Mapping spread and risk of avian influenza A (H7N9) in China

Based on the MaxEnt model the analysis of influencing factors and simulation of potential risk areas of human infection with avian influenza A (H7N9) in China

Exposure to infected animals and their contaminated environments may be the primary cause of human infection with the H7N9 avian influenza virus. However, the transmission characteristics and specific role of various influencing factors in the spread of the epidemic are not clearly understood. Therefore, it is of great significance for scientific research and practical application to explore the influencing factors related to the epidemic. Based on the data of relevant influencing factors and case sample points, this study used the MaxEnt model to test the correlation between human infection with H7N9 avian influenza and influencing factors in China from 2013 to 2017, and scientifically simulated and evaluated the potential risk areas of human infection with H7N9 avian influenza in China. The simulation results show that the epidemic risk is increasing year by year, and the eastern and southeastern coasts have always been high-risk areas. After verification, the model simulation results are generally consistent with the actual outbreak of the epidemic. Population density was the main influencing factor of the epidemic, and the secondary influencing factors included vegetation coverage, precipitation, altitude, poultry slaughter, production value, and temperature. The study revealed the spatial distribution and diffusion rules of the H7N9 epidemic and clarified the key influencing factors. In the future, more variables need to be included to improve the model and provide more accurate support for prevention and control strategies.

Risk distribution of human infections with avian influenza A (H5N1, H5N6, H9N2 and H7N9) viruses in China

Background

This study aimed to investigate epidemiologic characteristics of major human infection with avian influenza and explore the factors underlying the spatial distributions, particularly H5N6 and H9N2, as H9N2 could directly infect mankind and contribute partial or even whole internal genes to generate novel human-lethal reassortants such as H5N6. They pose potential threats to public health and agriculture.

Methods

This study collected cases of H5N1, H5N6, H9N2, and H7N9 in China, along with data on ecoclimatic, environmental, social and demographic factors at the provincial level. Boosted regression tree (BRT) models, a popular approach to ecological studies, has been commonly used for risk mapping of infectious diseases, therefore, it was used to investigate the association between these variables and the occurrence of human cases for each subtype, as well as to map the probabilities of human infections.

Results

A total of 1,123 H5N1, H5N6, H9N2, and H7N9 human cases have been collected in China from 2011 to 2024. Factors including density of pig and density of human population emerged as common significant predictors for H5N1 (relative contributions: 5.3, 5.8%), H5N6 (10.8, 6.4%), H9N2 (11.2, 7.3%), and H7N9 (9.4, 8.0%) infection. Overall, each virus has its own ecological and social drivers. The predicted distribution probabilities for H5N1, H5N6, H9N2, and H7N9 presence are highest in Guangxi, Sichuan, Guangdong, and Jiangsu, respectively, with values of 0.86, 0.96, 0.93 and 0.99.

Conclusion

This study highlighted the important role of social and demographic factors in the infection of different avian influenza, and suggested that monitoring and control of predicted high-risk areas should be prioritized.

Epidemiological characteristics of ventilator-associated pneumonia in neurosurgery: A 10-year surveillance study in a Chinese tertiary hospital

Background

Ventilator-associated pneumonia (VAP) is a significant and common health concern. The epidemiological landscape of VAP is poorly understood in neurosurgery patients. This study aimed to explore the epidemiology of VAP in this population and devise targeted surveillance, treatment, and control efforts.

Methods

A 10-year retrospective study spanning 2011 to 2020 was performed in a large Chinese tertiary hospital. Surveillance data was collected from neurosurgical patients and analyzed to map the demographic and clinical characteristics of VAP and describe the distribution and antimicrobial resistance profile of leading pathogens. Risk factors associated with the presence of VAP were explored using boosted regression tree (BRT) models.

Results

Three hundred ten VAP patients were identified. The 10-year incidence of VAP was 16.21 per 1000 ventilation days. All-cause mortality was 6.1%. The prevalence of gram-negative bacteria, fungi, and gram-positive bacteria among the 357 organisms isolated from VAP patients was 86.0%, 7.6%, and 6.4%, respectively; most were multidrug-resistant organisms. Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa were the most common pathogens. The prevalence of carbapenem-resistant A. baumannii, P. aeruginosa, and K. pneumoniae was high and increased over time in the study period. The BRT models revealed that VAP was associated with number of days of ventilator use (relative contribution, 47.84 ± 7.25), Glasgow Coma Scale score (relative contribution, 24.72 ± 5.67), and tracheotomy (relative contribution, 21.50 ± 2.69).

Conclusions

Our findings provide a better understanding of the epidemiology of VAP and its risk factors in neurosurgery patients.

Ecology of Middle East respiratory syndrome coronavirus, 2012-2020: A machine learning modelling analysis

The ongoing enzootic circulation of the Middle East respiratory syndrome coronavirus (MERS-CoV) in the Middle East and North Africa is increasingly raising the concern about the possibility of its recombination with other human-adapted coronaviruses, particularly the pandemic SARS-CoV-2. We aim to provide an updated picture about ecological niches of MERS-CoV and associated socio-environmental drivers. Based on 356 confirmed MERS cases with animal contact reported to the WHO and 63 records of animal infections collected from the literature as of 30 May 2020, we assessed ecological niches of MERS-CoV using an ensemble model integrating three machine learning algorithms. With a high predictive accuracy (area under receiver operating characteristic curve = 91.66% in test data), the ensemble model estimated that ecologically suitable areas span over the Middle East, South Asia and the whole North Africa, much wider than the range of reported locally infected MERS cases and test-positive animal samples. Ecological suitability for MERS-CoV was significantly associated with high levels of bareland coverage (relative contribution = 30.06%), population density (7.28%), average temperature (6.48%) and camel density (6.20%). Future surveillance and intervention programs should target the high-risk populations and regions informed by updated quantitative analyses.

A Study on a Neural Network Risk Simulation Model Construction for Avian Influenza A (H7N9) Outbreaks in Humans in China during 2013-2017

The main purposes of this study were to explore the spatial distribution characteristics of H7N9 human infections during 2013-2017, and to construct a neural network risk simulation model of H7N9 outbreaks in China and evaluate their effects. First, ArcGIS 10.6 was used for spatial autocorrelation analysis, and cluster patterns ofH7N9 outbreaks were analyzed in China during 2013-2017 to detect outbreaks' hotspots. During the study period, the incidence of H7N9 outbreaks in China was high in the eastern and southeastern coastal areas of China, with a tendency to spread to the central region. Moran's I values of global spatial autocorrelation of H7N9 outbreaks in China from 2013 to 2017 were 0.080128, 0.073792, 0.138015, 0.139221 and 0.050739, respectively (p < 0.05) indicating a statistically significant positive correlation of the epidemic. Then, SPSS 20.0 was used to analyze the correlation between H7N9 outbreaks in China and population, livestock production, the distance between the case and rivers, poultry farming, poultry market, vegetation index, etc. Statistically significant influencing factors screened out by correlation analysis were population of the city, average vegetation of the city, and the distance between the case and rivers (p < 0.05), which were included in the neural network risk simulation model of H7N9 outbreaks in China. The simulation accuracy of the neural network risk simulation model of H7N9 outbreaks in China from 2013 to 2017 were 85.71%, 91.25%, 91.54%, 90.49% and 92.74%, and the AUC were 0.903, 0.976, 0.967, 0.963 and 0.970, respectively, showing a good simulation effect of H7N9 epidemics in China. The innovation of this study lies in the epidemiological study of H7N9 outbreaks by using a variety of technical means, and the construction of a neural network risk simulation model of H7N9 outbreaks in China. This study could provide valuable references for the prevention and control of H7N9 outbreaks in China.

Epidemic characteristics and transmission risk prediction of brucellosis in Xi'an city, Northwest China

Human brucellosis (HB) has re-emerged in China since the mid-1990s, and exhibited an apparent geographic expansion shifted from the traditional livestock regions to the inland areas of China. It is often neglected in non-traditional epidemic areas, posing a serious threat to public health in big cities. We carried out a retrospective epidemiological study in Xi'an, the largest city in northwestern China. It utilizes long-term surveillance data on HB during 2008–2021 and investigation data during 2014–2021. A total of 1989 HB cases were reported in Xi'an, consisting of 505 local cases, i.e., those located in Xi'an and 1,484 non-local cases, i.e., those located in other cities. Significantly epidemiological heterogeneity was observed between them, mainly owing to differences in the gender, occupation, diagnostic delays, and reporting institutions. Serological investigations suggested that 59 people and 1,822 animals (sheep, cattle, and cows) tested positive for brucellosis from 2014 to 2021, with the annual average seroprevalence rates were 1.38 and 1.54%, respectively. The annual animal seroprevalence rate was positively correlated with the annual incidence of non-local HB cases. Multivariate boosted regression tree models revealed that gross domestic product, population density, length of township roads, number of farms, and nighttime lights substantially contributed to the spatial distribution of local HB. Approximately 7.84 million people inhabited the potential infection risk zones in Xi'an. Our study highlights the reemergence of HB in non-epidemic areas and provides a baseline for large and medium-sized cities to identify regions, where prevention and control efforts should be prioritized in the future.

Climatic influence on the magnitude of COVID-19 outbreak: a stochastic model-based global analysis

We investigate the climatic influence on COVID-19 transmission risks in 228 cities globally across three climatic zones. The results, based on the application of a Boosted Regression Tree algorithm method, show that average temperature and average relative humidity explain significant variations in COVID-19 transmission across temperate and subtropical regions, whereas in the tropical region, the average diurnal temperature range and temperature seasonality significantly predict the infection outbreak. The number of positive cases showed a decrease sharply above an average temperature of 10°C in the cities of France, Turkey, the US, the UK, and Germany. Among the tropical countries, COVID-19 in Indian cities is most affected by mean diurnal temperature, and those in Brazil by temperature seasonality. The findings have implications on public health interventions, and contribute to the ongoing scientific and policy discourse on the complex interplay of climatic factors determining the risks of COVID-19 transmission.

Mapping the Distributions of Mosquitoes and Mosquito-Borne Arboviruses in China

The geographic expansion of mosquitos is associated with a rising frequency of outbreaks of mosquito-borne diseases (MBD) worldwide. We collected occurrence locations and times of mosquito species, mosquito-borne arboviruses, and MBDs in the mainland of China in 1954−2020. We mapped the spatial distributions of mosquitoes and arboviruses at the county level, and we used machine learning algorithms to assess contributions of ecoclimatic, socioenvironmental, and biological factors to the spatial distributions of 26 predominant mosquito species and two MBDs associated with high disease burden. Altogether, 339 mosquito species and 35 arboviruses were mapped at the county level. Culex tritaeniorhynchus is found to harbor the highest variety of arboviruses (19 species), followed by Anopheles sinensis (11) and Culex pipiens quinquefasciatus (9). Temperature seasonality, annual precipitation, and mammalian richness were the three most important contributors to the spatial distributions of most of the 26 predominant mosquito species. The model-predicted suitable habitats are 60–664% larger in size than what have been observed, indicating the possibility of severe under-detection. The spatial distribution of major mosquito species in China is likely to be under-estimated by current field observations. More active surveillance is needed to investigate the mosquito species in specific areas where investigation is missing but model-predicted probability is high.

Exploring the determinants of influenza A/H7N9 control intervention efficacy in China: disentangling the effect of the "1110" policy and poultry vaccination

: The influenza A virus of the H7N9 subtype (FLUAV H7N9) emerged in Eastern China provinces in 2013 causing illness in both poultry and humans. Most reported FLUAV H7N9 human cases were related to those associated with the live poultry market chain. From 2013 to 2017, there were five epidemic waves of human infections, and from the end of 2016, the number of human cases increased sharply. To control FLUAV H7N9 in the market chain, the so-called "1110" policy at live poultry markets and a national vaccination programme were implemented. The relative efficacy of these two measures on the number of poultry and human infections has not been quantified and compared. To explore their efficacy, a cross-sectional study was conducted in six provinces of China, and the vaccination and surveillance data of H7N9 were analysed. Our survey data showed that poultry vendors were not widely aware of and did not accept the "1110" policy. For subjective and objective factors, some measures of the "1110" policy were not implemented in live bird markets (LBMs). However, the national vaccination programme achieved good immune effects and sharply decreased poultry FLUAV H7N9 infections. The detection rates of FLUAV H7N9 in LBMs and farms gradually decreased since the vaccination programme was implemented. Our analysis also indicated that human infections were closely related to poultry virus carriage rates; therefore, controlling FLUAV H7N9 circulation in poultry was an effective measure to control FLUAV H7N9 infections in humans. Although LBMs play a significant role in human infections, the management measures may not be implemented efficiently; hence, we need to conduct more investigations before developing related policies. This article is protected by copyright. All rights reserved.

Geospatial modelling to estimate the territory at risk of establishment of influenza type A in Mexico - An ecological study

The aim of this study was to estimate the territory at risk of establishment of influenza type A (EOITA) in Mexico, using geospatial models. A spatial database of 1973 outbreaks of influenza worldwide was used to develop risk models accounting for natural (natural threat), anthropic (man-made) and environmental (combination of the above) transmission. Then, a virus establishment risk model; an introduction model of influenza A developed in another study; and the three models mentioned were utilized using multi-criteria spatial evaluation supported by geographically weighted regression (GWR), receiver operating characteristic analysis and Moran's I. The results show that environmental risk was concentrated along the Gulf and Pacific coasts, the Yucatan Peninsula and southern Baja California. The identified risk for EOITA in Mexico were: 15.6% and 4.8%, by natural and anthropic risk, respectively, while 18.5% presented simultaneous environmental, natural and anthropic risk. Overall, 28.1% of localities in Mexico presented a High/High risk for the establishment of influenza type A (area under the curve=0.923, P<0.001; GWR, r2=0.840, P<0.001; Moran's I =0.79, P<0.001). Hence, these geospatial models were able to robustly estimate those areas susceptible to EOITA, where the results obtained show the relation between the geographical area and the different effects on health. The information obtained should help devising and directing strategies leading to efficient prevention and sound administration of both human and financial resources.

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.

Mitigating Future Respiratory Virus Pandemics: New Threats and Approaches to Consider

Despite many recent efforts to predict and control emerging infectious disease threats to humans, we failed to anticipate the zoonotic viruses which led to pandemics in 2009 and 2020. The morbidity, mortality, and economic costs of these pandemics have been staggering. We desperately need a more targeted, cost-efficient, and sustainable strategy to detect and mitigate future zoonotic respiratory virus threats. Evidence suggests that the transition from an animal virus to a human pathogen is incremental and requires a considerable number of spillover events and considerable time before a pandemic variant emerges. This evolutionary view argues for the refocusing of public health resources on novel respiratory virus surveillance at human-animal interfaces in geographical hotspots for emerging infectious diseases. Where human-animal interface surveillance is not possible, a secondary high-yield, cost-efficient strategy is to conduct novel respiratory virus surveillance among pneumonia patients in these same hotspots. When novel pathogens are discovered, they must be quickly assessed for their human risk and, if indicated, mitigation strategies initiated. In this review, we discuss the most common respiratory virus threats, current efforts at early emerging pathogen detection, and propose and defend new molecular pathogen discovery strategies with the goal of preempting future pandemics.

Mapping ticks and tick-borne pathogens in China

Understanding ecological niches of major tick species and prevalent tick-borne pathogens is crucial for efficient surveillance and control of tick-borne diseases. Here we provide an up-to-date review on the spatial distributions of ticks and tick-borne pathogens in China. We map at the county level 124 tick species, 103 tick-borne agents, and human cases infected with 29 species (subspecies) of tick-borne pathogens that were reported in China during 1950-2018. Haemaphysalis longicornis is found to harbor the highest variety of tick-borne agents, followed by Ixodes persulcatus, Dermacentor nutalli and Rhipicephalus microplus. Using a machine learning algorithm, we assess ecoclimatic and socioenvironmental drivers for the distributions of 19 predominant vector ticks and two tick-borne pathogens associated with the highest disease burden. The model-predicted suitable habitats for the 19 tick species are 14‒476% larger in size than the geographic areas where these species were detected, indicating severe under-detection. Tick species harboring pathogens of imminent threats to public health should be prioritized for more active field surveillance.

Mapping the global potential transmission hotspots for severe fever with thrombocytopenia syndrome by machine learning methods

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with increasing spread. Currently SFTS transmission has expanded beyond Asian countries, however, with definitive global extents and risk patterns remained obscure. Here we established an exhaustive database that included globally reported locations of human SFTS cases and the competent vector, Haemaphysalis longicornis (H. longicornis), as well as the explanatory environmental variables, based on which, the potential geographic range of H. longicornis and risk areas for SFTS were mapped by applying two machine learning methods. Ten predictors were identified contributing to global distribution for H. longicornis with relative contribution ≥1%. Outside contemporary known distribution, we predict high receptivity to H. longicornis across two continents, including northeastern USA, New Zealand, parts of Australia, and several Pacific islands. Eight key drivers of SFTS cases occurrence were identified, including elevation, predicted probability of H. longicornis presence, two temperature-related factors, two precipitation-related factors, the richness of mammals and percentage coverage of water bodies. The globally model-predicted risk map of human SFTS occurrence was created and validated effective for discriminating the actual affected and unaffected areas (median predictive probability 0.74 vs. 0.04, P < 0.001) in three countries with reported cases outside China. The high-risk areas (probability ≥50%) were predicted mainly in east-central China, most parts of the Korean peninsula and southern Japan, and northern New Zealand. Our findings highlight areas where an intensive vigilance for potential SFTS spread or invasion events should be advocated, owing to their high receptibility to H. longicornis distribution.

Spatial modeling could not differentiate early SARS-CoV-2 cases from the distribution of humans on the basis of climate in the United States

The SARS-CoV-2 coronavirus is wreaking havoc globally, yet, as a novel pathogen, knowledge of its biology is still emerging. Climate and seasonality influence the distributions of many diseases, and studies suggest at least some link between SARS-CoV-2 and weather. One such study, building species distribution models (SDMs), predicted SARS-CoV-2 risk may remain concentrated in the Northern Hemisphere, shifting northward in summer months. Others have highlighted issues with SARS-CoV-2 SDMs, notably: the primary niche of the virus is the host it infects, climate may be a weak distributional predictor, global prevalence data have issues, and the virus is not in population equilibrium. While these issues should be considered, we believe climate's relationship with SARS-CoV-2 is still worth exploring, as it may have some impact on the distribution of cases. To further examine if there is a link to climate, we build model projections with raw SARS-CoV-2 case data and population-scaled case data in the USA. The case data were from across March 2020, before large travel restrictions and public health policies were impacting cases across the country. We show that SDMs built from population-scaled case data cannot be distinguished from control models (built from raw human population data), while SDMs built on raw case data fail to predict the known distribution of cases in the U.S. from March. The population-scaled analyses indicate that climate did not play a central role in early U.S. viral distribution and that human population density was likely the primary driver. We do find slightly more population-scaled viral cases in cooler areas. Ultimately, the temporal and geographic constraints on this study mean that we cannot rule out climate as a partial driver of the SARS-CoV-2 distribution. Climate's role on SARS-CoV-2 should continue to be cautiously examined, but at this time we should assume that SARS-CoV-2 will continue to spread anywhere in the U.S. where governmental policy does not prevent spread.

Spatial modeling could not differentiate early SARS-CoV-2 cases from the distribution of humans on the basis of climate in the United States

The SARS-CoV-2 coronavirus is wreaking havoc globally, yet, as a novel pathogen, knowledge of its biology is still emerging. Climate and seasonality influence the distributions of many diseases, and studies suggest at least some link between SARS-CoV-2 and weather. One such study, building species distribution models (SDMs), predicted SARS-CoV-2 risk may remain concentrated in the Northern Hemisphere, shifting northward in summer months. Others have highlighted issues with SARS-CoV-2 SDMs, notably: the primary niche of the virus is the host it infects, climate may be a weak distributional predictor, global prevalence data have issues, and the virus is not in population equilibrium. While these issues should be considered, we believe climate's relationship with SARS-CoV-2 is still worth exploring, as it may have some impact on the distribution of cases. To further examine if there is a link to climate, we build model projections with raw SARS-CoV-2 case data and population-scaled case data in the USA. The case data were from across March 2020, before large travel restrictions and public health policies were impacting cases across the country. We show that SDMs built from population-scaled case data cannot be distinguished from control models (built from raw human population data), while SDMs built on raw case data fail to predict the known distribution of cases in the U.S. from March. The population-scaled analyses indicate that climate did not play a central role in early U.S. viral distribution and that human population density was likely the primary driver. We do find slightly more population-scaled viral cases in cooler areas. Ultimately, the temporal and geographic constraints on this study mean that we cannot rule out climate as a partial driver of the SARS-CoV-2 distribution. Climate's role on SARS-CoV-2 should continue to be cautiously examined, but at this time we should assume that SARS-CoV-2 will continue to spread anywhere in the U.S. where governmental policy does not prevent spread.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Could Environment Affect the Mutation of H1N1 Influenza Virus?

H1N1 subtype influenza A viruses are the most common type of influenza A virus to infect humans. The two major outbreaks of the virus in 1918 and 2009 had a great impact both on human health and social development. Though data on their complete genome sequences have recently been obtained, the evolution and mutation of A/H1N1 viruses remain unknown to this day. Among many drivers, the impact of environmental factors on mutation is a novel hypothesis worth studying. Here, a geographically disaggregated method was used to explore the relationship between environmental factors and mutation of A/H1N1 viruses from 2000-2019. All of the 11,721 geo-located cases were examined and the data was analysed of six environmental elements according to the time and location (latitude and longitude) of those cases. The main mutation value was obtained by comparing the sequence of the influenza virus strain with the earliest reported sequence. It was found that environmental factors systematically affect the mutation of A/H1N1 viruses. Minimum temperature displayed a nonlinear, rising association with mutation, with a maximum ~15 °C. The effects of precipitation and social development index (nighttime light) were more complex, while population density was linearly and positively correlated with mutation of A/H1N1 viruses. Our results provide novel insight into understanding the complex relationships between mutation of A/H1N1 viruses and environmental factors.

Low-Cost Battery-Powered and User-Friendly Real-Time Quantitative PCR System for the Detection of Multigene

Real-time polymerase chain reaction (PCR) is the standard for nucleic acid detection and plays an important role in many fields. A new chip design is proposed in this study to avoid the use of expensive instruments for hydrophobic treatment of the surface, and a new injection method solves the issue of bubbles formed during the temperature cycle. We built a battery-powered real-time PCR device to follow polymerase chain reaction using fluorescence detection and developed an independently designed electromechanical control system and a fluorescence analysis software to control the temperature cycle, the photoelectric detection coupling, and the automatic analysis of the experimental data. The microchips and the temperature cycling system cost USD 100. All the elements of the device are available through open access, and there are no technical barriers. The simple structure and manipulation allows beginners to build instruments and perform PCR tests after only a short tutorial. The device is used for analysis of the amplification curve and the melting curve of multiple target genes to demonstrate that our instrument has the same accuracy and stability as a commercial instrument.

Protection of wetlands as a strategy for reducing the spread of avian influenza from migratory waterfowl

Highly pathogenic avian influenza (HPAI) H5N1 has led to the death or destruction of millions of domesticated and wild birds and caused hundreds of human deaths worldwide. As with other HPAIs, H5N1 outbreaks among poultry have generally been caused by contact with infected migratory waterfowl at the interface of wildlands and human-dominated landscapes. Using a case-control epidemiological approach, we analyzed the relation between habitat protection and H5N1 outbreaks in China from 2004 to 2017. We found that while proximity to unprotected waterfowl habitats and rice paddy generally increased outbreak risk, proximity to the most highly protected habitats (e.g., Ramsar-designated lakes and wetlands) had the opposite effect. Protection likely involves two mechanisms: the separation of wild waterfowl and poultry populations and the diversion of wild waterfowl from human-dominated landscapes toward protected natural habitats. Wetland protection could therefore be an effective means to control avian influenza while also contributing to avian conservation.

Parasite associations predict infection risk: incorporating co-infections in predictive models for neglected tropical diseases

BACKGROUND

Schistosomiasis and infection by soil-transmitted helminths are some of the world's most prevalent neglected tropical diseases. Infection by more than one parasite (co-infection) is common and can contribute to clinical morbidity in children. Geostatistical analyses of parasite infection data are key for developing mass drug administration strategies, yet most methods ignore co-infections when estimating risk. Infection status for multiple parasites can act as a useful proxy for data-poor individual-level or environmental risk factors while avoiding regression dilution bias. Conditional random fields (CRF) is a multivariate graphical network method that opens new doors in parasite risk mapping by (i) predicting co-infections with high accuracy; (ii) isolating associations among parasites; and (iii) quantifying how these associations change across landscapes.

METHODS

We built a spatial CRF to estimate infection risks for Ascaris lumbricoides, Trichuris trichiura, hookworms (Ancylostoma duodenale and Necator americanus) and Schistosoma mansoni using data from a national survey of Rwandan schoolchildren. We used an ensemble learning approach to generate spatial predictions by simulating from the CRF's posterior distribution with a multivariate boosted regression tree that captured non-linear relationships between predictors and covariance in infection risks. This CRF ensemble was compared against single parasite gradient boosted machines to assess each model's performance and prediction uncertainty.

RESULTS

Parasite co-infections were common, with 19.57% of children infected with at least two parasites. The CRF ensemble achieved higher predictive power than single-parasite models by improving estimates of co-infection prevalence at the individual level and classifying schools into World Health Organization treatment categories with greater accuracy. The CRF uncovered important environmental and demographic predictors of parasite infection probabilities. Yet even after capturing demographic and environmental risk factors, the presences or absences of other parasites were strong predictors of individual-level infection risk. Spatial predictions delineated high-risk regions in need of anthelminthic treatment interventions, including areas with higher than expected co-infection prevalence.

CONCLUSIONS

Monitoring studies routinely screen for multiple parasites, yet statistical models generally ignore this multivariate data when assessing risk factors and designing treatment guidelines. Multivariate approaches can be instrumental in the global effort to reduce and eventually eliminate neglected helminth infections in developing countries.

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.

[Using geo-intelligence to estimate risk of introduction of influenza type A in MexicoCenário de risco de introdução do vírus da influenza A no México estimado com o uso de inteligência geográfica]

Objetivo.

Estimar el escenario potencial probabilístico de introducción del agente causal de la influenza tipo A en México mediante geointeligencia sanitaria.

Métodos.

Estudio ecológico en el que consideran 1 973 brotes de influenza con alto grado de patogenicidad en el mundo durante el período 2014-2016. Se desarrolló un modelado geoespacial con herramientas de la geointeligencia, como la representación espacial, modelo de conexidad, caracterización espacial de la fuente de inoculo con el modelo de máxima entropía y la curva característica de operación receptora (COR) mediante la evaluación espacial multicriterio y se validó con el índice de Moran y la regresión geográficamente ponderada.

Resultados.

Se estimaron las isocronas de riesgo sanitario con una distancia de 548 km y su crecimiento exponencial; hasta la cuarta isócrona se identificaron las costas este y oeste de Estados Unidos de América (EEUU) y una porción de América Central como posible superficie que favorece la introducción del patógeno. Se obtuvo, también, una curva COR = 0,923, se identificaron dos períodos de riesgo de introducción (setiembre-marzo) y (abril-agosto) con trayectorias de norte-sur y sur-norte respectivamente, con alta autocorrelación positiva para el modelado geoespacial, y se estimó un escenario donde más de la mitad de México se encuentra en un riesgo alto de introducción, con 78 millones de personas expuestas. Se identificó una asociación positiva entre las áreas de riesgo significativo (P < 0,001).

Conclusión.

Se evidencia que más de 50% del territorio mexicano se encuentra en riesgo de introducción del agente causal de la influenza tipo A, con aproximadamente 70% de la población expuesta.

Association between meteorological factors, spatiotemporal effects, and prevalence of influenza A subtype H7 in environmental samples in Zhejiang province, China

BACKGROUND

Human infection with the H7N9 virus has been reported recurrently since spring 2013. Given low pathogenicity of the virus in poultry, the outbreak cannot be noticed easily until a case of human infection is reported. Studies showed that the prevalence of influenza A subtype H7 in environmental samples is associated with the number of human H7N9 infection, with the latter associated with meteorological factors. Understanding the association between meteorological factors and the prevalence of H7 subtype in the environmental samples can shed light on how the virus propagates in the environment for disease control.

METHOD

Environmental samples and meteorological data (precipitation, temperature, relative humidity, sunshine duration, and wind speed) collected in Zhejiang province, China, during 2013-2017 were used. A Bayesian hierarchical binomial logistic spatiotemporal model which captures spatiotemporal effects was adopted to model the prevalence of H7 subtype with the meteorological factors.

RESULTS

The monthly overall prevalence of H7 subtype in the environmental samples was usually <30%. Compared with the odds at median, moderately low precipitation (49.19-115.60 mm), moderately long sunshine duration (4.22-9.25 h) and low temperature (<9.33 °C) were statistically significantly associated with a higher adjusted odds of detecting an H7-positive sample, whereas moderately high precipitation (119.51-146.85 mm), short and moderately short sunshine duration (<1.77 h; 4.00-4.17 h), and high temperature (>23.09 °C) were statistically significantly associated with a lower adjusted odds. The adjusted odds increased multiplicatively by 1.11 per 1% increase in relative humidity.

CONCLUSION

Since the prevalence of H7 subtype in environmental samples was associated with meteorological conditions and the number of human H7N9 infection, an environmental surveillance program which incorporates meteorological conditions in planning allows for early detection of the spread of the virus in the environment and better preparation for the outbreak in the human population.

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.

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.

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.

Identification of meteorological factors associated with human infection with avian influenza A H7N9 virus in Zhejiang Province, China

BACKGROUND

Since the first reported human infection with an avian-origin influenza A (H7N9) virus in China in early 2013, there have been recurrent outbreaks of the virus in the country. Previous studies have shown that meteorological factors are associated with the risk of human infection with the virus; however, their possible nonlinear and lagged effects were not commonly taken into account.

METHOD

To quantify the effect of meteorological factors on the risk of human H7N9 infection, daily laboratory-confirmed cases of human H7N9 infection and meteorological factors including total rainfall, average wind speed, average temperature, average relative humidity, and sunshine duration of the 11 sub-provincial/prefecture cities in Zhejiang during the first four outbreaks (13 March 2013-30 June 2016) were analyzed. Separate models were built for the 6 sub-provincial/prefecture cities with the greatest number of reported cases using a combination of logistic generalized additive model and distributed lag nonlinear models, which were then pooled by a multivariate meta-regression model to determine their overall effects.

RESULTS

According to the meta-regression model, for rainfall, the log adjusted overall cumulative odds ratio was statistically significant when log of rainfall was >4.0, peaked at 5.3 with a value of 12.42 (95% confidence intervals (CI): [3.23, 21.62]). On the other hand, when wind speed was 2.1-3.0 m/s or 6.3-7.1 m/s, the log adjusted overall cumulative odds ratio was statistically significant, peaked at 7.1 m/s with a value of 6.75 (95% CI: [0.03, 13.47]). There were signs of nonlinearity and lag effects in their associations with the risk of infection.

CONCLUSION

As rainfall and wind speed were found to be associated with the risk of human H7N9 infection, weather conditions should be taken into account when it comes to disease surveillance, allowing prompt actions when an outbreak takes place.

Coupling dynamics of epidemic spreading and information diffusion on complex networks

The interaction between disease and disease information on complex networks has facilitated an interdisciplinary research area. When a disease begins to spread in the population, the corresponding information would also be transmitted among individuals, which in turn influence the spreading pattern of the disease. In this paper, firstly, we analyze the propagation of two representative diseases (H7N9 and Dengue fever) in the real-world population and their corresponding information on Internet, suggesting the high correlation of the two-type dynamical processes. Secondly, inspired by empirical analyses, we propose a nonlinear model to further interpret the coupling effect based on the SIS (Susceptible-Infected-Susceptible) model. Both simulation results and theoretical analysis show that a high prevalence of epidemic will lead to a slow information decay, consequently resulting in a high infected level, which shall in turn prevent the epidemic spreading. Finally, further theoretical analysis demonstrates that a multi-outbreak phenomenon emerges via the effect of coupling dynamics, which finds good agreement with empirical results. This work may shed light on the in-depth understanding of the interplay between the dynamics of epidemic spreading and information diffusion.

Changing Geographic Patterns and Risk Factors for Avian Influenza A(H7N9) Infections in Humans, China

The fifth epidemic wave of avian influenza A(H7N9) virus in China during 2016–2017 demonstrated a geographic range expansion and caused more human cases than any previous wave. The factors that may explain the recent range expansion and surge in incidence remain unknown. We investigated the effect of anthropogenic, poultry, and wetland variables on all epidemic waves. Poultry predictor variables became much more important in the last 2 epidemic waves than they were previously, supporting the assumption of much wider H7N9 transmission in the chicken reservoir. We show that the future range expansion of H7N9 to northern China may increase the risk of H7N9 epidemic peaks coinciding in time and space with those of seasonal influenza, leading to a higher risk of reassortments than before, although the risk is still low so far.

Independent and interactive effects of ambient temperature and absolute humidity on the risks of avian influenza A(H7N9) infection in China

The emergence of avian influenza A(H7N9) virus poses a pandemic threat to human beings. It was proposed that meteorological factors might be important environmental factors favoring the occurrence of H7N9 infection, but evidence is still inadequate. In this study, we aimed to investigate the independent and interactive effects of ambient temperature (TM) and absolute humidity (AH) on H7N9 infection risks in China. The individual information of all reported H7N9 cases and daily meteorological data in five provinces/municipality (Zhejiang, Jiangsu, Shanghai, Fujian, and Guangdong) in China during 2013-2016 were collected. We employed a case-crossover study design, in which the 7-10days before the onset date of each H7N9 case was defined as the hazard period, and 4weeks before the hazard period was taken as the control period. The average levels of meteorological factors were calculated during the hazard and control periods. A Cox regression model was used to estimate the independent and interactive effects of TM and vapor pressure (VP), an indicator of AH, on H7N9 infection risks. A total of 738 H7N9 cases were included in the present study. Significantly nonlinear negative associations of TM and VP with H7N9 infection risks were observed in all cases, and in cases from northern and southern regions. There were significant interactive effects between TM and VP on H7N9 infection risks, and the risks of H7N9 infection were higher in cold-dry days than other days. We further observed different risky windows of H7N9 infection in the northern (TM: 0-18°C, VP: 313mb) and southern areas (TM: 7-21°C, VP: 3-17mb). We concluded that ambient temperature and absolute humidity had significant independent and interactive effects on H7N9 infection risks in China, and the risks of H7N9 infection were higher in cold-dry days. The risky windows of H7N9 infection were different in the northern and southern areas.

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'.

Mapping the epidemic changes and risks of hemorrhagic fever with renal syndrome in Shaanxi Province, China, 2005-2016

Hemorrhagic fever with renal syndrome (HFRS) is a major rodent-borne zoonosis. Each year worldwide, 60,000–100,000 HFRS human cases are reported in more than seventy countries with almost 90% these cases occurring in China. Shaanxi Province in China has been among the most seriously affected areas since 1955. During 2009–2013, Shaanxi reported 11,400 human cases, the most of all provinces in China. Furthermore, the epidemiological features of HFRS have changed over time. Using long-term data of HFRS from 2005 to 2016, we carried out this retrospective epidemiological study combining ecological assessment models in Shaanxi. We found the majority of HFRS cases were male farmers who acquired infection in Guanzhong Plain, but the geographic extent of the epidemic has slowly spread northward. The highest age-specific attack rate since 2011 was among people aged 60–74 years, and the percentage of HFRS cases among the elderly increased from 12% in 2005 to 25% in 2016. We highly recommend expanding HFRS vaccination to people older than 60 years to better protect against the disease. Multivariate analysis revealed artificial area, cropland, pig and population density, GDP, and climate conditions (relative humidity, precipitation, and wind speed) as significant risk factors in the distribution of HFRS.

Human Infection with Influenza A(H7N9) Virus during 3 Major Epidemic Waves, China, 2013-2015

Variation in risk for death might be associated with differences in case ascertainment, changes in clinical management, or virus genetic diversity.

Since March 2013, a novel influenza A(H7N9) virus has caused 3 epidemic waves of human infection in mainland China. We analyzed data from patients with laboratory-confirmed influenza A(H7N9) virus infection to estimate the risks for severe outcomes after hospitalization across the 3 waves. We found that hospitalized patients with confirmed infections in waves 2 and 3 were younger and more likely to be residing in small cities and rural areas than were patients in wave 1; they also had a higher risk for death, after adjustment for age and underlying medical conditions. Risk for death among hospitalized patients during waves 2 and 3 was lower in Jiangxi and Fujian Provinces than in eastern and southern provinces. The variation in risk for death among hospitalized case-patients in different areas across 3 epidemic waves might be associated with differences in case ascertainment, changes in clinical management, or virus genetic diversity.

Spatio-temporal pattern analysis for evaluation of the spread of human infections with avian influenza A(H7N9) virus in China, 2013-2014

BACKGROUND

A large number (n = 460) of A(H7N9) human infections have been reported in China from March 2013 through December 2014, and H7N9 outbreaks in humans became an emerging issue for China health, which have caused numerous disease outbreaks in domestic poultry and wild bird populations, and threatened human health severely. The aims of this study were to investigate the directional trend of the epidemic and to identify the significant presence of spatial-temporal clustering of influenza A(H7N9) human cases between March 2013 and December 2014.

METHODS

Three distinct epidemic phases of A(H7N9) human infections were identified in this study. In each phase, standard deviational ellipse analysis was conducted to examine the directional trend of disease spreading, and retrospective space-time permutation scan statistic was then used to identify the spatio-temporal cluster patterns of H7N9 outbreaks in humans.

RESULTS

The ever-changing location and the increasing size of the three identified standard deviational ellipses showed that the epidemic moved from east to southeast coast, and hence to some central regions, with a future epidemiological trend of continue dispersing to more central regions of China, and a few new human cases might also appear in parts of the western China. Furthermore, A(H7N9) human infections were clustering in space and time in the first two phases with five significant spatio-temporal clusters (p < 0.05), but there was no significant cluster identified in phase III.

CONCLUSIONS

There was a new epidemiologic pattern that the decrease in significant spatio-temporal cluster of A(H7N9) human infections was accompanied with an obvious spatial expansion of the outbreaks during the study period, and identification of the spatio-temporal patterns of the epidemic can provide valuable insights for better understanding the spreading dynamics of the disease in China.

A Systematic Review and Meta-Analysis of Practices Exposing Humans to Avian Influenza Viruses, Their Prevalence, and Rationale

Almost all human infections by avian influenza viruses (AIVs) are transmitted from poultry. A systematic review was conducted to identify practices associated with human infections, their prevalence, and rationale. Observational studies were identified through database searches. Meta-analysis produced combined odds ratio estimates. The prevalence of practices and rationales for their adoptions were reported. Of the 48,217 records initially identified, 65 articles were included. Direct and indirect exposures to poultry were associated with infection for all investigated viral subtypes and settings. For the most frequently reported practices, association with infection seemed stronger in markets than households, for sick and dead than healthy poultry, and for H7N9 than H5N1. Practices were often described in general terms and their frequency and intensity of contact were not provided. The prevalence of practices was highly variable across studies, and no studies comprehensively explored reasons behind the adoption of practices. Combining epidemiological and targeted anthropological studies would increase the spectrum and detail of practices that could be investigated and should aim to provide insights into the rationale(s) for their existence. A better understanding of these rationales may help to design more realistic and acceptable preventive public health measures and messages.

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

Background

Zoonotic avian influenza poses a major risk to China, and other parts of the world. H5N1 has remained endemic in China and globally for nearly two decades, and in 2013, a novel zoonotic influenza A subtype H7N9 emerged in China. This study aimed to improve upon our current understanding of the spreading mechanisms of H7N9 and H5N1 by generating spatial risk profiles for each of the two virus subtypes across mainland China.

Methods and findings

In this study, we (i) developed a refined data set of H5N1 and H7N9 locations with consideration of animal/animal environment case data, as well as spatial accuracy and precision; (ii) used this data set along with environmental variables to build species distribution models (SDMs) for each virus subtype in high resolution spatial units of 1km² cells using Maxent; (iii) developed a risk modelling framework which integrated the results from the SDMs with human and chicken population variables, which was done to quantify the risk of zoonotic transmission; and (iv) identified areas at high risk of H5N1 and H7N9 transmission. We produced high performing SDMs (6 of 8 models with AUC > 0.9) for both H5N1 and H7N9. In all our SDMs, H7N9 consistently showed higher AUC results compared to H5N1, suggesting H7N9 suitability could be better explained by environmental variables. For both subtypes, high risk areas were primarily located in south-eastern China, with H5N1 distributions found to be more diffuse and extending more inland compared to H7N9.

Conclusions

We provide projections of our risk models to public health policy makers so that specific high risk areas can be targeted for control measures. We recommend comparing H5N1 and H7N9 prevalence rates and survivability in the natural environment to better understand the role of animal and environmental transmission in human infections.

H9N2 Avian Influenza Virus Protein PB1 Enhances the Immune Responses of Bone Marrow-Derived Dendritic Cells by Down-Regulating miR375

Polymerase basic protein 1 (PB1), the catalytic core of the influenza A virus RNA polymerase complex, is essential for viral transcription and replication. Dendritic cells (DCs) possess important antigen presenting ability and a crucial role in recognizing and clearing virus. MicroRNA (miRNA) influence the development of DCs and their ability to present antigens as well as the ability of avian influenza virus (AIV) to infect host cells and replicate. Here, we studied the molecular mechanism underlying the miRNA-mediated regulation of immune function in mouse DCs. We first screened for and verified the induction of miRNAs in DCs after PB1 transfection. Results showed that the viral protein PB1 down-regulated the expression of miR375, miR146, miR339, and miR679 in DCs, consistent with the results of H9N2 virus treatment; however, the expression of miR222 and miR499, also reduced in the presence of PB1, was in contrast to the results of H9N2 virus treatment. Our results suggest that PB1 enhanced the ability of DCs to present antigens, activate lymphocytes, and secrete cytokines, while miR375 over-expression repressed activation of DC maturation. Nevertheless, PB1 could not promote DC maturation once miR375 was inhibited. Finally, we revealed that PB1 inhibited the P-Jnk/Jnk signaling pathway, but activated the p-Erk/Erk signaling pathway. While inhibition of miR375 -activated the p-Erk/Erk and p-p38/p38 signaling pathway, but repressed the P-Jnk/Jnk signaling pathway. Taken together, results of our studies shed new light on the roles and mechanisms of PB1 and miR375 in regulating DC function and suggest new strategies for combating AIV.

Model-informed risk assessment for Zika virus outbreaks in the Asia-Pacific regions

Recently, Zika virus (ZIKV) has been recognized as a significant threat to global public health. The disease was present in large parts of the Americas, the Caribbean, and also the western Pacific area with southern Asia during 2015 and 2016. However, little is known about the factors affecting the transmission of ZIKV. We used Gradient Boosted Regression Tree models to investigate the effects of various potential explanatory variables on the spread of ZIKV, and used current with historical information from a range of sources to assess the risks of future ZIKV outbreaks. Our results indicated that the probability of ZIKV outbreaks increases with vapor pressure, the occurrence of Dengue virus, and population density but decreases as health expenditure, GDP, and numbers of travelers. The predictive results revealed the potential risk countries of ZIKV infection in the Asia-Pacific regions between October 2016 and January 2017. We believe that the high-risk conditions would continue in South Asia and Australia over this period. By integrating information on eco-environmental, social-economical, and ZIKV-related niche factors, this study estimated the probability for locally acquired mosquito-borne ZIKV infections in the Asia-Pacific region and improves the ability to forecast, and possibly even prevent, future outbreaks of ZIKV.

Quantifying predictors for the spatial diffusion of avian influenza virus in China

Background

Avian influenza virus (AIV) causes both severe outbreaks and endemic disease among poultry and has caused sporadic human infections in Asia, furthermore the routes of transmission in avian species between geographic regions can be numerous and complex. Using nucleotide sequences from the internal protein coding segments of AIV, we performed a Bayesian phylogeographic study to uncover regional routes of transmission and factors predictive of the rate of viral diffusion within China.

Results

We found that the Central area and Pan-Pearl River Delta were the two main sources of AIV diffusion, while the East Coast areas especially the Yangtze River delta, were the major targets of viral invasion. Next we investigated the extent to which economic, agricultural, environmental and climatic regional data was predictive of viral diffusion by fitting phylogeographic discrete trait models using generalised linear models.

Conclusions

Our results highlighted that the economic-agricultural predictors, especially the poultry population density and the number of farm product markets, are the key determinants of spatial diffusion of AIV in China; high human density and freight transportation are also important predictors of high rates of viral transmission; Climate features (e.g. temperature) were correlated to the viral invasion in the destination to some degree; while little or no impacts were found from natural environment factors (such as surface water coverage). This study uncovers the risk factors and enhances our understanding of the spatial dynamics of AIV in bird populations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-016-0845-3) contains supplementary material, which is available to authorized users.

Estimating Risks of Inapparent Avian Exposure for Human Infection: Avian Influenza Virus A (H7N9) in Zhejiang Province, China

Inapparent avian exposure was suspected for the sporadic infection of avian influenza A(H7N9) occurring in China. This type of exposure is usually unnoticed and difficult to model and measure. Infected poultry with avian influenza H7N9 virus typically remains asymptomatic, which may facilitate infection through inapparent poultry/bird exposure, especially in a country with widespread practice of backyard poultry. The present study proposed a novel approach that integrated ecological and case-control methods to quantify the risk of inapparent avian exposure on human H7N9 infection. Significant associations of the infection with chicken and goose densities, but not with duck density, were identified after adjusting for spatial clustering effects of the H7N9 cases across multiple geographic scales of neighborhood, community, district and city levels. These exposure risks varied geographically in association with proximity to rivers and lakes that were also proxies for inapparent exposure to avian-related environment. Males, elderly people, and farmers were high-risk subgroups for the virus infection. These findings enable health officials to target educational programs and awareness training in specific locations to reduce the risks of inapparent exposure.

H9N2 avian influenza virus enhances the immune responses of BMDCs by down-regulating miR29c

Avian influenza virus (AIV) of the subtypes H9 and N2 is well recognised and caused outbreaks-due to its high genetic variability and high rate of recombination with other influenza virus subtypes. The pathogenicity of H9N2 AIV depends on the host immune response. Dendritic cells (DCs) are major antigen presenting cells that can significantly inhibit H9N2 AIV replication. MicroRNAs (miRNAs) influence the ability of DCs to present antigens, as well as the ability of AIVs to infect host cells and replicate. Here, we studied the molecular mechanism underlying the miRNA-mediated regulation of immune function of mouse DCs. We first screened for and verified the induction of miRNAs in DCs after H9N2 AIVstimulation. We also constructed miR29c, miR339 and miR222 over-expression vector and showed that only the induction of miR29c lead to a hugely increased expression of surface marker MHCII and CD40. Whilst the inhibition of miR29c, miR339 and miR222 in mouse DCs would repressed the expression of DCs surface markers. Moreover, we found that miR29c stimulation not only up-regulate MHCII and CD40, but also enhance the ability of DCs to activate lymphocytes and secrete cytokines IL-6 or TNF-a. Furthermore, we found that Tarbp1 and Rfx7 were targeted and repressed by miR29c. Finally, we revealed that the inhibition of miR29c marvelously accelerated virus replication. Together, our data shed new light on the roles and mechanisms of miR29c in regulating DC function and suggest new strategies for combating AIVs.

H7N9 and H5N1 avian influenza suitability models for China: accounting for new poultry and live-poultry markets distribution data

In the last two decades, two important avian influenza viruses infecting humans emerged in China, the highly pathogenic avian influenza (HPAI) H5N1 virus in the late nineties, and the low pathogenic avian influenza (LPAI) H7N9 virus in 2013. China is home to the largest population of chickens (4.83 billion) and ducks (0.694 billion), representing, respectively 23.1 and 58.6% of the 2013 world stock, with a significant part of poultry sold through live-poultry markets potentially contributing to the spread of avian influenza viruses. Previous models have looked at factors associated with HPAI H5N1 in poultry and LPAI H7N9 in markets. However, these have not been studied and compared with a consistent set of predictor variables. Significant progress was recently made in the collection of poultry census and live-poultry market data, which are key potential factors in the distribution of both diseases. Here we compiled and reprocessed a new set of poultry census data and used these to analyse HPAI H5N1 and LPAI H7N9 distributions with boosted regression trees models. We found a limited impact of the improved poultry layers compared to models based on previous poultry census data, and a positive and previously unreported association between HPAI H5N1 outbreaks and the density of live-poultry markets. In addition, the models fitted for the HPAI H5N1 and LPAI H7N9 viruses predict a high risk of disease presence for the area around Shanghai and Hong Kong. The main difference in prediction between the two viruses concerned the suitability of HPAI H5N1 in north-China around the Yellow sea (outlined with Tianjin, Beijing, and Shenyang city) where LPAI H7N9 has not spread intensely.

Ecological Niche Modeling of Risk Factors for H7N9 Human Infection in China

China was attacked by a serious influenza A (H7N9) virus in 2013. The first human infection case was confirmed in Shanghai City and soon spread across most of eastern China. Using the methods of Geographic Information Systems (GIS) and ecological niche modeling (ENM), this research quantitatively analyzed the relationships between the H7N9 occurrence and the main environmental factors, including meteorological variables, human population density, bird migratory routes, wetland distribution, and live poultry farms, markets, and processing factories. Based on these relationships the probability of the presence of H7N9 was predicted. Results indicated that the distribution of live poultry processing factories, farms, and human population density were the top three most important determinants of the H7N9 human infection. The relative contributions to the model of live poultry processing factories, farms and human population density were 39.9%, 17.7% and 17.7%, respectively, while the maximum temperature of the warmest month and mean relative humidity had nearly no contribution to the model. The paper has developed an ecological niche model (ENM) that predicts the spatial distribution of H7N9 cases in China using environmental variables. The area under the curve (AUC) values of the model were greater than 0.9 (0.992 for the training samples and 0.961 for the test data). The findings indicated that most of the high risk areas were distributed in the Yangtze River Delta. These findings have important significance for the Chinese government to enhance the environmental surveillance at multiple human poultry interfaces in the high risk area.

Modelling African swine fever presence and reported abundance in the Russian Federation using national surveillance data from 2007 to 2014

African swine fever (ASF) is a viral disease of swine that has been present in the Russian Federation since 2007. Counts of ASF outbreaks reported in the Southern regions of the country (2007-2014) were aggregated to a grid of hexagons, and a zero-inflated Poisson model accounting for spatial dependence between hexagons was used to identify factors associated with the presence of ASF outbreaks and factors associated with the number of ASF reports in affected hexagons. Increasing density of pigs raised on low biosecurity farms was found to be positively associated with the probability of occurrence of at least one ASF outbreak in a hexagon and with the average number of reported ASF outbreaks amongst affected hexagons. Increasing human population density and increasing distance from the closest diagnostic laboratory were additional variables associated with number of reported ASF outbreaks amongst affected hexagons. The model was shown to have good predictive ability.

A new reassortment of influenza A (H7N9) virus causing human infection in Beijing, 2014

A 73-year-old man was confirmed to have an influenza A (H7N9) virus infection, and the causative agent A/Beijing/02/2014(H7N9) virus was isolated. Genetic and phylogenetic analyses revealed that the virus belonged to a novel genotype, which probably emerged and further reassorted with other H9 or H7 viruses in poultry before transmitting to humans. This virus caused a severe infection with high levels of cytokines and neutralizing antibodies. Eventually, the patient was cured after serially combined treatments. Taken together, our findings indicated that this novel genotype of the human H7N9 virus did not evolve directly from the first Beijing isolate A/Beijing/01/2013(H7N9), suggesting that the H7N9 virus has not obtained the ability for human-to-human transmissibility and the virus only evolves in poultry and then infects human by direct contact. Hence, the major measures to prevent human H7N9 virus infection are still to control and standardize the live poultry trade. Early antiviral treatment with combination therapies, including mechanical ventilation, nutrition support and symptomatic treatment, are effective for H7N9 infection.