BOARD INVITED REVIEW: Prospects for improving management of animal disease introductions using disease-dynamic models

Reconstruction and analysis of the transmission network of African swine fever in People's Republic of China, August 2018-September 2019

Introduction of African swine fever (ASF) to China in mid-2018 and the subsequent transboundary spread across Asia devastated regional swine production, affecting live pig and pork product-related markets worldwide. To explore the spatiotemporal spread of ASF in China, we reconstructed possible ASF transmission networks using nearest neighbour, exponential function, equal probability, and spatiotemporal case-distribution algorithms. From these networks, we estimated the reproduction numbers, serial intervals, and transmission distances of the outbreak. The mean serial interval between paired units was around 29 days for all algorithms, while the mean transmission distance ranged 332 -456 km. The reproduction numbers for each algorithm peaked during the first two weeks and steadily declined through the end of 2018 before hovering around the epidemic threshold value of 1 with sporadic increases during 2019. These results suggest that 1) swine husbandry practices and production systems that lend themselves to long-range transmission drove ASF spread; 2) outbreaks went undetected by the surveillance system. Efforts by China and other affected countries to control ASF within their jurisdictions may be aided by the reconstructed spatiotemporal model. Continued support for strict implementation of biosecurity standards and improvements to ASF surveillance is essential for halting transmission in China and spread across Asia.

Areas within the United States at the Highest Risk for African Swine Fever, Classical Swine Fever, and Foot-and-Mouth Disease Introduction

Domestic livestock production is a major component of the agricultural sector, contributing to food security and human health and nutrition and serving as the economic livelihood for millions worldwide. The impact of disease on global systems and processes cannot be understated, as illustrated by the effects of the COVID-19 global pandemic through economic and social system shocks and food system disruptions. This study outlines a method to identify the most likely sites of introduction into the United States for three of the most concerning foreign animal diseases: African swine fever (ASF), classical swine fever (CSF), and foot-and-mouth disease (FMD). We first created an index measuring the amount of potentially contaminated meat products entering the regions of interest using the most recently available Agricultural Quarantine Inspection Monitoring (AQIM) air passenger inspection dataset, the AQIM USPS/foreign mail, and the targeted USPS/foreign mail interception datasets. The risk of introduction of a given virus was then estimated using this index, as well as the density of operations of the livestock species and the likelihood of infected material contaminating the local herds. Using the most recently available version of the datasets, the most likely places of introduction for ASF and CSF were identified to be in central Florida, while FMD was estimated to have been most likely introduced to swine in western California and to cattle in northeastern Texas. The method illustrated in this study is important as it may provide insights on risk and can be used to guide surveillance activities and optimize the use of limited resources to combat the establishment of these diseases in the U.S.

Adaptive risk-based targeted surveillance for foreign animal diseases at the wildlife-livestock interface

Animal disease surveillance is an important component of the national veterinary infrastructure to protect animal agriculture and facilitates identification of foreign animal disease (FAD) introduction. Once introduced, pathogens shared among domestic and wild animals are especially challenging to manage due to the complex ecology of spillover and spillback. Thus, early identification of FAD in wildlife is critical to minimize outbreak severity and potential impacts on animal agriculture as well as potential impacts on wildlife and biodiversity. As a result, national surveillance and monitoring programs that include wildlife are becoming increasingly common. Designing surveillance systems in wildlife or, more importantly, at the interface of wildlife and domestic animals, is especially challenging because of the frequent lack of ecological and epidemiological data for wildlife species and technical challenges associated with a lack of non-invasive methodologies. To meet the increasing need for targeted FAD surveillance and to address gaps in existing wildlife surveillance systems, we developed an adaptive risk-based targeted surveillance approach that accounts for risks in source and recipient host populations. The approach is flexible, accounts for changing disease risks through time, can be scaled from local to national extents and permits the inclusion of quantitative data or when information is limited to expert opinion. We apply this adaptive risk-based surveillance framework to prioritize areas for surveillance in wild pigs in the United States with the objective of early detection of three diseases: classical swine fever, African swine fever and foot-and-mouth disease. We discuss our surveillance framework, its application to wild pigs and discuss the utility of this framework for surveillance of other host species and diseases.

Targeted sampling reduces the uncertainty in force of infection estimates from serological surveillance

Age bins are frequently used in serological studies of infectious diseases in wildlife to deal with uncertainty in the age of sampled animals. This study analyzed how age binning and targeted sampling in serological surveillance affect the width of the 95% confidence interval (CI) of the estimated force of infection (FOI) of infectious diseases. We indicate that the optimal target population with the narrowest 95% CI differs depending on the expected FOI using computer simulations and mathematical models. In addition, our findings show that we can substantially reduce the number of animals required to infer transmission risk by tailoring targeted, age-based sampling to specific epidemiological situations.

Optimizing response to an introduction of African swine fever in wild pigs

African swine fever virus (ASFv) is a virulent pathogen that threatens domestic swine industries globally and persists in wild boar populations in some countries. Persistence in wild boar can challenge elimination and prevent disease-free status, making it necessary to address wild swine in proactive response plans. In the U.S., invasive wild pigs are abundant and found across a wide range of ecological conditions that could drive different epidemiological dynamics among populations. Information on size of control areas required to rapidly eliminate ASFv in wild pigs and how this area should change with management constraints and local ecology are needed to optimize response planning. We developed a spatially-explicit disease transmission model contrasting wild pig movement and contact ecology in two ecosystems in southeastern U.S. We simulated ASFv spread and determined optimal response area (reported as radius of a circle) for eliminating ASFv rapidly over a range of detection times (when ASFv is detected relative to true date of introduction), culling capacities (proportion of wild pigs in the culling zone removed weekly), and wild pig densities. Large radii for response areas (14 km) were needed under most conditions but could be shortened with early detection (≤ 8 weeks) and high culling capacities (≥ 15% weekly). Under most conditions ASFv was eliminated in less than 22 weeks using optimal control radii, although ecological conditions with high rates of wild pig movement required higher culling capacities (≥ 10% weekly) for elimination within one year. Results highlight the importance of adjusting response plans based on local ecology and show wild pig movement is a better predictor of optimal response area than numbers of ASFv cases early in the outbreak trajectory. Our framework provides a tool for determining optimal control plans in different areas, guiding expectations of response impacts, and planning resources needed for rapid elimination. This article is protected by copyright. All rights reserved.

Investigating public support for biosecurity measures to mitigate pathogen transmission through the herpetological trade

The expanding global trade in herpetofauna has contributed to new infectious disease dynamics and pathways that allow for the rapid spread of pathogens geographically. Improved biosecurity is needed to mitigate adverse biodiversity, economic and human health impacts associated with pathogen transmission through the herpetological trade. However, general lack of knowledge of the pathogen transmission risks associated with the global trade in herpetofauna and public opposition to biosecurity measures are critical obstacles to successfully preventing pathogen transmission. In 2019 we administered a survey to 2,007 members of the public in the United States of America to ascertain their support for interventions to prevent the spread of Batrachochytrium dendrobatidis (Bd), Batrachochytrium salamandrivorans (Bsal), ranaviruses, and Salmonella through the herpetological trade. We presented survey respondents with different potential hazards associated with pathogen transmission through this trade, namely ecological, economic, and human health impacts. We used structural equation models to determine how these different hazards and respondents’ characteristics influenced respondents’ support for quarantine and veterinary observation of herpetofauna imported into the United States, mandatory tests for diseases of concern, and best practices to reduce stress and improve the care of live herpetofauna during transport to the United States. Respondents’ values and their perceived susceptibility and sensitivity to different hazards associated with pathogen transmission were key determinants of their support for biosecurity. Respondents with strong biospheric and altruistic values demonstrated sensitivity to ecological and human health impacts associated with pathogen transmission, whereas respondents with strong egoistic values demonstrated sensitivity to economic impacts. Respondents had limited knowledge of Bd, Bsal or ranaviruses, the size of the herpetological trade, or how this trade may contribute to pathogen transmission. Improved outreach and education on pathogen transmission through the herpetological trade is required, but it is important that messages are tailored to people with different values to elicit their support for biosecurity.

Modelling the role of mortality-based response triggers on the effectiveness of African swine fever control strategies

African swine fever (ASF) is considered the most impactful transboundary swine disease. In the absence of effective vaccines, control strategies are heavily dependent on mass depopulation and shipment restrictions. Here, we developed a nested multiscale model for the transmission of ASF, combining a spatially explicit network model of animal shipments with a deterministic compartmental model for the dynamics of two ASF strains within 3 km × 3 km pixels in one Brazilian state. The model outcomes are epidemic duration, number of secondary infected farms and pigs, and distance of ASF spread. The model also shows the spatial distribution of ASF epidemics. We analyzed quarantine-based control interventions in the context of mortality trigger thresholds for the deployment of control strategies. The mean epidemic duration of a moderately virulent strain was 11.2 days, assuming the first infection is detected (best-case scenario), and 15.9 days when detection is triggered at 10% mortality. For a highly virulent strain, the epidemic duration was 6.5 days and 13.1 days, respectively. The distance from the source to infected locations and the spatial distribution was not dependent on strain virulence. Under the best-case scenario, we projected an average number of infected farms of 23.77 farms and 18.8 farms for the moderate and highly virulent strains, respectively. At 10% mortality-trigger, the predicted number of infected farms was on average 46.27 farms and 42.96 farms, respectively. We also demonstrated that the establishment of ring quarantine zones regardless of size (i.e. 5 km, 15 km) was outperformed by backward animal movement tracking. The proposed modelling framework provides an evaluation of ASF epidemic potential, providing a ranking of quarantine-based control strategies that could assist animal health authorities in planning the national preparedness and response plan.

Continental-scale dynamics of avian influenza in U.S. waterfowl are driven by demography, migration, and temperature

Emerging diseases of wildlife origin are increasingly spilling over into humans and domestic animals. Surveillance and risk assessments for transmission between these populations are informed by a mechanistic understanding of the pathogens in wildlife reservoirs. For avian influenza viruses (AIV), much observational and experimental work in wildlife has been conducted at local scales, yet fully understanding their spread and distribution requires assessing the mechanisms acting at both local, (e.g., intrinsic epidemic dynamics), and continental scales, (e.g., long-distance migration). Here, we combined a large, continental-scale data set on low pathogenic, Type A AIV in the United States with a novel network-based application of bird banding/recovery data to investigate the migration-based drivers of AIV and their relative importance compared to well-characterized local drivers (e.g., demography, environmental persistence). We compared among regression models reflecting hypothesized ecological processes and evaluated their ability to predict AIV in space and time using within and out-of-sample validation. We found that predictors of AIV were associated with multiple mechanisms at local and continental scales. Hypotheses characterizing local epidemic dynamics were strongly supported, with age, the age-specific aggregation of migratory birds in an area and temperature being the best predictors of infection. Hypotheses defining larger, network-based features of the migration processes, such as clustering or between-cluster mixing explained less variation but were also supported. Therefore, our results support a role for local processes in driving the continental distribution of AIV.

A framework for surveillance of emerging pathogens at the human-animal interface: Pigs and coronaviruses as a case study

Pigs (Sus scrofa) may be important surveillance targets for risk assessment and risk-based control planning against emerging zoonoses. Pigs have high contact rates with humans and other animals, transmit similar pathogens as humans including CoVs, and serve as reservoirs and intermediate hosts for notable human pandemics. Wild and domestic pigs both interface with humans and each other but have unique ecologies that demand different surveillance strategies. Three fundamental questions shape any surveillance program: where, when, and how can surveillance be conducted to optimize the surveillance objective? Using theory of mechanisms of zoonotic spillover and data on risk factors, we propose a framework for determining where surveillance might begin initially to maximize a detection in each host species at their interface. We illustrate the utility of the framework using data from the United States. We then discuss variables to consider in refining when and how to conduct surveillance. Recent advances in accounting for opportunistic sampling designs and in translating serology samples into infection times provide promising directions for extracting spatio-temporal estimates of disease risk from typical surveillance data. Such robust estimates of population-level disease risk allow surveillance plans to be updated in space and time based on new information (adaptive surveillance) thus optimizing allocation of surveillance resources to maximize the quality of risk assessment insight.

Distribution of Viable Bacteria in the Dust-Generating Natural Source Area of the Gobi Region, Mongolia

The Gobi Desert is a major source of dust events, whose frequency of occurrence and damage caused have recently significantly increased. In the present study, we investigated the types of live bacteria present in the surface soil of the Gobi Desert in Mongolia, and determined their genetic identification as well as their geographical distribution. During the survey, four different topographies (dry lake bed, wadi, well, and desert steppe) were selected, and land characteristics were monitored for moisture and temperature. The surface soil was aerobically cultured to isolate bacterial colonies, and their 16s rDNA regions were sequenced. The sequence data were identified through NCBI-BLAST analysis and generated phylogenetic trees. The results revealed two phyla and seven families of isolates from the sample points. Each isolate was characterized by their corresponding sample site. The characteristics of land use and soil surface bacteria were compared. Most of the bacteria originated from the soil, however, animal-derived bacteria were also confirmed in areas used by animals. Our findings confirmed the existence of live bacteria in the dust-generating area, suggesting that their presence could affect animal and human health. Therefore, it is necessary to further investigate dust microbes based on the One Health concept.

Predicting spread and effective control measures for African swine fever-Should we blame the boars?

An ongoing, continually spreading, outbreak of African swine fever (ASF), following its identification in Georgia in 2007, has resulted in 17 European and 12 Asian countries reporting cases by April 2020, with cases occurring in both wild boar and domestic pigs. Curtailing further spread of ASF requires understanding of the transmission pathways of the disease. ASF is self-sustaining in the wild boar population, and they have been implicated as one of the main drivers of transmission within Europe. We developed a spatially explicit model to estimate the risk of infection with ASF in wild boar and pigs due to natural movement of wild boar that is applicable across the whole of Europe. We demonstrate the model by using it to predict the probability that early cases of ASF in Poland were caused by wild boar dispersion. The risk of infection in 2015 is computed due to wild boar cases in Poland in 2014, compared against reported cases in 2015, and then the procedure is repeated for 2015-2016. We find that long- and medium-distance spread of ASF (i.e. >30 km) is unlikely to have occurred due to wild boar dispersal, due in part to the generally short distances wild boar will travel (<20 km on average). We also predict the relative success of different control strategies in 2015, if they were implemented in 2014. Results suggest that hunting of wild boar reduces the number of new cases, but a larger region is at risk of ASF compared with no control measure. Alternatively, introducing wild boar-proof fencing reduces the size of the region at risk in 2015, but not the total number of cases. Overall, our model suggests wild boar movement is only responsible for local transmission of disease; thus, other pathways are more dominant in medium- and long-distance spread of the disease.