Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia

Pathogenicity and transmission of novel highly pathogenic H7N2 variants originating from H7N9 avian influenza viruses in chickens

The H7 subtype avian influenza viruses are circulating widely worldwide, causing significant economic losses to the poultry industry and posing a serious threat to human health. In 2019, H7N2 and H7N9 co-circulated in Chinese poultry, yet the risk of H7N2 remained unclear. We isolated and sequenced four H7N2 viruses from chickens, revealing them as novel reassortants with H7N9-derived HA, M, NS genes and H9N2-derived PB2, PB1, PA,NP, NA genes. To further explore the key segment of pathogenicity, H7N2-H7N9NA and H7N2-H9N2HA single-substitution were constructed. Pathogenicity study showed H7N2 isolates to be highly pathogenic in chickens, with H7N2-H7N9NA slightly weaker than H7N2-Wild type. Transcriptomic analysis suggested that H7N9-derived HA genes primarily drove the high pathogenicity of H7N2 isolates, eliciting a strong inflammatory response. These findings underscored the increased threat posed by reassorted H7N2 viruses to chickens, emphasizing the necessity of long-term monitoring of H7 subtype avian influenza viruses.

Evaluating the epizootic and zoonotic threat of an H7N9 low-pathogenicity avian influenza virus (LPAIV) variant associated with enhanced pathogenicity in turkeys

Between 2013 and 2017, the A/Anhui/1/13-lineage (H7N9) low-pathogenicity avian influenza virus (LPAIV) was epizootic in chickens in China, causing mild disease, with 616 fatal human cases. Despite poultry vaccination, H7N9 has not been eradicated. Previously, we demonstrated increased pathogenesis in turkeys infected with H7N9, correlating with the emergence of the L217Q (L226Q H3 numbering) polymorphism in the haemagglutinin (HA) protein. A Q217-containing virus also arose and is now dominant in China following vaccination. We compared infection and transmission of this Q217-containing 'turkey-adapted' (ty-ad) isolate alongside the H7N9 (L217) wild-type (wt) virus in different poultry species and investigated the zoonotic potential in the ferret model. Both wt and ty-ad viruses demonstrated similar shedding and transmission in turkeys and chickens. However, the ty-ad virus was significantly more pathogenic than the wt virus in turkeys but not in chickens, causing 100 and 33% mortality in turkeys respectively. Expanded tissue tropism was seen for the ty-ad virus in turkeys but not in chickens, yet the viral cell receptor distribution was broadly similar in the visceral organs of both species. The ty-ad virus required exogenous trypsin for in vitro replication yet had increased replication in primary avian cells. Replication was comparable in mammalian cells, and the ty-ad virus replicated successfully in ferrets. The L217Q polymorphism also affected antigenicity. Therefore, H7N9 infection in turkeys can generate novel variants with increased risk through altered pathogenicity and potential HA antigenic escape. These findings emphasize the requirement for enhanced surveillance and understanding of A/Anhui/1/13-lineage viruses and their risk to different species.

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.

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.

An overview of avian influenza surveillance strategies and modes

The global epidemic of avian influenza has imposed a substantial disease burden, inciting substantial societal panic and economic losses. The high variability and associated uncertainty of the influenza virus present significant challenges in its prevention and control. As a pivotal strategy for the mitigation of avian influenza, the surveillance network has shown considerable growth at both global and regional levels. This includes the expansion of surveillance coverage, continuous refinement of monitoring content and scope, and rapid enhancement of monitoring quality. Although the ultimate goal of avian influenza surveillance remains uniform, strategies and models vary, reflecting regional or national differences in surveillance system frameworks and their implementation. This review collates and examines the features and experiences of global, regional, and national avian influenza surveillance efforts. Furthermore, it delves into the surveillance system modalities in light of the "One Health" concept, which includes the establishment and enhancement of interdisciplinary and cross-sectoral coordination and cooperation among medical, veterinary, and public health institutions, and the sharing of surveillance information for timely alerts.

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.

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.

Quantifying risk factors and potential geographic extent of African swine fever across the world

African swine fever (ASF) has spread to many countries in Africa, Europe and Asia in the past decades. However, the potential geographic extent of ASF infection is unknown. Here we combined a modeling framework with the assembled contemporary records of ASF cases and multiple covariates to predict the risk distribution of ASF at a global scale. Local spatial variations in ASF risk derived from domestic pigs is influenced strongly by livestock factors, while the risk of having ASF in wild boars is mainly associated with natural habitat covariates. The risk maps show that ASF is to be ubiquitous in many areas, with a higher risk in areas in the northern hemisphere. Nearly half of the world’s domestic pigs (1.388 billion) are in the high-risk zones. Our results provide a better understanding of the potential distribution beyond the current geographical scope of the disease.

Coinfection of Chickens with H9N2 and H7N9 Avian Influenza Viruses Leads to Emergence of Reassortant H9N9 Virus with Increased Fitness for Poultry and a Zoonotic Potential

An H7N9 low-pathogenicity avian influenza virus (LPAIV) emerged in 2013 through genetic reassortment between H9N2 and other LPAIVs circulating in birds in China. This virus causes inapparent clinical disease in chickens, but zoonotic transmission results in severe and fatal disease in humans. To examine a natural reassortment scenario between H7N9 and G1 lineage H9N2 viruses predominant in the Indian subcontinent, we performed an experimental coinfection of chickens with A/Anhui/1/2013/H7N9 (Anhui/13) virus and A/Chicken/Pakistan/UDL-01/2008/H9N2 (UDL/08) virus. Plaque purification and genotyping of the reassortant viruses shed via the oropharynx of contact chickens showed H9N2 and H9N9 as predominant subtypes. The reassortant viruses shed by contact chickens also showed selective enrichment of polymerase genes from H9N2 virus. The viable "6+2" reassortant H9N9 (having nucleoprotein [NP] and neuraminidase [NA] from H7N9 and the remaining genes from H9N2) was successfully shed from the oropharynx of contact chickens, plus it showed an increased replication rate in human A549 cells and a significantly higher receptor binding to α2,6 and α2,3 sialoglycans compared to H9N2. The reassortant H9N9 virus also had a lower fusion pH, replicated in directly infected ferrets at similar levels compared to H7N9 and transmitted via direct contact. Ferrets exposed to H9N9 via aerosol contact were also found to be seropositive, compared to H7N9 aerosol contact ferrets. To the best of our knowledge, this is the first study demonstrating that cocirculation of H7N9 and G1 lineage H9N2 viruses could represent a threat for the generation of novel reassortant H9N9 viruses with greater virulence in poultry and a zoonotic potential. IMPORTANCE We evaluated the consequences of reassortment between the H7N9 and the contemporary H9N2 viruses of the G1 lineage that are enzootic in poultry across the Indian subcontinent and the Middle East. Coinfection of chickens with these viruses resulted in the emergence of novel reassortant H9N9 viruses with genes derived from both H9N2 and H7N9 viruses. The "6+2" reassortant H9N9 (having NP and NA from H7N9) virus was shed from contact chickens in a significantly higher proportion compared to most of the reassortant viruses, showed significantly increased replication fitness in human A549 cells, receptor binding toward human (α2,6) and avian (α2,3) sialic acid receptor analogues, and the potential to transmit via contact among ferrets. This study demonstrated the ability of viruses that already exist in nature to exchange genetic material, highlighting the potential emergence of viruses from these subtypes with zoonotic potential.

Distinct but connected avian influenza virus activities in wetlands and live poultry markets in Bangladesh, 2018-2019

From April 2018 to October 2019, we continued active surveillance for influenza viruses in Bangladeshi live poultry markets (LPMs) and in Tanguar Haor, a wetland region of Bangladesh where domestic ducks have frequent contact with migratory birds. The predominant virus subtypes circulating in the LPMs were low pathogenic avian influenza (LPAI) H9N2 and clade 2.3.2.1a highly pathogenic avian influenza (HPAI) H5N1 viruses of the H5N1-R1 genotype, like those found in previous years. Viruses of the H5N1-R2 genotype, which were previously reported as co-circulating with H5N1-R1 genotype viruses in LPM, were not detected. In addition to H9N2 viruses, which were primarily found in chicken and quail, H2N2, H3N8 and H11N3 LPAI viruses were detected in LPMs, exclusively in ducks. Viruses in domestic ducks and/or wild birds in Tanguar Haor were more diverse, with H1N1, H4N6, H7N1, H7N3, H7N4, H7N6, H8N4, H10N3, H10N4 and H11N3 detected. Phylogenetic analyses of these LPAI viruses suggested that some were new to Bangladesh (H2N2, H7N6, H8N4, H10N3 and H10N4), likely introduced by migratory birds of the Central Asian flyway. Our results show a complex dynamic of viral evolution and diversity in Bangladesh based on factors such as host populations and geography. The LPM environment was characterised by maintenance of viruses with demonstrated zoonotic potential and H5N1 genotype turnover. The wetland environment was characterised by greater viral gene pool diversity but a lower overall influenza virus detection rate. The genetic similarity of H11N3 viruses in both environments demonstrates that LPM and wetlands are connected despite their having distinct influenza ecologies.

Regional Distribution of Non-human H7N9 Avian Influenza Virus Detections in China and Construction of a Predictive Model

Introduction

H7N9 avian influenza has broken out in Chinese poultry 10 times since 2013 and impacted the industry severely. Although the epidemic is currently under control, there is still a latent threat.

Material and Methods

Epidemiological surveillance data for non-human H7N9 avian influenza from April 2013 to April 2020 were used to analyse the regional distribution and spatial correlations of positivity rates in different months and years and before and after comprehensive immunisation. In addition, positivity rate monitoring data were disaggregated into a low-frequency and a high-frequency trend sequence by wavelet packet decomposition (WPD). The particle swarm optimisation algorithm was adopted to optimise the least squares support-vector machine (LS-SVM) model parameters to predict the low-frequency trend sequence, and the autoregressive integrated moving average (ARIMA) model was used to predict the high-frequency one. Ultimately, an LS-SVM-ARIMA combined model based on WPD was constructed.

Results

The virus positivity rate was the highest in late spring and early summer, and overall it fell significantly after comprehensive immunisation. Except for the year 2015 and the single month of December from 2013 to 2020, there was no significant spatiotemporal clustering in cumulative non-human H7N9 avian influenza virus detections. Compared with the ARIMA and LS-SVM models, the LS-SVM-ARIMA combined model based on WPD had the highest prediction accuracy. The mean absolute and root mean square errors were 2.4% and 2.0%, respectively.

Conclusion

Low error measures prove the validity of this new prediction method and the combined model could be used for inference of future H7N9 avian influenza virus cases. Live poultry markets should be closed in late spring and early summer, and comprehensive H7N9 immunisation continued.

The epidemic volatility index, a novel early warning tool for identifying new waves in an epidemic

Early warning tools are crucial for the timely application of intervention strategies and the mitigation of the adverse health, social and economic effects associated with outbreaks of epidemic potential such as COVID-19. This paper introduces, the Epidemic Volatility Index (EVI), a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold. Data on the daily confirmed cases of COVID-19 are used to demonstrate the use of EVI. Results from the COVID-19 epidemic in Italy and New York State are presented here, based on the number of confirmed cases of COVID-19, from January 22, 2020, until April 13, 2021. Live daily updated predictions for all world countries and each of the United States of America are publicly available online. For Italy, the overall sensitivity for EVI was 0.82 (95% Confidence Intervals: 0.75; 0.89) and the specificity was 0.91 (0.88; 0.94). For New York, the corresponding values were 0.55 (0.47; 0.64) and 0.88 (0.84; 0.91). Consecutive issuance of early warnings is a strong indicator of main epidemic waves in any country or state. EVI’s application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting new waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act swiftly and thereby enhance containment of outbreaks.

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.

Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm

The timely and accurate mapping of paddy rice is important to ensure food security and to protect the environment for sustainable development. Existing paddy rice mapping methods are often remote sensing technologies based on optical images. However, the availability of high-quality remotely sensed paddy rice growing area data is limited due to frequent cloud cover and rain over the southwest China. In order to overcome these limitations, we propose a paddy rice field mapping method by combining a spatiotemporal fusion algorithm and a phenology-based algorithm. First, a modified neighborhood similar pixel interpolator (MNSPI) time series approach was used to remove clouds on Sentinel-2 and Landsat 8 OLI images in 2020. A flexible spatiotemporal data fusion (FSDAF) model was used to fuse Sentinel-2 data and MODIS data to obtain multi-temporal Sentinel-2 images. Then, the fused remote sensing data were used to construct fusion time series data to produce time series vegetation indices (NDVI\LSWI) having a high spatiotemporal resolution (10 m and ≤16 days). On this basis, the unique physical characteristics of paddy rice during the transplanting period and other auxiliary data were combined to map paddy rice in Yongchuan District, Chongqing, China. Our results were validated by field survey data and showed a high accuracy of the proposed method indicated by an overall accuracy of 93% and the Kappa coefficient of 0.85. The paddy rice planting area map was also consistent with the official data of the third national land survey; at the town level, the correlation between official survey data and paddy rice area was 92.5%. The results show that this method can effectively map paddy rice fields in a cloudy and rainy area.

Duck-origin H5N6 avian influenza viruses induce different pathogenic and inflammatory effects in mice

Since 2013, H5N6 highly pathogenic avian influenza viruses have caused considerable economic losses in the poultry industry and have caused 24 laboratory-confirmed human cases. In this study, we isolated nine (B1-B9) H5N6 viruses from healthy ducks in Guangdong Province, Southern China from December 2018 to April 2019. Phylogenetic analysis revealed that B1, B2, B3, B4, B5, B7, B8, and B9 clustered into the G1.1 genotype and shared high sequence similarity with human H5N6 isolates from Southern China in 2017 and 2018. Meanwhile, B6 clustered into the G1.1.9 genotype. The hemagglutinin (HA), neuraminidase (NA) and nonstructural protein (NS) gene segments of B6 were closely related to the human H5N6 isolates, while the other genomic segments were closely related to H5N6 viruses isolated from waterfowl in Southern China. Compared to B7, B6 had higher pathogenicity and induced stronger inflammatory responses in mice. B6 carried a full-length PB1-F2 protein (90 aa), while the rest carried an 11-amino acid C-terminal-truncated PB1-F2. The PB1-F2 protein may increase the virulence of B6 compared to that of B7. Our findings provide insight into the pathogenic mechanisms of H5N6 viruses in mammals and emphasize the need for continued surveillance of circulating H5N6 viruses in ducks.

Analysis of the movement of live broilers in Guangxi, China and implications for avian influenza control

Most Chinese provinces have a daily-updated database of live animal movements; however, the data are not efficiently utilized to support interventions to control H7N9 and other avian influenzas. Based on official records, this study assessed the spatio-temporal patterns of live broilers moved out of and within Guangxi in 2017. The yearly and monthly networks were analyzed for inter- and intra-provincial movements, respectively. Approximately 200,000 movements occurred in 2017, involving the transport of 200 million live broilers from Guangxi. Although Guangxi exported to 24 out of 32 provinces of China, 95% of inter-provincial movements occurred with three bordering provinces. Within Guangxi, counties were highly connected through the live broiler movements, creating conditions for rapid virus spreading throughout the province. Interestingly, a peak in movements during the Chinese Lunar New Year celebrations, late January in 2017, was not observed in this study, likely due to H7N9-related control measures constraining live bird trading. Both intra- and inter-provincial movements in March 2017 were significantly higher than in other months of that year, suggesting that dramatic price changes may influence the movement's network and reshape the risk pathways. However, despite these variations, the same small proportion of counties (less than 20%) exporting/importing more than 90% of inter- and intra-provincial movements remains the same throughout the year. Interventions, particularly surveillance and improving biosecurity, targeted to those counties are thus likely to be more effective for avian influenza risk mitigation than implemented indiscriminately. Additionally, simulations further demonstrated that targeting counties according to their degree or betweenness in the movement network would be the most efficient way to limit disease transmission via broiler movements. The study findings provide evidence to support the design of risk-based control interventions for H7N9 and all other avian influenza viruses in broiler value chains in Guangxi.

Rainfall driven and wild-bird mediated avian influenza virus outbreaks in Australian poultry

Globally, outbreaks of Avian Influenza Virus (AIV) in poultry continue to burden economies and endanger human, livestock and wildlife health. Wild waterbirds are often identified as possible sources for poultry infection. Therefore, it is important to understand the ecological and environmental factors that directly influence infection dynamics in wild birds, as these factors may thereby indirectly affect outbreaks in poultry. In Australia, where large parts of the country experience erratic rainfall patterns, intense rainfalls lead to wild waterfowl breeding events at temporary wetlands and increased proportions of immunologically naïve juvenile birds. It is hypothesized that after breeding, when the temporary wetlands dry, increasing densities of immunologically naïve waterbirds returning to permanent water bodies might strongly contribute to AIV prevalence in wild waterfowl in Australia. Since rainfall has been implicated as an important environmental driver in AIV dynamics in wild waterbirds in southeast Australia and wild waterbirds are identified globally to have a role in virus spillover into poultry, we hypothesise that rainfall events have an indirect effect on AIV outbreaks in poultry in southeast Australia. In this study we investigated this hypothesis by examining the correlation between the timing of AIV outbreaks in poultry in and near the Murray-Darling basin in relation to temporal patterns in regional rainfall since 1970. Our findings support our hypothesis and suggest that the risk of AIV outbreaks in poultry increases after a period of high rainfall, with peak AIV risk two years after the onset of the high-rainfall period. This is presumably triggered by increased rates of waterbird breeding and consequent higher proportions of immunologically naïve juvenile waterbirds entering the population directly after major rainfall events, which subsequently aggregate near permanent water bodies when the landscape dries out.

Reducing exposure to COVID-19 by improving access to fever clinics: an empirical research of the Shenzhen area of China

BACKGROUND

The current 2019 coronavirus disease (COVID-19) pandemic is hitting citizen's life and health like never before, with its significant loss to human life and a huge economic toll. In this case, the fever clinics (FCs) were still preserved as one of the most effective control measures in China, but this work is based on experience and lacks scientific and effective guidance. Here, we use travel time to link facilities and populations at risk of COVID-19 and identify the dynamic allocation of patients' medical needs, and then propose the optimized allocation scheme of FCs.

METHODS

We selected Shenzhen, China, to collect geospatial resources of epidemic communities (ECs) and FCs to determine the ECs' cumulative opportunities of visiting FCs, as well as evaluate the rationality of medical resources in current ECs. Also, we use the Location Set Covering Problem (LSCP) model to optimize the allocation of FCs and evaluate efficiency.

RESULTS

Firstly, we divide the current ECs into 3 groups based on travel time and cumulative opportunities of visiting FCs within 30 min: Low-need communities (22.06%), medium-need communities (59.8%), and high-need communities (18.14%) with 0,1-2 and no less than 3 opportunities of visiting FCs. Besides, our work proposes two allocation schemes of fever clinics through the LSCP model. Among which, selecting secondary and above hospitals as an alternative in Scheme 1, will increase the coverage rate of hospitals in medium-need and high-need communities from 59.8% to 80.88%. In Scheme 2, selecting primary and above hospitals as an alternative will increase the coverage rate of hospitals in medium-need and high-need communities to 85.29%, with the average travel time reducing from 22.42 min to 17.94 min.

CONCLUSIONS

The optimized allocation scheme can achieve two objectives: a. equal access to medical services for different types of communities has improved while reducing the overutilization of high-quality medical resources. b. the travel time for medical treatment in the community has reduced, thus improving medical accessibility. On this basis, during the early screening in prevention and control of the outbreak, the specific suggestions for implementation in developing and less developed countries are made.

Twenty-year trends in antimicrobial resistance from aquaculture and fisheries in Asia

Antimicrobial resistance (AMR) is a growing threat to human and animal health. However, in aquatic animals-the fastest growing food animal sector globally-AMR trends are seldom documented, particularly in Asia, which contributes two-thirds of global food fish production. Here, we present a systematic review and meta-analysis of 749 point prevalence surveys reporting antibiotic-resistant bacteria from aquatic food animals in Asia, extracted from 343 articles published in 2000-2019. We find concerning levels of resistance to medically important antimicrobials in foodborne pathogens. In aquaculture, the percentage of antimicrobial compounds per survey with resistance exceeding 50% (P50) plateaued at 33% [95% confidence interval (CI) 28 to 37%] between 2000 and 2018. In fisheries, P50 decreased from 52% [95% CI 39 to 65%] to 22% [95% CI 14 to 30%]. We map AMR at 10-kilometer resolution, finding resistance hotspots along Asia's major river systems and coastal waters of China and India. Regions benefitting most from future surveillance efforts are eastern China and India. Scaling up surveillance to strengthen epidemiological evidence on AMR and inform aquaculture and fisheries interventions is needed to mitigate the impact of AMR globally.

Transboundary Animal Diseases, an Overview of 17 Diseases with Potential for Global Spread and Serious Consequences

Animals provide food and other critical resources to most of the global population. As such, diseases of animals can cause dire consequences, especially disease with high rates of morbidity or mortality. Transboundary animal diseases (TADs) are highly contagious or transmissible, epidemic diseases, with the potential to spread rapidly across the globe and the potential to cause substantial socioeconomic and public health consequences. Transboundary animal diseases can threaten the global food supply, reduce the availability of non-food animal products, or cause the loss of human productivity or life. Further, TADs result in socioeconomic consequences from costs of control or preventative measures, and from trade restrictions. A greater understanding of the transmission, spread, and pathogenesis of these diseases is required. Further work is also needed to improve the efficacy and cost of both diagnostics and vaccines. This review aims to give a broad overview of 17 TADs, providing researchers and veterinarians with a current, succinct resource of salient details regarding these significant diseases. For each disease, we provide a synopsis of the disease and its status, species and geographic areas affected, a summary of in vitro or in vivo research models, and when available, information regarding prevention or treatment.

Different intervention strategies toward live poultry markets against avian influenza A (H7N9) virus: Model-based assessment

BACKGROUND

Different interventions targeting live poultry markets (LPMs) are applied in China for controlling avian influenza A (H7N9), including LPM closure and "1110" policy (i.e., daily cleaning, weekly disinfection, monthly rest day, zero poultry stock overnight). However, the interventions' effectiveness has not been comprehensively assessed.

METHODS

Based on the available data (including reported cases, domestic poultry volume, and climate) collected in Guangdong Province between October 2013 and June 2017, we developed a new compartmental model that enabled us to infer H7N9 transmission dynamics. The model incorporated the intrinsic interplay among humans and poultry as well as the impacts of absolute humidity and LPM intervention, in which intervention strategies were parameterized and estimated by Markov chain Monte Carlo method.

RESULTS

There were 258 confirmed human H7N9 cases in Guangdong during the study period. If without interventions, the number would reach 646 (95%CI, 575-718) cases. Temporal, seasonal and permanent closures of LPMs can substantially reduce transmission risk, which might respectively reduce human infections by 67.2% (95%CI, 64.3%-70.1%), 75.6% (95%CI, 73.8%-77.5%), 86.6% (95%CI, 85.7-87.6%) in total four epidemic seasons, and 81.9% (95%CI, 78.7%-85.2%), 91.5% (95%CI, 89.9%-93.1%), 99.0% (95%CI, 98.7%-99.3%) in the last two epidemic seasons. Moreover, implementing the "1110" policy from 2014 to 2017 would reduce the cases by 34.1% (95%CI, 20.1%-48.0%), suggesting its limited role in preventing H7N9 transmission.

CONCLUSIONS

Our study quantified the effects of different interventions and execution time toward LPMs for controlling H7N9 transmission. The results highlighted the importance of closing LPMs during epidemic period, and supported permanent closure as a long-term plan.

Legislation advancement of one health in China in the context of the COVID-19 pandemic: From the perspective of the wild animal conservation law

The outbreak of COVID-19 epidemic is endangering the health of all humans and requires the urgent attention and active response of all countries and all areas of society. Existing studies have shown that wild animals are one of the sources of high-risk virus infection affecting human health, and human activities have largely shaped the routes of virus transmission. To protect wildlife is to protect human health. We should follow the concept of One Health to make corresponding legislation, so as to better coordinate the relationship among human health, animal health and environmental health. Since the outbreak of COVID-19 epidemic, China has taken many effective measures to prevent its spreading, including revision of the Wild Animal Conservation Law. All sectors of the Chinese society have issued a strong appeal to pursue One Health and even specific legislative proposals. Because the current Wild Animal Conservation Law fails to properly reflect the concept of One Health, which is the root cause of the imperfect design of the system and the key to the unsatisfactory effectiveness of the legal application. China's new Wild Animal Conservation Law is expected to make a large-scale and systematic revision, which should fully implement the concept of One Health.

Optimizing the early detection of low pathogenic avian influenza H7N9 virus in live bird markets

In Southeast Asia, surveillance at live bird markets (LBMs) has been identified as crucial for detecting avian influenza viruses (AIV) and reducing the risk of human infections. However, the design of effective surveillance systems in LBMs remains complex given the rapid turn-over of poultry. We developed a deterministic transmission model to provide guidance for optimizing AIV surveillance efforts. The model was calibrated to fit one of the largest LBMs in northern Vietnam at high risk of low pathogenic H7N9 virus introduction from China to identify the surveillance strategy that optimizes H7N9 detection. Results show that (i) using a portable diagnostic device would slightly reduce the number of infected birds leaving the LBM before the first detection, as compared to a laboratory-based diagnostic strategy, (ii) H7N9 detection could become more timely by sampling birds staying overnight, just before new susceptible birds are introduced at the beginning of a working day, and (iii) banning birds staying overnight would represent an effective intervention to reduce the risk of H7N9 spread but would decrease the likelihood of virus detection if introduced. These strategies should receive high priority in Vietnam and other Asian countries at risk of H7N9 introduction.

Spatial and temporal analysis of human infection with the avian influenza A (H7N9) virus in China and research on a risk assessment agent-based model

OBJECTIVES

From 2013 to 2017, the avian influenza A (H7N9) virus frequently infected people in China, which seriously affected the public health of society. This study aimed to analyze the spatial characteristics of human infection with the H7N9 virus in China and assess the risk areas of the epidemic.

METHODS

Using kernel density estimation, standard deviation ellipse analysis, spatial and temporal scanning cluster analysis, and Pearson correlation analysis, the spatial characteristics and possible risk factors of the epidemic were studied. Meteorological factors, time (month), and environmental factors were combined to establish an epidemic risk assessment proxy model to assess the risk range of an epidemic.

RESULTS

The epidemic situation was significantly correlated with atmospheric pressure, temperature, and daily precipitation (P < 0.05), and there were six temporal and spatial clusters. The fitting accuracy of the epidemic risk assessment agent-based model for lower-risk, low-risk, medium-risk, and high-risk was 0.795, 0.672, 0.853, 0.825, respectively.

CONCLUSIONS

This H7N9 epidemic was found to have more outbreaks in winter and spring. It gradually spread to the inland areas of China. This model reflects the risk areas of human infection with the H7N9 virus.

Infection and risk factors of human and avian influenza in pigs in south China

The coinfection of swine influenza (SI) strains and avian/human-source influenza strains in piggeries can contribute to the evolution of new influenza viruses with pandemic potential. This study analyzed surveillance data on SI in south China and explored the spatial predictor variables associated with different influenza infection scenarios in counties within the study area. Blood samples were collected from 7670 pigs from 534 pig farms from 2015 to 2017 and tested for evidence of infection with influenza strains from swine, human and avian sources. The herd prevalences for EA H1N1, H1N1pdm09, classic H1N1, HS-like H3N2, seasonal human H1N1 and avian influenza H9N2 were 88.5, 64.5, 60.3, 57.8, 12.9 and 10.3 %, respectively. Anthropogenic factors including detection frequency, chicken density, duck density, pig density and human population density were found to be better predictor variables for three influenza infection scenarios (infection with human strains, infection with avian strains, and coinfection with H9N2 avian strain and at least one swine strain) than were meteorological and geographical factors. Predictive risk maps generated for the four provinces in south China highlighted that the areas with a higher risk of the three infection scenarios were predominantly clustered in the delta area of the Pearl River in Guangdong province and counties surrounding Poyang Lake in Jiangxi province. Identification of higher risk areas can inform targeted surveillance for influenza in humans and pigs, helping public health authorities in designing risk-based SI control strategies to address the pandemic influenza threat in south China.

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.

Mapping Paddy Rice with Satellite Remote Sensing: A Review

Paddy rice is a staple food of three billion people in the world. Timely and accurate estimation of the paddy rice planting area and paddy rice yield can provide valuable information for the government, planners and decision makers to formulate policies. This article reviews the existing paddy rice mapping methods presented in the literature since 2010, classifies these methods, and analyzes and summarizes the basic principles, advantages and disadvantages of these methods. According to the data sources used, the methods are divided into three categories: (I) Optical mapping methods based on remote sensing; (II) Mapping methods based on microwave remote sensing; and (III) Mapping methods based on the integration of optical and microwave remote sensing. We found that the optical remote sensing data sources are mainly MODIS, Landsat, and Sentinel-2, and the emergence of Sentinel-1 data has promoted research on radar mapping methods for paddy rice. Multisource data integration further enhances the accuracy of paddy rice mapping. The best methods are phenology algorithms, paddy rice mapping combined with machine learning, and multisource data integration. Innovative methods include the time series similarity method, threshold method combined with mathematical models, and object-oriented image classification. With the development of computer technology and the establishment of cloud computing platforms, opportunities are provided for obtaining large-scale high-resolution rice maps. Multisource data integration, paddy rice mapping under different planting systems and the connection with global changes are the focus of future development priorities.

Preparing for Emerging Zoonotic Viruses

The risk of emergence and spread of novel human pathogens originating from an animal reservoir has increased in the past decades. However, the unpredictable nature of disease emergence makes surveillance and preparedness challenging. Knowledge of general risk factors for emergence and spread, combined with local level data is needed to develop a risk-based methodology for early detection. This involves the implementation of the One Health approach, integrating human, animal and environmental health sectors, as well as social sciences, bioinformatics and more. Recent technical advances, such as metagenomic sequencing, will aid the rapid detection of novel pathogens on the human-animal interface.

Genetic diversity of the H5N1 viruses in live bird markets, Indonesia

Background

The live bird market (LBM) plays an important role in the dynamic evolution of the avian influenza H5N1 virus.

Objectives

The main objective of this study was to monitor the genetic diversity of the H5N1 viruses in LBMs in Indonesia.

Methods

Therefore, the disease surveillance was conducted in the area of Banten, West Java, Central Java, East Java, and Jakarta Province, Indonesia from 2014 to 2019. Subsequently, the genetic characterization of the H5N1 viruses was performed by sequencing all 8 segments of the viral genome.

Results

As a result, the H5N1 viruses were detected in most of LBMs in both bird' cloacal and environmental samples, in which about 35% of all samples were positive for influenza A and, subsequently, about 52% of these samples were positive for H5 subtyping. Based on the genetic analyses of 14 viruses isolated from LBMs, genetic diversities of the H5N1 viruses were identified including clades 2.1.3 and 2.3.2 as typical predominant groups as well as reassortant viruses between these 2 clades.

Conclusions

As a consequence, zoonotic transmission to humans in the market could be occurred from the exposure of infected birds and/or contaminated environments. Moreover, new virus variants could emerge from the LBM environment. Therefore, improving pandemic preparedness raised great concerns related to the zoonotic aspect of new influenza variants because of its high adaptivity and efficiency for human infection.

Spatial and temporal clusters of avian influenza a (H7N9) virus in humans across five epidemics in mainland China: an epidemiological study of laboratory-confirmed cases

Background

Avian influenza A (H7N9) virus was first reported in mainland China in 2013, and alarming in 2016–17 due to the surge across a wide geographic area. Our study aimed to identify and explore the spatial and temporal variation across five epidemics to reinforce the epidemic prevention and control.

Methods

We collected spatial and temporal information about all laboratory-confirmed human cases of A (H7N9) virus infection reported in mainland China covering 2013–17 from the open source. The autocorrelation analysis and intensity of cases were used to analyse the spatial cluster while circular distribution method was used to analyse the temporal cluster.

Results

Across the five epidemics, a total of 1553 laboratory-confirmed human cases with A (H7N9) virus were reported in mainland China. The global Moran’s I index values of five epidemic were 0.610, 0.132, 0.308, 0.306, 0.336 respectively, among which the differences were statistically significant. The highest intensity was present in the Yangtze River Delta region and the Pearl River Delta region, and the range enlarged from the east of China to inner provinces and even the west of China across the five epidemics. The temporal clusters of the five epidemics were statistically significant, and the peak period was from the end of January to April with the first and the fifth epidemic later than the mean peak period.

Conclusions

Spatial and temporal clusters of avian influenza A (H7N9) virus in humans are obvious, moreover the regions existing clusters may enlarge across the five epidemics. Yangtze River Delta region and the Pearl River Delta region have the spatial cluster and the peak period is from January to April. The government should facilitate the tangible improvement for the epidemic preparedness according to the characteristics of spatial and temporal clusters of patients with avian influenza A (H7N9) virus.

Geographical variation in the risk of H7N9 human infections in China: implications for risk-based surveillance

The influenza A (H7N9) subtype remains a public health problem in China affecting individuals in contact with live poultry, particularly at live bird markets. Despite enhanced surveillance and biosecurity at LBMs H7N9 viruses are now more widespread in China. This study aims to quantify the temporal relationship between poultry surveillance results and the onset of human H7N9 infections during 2013-2017 and to estimate risk factors associated with geographical risk of H7N9 human infections in counties in Southeast China. Our results suggest that poultry surveillance data can potentially be used as early warning indicators for human H7N9 notifications. Furthermore, we found that human H7N9 incidence at county-level was significantly associated with the presence of wholesale LBMs, the density of retail LBMs, the presence of poultry virological positives, poultry movements from high-risk areas, as well as chicken population density and human population density. The results of this study can influence the current AI H7N9 control program by supporting the integration of poultry surveillance data with human H7N9 notifications as an early warning of the timing and areas at risk for human infection. The findings also highlight areas in China where monitoring of poultry movement and poultry infections could be prioritized.

Current and Projected Distributions of Aedes aegypti and Ae. albopictus in Canada and the U.S

BACKGROUND

Aedes aegypti and Ae. albopictus are mosquito vectors of more than 22 arboviruses that infect humans.

OBJECTIVES

Our objective was to develop regional ecological niche models for Ae. aegypti and Ae. albopictus in the conterminous United States and Canada with current observed and simulated climate and land-use data using boosted regression trees (BRTs).

METHODS

We used BRTs to assess climatic suitability for Ae. albopictus and Ae. aegypti mosquitoes in Canada and the United States under current and future projected climates.

RESULTS

Models for both species were mostly influenced by minimum daily temperature and demonstrated high accuracy for predicting their geographic ranges under the current climate. The northward range expansion of suitable niches for both species was projected under future climate models. Much of the United States and parts of southern Canada are projected to be suitable for both species by 2100, with Ae. albopictus projected to expand its range north earlier this century and further north than Ae. aegypti.

DISCUSSION

Our projections suggest that the suitable ecological niche for Aedes will expand with climate change in Canada and the United States, thus increasing the risk of Aedes-transmitted arboviruses. Increased surveillance for these vectors and the pathogens they carry would be prudent. https://doi.org/10.1289/EHP5899.

Proceedings Paper-Avian Diseases 10th AI Symposium Issue Development and Application of Real-Time PCR Assays for Specific Detection of Contemporary Avian Influenza Virus Subtypes N5, N6, N7, N8, and N9

Previously published NA subtype-specific real-time reverse-transcriptase PCRs (RRT-PCRs) were further validated for the detection of five avian influenza virus (AIV) NA subtypes, namely N5, N6, N7, N8, and N9. Testing of 30 AIV isolates of all nine NA subtypes informed the assay assessments, with the N5 and N9 RRT-PCRs retained as the original published assays while the N7 and N8 assays were modified in the primer-probe sequences to optimize detection of current threats. The preferred N6 RRT-PCR was either the original or the modified variant, depending on the specific H5N6 lineage. Clinical specimen (n = 137) testing revealed the ability of selected N5, N6, and N8 RRT-PCRs to sensitively detect clade 2.3.4.4b highly pathogenic AIV (HPAIV) infections due to H5N5, H5N6, and H5N8 subtypes, respectively, all originating from European poultry and wild bird cases during 2016-2018. Similar testing (n = 32 clinical specimens) also showed the ability of N7 and N9 RRT-PCRs to sensitively detect European H7N7 HPAIV and China-origin H7N9 low pathogenicity AIV infections, respectively.

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.

Heat Stress Causes Immune Abnormalities via Massive Damage to Effect Proliferation and Differentiation of Lymphocytes in Broiler Chickens

Broiler chickens are highly sensitive to high ambient temperatures due to their feathers, lack of skin sweat glands, and high productivity. Heat stress (HS) is a major concern for the poultry industry because it negatively affects growth as well as immune functions, which increase the potential risk of infectious disease outbreaks. Therefore, it is vital to elucidate HS's effect on the avian immune system, especially considering the global rise in average surface temperature. Our study identified a series of immunological disorders in heat-stressed broiler chickens. We exposed 22-day-old broiler chickens to a continuous HS condition (34.5 ± 0.5°C) for 14 days and immunized them with a prototype bovine serum albumin (BSA) antigen. The plasma and lymphoid tissues (thymus, bursa of Fabricius, and spleen) were harvested at the end of the experiments to investigate the induction of BSA-specific immune responses. Our results revealed that plasma titers of immunoglobulin (Ig)Y, IgM, and IgA antibodies specific for BSA were lower than those of thermoneutral chickens immunized with BSA. Furthermore, the spleens of the heat-stressed broiler chickens displayed severe depression of Bu1⁺ B cells and CD3⁺ T cells, including CD4⁺ T cells and CD8⁺ T cells, and lacked a fully developed germinal center (GC), which is crucial for B cell proliferation. These immunological abnormalities might be associated with severe depression of CD4⁻CD8⁻ or CD4⁺CD8⁺ cells, which are precursors of either helper or killer T cells in the thymus and Bu1⁺ B cells in the bursa of Fabricius. Importantly, HS severely damaged the morphology of the thymic cortex and bursal follicles, where functional maturation of T and B cells occur. These results indicate that HS causes multiple immune abnormalities in broiler chickens by impairing the developmental process and functional maturation of T and B cells in both primary and secondary lymphoid tissues.

Disease Risk from Human-Environment Interactions: Environment and Development Economics for Joint Conservation-Health Policy

Emergence of COVID-19 joins a collection of evidence that local and global health are influenced by human interactions with the natural environment. Frameworks that simultaneously model decisions to interact with natural systems and environmental mechanisms of zoonotic disease spread allow for identification of policy levers to mitigate disease risk and promote conservation. Here, we highlight opportunities to broaden existing conservation economics frameworks that represent human behavior to include disease transmission in order to inform conservation-disease risk policy. Using examples from wildlife markets and forest extraction, we call for environment, resource, and development economists to develop and analyze empirically-grounded models of people's decisions about interacting with the environment, with particular attention to LMIC settings and ecological-epidemiological risk factors. Integrating the decisions that drive human-environment interactions with ecological and epidemiological research in an interdisciplinary approach to understanding pathogen transmission will inform policy needed to improve both conservation and disease spread outcomes.

Association between meteorological variations and activities of influenza A and B across different climate zones: a multi-region modelling analysis across the globe

OBJECTIVE

To elucidate the effects of meteorological variations on the activity of influenza A and B in 11 sites across different climate regions.

METHODS

Daily numbers of laboratory-confirmed influenza A and B cases from 2011-2015 were collected from study sites where the corresponding daily mean temperature, relative humidity, wind speed and daily precipitation amount were used for boosted regression trees analysis on the marginal associations and the interaction effects.

RESULTS

Cold temperature was a major determinant that favored both influenza A and B in temperate and subtropical sites. Temperature-to-influenza A, but not influenza B, exhibited a U-shape association in subtropical and tropical sites. High relative humidity was also associated with influenza activities but was less consistent with influenza B activity. Compared with relative humidity, absolute humidity had a stronger association - it was negatively associated with influenza B activity in temperate zones, but was positively associated with both influenza A and B in subtropical and tropical zones.

CONCLUSION

The association between meteorological factors and with influenza activity is virus type specific and climate dependent. The heavy influence of temperature on influenza activity across climate zones implies that global warming is likely to have an impact on the influenza burden.

A review of knowledge discovery process in control and mitigation of avian influenza

In the last several decades, avian influenza virus has caused numerous outbreaks around the world. These outbreaks pose a significant threat to the poultry industry and also to public health. When an avian influenza (AI) outbreak occurs, it is critical to make informed decisions about the potential risks, impact, and control measures. To this end, many modeling approaches have been proposed to acquire knowledge from different sources of data and perspectives to enhance decision making. Although some of these approaches have shown to be effective, they do not follow the process of knowledge discovery in databases (KDD). KDD is an iterative process, consisting of five steps, that aims at extracting unknown and useful information from the data. The present review attempts to survey AI modeling methods in the context of KDD process. We first divide the modeling techniques used in AI into two main categories: data-intensive modeling and small-data modeling. We then investigate the existing gaps in the literature and suggest several potential directions and techniques for future studies. Overall, this review provides insights into the control of AI in terms of the risk of introduction and spread of the virus.

Predicting Risk of Imported Disease with Demographics: Geospatial Analysis of Imported Malaria in Minnesota, 2010-2014

Although immigrants who visit friends and relatives (VFRs) account for most of the travel-acquired malaria cases in the United States, there is limited evidence on community-level risk factors and best practices for prevention appropriate for various VFR groups. Using 2010–2014 malaria case reports, sociodemographic census data, and health services data, we explored and mapped community-level characteristics to understand who is at risk and where imported malaria infections occur in Minnesota. We examined associations with malaria incidence using Poisson and negative binomial regression. Overall, mean incidence was 0.4 cases per 1,000 sub-Saharan African (SSA)–born in communities reporting malaria, with cases concentrated in two areas of Minneapolis–St. Paul. We found moderate and positive associations between imported malaria and counts of SSA- and Asian-born populations, respectively. Our findings may inform future studies to understand the knowledge, attitudes, and practices of VFR travelers and facilitate and focus intervention strategies to reduce imported malaria in the United States.

Clinical indices and mortality of hospitalized avian influenza A (H7N9) patients in Guangdong, China

Background:

Six epidemic waves of human infection with avian influenza A (H7N9) virus have emerged in China with high mortality. However, study on quantitative relationship between clinical indices in ill persons and H7N9 outcome (fatal and non-fatal) is still unclear. A retrospective cohort study was conducted to collect laboratory-confirmed cases with H7N9 viral infection from 2013 to 2015 in 23 hospitals across 13 cities in Guangdong Province, China.

Methods:

Multivariable logistic regression model and classification tree model analyses were used to detect the threshold of selected clinical indices and risk factors for H7N9 death. The receiver operating characteristic curve (ROC) and analyses were used to compare survival and death distributions and differences between indices. A total of 143 cases with 90 survivors and 53 deaths were investigated.

Results:

Average age (Odds Ratio (OR) = 1.036, 95% Confidence Interval (CI) = 1.016–1.057), interval days between dates of onset and confirmation (OR = 1.078, 95% CI = 1.004–1.157), interval days between onset and oseltamivir treatment (OR = 5.923, 95% CI = 1.877–18.687), body temperature (BT) (OR = 3.612, 95% CI = 1.914–6.815), white blood cell count (WBC) (OR = 1.212, 95% CI = 1.092–1.346) were significantly associated with H7N9 death after adjusting for confounders. The chance of death from H7N9 infection was 80.0% if BT was over 38.1 °C, and chance of death is 67.4% if WBC count was higher than 9.5 (10⁹/L). Only 27.1% of patients who began oseltamivir treatment less than 9.5 days after disease onset died, compared to 68.8% of those who started treatment more than 15.5 days after onset.

Conclusions:

The intervals between date of onset and confirmation of diagnosis, between date of onset to oseltamivir treatment, age, BT and WBC are found to be the best predictors of H7N9 mortality.

A systematic review of MERS-CoV seroprevalence and RNA prevalence in dromedary camels: Implications for animal vaccination

•

Most adult dromedaries in Africa and the Middle East have been infected with MERS-CoV.

•

Seroprevalence increases with age, while active infection is more common in calves.

•

Prevalence is higher at sites where different dromedary populations mix.

•

Further study is needed to determine if prevalence of infection varies seasonally.

Human infection with Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is driven by recurring dromedary-to-human spill-over events, leading decision-makers to consider dromedary vaccination. Dromedary vaccine candidates in the development pipeline are showing hopeful results, but gaps in our understanding of the epidemiology of MERS-CoV in dromedaries must be addressed to design and evaluate potential vaccination strategies. We aim to bring together existing measures of MERS-CoV infection in dromedary camels to assess the distribution of infection, highlighting knowledge gaps and implications for animal vaccination. We systematically reviewed the published literature on MEDLINE, EMBASE and Web of Science that reported seroprevalence and/or prevalence of active MERS-CoV infection in dromedary camels from both cross-sectional and longitudinal studies. 60 studies met our eligibility criteria. Qualitative syntheses determined that MERS-CoV seroprevalence increased with age up to 80–100% in adult dromedaries supporting geographically widespread endemicity of MERS-CoV in dromedaries in both the Arabian Peninsula and countries exporting dromedaries from Africa. The high prevalence of active infection measured in juveniles and at sites where dromedary populations mix should guide further investigation – particularly of dromedary movement – and inform vaccination strategy design and evaluation through mathematical modelling.

Risk factors for H5 avian influenza virus prevalence on urban live bird markets in Jakarta, Indonesia-Evaluation of long-term environmental surveillance data

In the re-emergence of Highly Pathogenic Avian Influenza (HPAI), live bird markets have been identified to play a critical role. In this repeated cross-sectional study, we combined surveillance data collected monthly on Jakarta's live bird markets over a five-year period, with risk factors related to the structure and management of live bird markets, the trading and slaughtering of birds at these markets, and environmental and demographic conditions in the areas where the markets were located. Over the study period 36.7% (95% CI: 35.1, 38.3) of samples (N = 1315) tested HPAI H5 virus positive. Using General Estimation Equation approaches to account for repeated observations over time, we explored the association between HPAI H5 virus prevalence and potential risk factors. Markets where only live birds and carcasses were sold, but no slaughtering was conducted at or at the vicinity of the markets, had a significantly reduced chance of being positive for H5 virus (OR = 0.2, 95% CI 0.1-0.5). Also, markets, that used display tables for poultry carcasses made from wood, had reduced odds of being H5 virus positive (OR = 0.7, 95% CI 0.5-1.0), while having at least one duck sample included in the pool of samples collected at the market increased the chance of being H5 virus positive (OR = 5.7, 95% CI 3.6-9.2). Markets where parent stock was traded, were more at risk of being H5 virus positive compared to markets where broilers were traded. Finally, the human population density in the district, the average distance between markets and origins of poultry sold at markets and the total rainfall per month were all positively associated with higher H5 virus prevalence. In summary, our results highlight that a combination of factors related to trading and marketing processes and environmental pressures need to be considered to reduce H5 virus infection risk for customers at urban live bird markets. In particular, the relocation of slaughter areas to well-managed separate locations should be considered.

The epidemic potential of avian influenza A (H7N9) virus in humans in mainland China: A two-stage risk analysis

Background

From 2013 to 2017, more than one thousand avian influenza A (H7N9) confirmed cases with hundreds of deaths were reported in mainland China. To identify priorities for epidemic prevention and control, a risk assessing framework for subnational variations is needed to define the epidemic potential of A (H7N9).

Methods

We established a consolidated two-stage framework that outlined the potential epidemic of H7N9 in humans: The Stage 1, index-case potential, used a Boosted Regression Trees model to assess population at risk due to spillover from poultry; the Stage 2, epidemic potential, synthesized the variables upon a framework of the Index for Risk Management to measure epidemic potential based on the probability of hazards and exposure, the vulnerability and coping capacity.

Results

Provinces in southern and eastern China, especially Jiangsu, Zhejiang, Guangzhou, have high index-case potential of human infected with A (H7N9), while northern coastal provinces and municipalities with low morbidity, i.e. Tianjin and Liaoning, have an increasing risk of A (H7N9) infection. Provinces in central China are likely to have high potential of epidemic due to the high vulnerability and the lack of coping capacity.

Conclusions

This study provides a unified risk assessment of A (H7N9) to detect the two-stage heterogeneity of epidemic potential among different provinces in mainland China, allowing proactively evaluate health preparedness at subnational levels to improve surveillance, diagnostic capabilities, and health promotion.

Duck production systems and highly pathogenic avian influenza H5N8 in France, 2016-2017

In winter 2016-2017, Highly Pathogenic Avian Influenza (HPAI) H5N8 virus spread across Europe, causing unprecedented epizootics. France was massively affected, resulting in the culling of over 6 million poultry. Boosted regression tree (BRT) models were used to quantify the association between spatial risk factors and HPAI H5N8 infection in poultry holdings and to generate predictive maps for HPAI infection. Three datasets were combined to build the model: a dataset of the reported outbreaks in poultry, a dataset of the poultry holdings where the virus has not been reported and a set of relevant spatial risk factors, including poultry production and trade, and water bird habitat. Results identified key associations between the 'foie gras' production systems and HPAI H5N8 risk of occurrence and indicate that strengthening surveillance of fattening duck production systems and making the transportation of fattening ducks more secure would be key priority options for HPAI prevention and control.

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

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

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)