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

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

Spatial distribution of presence and absence and background samples.

<p>The zoomed area presents an example of the raw samples’ distribution before data cleaning.</p

Variable importance for the final models of the four DDMs: Maxent Presence Background (MPB), Maxent Presence-Absence (MPA), Random Forest Presence Background (RFPB) and Random Forest Presence-Absence (RFPA) of Avian Influence occurrence in California, USA.

<p>Variable importance was determined with percent contribution for Maxent and mean decrease in accuracy for Random Forest.</p

Risk maps generated from the four diseases distribution models and their means: Presence-background MaxEnt (MPB), presence-background Random Forest (RFPB), weighted mean of both presence-background models based on AUCc (MeanPB), corrected presence-absence MaxEnt (MPAc), corrected presence-absence Random Forest (RFPAc), weighted mean of both presence-absence models based on AUCc (MeanPAc).

<p>The green dots represent the centroids of the 2014–2015 HPAI outbreaks. The color gradient of each pixel represents the AI presence probability from clear red shading (low presence probability) to bright red shading (high presence probability). High resolution versions of the maps are available in Disease BioPortal (<a href="http://bioportal.ucdavis.edu" target="_blank">http://bioportal.ucdavis.edu</a>).</p

AUC and AUCc values for the four DDMs: Maxent Presence Bakground (MPB), Maxent Presence-Absence (MPA), Random Forest Presence Background (RFPB) and Random Forest Presence-Absence (RFPA).

<p>AUC and AUCc values for the four DDMs: Maxent Presence Bakground (MPB), Maxent Presence-Absence (MPA), Random Forest Presence Background (RFPB) and Random Forest Presence-Absence (RFPA).</p

Spearman’s correlation coefficient between the spatial predictions of the for the four DDMs, Maxent Presence Background (MPB), Corrected Maxent Presence-Absence (MPAc), Random Forest Presence Background (RFPB), Corrected Random Forest Presence-Absence (RFPAc) and, the model averages for Presence-Absence (WPAc) and Presence-Background (WPB) of Avian Influence occurrence in California, USA.

<p>Predictions were made for 500 × 500 m grid cells in California (n = 3,301,320).</p

Comparison of the three pathogens under surveillance.

<p>Comparison of the three pathogens under surveillance.</p

Best supported models (ΔAICc <2) of seroprevalence for the three pathogens.

<p>Best supported models (ΔAICc <2) of seroprevalence for the three pathogens.</p

Coinfection rates, with bars representing 95% confidence intervals, of FIV/<i>T. gondii</i> IgG coinfection, <i>T. gondii</i> IgG<i>/Bartonella</i> spp. coinfection, and FIV/<i>Bartonella</i> spp. coinfection, for bobcats (n = 228), pumas (n = 162), and domestic cats (n = 265).

<p>Coinfection rates, with bars representing 95% confidence intervals, of FIV/<i>T. gondii</i> IgG coinfection, <i>T. gondii</i> IgG<i>/Bartonella</i> spp. coinfection, and FIV/<i>Bartonella</i> spp. coinfection, for bobcats (n = 228), pumas (n = 162), and domestic cats (n = 265).</p

Seroprevalence, with bars representing 95% confidence intervals, of <i>Bartonella</i> spp., FIV, and <i>T. gondii</i> IgG for domestic cats, bobcats, and pumas at all study locations (FR = Front Range, CO; WS = Western Slope, CO; OC = Orange County, CA; SDRC = San Diego/Riverside Counties, CA; VC = Ventura County, CA).

<p>Sample sizes are listed above columns.</p