan Basin.

Global dynamical analysis of H5 subtype avian influenza model

In order to study the comprehensive influence of factors such as contact between resident birds and poultry, poultry recruitment, environment and other factors on the transmission and control of H5 subtype avian influenza virus, a dynamic model of resident birds and poultry is developed. First, the basic reproduction number [Formula: see text] is obtained. When [Formula: see text], the dynamic model have a unique positive equilibrium and the disease persisted. Second, the Lyapunov functions is constructed to determine the global stability of the disease-free equilibrium and the endemic equilibrium. The results of numerical simulation show that regular disinfection and sterilization can increase the mortality of virus and effectively prevent the occurrence of epidemic situation. Although closing the live poultry trading market is not the main measure to control the epidemic, but it can control the epidemic to a lower level. Therefore, the regular closure of trading markets and sterilization can prevent and control the spread of the epidemic.

Evolutionary Dynamics of H5 Highly Pathogenic Avian Influenza Viruses (Clade 2.3.4.4B) Circulating in Bulgaria in 2019-2021

The first detection of a Highly Pathogenic Avian Influenza (HPAI) H5N8 virus in Bulgaria dates back to December 2016. Since then, many outbreaks caused by HPAI H5 viruses from clade 2.3.4.4B have been reported in both domestic and wild birds in different regions of the country. In this study, we characterized the complete genome of sixteen H5 viruses collected in Bulgaria between 2019 and 2021. Phylogenetic analyses revealed a persistent circulation of the H5N8 strain for four consecutive years (December 2016-June 2020) and the emergence in 2020 of a novel reassortant H5N2 subtype, likely in a duck farm. Estimation of the time to the most recent common ancestor indicates that this reassortment event may have occurred between May 2019 and January 2020. At the beginning of 2021, Bulgaria experienced a new virus introduction in the poultry sector, namely a HPAI H5N8 that had been circulating in Europe since October 2020. The periodical identification in domestic birds of H5 viruses related to the 2016 epidemic as well as a reassortant strain might indicate undetected circulation of the virus in resident wild birds or in the poultry sector. To avoid the concealed circulation and evolution of viruses, and the risk of emergence of strains with pandemic potential, the implementation of control measures is of utmost importance, particularly in duck farms where birds display no clinical signs.

Longitudinal Analysis of Influenza A(H5) Sero-Surveillance in Myanmar Ducks, 2006-2019

Between 2006 and 2019, serological surveys in unvaccinated domestic ducks reared outdoors in Myanmar were performed, using a haemagglutination inhibition (HI) test, to confirm H5 avian influenza virus circulation and assess temporal and spatial distribution. Positive test results occurred every year that samples were collected. The annual proportion of positive farms ranged from 7.1% to 77.2%. The results revealed silent/sub-clinical influenza A (H5) virus circulation, even in years and States/Regions with no highly pathogenic avian influenza (HPAI) outbreaks reported. Further analysis of the 2018/19 results revealed considerable differences in seroconversion rates between four targeted States/Regions and between years, and showed seroconversion before and during the sampling period. By the end of the trial, a high proportion of farms were seronegative, leaving birds vulnerable to infection when sold. Positive results likely indicate infection with Gs/GD/96-lineage H5Nx HPAI viruses rather than other H5 subtype low-pathogenicity avian influenza viruses. The findings suggested persistent, but intermittent, circulation of Gs/GD/96-lineage H5Nx HPAI viruses in domestic ducks, despite the veterinary services' outbreak detection and control efforts. The role of wild birds in transmission remains unclear but there is potential for spill-over in both directions. The findings of this study assist the national authorities in the design of appropriate, holistic avian influenza control programs.

Geographically targeted surveillance of livestock could help prioritize intervention against antimicrobial resistance in China

The rise of antimicrobial resistance (AMR) in animals is being fuelled by the widespread use of veterinary antimicrobials. China is the largest global consumer of veterinary antimicrobials, and improving AMR surveillance strategies in this region could help prioritize intervention and preserve antimicrobial efficacy. Here we mapped AMR rates in pigs, chickens and cattle in China using 446 surveys of event-based surveillance between 2000 and 2019 for foodborne bacteria, in combination with geospatial models to identify locations where conducting new surveys could have the highest benefits. Using maps of uncertainty, we show that eastern China currently has the highest AMR rates, and southwestern and northeastern China would benefit the most from additional surveillance efforts. Instead of distributing new surveys evenly across administrative divisions, using geographically targeted surveillance could reduce AMR prediction uncertainty by two-fold. In a context of competing disease control priorities, our findings present a feasible option for optimizing surveillance efforts-and slowing the spread of AMR.

Modelling the Spatial Distribution of ASF-Positive Wild Boar Carcasses in South Korea Using 2019-2020 National Surveillance Data

Simple Summary

Since African swine fever (ASF) virus in wild boar populations can spill over to domestic pigs, it is crucial to understand the disease determinants in the wild compartment. However, the imperfect detection sensitivity of wild boar surveillance jeopardizes our ability to understand ASF spatial distribution. In this study, we used national surveillance data of ASF in wild boars collected in the Republic of Korea from 2019–2020 to model the spatial distribution of ASF-positive carcasses for two successive study periods associated with different surveillance intensity. The model allowed us to identify disease risk factors in the Republic of Korea, determine the spatial distribution of the risk of ASF, and estimate the sensitivity of surveillance. The outputs of this study are relevant to policy makers for developing and improving risk-based surveillance programs for ASF in wild boars.

Abstract

In September 2019, African swine fever (ASF) was reported in South Korea for the first time. Since then, more than 651 ASF cases in wild boars and 14 farm outbreaks have been notified in the country. Despite the efforts to eradicate ASF among wild boar populations, the number of reported ASF-positive wild boar carcasses have increased recently. The purpose of this study was to characterize the spatial distribution of ASF-positive wild boar carcasses to identify the risk factors associated with the presence and number of ASF-positive wild boar carcasses in the affected areas. Because surveillance efforts have substantially increased in early 2020, we divided the study into two periods (2 October 2019 to 19 January 2020, and 19 January to 28 April 2020) based on the number of reported cases and aggregated the number of reported ASF-positive carcasses into a regular grid of hexagons of 3-km diameter. To account for imperfect detection of positive carcasses, we adjusted spatial zero-inflated Poisson regression models to the number of ASF-positive wild boar carcasses per hexagon. During the first study period, proximity to North Korea was identified as the major risk factor for the presence of African swine fever virus. In addition, there were more positive carcasses reported in affected hexagons with high habitat suitability for wild boars, low heat load index (HLI), and high human density. During the second study period, proximity to an ASF-positive carcass reported during the first period was the only significant risk factor for the presence of ASF-positive carcasses. Additionally, low HLI and elevation were associated with an increased number of ASF-positive carcasses reported in the affected hexagons. Although the proportion of ASF-affected hexagons increased from 0.06 (95% credible interval (CrI): 0.05–0.07) to 0.09 (95% CrI: 0.08–0.10), the probability of reporting at least one positive carcass in ASF-affected hexagons increased from 0.49 (95% CrI: 0.41–0.57) to 0.73 (95% CrI: 0.66–0.81) between the two study periods. These results can be used to further advance risk-based surveillance strategies in the Republic of Korea.

Safety of a quadrivalent influenza vaccine in Vietnamese healthy subjects aged 6 months and older

Quadrivalent influenza vaccines (QIVs) provide protection against the two influenza A viruses (H1N1 and H3N2) and both co-circulating influenza B lineages. QIVs have been found safe, immunogenic, and efficacious in several phase III clinical trials. Here we assess the safety of QIV after vaccination in Vietnamese infants, children, and adults. Participants (n = 228) were asked to report any solicited adverse events (AEs) occurring within 7 days, unsolicited non-serious AEs occurring within 28 days post-vaccination, and serious adverse events (SAEs) at any time during the study. The study was completed by 224 participants (97.4%). Thirty-one children (39.7%) aged 6 − 35 months, 32 children (40.0%) aged 3 − 8 years, 2 participants (9.0%) aged 9 − 17 years, 5 participants (17.9%) aged 18 − 60 years, and 3 participants (15.0%) aged ≥60 years reported ≥1 solicited reaction within 7 days following vaccination. The most frequent-solicited AEs were injection-site tenderness or pain, appetite loss, fever, and abnormal crying in 6 − 35 month-olds, and fever, headache, and myalgia in other age groups. No severe-unsolicited AEs or vaccine-related SAEs were reported. These results suggest that QIV is well tolerated across age groups in Vietnam, and can be safely used to protect the Vietnamese population against influenza and its potentially serious complications.

Landscape effects and spatial patterns of avian influenza virus in Danish wild birds, 2006-2020

Avian influenza (AI) is a contagious disease of birds with zoonotic potential. AI virus (AIV) can infect most bird species, but clinical signs and mortality vary. Assessing the distribution and factors affecting AI presence can direct targeted surveillance to areas at risk of disease outbreaks, or help identify disease hotspots or areas with inadequate surveillance. Using virus surveillance data from passive and active AIV wild bird surveillance, 2006−2020, we investigated the association between the presence of AIV and a range of landscape factors and game bird release. Furthermore, we assessed potential bias in the passive AIV surveillance data submitted by the public, via factors related to public accessibility. Lastly, we tested the AIV data for possible hot‐ and cold spots within Denmark. The passive surveillance data was biased regarding accessibility to areas (distance to roads, cities and coast) compared to random locations within Denmark. For both the passive and active AIV surveillance data, we found significant (p < .01) associations with variables related to coast, wetlands and cities, but not game bird release. We used these variables to predict the risk of AIV presence throughout Denmark, and found high‐risk areas concentrated along the coast and fjords. For both passive and active surveillance data, low‐risk clusters were mainly seen in Jutland and northern Zealand, whereas high‐risk clusters were found in Jutland, Zealand, Funen and the southern Isles such as Lolland and Falster. Our results suggest that landscape affects AIV presence, as coastal areas and wetlands attract waterfowl and migrating birds and therefore might increase the potential for AIV transmission. Our findings have enabled us to create risk maps of AIV presence in wild birds and pinpoint high‐risk clusters within Denmark. This will aid targeted surveillance efforts within Denmark and potentially aid in planning the location of future poultry farms.

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.

Possible ramifications of climate variability on HPAI-H5N1 outbreak occurrence: Case study from the Menoufia, Egypt

Long endemicity of the Highly Pathogenic Avian Influenza (HPAI) H5N1 subtype in Egypt poses a lot of threats to public health. Contrary to what is previously known, outbreaks have been circulated continuously in the poultry sectors all year round without seasonality. These changes call the need for epidemiological studies to prove or deny the influence of climate variability on outbreak occurrence, which is the aim of this study. This work proposes a modern approach to examine the degree to which the HPAI-H5N1disease event is being influenced by climate variability as a potential risk factor using generalized estimating equations (GEEs). GEE model revealed that the effect of climate variability differs according to the timing of the outbreak occurrence. Temperature and relative humidity could have both positive and negative effects on disease events. During the cold seasons especially in the first quarter, higher minimum temperatures, consistently show higher risks of disease occurrence, because this condition stimulates viral activity, while lower minimum temperatures support virus survival in the other quarters of the year with the highest negative effect in the third quarter. On the other hand, relative humidity negatively affects the outbreak in the first quarter of the year as the humid weather does not support viral circulation, while the highest positive effect was found in the second quarter during which low humidity favors the disease event.

Incorporating heterogeneous sampling probabilities in continuous phylogeographic inference - Application to H5N1 spread in the Mekong region

MOTIVATION

The potentially low precision associated with the geographic origin of sampled sequences represents an important limitation for spatially explicit (i.e. continuous) phylogeographic inference of fast-evolving pathogens such as RNA viruses. A substantial proportion of publicly available sequences is geo-referenced at broad spatial scale such as the administrative unit of origin, rather than more precise locations (e.g. geographic coordinates). Most frequently, such sequences are either discarded prior to continuous phylogeographic inference or arbitrarily assigned to the geographic coordinates of the centroid of their administrative area of origin for lack of a better alternative.

RESULTS

We here implement and describe a new approach that allows to incorporate heterogeneous prior sampling probabilities over a geographic area. External data, such as outbreak locations, are used to specify these prior sampling probabilities over a collection of sub-polygons. We apply this new method to the analysis of highly pathogenic avian influenza H5N1 clade data in the Mekong region. Our method allows to properly include, in continuous phylogeographic analyses, H5N1 sequences that are only associated with large administrative areas of origin and assign them with more accurate locations. Finally, we use continuous phylogeographic reconstructions to analyse the dispersal dynamics of different H5N1 clades and investigate the impact of environmental factors on lineage dispersal velocities.

AVAILABILITY AND IMPLEMENTATION

Our new method allowing heterogeneous sampling priors for continuous phylogeographic inference is implemented in the open-source multi-platform software package BEAST 1.10.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Review of Antimicrobial Resistance in Poultry Farming within Low-Resource Settings

The emergence, spread, and persistence of antimicrobial resistance (AMR) remain a pressing global health issue. Animal husbandry, in particular poultry, makes up a substantial portion of the global antimicrobial use. Despite the growing body of research evaluating the AMR within industrial farming systems, there is a gap in understanding the emergence of bacterial resistance originating from poultry within resource-limited environments. As countries continue to transition from low- to middle income countries (LMICs), there will be an increased demand for quality sources of animal protein. Further promotion of intensive poultry farming could address issues of food security, but it may also increase risks of AMR exposure to poultry, other domestic animals, wildlife, and human populations. Given that intensively raised poultry can function as animal reservoirs for AMR, surveillance is needed to evaluate the impacts on humans, other animals, and the environment. Here, we provide a comprehensive review of poultry production within low-resource settings in order to inform future small-scale poultry farming development. Future research is needed in order to understand the full extent of the epidemiology and ecology of AMR in poultry within low-resource settings.

Modelling the pandemic: attuning models to their contexts

The evidence produced in mathematical models plays a key role in shaping policy decisions in pandemics. A key question is therefore how well pandemic models relate to their implementation contexts. Drawing on the cases of Ebola and influenza, we map how sociological and anthropological research contributes in the modelling of pandemics to consider lessons for COVID-19. We show how models detach from their implementation contexts through their connections with global narratives of pandemic response, and how sociological and anthropological research can help to locate models differently. This potentiates multiple models of pandemic response attuned to their emerging situations in an iterative and adaptive science. We propose a more open approach to the modelling of pandemics which envisages the model as an intervention of deliberation in situations of evolving uncertainty. This challenges the 'business-as-usual' of evidence-based approaches in global health by accentuating all science, within and beyond pandemics, as 'emergent' and 'adaptive'.

Transmission of highly pathogenic avian influenza in the nomadic free-grazing duck production system in Viet Nam

The presence of free-grazing ducks (FGD) has consistently been shown to be associated with highly pathogenic avian influenza virus (HPAIV) H5N1 outbreaks in South-East Asia. However, the lack of knowledge about the transmission pathways limits the effectiveness of control efforts. To address this gap, we developed a probabilistic transmission model of HPAIV H5N1 in the nomadic FGD production system in Viet Nam, assuming different scenarios to address parameter uncertainty. Results suggested that HPAIV H5N1 could spread within the nomadic FGD production system, with an estimated flock-level effective reproduction number (r_e) ranging from 2.16 (95% confidence interval (CI): 1.39-3.49) to 6.10 (95%CI: 3.93-9.85) depending on the scenario. Indirect transmission via boats and trucks was shown to be the main transmission route in all scenarios. Results suggest that r_e could be reduced below one with 95% confidence if 86% of FGD flocks were vaccinated in the best-case scenario or 95% in the worst-case scenario. If vaccination was combined with cleaning and disinfection of transport vehicles twice a week, vaccination coverage could be lowered to 60% in the best-case scenario. These findings are of particular relevance for prioritising interventions for effective control of HPAIV in nomadic free-grazing duck production systems.

Ecosystem Service Loss in Response to Agricultural Expansion in the Small Sanjiang Plain, Northeast China: Process, Driver and Management

Converting natural ecosystems to cultivated land, driven by human activities, has been considered a significant driver of limiting the delivery of ecosystem services (ES). The ES loss in the past was mainly caused by agricultural activities that have been taken to meet people’s needs in Northeast China. Quantifying historical declining ecosystem service values is essential to facilitate sustainable development. In this study, remote sensing images were used to investigate the history of cultivated land expansion over the last five decades. Additionally, ES variations caused by agricultural expansion since 1965 were quantified in the Small Sanjiang Plain (SSP), Northeast China. From the results, cultivated land expanded from 3.97% of the total SSP area to 66.40% from 1965 to 2015 (approximately 898.23 million ha), of which paddy field expanded drastically from 0% to 55.93%. Variations in cultivated land resulted in a loss of ecosystem service values by 11,893.85 million dollars, of which 62.98 million dollars were caused by the internal conversion between cultivation during 1965–2015. Agricultural expansion accelerated the export of agricultural products function, while it decreased almost all other functions, especially hydrological regulation and freshwater supply function. For future sustainability of the SSP, some suggestions, such as restoring natural ecosystems, planting trees between cultivated land, coculture systems, and winter-flooding of paddy rice were provided in our study.

Synergistic China-US Ecological Research is Essential for Global Emerging Infectious Disease Preparedness

The risk of a zoonotic pandemic disease threatens hundreds of millions of people. Emerging infectious diseases also threaten livestock and wildlife populations around the world and can lead to devastating economic damages. China and the USA-due to their unparalleled resources, widespread engagement in activities driving emerging infectious diseases and national as well as geopolitical imperatives to contribute to global health security-play an essential role in our understanding of pandemic threats. Critical to efforts to mitigate risk is building upon existing investments in global capacity to develop training and research focused on the ecological factors driving infectious disease spillover from animals to humans. International cooperation, particularly between China and the USA, is essential to fully engage the resources and scientific strengths necessary to add this ecological emphasis to the pandemic preparedness strategy. Here, we review the world's current state of emerging infectious disease preparedness, the ecological and evolutionary knowledge needed to anticipate disease emergence, the roles that China and the USA currently play as sources and solutions to mitigating risk, and the next steps needed to better protect the global community from zoonotic disease.

HopPER: an adaptive model for probability estimation of influenza reassortment through host prediction

Background

Influenza reassortment, a mechanism where influenza viruses exchange their RNA segments by co-infecting a single cell, has been implicated in several major pandemics since 19th century. Owing to the significant impact on public health and social stability, great attention has been received on the identification of influenza reassortment.

Methods

We proposed a novel computational method named HopPER (Host-prediction-based Probability Estimation of Reassortment), that sturdily estimates reassortment probabilities through host tropism prediction using 147 new features generated from seven physicochemical properties of amino acids. We conducted the experiments on a range of real and synthetic datasets and compared HopPER with several state-of-the-art methods.

Results

It is shown that 280 out of 318 candidate reassortants have been successfully identified. Additionally, not only can HopPER be applied to complete genomes but its effectiveness on incomplete genomes is also demonstrated. The analysis of evolutionary success of avian, human and swine viruses generated through reassortment across different years using HopPER further revealed the reassortment history of the influenza viruses.

Conclusions

Our study presents a novel method for the prediction of influenza reassortment. We hope this method could facilitate rapid reassortment detection and provide novel insights into the evolutionary patterns of influenza viruses.

Emerging infectious disease, the household built environment characteristics, and urban planning: Evidence on avian influenza in Vietnam

Recent concerns with pandemic outbreaks of human disease and their origins in animal populations have ignited concerns regarding connections between Emerging Infectious Diseases (EID) and development. As disasters, health, and infectious disease become part of planning concern (Matthew & McDonald, 2007), greater focus on household infrastructure and EID disease outbreaks among poultry is warranted. Following Spencer (2013), this study examines the relationship between the mix of household-scale water supplies, sanitation systems, and construction materials, and Highly Pathogenic Avian Influenza (HPAI) among poultry in a developing country: Vietnam. Findings of our multivariate logistic regressions suggest that a non-linear, Kuznets-shaped urban transition (Spencer, 2013) has an independent effect on the outbreak of HPAI, especially as it relates to household-level sanitation infrastructure. We conclude that the Kuznets-shape development of household infrastructure characteristics in Vietnam play a significant role in explaining where poultry outbreaks occur. Using secondary data from the Census of Population and Housing, and the Agricultural Census at the District and Commune levels for the country of Vietnam, we performed logistic regression to test the relationship between outbreaks of HPAI in poultry and newly-developed "coherence indices" (Spencer, 2013) of household water supply, sanitation, and construction materials that measure nonlinear, transitional development. Results show that district-scale coherence indices are negatively and independently correlated with HPAI outbreaks, especially for sanitation. Findings also suggest that community-scale coherence of urban infrastructures is a powerful tool for predicting where HPAI poultry outbreaks are likely to occur, thereby providing health planners new tools for efficient surveillance.

Quantifying the spatial risk of Avian Influenza introduction into British poultry by wild birds

The transmission of pathogens across the interface between wildlife and livestock presents a challenge to the development of effective surveillance and control measures. Wild birds, especially waterbirds such as the Anseriformes and Charadriiformes are considered to be the natural hosts of Avian Influenza (AI), and are presumed to pose one of the most likely vectors for incursion of AI into European poultry flocks. We have developed a generic quantitative risk map, derived from the classical epidemiological risk equation, to describe the relative, spatial risk of disease incursion into poultry flocks via wild birds. We then assessed the risk for AI incursion into British flocks. The risk map suggests that the majority of AI incursion risk is highly clustered within certain areas of Britain, including in the east, the south west and the coastal north-west of England. The clustering of high risk areas concentrates total risk in a relatively small land area; the top 33% of cells contribute over 80% of total incursion risk. This suggests that targeted risk-based sampling in a relatively small geographical area could be a much more effective and cost-efficient approach than representative sampling. The generic nature of the risk map method, allows rapid updating and application to other diseases transmissible between wild birds and poultry.

Game theory of vaccination and depopulation for managing livestock diseases and zoonoses on small-scale farms

Livestock producers adapt their farm management to epidemiological risks in different ways, through veterinary interventions but also by modulating their farm size and the removal rate of animals. The objective of this theoretical study was to elucidate how these behavioral adaptations may affect the epidemiology of highly-pathogenic avian influenza in domestic poultry and the outcome of the implemented control policies. We studied a symmetric population game where the players are broiler poultry farmers at risk of infection and where the between-farms disease transmission is both environmental and mediated by poultry trade. Three types of farmer behaviors were modelled: vaccination, depopulation, and cessation of poultry farming. We found that the transmission level of the disease through trade networks has strong qualitative effects on the system's epidemiological-economic equilibria. In the case of low trade-based transmission, when the monetary cost of infection is high, depopulation behavior can maintain a stable disease-free equilibrium. In addition, vaccination behavior can lead to eradication by private incentives alone - an outcome not seen for human diseases. In a scenario of high trade-based transmission, depopulation behavior has perverse epidemiological effects as it accelerates the spread of disease via poultry trade. In this situation, state interventions should focus on making vaccination technologies available at a low price rather than penalizing infected farms.

Agricultural land-uses consistently exacerbate infectious disease risks in Southeast Asia

Agriculture has been implicated as a potential driver of human infectious diseases. However, the generality of disease-agriculture relationships has not been systematically assessed, hindering efforts to incorporate human health considerations into land-use and development policies. Here we perform a meta-analysis with 34 eligible studies and show that people who live or work in agricultural land in Southeast Asia are on average 1.74 (CI 1.47-2.07) times as likely to be infected with a pathogen than those unexposed. Effect sizes are greatest for exposure to oil palm, rubber, and non-poultry based livestock farming and for hookworm (OR 2.42, CI 1.56-3.75), malaria (OR 2.00, CI 1.46-2.73), scrub typhus (OR 2.37, CI 1.41-3.96) and spotted fever group diseases (OR 3.91, CI 2.61-5.85). In contrast, no change in infection risk is detected for faecal-oral route diseases. Although responses vary by land-use and disease types, results suggest that agricultural land-uses exacerbate many infectious diseases in Southeast Asia.

Highly Pathogenic Avian Influenza A(H5N1) Outbreaks in West Java Indonesia 2015-2016: Clinical Manifestation and Associated Risk Factors

Knowledge of outbreaks and associated risk factors is helpful to improve control of the Highly Pathogenic Avian Influenza A(H5N1) virus (HPAI) in Indonesia. This study was conducted to detect outbreaks of HPAI H5N1 in endemically infected regions by enhanced passive surveillance, to describe the clinical manifestation of these outbreaks and identify associated risk factors. From November 2015 to November 2016, HPAI outbreak investigations were conducted in seven districts of West Java. In total 64 outbreaks were confirmed out of 75 reported suspicions and outbreak characteristics were recorded. The highest mortality was reported in backyard chickens (average 59%, CI95%: 49-69%). Dermal apoptosis and lesions (64%, CI95%: 52-76%) and respiratory signs (39%, CI95%: 27-51%) were the clinical signs observed overall most frequently, while neurological signs were most frequently observed in ducks (68%, CI95%: 47-90%). In comparison with 60 non-infected control farms, the rate of visitor contacts onto a farm was associated with the odds of HPAI infection. Moreover, duck farms had higher odds of being infected than backyard farms, and larger farms had lower odds than small farms. Results indicate that better external biosecurity is needed to reduce transmission of HPAI A(H5N1) in Indonesia.

Mapping Paddy Rice Planting Area in Northeastern China Using Spatiotemporal Data Fusion and Phenology-Based Method

Accurate paddy rice mapping with fine spatial detail is significant for ensuring food security and maintaining sustainable environmental development. In northeastern China, rice is planted in fragmented and patchy fields and its production has reached over 10% of the total amount of rice production in China, which has brought the increasing need for updated paddy rice maps in the region. Existing methods for mapping paddy rice are often based on remote sensing techniques by using optical images. However, it is difficult to obtain high quality time series remote sensing data due to the frequent cloud cover in rice planting area and low temporal sampling frequency of satellite imagery. Therefore, paddy rice maps are often developed using few Landsat or time series MODIS images, which has limited the accuracy of paddy rice mapping. To overcome these limitations, we presented a new strategy by integrating a spatiotemporal fusion algorithm and phenology-based algorithm to map paddy rice fields. First, we applied the spatial and temporal adaptive reflectance fusion model (STARFM) to fuse the Landsat and MODIS data and obtain multi-temporal Landsat-like images. From the fused Landsat-like images and the original Landsat images, we derived time series vegetation indices (VIs) with high temporal and high spatial resolution. Then, the phenology-based algorithm, considering the unique physical features of paddy rice during the flooding and transplanting phases/open-canopy period, was used to map paddy rice fields. In order to prove the effectiveness of the proposed strategy, we compared our results with those from other three classification strategies: (1) phenology-based classification based on original Landsat images only, (2) phenology-based classification based on original MODIS images only and (3) random forest (RF) classification based on both Landsat and Landsat-like images. The validation experiments indicate that our fusion-and phenology-based strategy could improve the overall accuracy of classification by 6.07% (from 92.12% to 98.19%) compared to using Landsat data only, and 8.96% (from 89.23% to 98.19%) compared to using MODIS data, and 4.66% (from93.53% to 98.19%) compared to using the RF algorithm. The results show that our new strategy, by integrating the spatiotemporal fusion algorithm and phenology-based algorithm, can provide an effective and robust approach to map paddy rice fields in regions with limited available images, as well as the areas with patchy and fragmented fields.

Poultry population dynamics and mortality risks in smallholder farms of the Mekong river delta region

BACKGROUND

Poultry farming is widely practiced by rural households in Vietnam and the vast majority of domestic birds are kept on small household farms. However, smallholder poultry production is constrained by several issues such as infectious diseases, including avian influenza viruses whose circulation remains a threat to public health. This observational study describes the demographic structure and dynamics of small-scale poultry farms of the Mekong river delta region.

METHOD

Fifty three farms were monitored over a 20-month period, with farm sizes, species, age, arrival/departure of poultry, and farm management practices recorded monthly.

RESULTS

Median flock population sizes were 16 for chickens (IQR: 10-40), 32 for ducks (IQR: 18-101) and 11 for Muscovy ducks (IQR: 7-18); farm size distributions for the three species were heavily right-skewed. Muscovy ducks were kept for long periods and outdoors, while chickens and ducks were farmed indoors or in pens. Ducks had a markedly higher removal rate (broilers: 0.14/week; layer/breeders: 0.05/week) than chickens and Muscovy ducks (broilers: 0.07/week; layer/breeders: 0.01-0.02/week) and a higher degree of specialization resulting in a substantially shorter life span. The rate of mortality due to disease did not differ much among species, with birds being less likely to die from disease at older ages, but frequency of disease symptoms differed by species. Time series of disease-associated mortality were correlated with population size for Muscovy ducks (Kendall's coefficient τ = 0.49, p-value < 0.01) and with frequency of outdoor grazing for ducks (τ = 0.33, p-value = 0.05).

CONCLUSION

The study highlights some challenges to disease control in small-scale multispecies poultry farms. The rate of interspecific contact and overlap between flocks of different ages is high, making small-scale farms a suitable environment for pathogens circulation. Muscovy ducks are farmed outdoors with little investment in biosecurity and few inter-farm movements. Ducks and chickens are more at-risk of introduction of pathogens through movements of birds from one farm to another. Ducks are farmed in large flocks with high turnover and, as a result, are more vulnerable to disease spread and require a higher vaccination coverage to maintain herd immunity.

Epidemiological Characteristics and Spatial Analysis of Tick-Borne Encephalitis in Jilin Province, China

Tick-borne encephalitis (TBE) is a viral infectious disease and has become a reemerging public health threat in recent years in northeastern China. However, no studies has characterized the epidemiologic features and explored the spatial dynamics and environmental factors of TBE cases in Jilin Province. In this study, we have described the epidemiological features of 846 reported human TBE cases from 2006 to 2016 in Jilin Province. There was an obvious single peak pattern of TBE cases from May to July in Jilin Province. More than 60% of TBE cases occurred in farmers, and the people in 50- to 59-year-old group had the high incidence of the disease. The results of Getis-Ord Gi* statistics demonstrated that the human TBE cases were more clustered in the northeastern border including Dunhua and Yanji cities and Antu and Wangqing counties, and southern areas including Huinan, Jingyu, Jiangyuan, and Liuhe counties in Jilin Province. We demonstrated that the temporal dynamics of TBE in Jilin was significantly associated with the dynamics of meteorological factors especially after 2009. The results from the auto-logistic regression analysis showed that the percentage coverage of forest, temperature, and autoregressive term were significantly associated with the occurrence of human TBE cases in Jilin Province. Our findings will provide a scientific evidence for the targeted prevention and control programs.

Spatial modelling for low pathogenicity avian influenza virus at the interface of wild birds and backyard poultry

Low pathogenicity avian influenza virus (LPAIV) is endemic in wild birds and poultry in Argentina, and active surveillance has been in place to prevent any eventual virus mutation into a highly pathogenic avian influenza virus (HPAIV), which is exotic in this country. Risk mapping can contribute effectively to disease surveillance and control systems, but it has proven a very challenging task in the absence of disease data. We used a combination of expert opinion elicitation, multicriteria decision analysis (MCDA) and ecological niche modelling (ENM) to identify the most suitable areas for the occurrence of LPAIV at the interface between backyard domestic poultry and wild birds in Argentina. This was achieved by calculating a spatially explicit risk index. As evidenced by the validation and sensitivity analyses, our model was successful in identifying high-risk areas for LPAIV occurrence. Also, we show that the risk for virus occurrence is significantly higher in areas closer to commercial poultry farms. Although the active surveillance systems have been successful in detecting LPAIV-positive backyard farms and wild birds in Argentina, our predictions suggest that surveillance efforts in those compartments could be improved by including high-risk areas identified by our model. Our research provides a tool to guide surveillance activities in the future, and presents a mixed methodological approach which could be implemented in areas where the disease is exotic or rare and a knowledge-driven modelling method is necessary.

High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data

Knowledge of where, when, and how much paddy rice is planted is crucial information for understating of regional food security, freshwater use, climate change, and transmission of avian influenza virus. We developed seasonal paddy rice maps at high resolution (10 m) for Bangladesh and Northeast India, typical cloud-prone regions in South Asia, using cloud-free Synthetic Aperture Radar (SAR) images from Sentinel-1 satellite, the Random Forest classifier, and the Google Earth Engine (GEE) cloud computing platform. The maps were provided for all the three distinct rice growing seasons of the region: Boro, Aus and Aman. The paddy rice maps were evaluated against the independent validation samples, and compared with the existing products from the International Rice Research Institute (IRRI) and the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) data. The generated paddy rice maps were spatially consistent with the compared maps and had a satisfactory accuracy over 90%. This study showed the potential of Sentinel-1 data and GEE on large scale paddy rice mapping in cloud-prone regions like tropical Asia.

Design Type(s)	image analysis objective • observational design
Measurement Type(s)	rice field
Technology Type(s)	satellite imaging
Factor Type(s)	season • geographic location
Sample Characteristic(s)	Bangladesh • paddy field • India

Machine-accessible metadata file describing the reported data (ISA-Tab format)

A spatial assessment of Nipah virus transmission in Thailand pig farms using multi-criteria decision analysis

BACKGROUND

Thailand's Central Plain is identified as a contact zone between pigs and flying foxes, representing a potential zoonotic risk. Nipah virus (NiV) has been reported in flying foxes in Thailand, but it has never been found in pigs or humans. An assessment of the suitability of NiV transmission at the spatial and farm level would be useful for disease surveillance and prevention. Multi-criteria decision analysis (MCDA), a knowledge-driven model, was used to map contact zones between local epizootic risk factors as well as to quantify the suitability of NiV transmission at the pixel and farm level.

RESULTS

Spatial risk factors of NiV transmission in pigs were identified by experts as being of three types, including i) natural host factors (bat preferred areas and distance to the nearest bat colony), ii) intermediate host factors (pig population density), and iii) environmental factors (distance to the nearest forest, distance to the nearest orchard, distance to the nearest water body, and human population density). The resulting high suitable areas were concentrated around the bat colonies in three provinces in the East of Thailand, including Chacheongsao, Chonburi, and Nakhonnayok. The suitability of NiV transmission in pig farms in the study area was quantified as ranging from very low to medium suitability.

CONCLUSIONS

We believe that risk-based surveillance in the identified priority areas may increase the chances of finding out NiV and other bat-borne pathogens and thereby optimize the allocation of financial resources for disease surveillance. In the long run, improvements of biosecurity in those priority areas may also contribute to preventing the spread of potential emergence of NiV and other bat-borne pathogens.

Experimental Evaluation of the Role of Ecologically-Relevant Hosts and Vectors in Japanese Encephalitis Virus Genotype Displacement

Japanese encephalitis virus (JEV) is a flavivirus that is maintained via transmission between Culex spp. mosquitoes and water birds across a large swath of southern Asia and northern Australia. Currently JEV is the leading cause of vaccine-preventable encephalitis in humans in Asia. Five genotypes of JEV (G-I–G-V) have been responsible for historical and current outbreaks in endemic regions, and G-I and G-III co-circulate throughout Southern Asia. While G-III has historically been the dominant genotype worldwide, G-I has gradually but steadily displaced G-III. The objective of this study was to better understand the phenomenon of genotype displacement for JEV by evaluating both avian host and mosquito vector susceptibilities to infection with representatives from both G-I and G-III. Since ducks and Culex quinquefasciatus mosquitoes are prevalent avian hosts and vectors perpetuating JEV transmission in JE endemic areas, experimental evaluation of virus replication in these species was considered to approximate the natural conditions necessary for studying the role of host, vectors and viral fitness in the JEV genotype displacement context. We evaluated viremia in ducklings infected with G-I and G-III, and did not detect differences in magnitude or duration of viremia. Testing the same viruses in mosquitoes revealed that the rates of infection, dissemination and transmission were higher in virus strains belonging to G-I than G-III, and that the extrinsic incubation period was shorter for the G-I strains. These data suggest that the characteristics of JEV infection of mosquitoes but not of ducklings, may have play a role in genotype displacement.

The live poultry trade and the spread of highly pathogenic avian influenza: Regional differences between Europe, West Africa, and Southeast Asia

In the past two decades, avian influenzas have posed an increasing international threat to human and livestock health. In particular, highly pathogenic avian influenza H5N1 has spread across Asia, Africa, and Europe, leading to the deaths of millions of poultry and hundreds of people. The two main means of international spread are through migratory birds and the live poultry trade. We focus on the role played by the live poultry trade in the spread of H5N1 across three regions widely infected by the disease, which also correspond to three major trade blocs: the European Union (EU), the Economic Community of West African States (ECOWAS), and the Association of Southeast Asian Nations (ASEAN). Across all three regions, we found per-capita GDP (a proxy for modernization, general biosecurity, and value-at-risk) to be risk reducing. A more specific biosecurity measure-general surveillance-was also found to be mitigating at the all-regions level. However, there were important inter-regional differences. For the EU and ASEAN, intra-bloc live poultry imports were risk reducing while extra-bloc imports were risk increasing; for ECOWAS the reverse was true. This is likely due to the fact that while the EU and ASEAN have long-standing biosecurity standards and stringent enforcement (pursuant to the World Trade Organization's Agreement on the Application of Sanitary and Phytosanitary Measures), ECOWAS suffered from a lack of uniform standards and lax enforcement.

Identifying key bird species and geographical hotspots of avian influenza A (H7N9) virus in China

BACKGROUND

In China since the first human infection of avian influenza A (H7N9) virus was identified in 2013, it has caused serious public health concerns due to its wide spread and high mortality rate. Evidence shows that bird migration plays an essential role in global spread of avian influenza viruses. Accordingly, in this paper, we aim to identify key bird species and geographical hotspots that are relevant to the transmission of avian influenza A (H7N9) virus in China.

METHODS

We first conducted phylogenetic analysis on 626 viral sequences of avian influenza A (H7N9) virus isolated in chicken, which were collected from the Global Initiative on Sharing All Influenza Data (GISAID), to reveal geographical spread and molecular evolution of the virus in China. Then, we adopted the cross correlation function (CCF) to explore the relationship between the identified influenza A (H7N9) cases and the spatiotemporal distribution of migratory birds. Here, the spatiotemporal distribution of bird species was generated based on bird observation data collected from China Bird Reports, which consists of 157 272 observation records about 1145 bird species. Finally, we employed a kernel density estimator to identify geographical hotspots of bird habitat/stopover that are relevant to the influenza A (H7N9) infections.

RESULTS

Phylogenetic analysis reveals the evolutionary and geographical patterns of influenza A (H7N9) infections, where cases in the same or nearby municipality/provinces are clustered together with small evolutionary differences. Moreover, three epidemic waves in chicken along the East Asian-Australasian flyway in China are distinguished from the phylogenetic tree. The CCF analysis identifies possible migratory bird species that are relevant to the influenza A(H7N9) infections in Shanghai, Jiangsu, Zhejiang, Fujian, Jiangxi, and Guangdong in China, where the six municipality/provinces account for 91.2% of the total number of isolated H7N9 cases in chicken in GISAID. Based on the spatial distribution of identified bird species, geographical hotspots are further estimated and illustrated within these typical municipality/provinces.

CONCLUSIONS

In this paper, we have identified key bird species and geographical hotspots that are relevant to the spread of influenza A (H7N9) virus. The results and findings could provide sentinel signal and evidence for active surveillance, as well as strategic control of influenza A (H7N9) transmission in China.

Dynamics of the 2004 avian influenza H5N1 outbreak in Thailand: The role of duck farming, sequential model fitting and control

The Highly Pathogenic Avian Influenza (HPAI) subtype H5N1 virus persists in many countries and has been circulating in poultry, wild birds. In addition, the virus has emerged in other species and frequent zoonotic spillover events indicate that there remains a significant risk to human health. It is crucial to understand the dynamics of the disease in the poultry industry to develop a more comprehensive knowledge of the risks of transmission and to establish a better distribution of resources when implementing control. In this paper, we develop a set of mathematical models that simulate the spread of HPAI H5N1 in the poultry industry in Thailand, utilising data from the 2004 epidemic. The model that incorporates the intensity of duck farming when assessing transmision risk provides the best fit to the spatiotemporal characteristics of the observed outbreak, implying that intensive duck farming drives transmission of HPAI in Thailand. We also extend our models using a sequential model fitting approach to explore the ability of the models to be used in “real time” during novel disease outbreaks. We conclude that, whilst predictions of epidemic size are estimated poorly in the early stages of disease outbreaks, the model can infer the preferred control policy that should be deployed to minimise the impact of the disease.

Use of Bayesian networks in predicting contamination of drinking water with E. coli in rural Vietnam

Background

More than 70 million Vietnamese rely on small-scale farming for some form of household income. Water on many of those farms is contaminated with waste, including animal manure, partly due to non-sustainable waste management. This increases the risk of water-related zoonotic disease transmission. The purpose of this research was to examine the impact of various demographic and management factors on the likelihood of finding Escherichia coli in drinking water sourced from wells and rainwater on farms in Vietnam.

Methods

A Bayesian Belief Network (BBN) was designed to describe association between various deterministic and probabilistic variables gathered from 600 small-scale integrated (SSI) farmers in Vietnam. The variables relate to E. coli content of their drinking water sourced on-farm from wells and rainwater, and stored in on-farm large vessels, including concrete water tanks. The BBN was developed using the Netica software tool; the model was calibrated and goodness of fit examined using concordance of predictability.

Results

Sensitivity analysis of the model revealed that choice variables, including engagement in mitigation of water contamination and livestock management activities, were particularly likely to influence endpoint values, reflecting the highly variable and impactful nature of preferences, attitudes and beliefs relating to mitigation strategies. Quantitative variables including numbers of livestock (particularly chickens) and income also had a high impact. The highest concordance (62%) was achieved with the BBN reported in this paper.

Conclusions

This BBN model of SSI farming in Vietnam is helpful in understanding the complexity of small-scale agriculture and how various factors work in concert to influence contamination of on-farm drinking water as indicated by the presence of E. coli. The model will also be useful for identifying and estimating the impact of policy options such as improved delivery of clean water management training for rural areas, particularly where such analysis is combined with other analytical and policy tools. With appropriate knowledge translation, the model results will be particularly useful in helping SSI farmers understand their options for engaging in public health mitigation strategies addressing clean water that do not significantly disrupt their agriculture-based livelihoods.

Comparative Epidemiology of Highly Pathogenic Avian Influenza Virus H5N1 and H5N6 in Vietnamese Live Bird Markets: Spatiotemporal Patterns of Distribution and Risk Factors

Highly pathogenic avian influenza (HPAI) H5N1 virus has been circulating in Vietnam since 2003, whilst outbreaks of HPAI H5N6 virus are more recent, having only been reported since 2014. Although the spatial distribution of H5N1 outbreaks and risk factors for virus occurrence has been extensively studied, there have been no comparative studies for H5N6. Data collected through active surveillance of Vietnamese live bird markets (LBMs) between 2011 and 2015 were used to explore and compare the spatiotemporal distributions of H5N1- and H5N6-positive LBMs. Conditional autoregressive models were developed to quantify spatiotemporal associations between agroecological factors and the two HPAI strains using the same set of predictor variables. Unlike H5N1, which exhibited a strong north–south divide, with repeated occurrence in the extreme south of a cluster of high-risk provinces, H5N6 was homogeneously distributed throughout Vietnam. Similarly, different agroecological factors were associated with each strain. Sample collection in the months of January and February and higher average maximum temperature were associated with higher likelihood of H5N1-positive market-day status. The likelihood of market days being positive for H5N6 increased with decreased river density, and with successive Rounds of data collection. This study highlights marked differences in spatial patterns and risk factors for H5N1 and H5N6 in Vietnam, suggesting the need for tailored surveillance and control approaches.

Potential Biological and Climatic Factors That Influence the Incidence and Persistence of Highly Pathogenic H5N1 Avian Influenza Virus in Egypt

Highly pathogenic H5N1 avian influenza virus (A/H5N1) of clade 2.2.1 is endemic in poultry in Egypt where the highest number of human infections worldwide was reported. During the last 12 years the Egyptian A/H5N1 evolved into several genotypes. In 2007-2014 vaccinated poultry suffered from antigenic drift variants of clade 2.2.1.1 and in 2014/2015 an unprecedented upsurge of A/H5N1 clade 2.2.1.2 occurred in poultry and humans. Factors contributing to the endemicity or re-emergence of A/H5N1 in poultry in Egypt remain unclear. Here, three potential factors were studied: climatic factors (temperature, relative humidity, and wind speed), biological fitness in vitro, and pathogenicity in domestic Pekin and Muscovy ducks. Statistical analyses using negative binomial regression models indicated that ambient temperature in winter months influenced the spread of A/H5N1 in different geographic areas analyzed in this study. In vitro, at 4 and 56°C 2.2.1.1 and recent 2.2.1.2 viruses were more stable than other viruses used in this study. Further, Pekin ducks were more resistant than Muscovy ducks and the viruses were excreted for up to 2 weeks post-infection assuming a strong role as a reservoir. Taken together, ambient temperature in winter months potentially contributes to increasing outbreaks in some regions in Egypt. Heat stability of clade 2.2.1.1 and recent 2.2.1.2 viruses probably favors their persistence at elevated temperatures. Importantly, asymptomatically infected Pekin ducks may play an important role in the spread of avian and human-like A/H5N1 in Egypt. Therefore, control measures including targeted surveillance and culling of silently infected Pekin ducks should be considered.

Matched case-control study of the influence of inland waters surrounding poultry farms on avian influenza outbreaks in Japan

To successfully control highly pathogenic avian influenza (HPAI), understanding the risk factors related to the incursion of the virus into poultry farms is essential. In this study, we focused on the presence of inland waters surrounding poultry farms as a potential risk factor of incursion of the virus. To evaluate the influence of inland waters surrounding poultry farms on HPAI outbreaks in Japan, a simple matched case-control study was conducted. The results of the conditional regression analyses indicated that the number of farms with neighbouring inland waters was significantly high among the affected farms during the 2016-2017 outbreak period. These results provide good grounds for strengthening biosecurity management at farms located near inland waters.

Identification of high risk areas for avian influenza outbreaks in California using disease distribution models

The coexistence of different types of poultry operations such as free range and backyard flocks, large commercial indoor farms and live bird markets, as well as the presence of many areas where wild and domestic birds co-exist, make California susceptible to avian influenza outbreaks. The 2014-2015 highly pathogenic Avian Influenza (HPAI) outbreaks affecting California and other states in the United States have underscored the need for solutions to protect the US poultry industry against this devastating disease. We applied disease distribution models to predict where Avian influenza is likely to occur and the risk for HPAI outbreaks is highest. We used observations on the presence of Low Pathogenic Avian influenza virus (LPAI) in waterfowl or water samples at 355 locations throughout the state and environmental variables relevant to the disease epidemiology. We used two algorithms, Random Forest and MaxEnt, and two data-sets Presence-Background and Presence-Absence data. The models performed well (AUCc > 0.7 for testing data), particularly those using Presence-Background data (AUCc > 0.85). Spatial predictions were similar between algorithms, but there were large differences between the predictions with Presence-Absence and Presence-Background data. Overall, predictors that contributed most to the models included land cover, distance to coast, and broiler farm density. Models successfully identified several counties as high-to-intermediate risk out of the 8 counties with observed outbreaks during the 2014-2015 HPAI epizootics. This study provides further insights into the spatial epidemiology of AI in California, and the high spatial resolution maps may be useful to guide risk-based surveillance and outreach efforts.

Conservation, development and the management of infectious disease: avian influenza in China, 2004-2012

There is growing evidence that wildlife conservation measures have mixed effects on the emergence and spread of zoonotic disease. Wildlife conservation has been found to have both positive (dilution) and negative (contagion) effects. In the case of avian influenza H5N1 in China, the focus has been on negative effects. Lakes and wetlands attracting migrating waterfowl have been argued to be disease hotspots. We consider the implications of waterfowl conservation for H5N1 infections in both poultry and humans between 2004 and 2012. We model both environmental and economic risk factors. Environmental risk factors comprise the conditions that structure interaction between wild and domesticated birds. Economic risk factors comprise the cost of disease, biosecurity measures and disease risk mitigation. We find that H5N1 outbreaks in poultry populations are indeed sensitive to the existence of wild-domesticated bird mixing zones, but not in the way we would expect from the literature. We find that risk is decreasing in protected migratory bird habitat. Since the number of human cases is increasing in the number of poultry outbreaks, as expected, the implication is that the protection of wetlands important for migratory birds offers unexpected human health benefits.This article is part of the themed issue 'Conservation, biodiversity and infectious disease: scientific evidence and policy implications'.

Lineage-specific epitope profiles for HPAI H5 pre-pandemic vaccine selection and evaluation

BACKGROUND

Multiple highly pathogenic avian influenza (HPAI) H5 viruses continue to co-circulate. This has complicated pandemic preparedness and confounded effective vaccine candidate selection and evaluation.

OBJECTIVES

In this study, we aimed to predict and map the diversity of CD8+ T-cell epitopes among H5 hemagglutinin (HA) gene lineages to estimate CD8+ T-cell immunity in humans induced by vaccine candidates.

METHODS

A dataset consisting of 1125 H5 HA sequences collected between 1996 and 2017 from avian and humans was assembled for phylogenetic and lineage-specific epitope analyses. Conserved epitopes were predicted from WHO-endorsed vaccine candidates and representative clade-defining strains by pairwise comparison with Immune Epitope Database (IEDB). The distribution of predicted epitopes was mapped to each HPAI H5 lineage. We assume that high similarity and conservancy of predicted epitopes from vaccine candidates among all circulating HPAI H5 lineages is correlated with high immunity.

RESULTS

A total of 49 conserved CD8+ T-cell epitopes were predicted at 28 different amino acid positions of the HA protein. Mapping these epitopes to the phylogenetic tree allowed us to develop epitope profiles, or "fingerprints," for each HPAI H5 lineage. Vaccine epitope percentage analyses showed some epitope profiles were highly conserved for all H5 isolates and may be valuable for universal vaccine design. However, the positions with low coverage may explain why the vaccine candidates do not always function well.

CONCLUSIONS

These findings demonstrate that our analytical approach to evaluate conserved CD8+ T-cell epitope prediction in a phylogenetic framework may provide important insights for computational design of vaccine selection and future epitope-based design.