Effects of regional differences and demography in modelling foot-and-mouth disease in cattle at the national scale.

Foot-and-mouth disease (FMD) is a fast-spreading viral infection that can produce large and costly outbreaks in livestock populations. Transmission occurs at multiple spatial scales, as can the actions used to control outbreaks. The US cattle industry is spatially expansive, with heterogeneous distributions of animals and infrastructure. We have developed a model that incorporates the effects of scale for both disease transmission and control actions, applied here in simulating FMD outbreaks in US cattle. We simulated infection initiating in each of the 3049 counties in the contiguous US, 100 times per county. When initial infection was located in specific regions, large outbreaks were more likely to occur, driven by infrastructure and other demographic attributes such as premises clustering and number of cattle on premises. Sensitivity analyses suggest these attributes had more impact on outbreak metrics than the ranges of estimated disease parameter values. Additionally, although shipping accounted for a small percentage of overall transmission, areas receiving the most animal shipments tended to have other attributes that increase the probability of large outbreaks. The importance of including spatial and demographic heterogeneity in modelling outbreak trajectories and control actions is illustrated by specific regions consistently producing larger outbreaks than others.

Mortality and Egg Production Patterns in the United States Prior to HP/LPAI H7N9 Detection.

In March 2017, two commercial broiler breeder operations were confirmed with H7N9 highly pathogenic avian influenza (HPAI), and an additional six commercial broiler breeder operations were found positive with an H7N9 low pathogenicity avian influenza virus (LPAIV) or an H7 LPAIV (N type not identified). To better understand conditions leading up to testing positive for AI, egg production and mortality data for the 6 mo before the outbreak were obtained from five case farms (two HPAIV-infected farms and three LPAIV-infected farms) and two control farms. Both HPAI farms experienced a sudden spike in mortality immediately before testing positive. Two LPAI farms experienced drops in egg production along with slight increases in mortality that occurred after a negative serologic test and before a positive PCR test. The third LPAI farm also had a notable drop in egg production with a coinciding increase in mortality before testing positive for AIV (last negative test date not available). Additionally, both HPAI farms and two LPAI farms reported mild respiratory illnesses in the weeks prior to testing positive for AI. Control farms did not experience similar drops in production or increase in mortality. Clinical signs on LPAI farms were mild and easily confused with background health patterns, suggesting the need for improved sensitivity to identify LPAI quickly. Applying a trigger of a 2% drop in egg production along with a mortality of 8 per 10 000 hens in individual barns showed that all case farms would be identified and uninfected farms would be falsely triggered on 1% of days monitored.

Mortalidad y patrones de producción de huevos en los Estados Unidos antes de la detección del virus de la influenza aviar H7N9 de baja y alta patogenicidad. En marzo del 2017, se confirmaron dos operaciones de reproductores pesados comerciales con influenza aviar altamente patógena H7N9 (HPAI) y adicionalmente se encontraron seis operaciones de reproductores pesados comerciales con un virus de influenza aviar de baja patogenicidad H7N9 (LPAIV) o con un virus de baja patogenicidad H7 con un subtipo de neuraminidasa no identificado. Para comprender mejor las condiciones que condujeron a un resultado positivo de influenza aviar, se recolectaron los datos de producción de huevo y mortalidad de cinco granjas que presentaron casos (dos granjas infectadas con el virus de alta patogenicidad y tres granjas infectadas con el virus de baja patogenicidad) y dos granjas control durante los seis meses anteriores al brote. Las dos grajas infectadas con el virus de alta patogenicidad experimentaron un aumento repentino en la mortalidad inmediatamente antes de que se determinaran como positivas. Dos granjas con el virus de baja patogenicidad experimentaron caídas en la producción de huevo junto con un ligero aumento en la mortalidad que se produjo después de que mostraran respuesta negativa a las pruebas serológicas y antes de resultar positivas mediante pruebas de RT-PCR. La tercera granja infectada con el virus de baja patogenicidad también tuvo una caída importante en la producción de huevo con un aumento coincidente en la mortalidad antes de que mostraran un resultado positivo para influenza aviar (la fecha de la última prueba negativa no se encontró disponible). Además, tanto las dos granjas con influenza aviar de alta patogenicidad como dos granjas con influenza aviar de baja patogenicidad reportaron enfermedades respiratorias leves en las semanas previas a la fecha cuando mostraron resultados positivos para la influenza aviar. Las granjas control no experimentaron caídas similares en la producción de huevo o aumento en la mortalidad. Los signos clínicos en las granjas de baja patogenicidad fueron leves y se confundieron fácilmente con los patrones de salud normales de la parvada, lo que sugiere la necesidad de mejorar la sensibilidad para identificar rápidamente la a la influenza aviar de baja patogenicidad. Con la aplicación de una alarma que se desencadene con una caída del 2% en la producción de huevo junto con una mortalidad de ocho por cada 10 mil gallinas en casetas individuales mostró que todas las granjas problema serían identificadas y que las granjas no infectadas podrían ser activadas falsamente en el 1% de los días monitoreados.

Need for speed: An optimized gridding approach for spatially explicit disease simulations.

Numerical models for simulating outbreaks of infectious diseases are powerful tools for informing surveillance and control strategy decisions. However, large-scale spatially explicit models can be limited by the amount of computational resources they require, which poses a problem when multiple scenarios need to be explored to provide policy recommendations. We introduce an easily implemented method that can reduce computation time in a standard Susceptible-Exposed-Infectious-Removed (SEIR) model without introducing any further approximations or truncations. It is based on a hierarchical infection process that operates on entire groups of spatially related nodes (cells in a grid) in order to efficiently filter out large volumes of susceptible nodes that would otherwise have required expensive calculations. After the filtering of the cells, only a subset of the nodes that were originally at risk are then evaluated for actual infection. The increase in efficiency is sensitive to the exact configuration of the grid, and we describe a simple method to find an estimate of the optimal configuration of a given landscape as well as a method to partition the landscape into a grid configuration. To investigate its efficiency, we compare the introduced methods to other algorithms and evaluate computation time, focusing on simulated outbreaks of foot-and-mouth disease (FMD) on the farm population of the USA, the UK and Sweden, as well as on three randomly generated populations with varying degree of clustering. The introduced method provided up to 500 times faster calculations than pairwise computation, and consistently performed as well or better than other available methods. This enables large scale, spatially explicit simulations such as for the entire continental USA without sacrificing realism or predictive power.

Utility of mosquito surveillance data for spatial prioritization of vector control against dengue viruses in three Brazilian cities.

BACKGROUND: Vector control remains the primary defense against dengue fever. Its success relies on the assumption that vector density is related to disease transmission. Two operational issues include the amount by which mosquito density should be reduced to minimize transmission and the spatio-temporal allotment of resources needed to reduce mosquito density in a cost-effective manner. Recently, a novel technology, MI-Dengue, was implemented city-wide in several Brazilian cities to provide real-time mosquito surveillance data for spatial prioritization of vector control resources. We sought to understand the role of city-wide mosquito density data in predicting disease incidence in order to provide guidance for prioritization of vector control work. METHODS: We used hierarchical Bayesian regression modeling to examine the role of city-wide vector surveillance data in predicting human cases of dengue fever in space and time. We used four years of weekly surveillance data from Vitoria city, Brazil, to identify the best model structure. We tested effects of vector density, lagged case data and spatial connectivity. We investigated the generality of the best model using an additional year of data from Vitoria and two years of data from other Brazilian cities: Governador Valadares and Sete Lagoas. RESULTS: We found that city-wide, neighborhood-level averages of household vector density were a poor predictor of dengue-fever cases in the absence of accounting for interactions with human cases. Effects of city-wide spatial patterns were stronger than within-neighborhood or nearest-neighborhood effects. Readily available proxies of spatial relationships between human cases, such as economic status, population density or between-neighborhood roadway distance, did not explain spatial patterns in cases better than unweighted global effects. CONCLUSIONS: For spatial prioritization of vector controls, city-wide spatial effects should be given more weight than within-neighborhood or nearest-neighborhood connections, in order to minimize city-wide cases of dengue fever. More research is needed to determine which data could best inform city-wide connectivity. Once these data become available, MI-dengue may be even more effective if vector control is spatially prioritized by considering city-wide connectivity between cases together with information on the location of mosquito density and infected mosquitos.

Sources of bovine tuberculosis in the United States.

Despite control and eradication efforts, bovine tuberculosis continues to be identified at low levels among cattle in the United States. We evaluated possible external sources of infection by characterizing the genetic relatedness of bovine tuberculosis from a national database of reported infections, comparing strains circulating among US cattle with those of imported cattle, and farmed and wild cervids. Farmed cervids maintained a genetically distinct Mycobacterium bovis strain, and cattle occasionally became infected with this strain. In contrast, wild cervids acted as an epidemiologically distinct group, instead hosting many of the same strains found in cattle, and the data did not show a clear transmission direction. Cattle from Mexico hosted a higher overall richness of strains than US cattle, and many of those strains were found in both US and Mexican cattle. However, these two populations appeared to be well-mixed with respect to their M. bovis lineages, and higher resolution data is necessary to infer the direction of recent transmission. Overall patterns of both host and geographic distributions were highly variable among strains, suggesting that different sources or transmission mechanisms are contributing to maintaining different strains.

Identification of Borrelia burgdorferi ospC genotypes in host tissue and feeding ticks by terminal restriction fragment length polymorphisms.

We developed a high-throughput method based on terminal restriction fragment length polymorphisms (T-RFLP) to identify ospC genotypes from field-collected samples of Borrelia burgdorferi. We first validated the method by analyzing B. burgdorferi ospC previously identified by sequencing. We then analyzed and compared ospC genotypes detected from ear biopsy tissue from natural populations of the white-footed mouse, a major B. burgdorferi reservoir host species in the eastern United States, and larval ticks feeding on those individual mice. The T-RFLP method enabled us to distinguish all 17 ospC genotypes tested, as well as mixed samples containing more than one genotype. Analysis costs compare favorably to those of alternative ospC identification methods. The T-RFLP method will facilitate large-scale field studies to advance our understanding of genotype-specific transmission patterns.

Predicted outcomes of vaccinating wildlife to reduce human risk of Lyme disease.

Vaccination efforts for Lyme disease prevention in humans have focused on wildlife reservoirs to target the causative agent, Borrelia burgdorferi, for elimination in vector ticks. Multiple host species are involved in the transmission and maintenance of the bacterium, but not all host species can be vaccinated effectively. To evaluate vaccinating a subset of hosts in the context of host-tick interactions, we constructed and evaluated a dynamic model of B. burgdorferi transmission in mice. Our analyses indicate that on average, a mouse-targeted vaccine is expected to proportionally reduce infection prevalence among ticks by 56%. However, relative to mouse vaccination, human risk of exposure is dominated by the number of tick bites received per person, the proportion of tick blood meals taken from the highly reservoir-competent white-footed mouse relative to other hosts, and the average number of tick bites per mouse. Variation in these factors reduces the predictability of vaccination outcomes. Additionally, contributions of nonmouse hosts to pathogen maintenance preclude elimination of B. burgdorferi through mouse vaccination alone. Our findings indicate that to increase the impact of wildlife vaccination, reducing tick populations by acaricide application, in addition to targeting additional reservoir-competent host species, should be employed.

Regional variation in immature Ixodes scapularis parasitism on North American songbirds: implications for transmission of the Lyme pathogen, Borrelia burgdorferi.

Borrelia burgdorferi, the etiological agent of Lyme disease, is transmitted among hosts by the black-legged tick, Ixodes scapularis, a species that regularly parasitizes various vertebrate hosts, including birds, in its immature stages. Lyme disease risk in the United States is highest in the Northeast and in the upper Midwest where I. scapularis ticks are most abundant. Because birds might be important to the range expansion of I. scapularis and B. burgdorferi, we explored spatial variation in patterns of I. scapularis parasitism on songbirds, as well as B. burgdorferi infection in bird-derived I. scapularis larvae. We sampled birds at 23 sites in the eastern United States to describe seasonal patterns of I. scapularis occurrence on birds, and we screened a subset of I. scapularis larvae for presence of B. burgdorferi. Timing of immature I. scapularis occurrence on birds is consistent with regional variation in host-seeking activity with a generally earlier peak in larval parasitism on birds in the Midwest. Significantly more I. scapularis larvae occurred on birds that were contemporaneously parasitized by nymphs in the Midwest than the Northeast, and the proportion of birds that yielded B. burgdorferi-infected larvae was also higher in the Midwest. We conclude that regional variation in immature I. scapularis phenology results in different temporal patterns of parasitism on birds, potentially resulting in differential importance of birds to B. burgdorferi transmission dynamics among regions.

Genotypic diversity of Borrelia burgdorferi strains detected in Ixodes scapularis larvae collected from North American songbirds.

We genotyped Borrelia burgdorferi strains detected in larvae of Ixodes scapularis removed from songbirds and compared them with those found in host-seeking I. scapularis nymphs sampled throughout the eastern United States. Birds are capable of transmitting most known genotypes, albeit at different frequencies than expected based on genotypes found among host-seeking nymphs.