Edited by: Qing Pan, George Washington University, USA
Reviewed by: Timothy Joe Wade, United States Environmental Protection Agency, USA; Andrew Anthony Hill, Royal Veterinary College, UK
This article was submitted to Epidemiology, a section of the journal Frontiers in Public Health.
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Emergence of avian influenza viruses with high lethality to humans, such as the currently circulating highly pathogenic A(H5N1) (emerged in 1996) and A(H7N9) cause serious concern for the global economic and public health sectors. Understanding the spatial and temporal interface between wild and domestic populations, from which these viruses emerge, is fundamental to taking action. This information, however, is rarely considered in influenza risk models, partly due to a lack of data. We aim to identify areas of high transmission risk between domestic poultry and wild waterfowl in China, the epicenter of both viruses. Two levels of models were developed: one that predicts hotspots of novel virus emergence between domestic and wild birds, and one that incorporates H5N1 risk factors, for which input data exists. Models were produced at 1 and 30 km spatial resolution, and two temporal seasons. Patterns of risk varied between seasons with higher risk in the northeast, central-east, and western regions of China during spring and summer, and in the central and southeastern regions during winter. Monte-Carlo uncertainty analyses indicated varying levels of model confidence, with lowest errors in the densely populated regions of eastern and southern China. Applications and limitations of the models are discussed within.
Emerging infectious diseases in wildlife are a growing concern to human health. More than 75% of known emerging pathogens are zoonotic, or transmissible from animal to humans (
Two currently circulating avian influenza viruses, highly pathogenic A(H5N1) and low pathogenic A(H7N9) (hereafter H5N1 and H7N9) are of particular concern due to their high case-fatality rates (approximately 60 and 30% currently), and economic impact to the livestock industry and public health system (
Wild birds, generally waterfowl and shorebirds (Orders Anseriformes and Charadriiformes), are the natural reservoir for low pathogenic avian influenza viruses (LPAIV) (
A recent review of H5N1 risk models (
Transmission risk models were developed for China based on the importance of this region for H5N1 and H7N9, and the potential for emergence of novel viruses. China’s anthropogenic and natural landscapes differ greatly across the country, allowing for varying levels of disease risk, both spatially and temporally.
In addition to developing nationwide models for China, we focused on two areas of interest regarding transmission at the wild and domestic bird interface: Poyang Lake (PYL) Region in southeastern China and Qinghai Lake (QHL) in northwestern China. PYL, located in along the Yangtze River basin, is a complex wetland system that supports 8.8 million people, 14 million ducks, and 100,000 wintering migratory waterbirds, including 90% of the global population of endangered Siberian Cranes (
We developed three levels of models to predict risk for disease transmission between poultry and wild waterfowl in China (Figure
Level 2 and 3 models incorporate H5N1 parameters. The models include four basic components that relate to classic compartmental models in epidemiology (
Nine parameters were derived for the risk models (Table
Parameter | Description | Value range for 1 km (mean, SD) | Value range for 30 km | Notes (Reference) |
---|---|---|---|---|
Terrestrial poultry density | 0–9418 (379.4, 745.7) chickens/km2 | 0–5871 chickens/km2 | Chicken densities for China ( |
|
Aquatic poultry density | 0–2796 (86.2, 164.7) ducks and geese/km2 | 0–2796 Ducks and geese/km2 | Duck and goose densities for China ( |
|
Effective waterfowl population | Distributions from (Prosser, in review) | |||
Breeding season: |
Wprw i 0 to 0.39 | Population estimates from ( |
||
Wintering season: |
Prevalence rates from ( |
|||
Contaminant containment, terrestrial poultry (biosecure threshold |
Biosecure = 0.75 and 0.25; non-biosecure = 1 (unitless) | Biosecure = 0.5; non-biosecure = 1 | Biosecure threshold of 5000 chickens per km2. Reduction of population by 0.25 or 0.75 given biosecure designation | |
Biosecurity, terrestrial poultry thresholds: |
Tri-part equation: At densities ≤ 50, 100% of population is backyard poultry From 50 to 1000, half are backyard poultry At greater than 1000, backyard poultry is limited to 1000 | |||
Viral shedding rate, terrestrial poultry | 101.4 and 109.8 EID50 | 100, 109.8, 106.8 EID50 | Viral shedding rates per individual per day from ( |
|
Viral shedding rate, aquatic poultry | 101 and 105.7 EID50 | 0, 106.5, 102.98 EID50 | Viral shedding rates per individual per day from ( |
|
Viral shedding rate, wild waterfowl | 102.5 and 106.5 EID50 | 102.5, 106.5, 104.77 EID50 | Viral shedding rates per individual per day from ( |
|
Viral uptake = consumption rate of virus in the environment/minimum load for infection | 10−15/(104.7–101.8) EID50 | 1.58e−17, 1.99e−20, 1.99e−20 ∑50 | Consumption rate of virus in environment 10-15 ( |
We modeled terrestrial (
Two biosecurity scalars were developed to reduce the effective poultry populations: a contaminant containment parameter (
Virus shedding rates for terrestrial poultry (
We developed two Level 1 models, both of which are based on presence or absence of bird groups and not effective population numbers. The first model generated a simple binary risk map with the assumptions that (a) transmission risk is bi-directional (equal probability) between poultry and waterfowl and (b) transmission would only occur if both poultry and waterfowl were predicted to be “present”:
Level 2 and 3 models are based on where poultry and waterfowl are found together on the landscape, but also include effective population size, H5N1 shedding rates, and virus uptake for each group. We developed uni-directional equations for transmission potential from poultry to waterfowl versus waterfowl to poultry (Eqs 3 and 4) due to differences in farming structure and movement of virus through the environment (see above). The equations include compartments (grouped by brackets below) for the amount of virus entering the environment from infected birds (effective populations of infected birds times shedding rates) and amount of virus being taken up by susceptible individuals (effective susceptible populations times the uptake rate):
The Level 1 and 2 deterministic equations were modeled at 1 km resolution in a geographic framework using ArcGIS 10.0 (ESRI, Redlands California) and Python (
Level 1 models, identifying locations where poultry and wild waterfowl co-occur showed distinct patterns across China (Figure
Transmission risk for the 1 km deterministic poultry to wild equations (Eq. 3,
Model | Eq. 3 breeding season | Eq. 3 wintering season | Eq. 4 breeding season | Eq. 4 wintering season |
---|---|---|---|---|
Level 2 (deterministic) | 3.82E−10 | 7.13E−10 | 1.48E−13 | 3.13E−13 |
Level 3 (Monte-Carlo) | 1.18E−09 | 1.66E−09 | 6.03E−13 | 8.39E−13 |
Coefficient of variation | 144 | 147 | 219 | 223 |
Patterns in uncertainty of the Level 3 Monte-Carlo simulation models were similar across seasons and uni-directional equations. We carefully investigated mean input and output for the models between the Level 2 deterministic and Level 3 Monte-Carlo simulations (
(A) Parameter | 1 km Deterministic | 30 km Deterministic | 30 km Monte-Carlo | |
---|---|---|---|---|
379 | 378 | 379 | ||
86 | 86 | 86 | ||
0.01 | 0.01 | 0.13 | 0.03 | |
0.006 | 0.006 | 0.099 | 0.037 | |
183 | 184 | 395 | 227 | |
1.00 | 1.00 | 0.83 | 0.83 |
The objective of this study was to lay the foundation for a systematic modeling approach to investigate and predict spatial and temporal patterns of disease transmission risk between poultry and wild waterfowl populations in China. We explicitly took a multi-level approach toward modeling transmission risk between wild and domestic waterfowl in China. The Level 1 deterministic models demonstrated, at a fine resolution, predicted locations of co-occurrence of wild and domestic waterfowl distributions. High risk hotspots during the wintering season were observed in the southern and eastern lowland regions of China (Figure
Level 2 and 3 models, although unrealistically simplistic for predicting absolute risk of transmission, takes a first attempt at incorporating H5N1-specific parameters and population numbers for the different bird groups. Virus shedding and uptake rates were included as constants in the model. As our knowledge increases, these constants can be replaced with inputs parameterized to reflect geographic heterogeneity. In particular, the numerator of the uptake term seems unrealistically low (10−15). This term was taken directly from a traditional compartmental epidemiological model (
The effect of the addition of seasonal bird population size was apparent in the model results for QHL and PYL focal areas (Figure
Although waterfowl species were considered as a composite, the contribution of significant species was still evident in certain areas. A concentrated section of risk was observed in northeastern China wintering models (Figures
The virus shedding and uptake rates spanned four to ten orders of magnitude. Due to the large range in values, and that we extracted them directly from the literature, we chose to keep these rates fixed in the Monte-Carlo models so we could more clearly assess the effects of our modeled input parameters (wild bird and poultry distributions, and biosecurity and contaminant containment parameters). One term of particular interest was the biosecurity term (
Level 3 models showed a similar pattern across China to the deterministic models, confirming that the number of simulations was sufficient for the mean values to converge toward results of the deterministic models (
These models should be considered as a starting point to refine predictions of risk for H5N1 outbreaks. In contrast to existing temporal dynamic models of H5N1 transmission (
With growing interest in predicting the risk of future transmission of H5N1 or other emerging strains such as H7N9, all three levels of model have utility. Level 1 models identify areas where transmission can occur due to co-occurrence of wild and domestic species. Level 2 models incorporate population size, prevalence, transmission, and biosecurity parameters. The model structure allows inputs to be changed and new maps to be created. Level 3 models show that the coarser county scale is appropriate for informing surveillance and prevention measures, and is more realistic for assessment. Identification of locations of greater uncertainty in the predictions also helps inform policy decision-making. Applications for the findings of this study may include use by health experts and wildlife officials who are interested in using the poultry to wild risk models (Figure
A desirable step would be to validate the model predictions using avian surveillance and outbreak data. A strong match exists between outbreak locations and our predicted risk areas (Figure
Through a structured approach to predicting transmission risk between domestic poultry and wild waterfowl in China, we were able to separate the spatial relationships between poultry and waterfowl from the disease-specific factors to better understand the contributions of each to transmission risk. We explicitly incorporated uncertainty measures with our risk predictions and conducted sensitivity analyses to understand the effects of uncertainty on the model outputs. It is the first analysis of its kind and one of the few that focuses specifically on interactions between the wild and domestic bird populations, providing a unique contribution to our growing knowledge on the topic of wild birds and avian influenza transmission.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This study was supported by funding from the United States Geological Survey Victims and Vectors Program. We thank Ruth DeFries of Columbia University for expertise in remote sensing, and Lisa Vormwald and two anonymous reviewers for strengthening earlier versions of this manuscript. The use of trade, product, or firm names in this publication is for descriptive purposes only and does not imply endorsement by the U.S. Government.