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

Airborne Avian Influenza Virus in Ambient Air in the Winter Habitats of Migratory Birds

Outbreaks of avian influenza virus (AIV) have raised public concerns recently. Airborne AIV has been evaluated in live poultry markets and case farms; however, no study has discussed airborne AIV in ambient air in the winter habitats of migratory birds. Therefore, this study aimed to evaluate airborne AIV, specifically H5, H7, and H9, in a critical winter habitat of migratory birds and assess the factors influencing airborne AIV transmission in ambient air to provide novel insights into the epidemiology of avian influenza. A total of 357 ambient air samples were collected in the Aogu Wetland, Taiwan, Republic of China, between October 2017 and December 2019 and analyzed using quantitative real-time polymerase chain reaction. The effects of environmental factors including air pollutants, meteorological factors, and the species of the observed migratory birds on the concentration of airborne AIV were also analyzed. To our knowledge, this is the first study to investigate the relationship between airborne AIV in ambient air and the influence factors in the winter habitats of migratory birds, demonstrating the benefits of environmental sampling for infectious disease epidemiology. The positive rate of airborne H7 (12%) was higher than that of H5 (8%) and H9 (10%). The daily mean temperature and daily maximum temperature had a significant negative correlation with influenza A, H7, and H9. Cold air masses and bird migration were significantly associated with airborne H9 and H7, respectively. In addition, we observed a significant correlation between AIV and the number of pintails, common teals, Indian spot-billed ducks, northern shovelers, Eurasian wigeons, tufted ducks, pied avocets, black-faced spoonbills, and great cormorants. In conclusion, we demonstrated the potential for alternative surveillance approaches (monitoring bird species) as an indicator for influenza-related risks and identified cold air masses and the presence of specific bird species as potential drivers of the presence and/or the airborne concentration of AIV.

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.

Construction of Influenza Early Warning Model Based on Combinatorial Judgment Classifier: A Case Study of Seasonal Influenza in Hong Kong

OBJECTIVE

The annual influenza epidemic is a heavy burden on the health care system, and has increasingly become a major public health problem in some areas, such as Hong Kong (China). Therefore, based on a variety of machine learning methods, and considering the seasonal influenza in Hong Kong, the study aims to establish a Combinatorial Judgment Classifier (CJC) model to classify the epidemic trend and improve the accuracy of influenza epidemic early warning.

METHODS

The characteristic variables were selected using the single-factor statistical method to establish the influencing factor system of an influenza outbreak. On this basis, the CJC model was proposed to provide an early warning for an influenza outbreak. The characteristic variables in the final model included atmospheric pressure, absolute maximum temperature, mean temperature, absolute minimum temperature, mean dew point temperature, the number of positive detections of seasonal influenza viruses, the positive percentage among all respiratory specimens, and the admission rates in public hospitals with a principal diagnosis of influenza.

RESULTS

The accuracy of the CJC model for the influenza outbreak trend reached 96.47%, the sensitivity and specificity change rates of this model were lower than those of other models. Hence, the CJC model has a more stable prediction performance. In the present study, the epidemic situation and meteorological data of Hong Kong in recent years were used as the research objects for the construction of the model index system, and a lag correlation was found between the influencing factors and influenza outbreak. However, some potential risk factors, such as geographical nature and human factors, were not incorporated, which ideally affected the prediction performance to some extent.

CONCLUSION

In general, the CJC model exhibits a statistically better performance, when compared to some classical early warning algorithms, such as Support Vector Machine, Discriminant Analysis, and Ensemble Classfiers, which improves the performance of the early warning of seasonal influenza.

Prediction of Human Brucellosis in China Based on Temperature and NDVI

Brucellosis occurs periodically and causes great economic and health burdens. Brucellosis prediction plays an important role in its prevention and treatment. This paper establishes relationships between human brucellosis (HB) and land surface temperature (LST), and the normalized difference vegetation index (NDVI). A seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) model is constructed to predict trends in brucellosis rates. The fitted results (Akaike Information Criterion (AIC) = 807.58, Schwarz Bayes Criterion (SBC) = 819.28) showed obvious periodicity and a rate of increase of 138.68% from January 2011 to May 2016. We found a significant effect between HB and NDVI. At the same time, the prediction part showed that the highest monthly incidence per year has a decreasing trend after 2015. This may be because of the brucellosis prevention and control measures taken by the Chinese Government. The proposed model allows the early detection of brucellosis outbreaks, allowing more effective prevention and control.

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.

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.

Landscape epidemiology in urban environments: The example of rodent-borne Trypanosoma in Niamey, Niger

Trypanosomes are protozoan parasites found worldwide, infecting humans and animals. In the past decade, the number of reports on atypical human cases due to Trypanosoma lewisi or T. lewisi-like has increased urging to investigate the multiple factors driving the disease dynamics, particularly in cities where rodents and humans co-exist at high densities. In the present survey, we used a species distribution model, Maxent, to assess the spatial pattern of Trypanosoma-positive rodents in the city of Niamey. The explanatory variables were landscape metrics describing urban landscape composition and physiognomy computed from 8 land-cover classes. We computed the metrics around each data location using a set of circular buffers of increasing radii (20m, 40m, 60m, 80m and 100m). For each spatial resolution, we determined the optimal combination of feature class and regularization multipliers by fitting Maxent with the full dataset. Since our dataset was small (114 occurrences) we expected an important uncertainty associated to data partitioning into calibration and evaluation datasets. We thus performed 350 independent model runs with a training dataset representing a random subset of 80% of the occurrences and the optimal Maxent parameters. Each model yielded a map of habitat suitability over Niamey, which was transformed into a binary map implementing a threshold maximizing the sensitivity and the specificity. The resulting binary maps were combined to display the proportion of models that indicated a good environmental suitability for Trypanosoma-positive rodents. Maxent performed better with landscape metrics derived from buffers of 80m. Habitat suitability for Trypanosoma-positive rodents exhibited large patches linked to urban features such as patch richness and the proportion of landscape covered by concrete or tarred areas. Such inferences could be helpful in assessing areas at risk, setting of monitoring programs, public and medical staff awareness or even vaccination campaigns.

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.