Erin E. Gorsich

EVALUATION OF THE SENSITIVITY AND SPECIFICITY OF AN ENZYME-LINKED IMMUNOSORBENT ASSAY FOR DIAGNOSING BRUCELLOSIS IN AFRICAN BUFFALO (SYNCERUS CAFFER)

Abstract Brucellosis is a disease of veterinary and public health importance worldwide. In sub-Saharan Africa, where the bacterium Brucella abortus has been identified in several free-ranging wildlife species, successful disease control may be dependent on accurate detection in wildlife reservoirs, including African buffalo (Syncerus caffer). We estimated the sensitivity and specificity of a commercial enzyme-linked immunosorbent assay (ELISA) (IDEXX Brucellosis Serum Ab test, IDEXX Laboratories, Westbrook, Maine, USA) for B. abortus based on a data set of 571 serum samples from 258 buffalo in the Kruger National Park, South Africa. We defined a pseudogold standard test result as those buffalo that were consistently positive or negative on two additional serologic tests, namely, the rose bengal test (RBT) and the complement fixation test (CFT). The ELISAs cutoff value was selected using receiver operating characteristics analysis, the pseudogold standard, and a threshold criterion that maximizes the total sensitivity and specificity. Then, we estimated the sensitivity and specificity of all three tests using Bayesian inference and latent class analysis. The ELISA had an estimated sensitivity of 0.928 (95% Bayesian posterior credibility interval [95% BCI] = 0.869–0.974) and specificity of 0.870 (95% BCI = 0.836–0.900). Compared with the ELISA, the RBT had a higher estimated sensitivity of 0.986 (95% BCI = 0.928–0.999), and both the RBT and CFT had higher specificities, estimated to be 0.992 (95% BCI = 0.971–0.996) and 0.998 (95% BCI = 0.992–0.999), respectively. Therefore, no single serologic test perfectly detected the antibody. However, after adjustment of cutoff values for South African conditions, the IDEXX Brucellosis Serum Ab Test may be a valuable additional screening test for brucellosis in Kruger National Parks African buffalo.

Enemies and Turncoats: Bovine tuberculosis exposes pathogenic potential of Rift Valley fever virus in a common host, African Buffalo (Syncerus caffer)

The ubiquity and importance of parasite co-infections in populations of free-living animals is beginning to be recognized, but few studies have demonstrated differential fitness effects of single infection versus co-infection in free-living populations. We investigated interactions between the emerging bacterial disease bovine tuberculosis (BTB) and the previously existing viral disease Rift Valley fever (RVF) in a competent reservoir host, African buffalo, combining data from a natural outbreak of RVF in captive buffalo at a buffalo breeding facility in 2008 with data collected from a neighbouring free-living herd of African buffalo in Kruger National Park. RVF infection was twice as likely in individual BTB+ buffalo as in BTB− buffalo, which, according to a mathematical model, may increase RVF outbreak size at the population level. In addition, co-infection was associated with a far higher rate of fetal abortion than other infection states. Immune interactions between BTB and RVF may underlie both of these interactions, since animals with BTB had decreased innate immunity and increased pro-inflammatory immune responses. This study is one of the first to demonstrate how the consequences of emerging infections extend beyond direct effects on host health, potentially altering the dynamics and fitness effects of infectious diseases that had previously existed in the ecosystem on free-ranging wildlife populations.

Within guild co-infections influence parasite community membership : a longitudinal study in African Buffalo

Experimental studies in laboratory settings have demonstrated a critical role of parasite interactions in shaping parasite communities. The sum of these interactions can produce diverse effects on individual hosts as well as influence disease emergence and persistence at the population level. A predictive framework for the effects of parasite interactions in the wild remains elusive, largely because of limited longitudinal or experimental data on parasite communities of free-ranging hosts. This 4-year study followed a community of haemoparasites in free-ranging African buffalo (Syncerus caffer). We detected infection by 11 haemoparasite species using PCR-based diagnostic techniques, and analyzed drivers of infection patterns using generalized linear mixed models to understand the role of host characteristics and season on infection likelihood. We tested for (i) effects of co-infection by other haemoparasites (within guild) and (ii) effects of parasites infecting different tissue types (across guild). We found that within guild co-infections were the strongest predictors of haemoparasite infections in the buffalo; but that seasonal and host characteristics also had important effects. In contrast, the evidence for across-guild effects of parasites utilizing different tissue on haemoparasite infection was weak. These results provide a nuanced view of the role of co-infections in determining haemoparasite infection patterns in free living mammalian hosts. Our findings suggest a role for interactions among parasites infecting a single tissue type in determining infection patterns.

Nematode–coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy

Co-infections are common in natural populations and interactions among co-infecting parasites can significantly alter the transmission and host fitness costs of infection. Because both exposure and susceptibility vary over time, predicting the consequences of parasite interactions on host fitness and disease dynamics may require detailed information on their effects across different environmental (season) and host demographic (age, sex) conditions. This study examines five years of seasonal health and co-infection patterns in African buffalo (Syncerus caffer). We use data on two groups of gastrointestinal parasites, coccidia and nematodes, to test the hypothesis that co-infection and season interact to influence (1) parasite prevalence and intensity and (2) three proxies for host fitness: host pregnancy, host body condition, and parasite aggregation. Our results suggest that season-dependent interactions between nematodes and coccidia affect the distribution of infections. Coccidia prevalence, coccidia intensity and nematode prevalence were sensitive to factors that influence host immunity and exposure (age, sex, and season) but nematode intensity was most strongly predicted by co-infection with coccidia and its interaction with season. The influence of co-infection on host body condition and parasite aggregation occurred in season-dependent manner. Co-infected buffalo in the early wet season were in worse condition, had a less aggregated distribution of nematode parasites, and lower nematode infection intensity than buffalo infected with nematodes alone. We did not detect an effect of infection or co-infection on host pregnancy. These results suggest that demographic and seasonal variation may mediate the effects of parasites, and their interactions, on the distribution and fitness costs of infection.

Opposite outcomes of coinfection at individual and population scales

Significance Infection with multiple parasite species is common and the majority of coinfections involve at least one long-lasting infection. Our data-driven model of chronic coinfection dynamics shows that accurate prediction at the population level requires quantifying both the individual-level transmission and mortality consequences of coinfection. The infections characterized in this study compete at the population level. When one pathogen facilitates both the transmission and progression of the second pathogen, the prevalence of the first pathogen is reduced. This mechanism of competition is unique compared with previously described mechanisms and occurs without cross-immunity, resource competition within the host, or convalescence. We recommend assessing the generality of this mechanism, which could have important consequences for other chronic, immunosuppressive pathogens such as HIV or TB. Coinfecting parasites and pathogens remain a leading challenge for global public health due to their consequences for individual-level infection risk and disease progression. However, a clear understanding of the population-level consequences of coinfection is lacking. Here, we constructed a model that includes three individual-level effects of coinfection: mortality, fecundity, and transmission. We used the model to investigate how these individual-level consequences of coinfection scale up to produce population-level infection patterns. To parameterize this model, we conducted a 4-y cohort study in African buffalo to estimate the individual-level effects of coinfection with two bacterial pathogens, bovine tuberculosis (bTB) and brucellosis, across a range of demographic and environmental contexts. At the individual level, our empirical results identified bTB as a risk factor for acquiring brucellosis, but we found no association between brucellosis and the risk of acquiring bTB. Both infections were associated with reductions in survival and neither infection was associated with reductions in fecundity. The model reproduced coinfection patterns in the data and predicted opposite impacts of coinfection at individual and population scales: Whereas bTB facilitated brucellosis infection at the individual level, our model predicted the presence of brucellosis to have a strong negative impact on bTB at the population level. In modeled populations where brucellosis was present, the endemic prevalence and basic reproduction number (R0) of bTB were lower than in populations without brucellosis. Therefore, these results provide a data-driven example of competition between coinfecting pathogens that occurs when one pathogen facilitates secondary infections at the individual level.

Mapping U.S. cattle shipment networks: Spatial and temporal patterns of trade communities from 2009 to 2011

The application of network analysis to cattle shipments broadens our understanding of shipment patterns beyond pairwise interactions to the network as a whole. Such a quantitative description of cattle shipments in the U.S. can identify trade communities, describe temporal shipment patterns, and inform the design of disease surveillance and control strategies. Here, we analyze a longitudinal dataset of beef and dairy cattle shipments from 2009 to 2011 in the United States to characterize communities within the broader cattle shipment network, which are groups of counties that ship mostly to each other. Because shipments occur over time, we aggregate the data at various temporal scales to examine the consistency of network and community structure over time. Our results identified nine large (>50 counties) communities based on shipments of beef cattle in 2009 aggregated into an annual network and nine large communities based on shipments of dairy cattle. The size and connectance of the shipment network was highly dynamic; monthly networks were smaller than yearly networks and revealed seasonal shipment patterns consistent across years. Comparison of the shipment network over time showed largely consistent shipping patterns, such that communities identified on annual networks of beef and diary shipments from 2009 still represented 41-95% of shipments in monthly networks from 2009 and 41-66% of shipments from networks in 2010 and 2011. The temporal aspects of cattle shipments suggest that future applications of the U.S. cattle shipment network should consider seasonal shipment patterns. However, the consistent within-community shipping patterns indicate that yearly communities could provide a reasonable way to group regions for management.

“Deadly mosquito” or “living freshwater”?

Organizations fighting disease often use catchy taglines such as “Which animal kills most people?,” as the Gates Foundation did at its 2018 annual campaign against malaria ([ 1 ][1]). These questions pique our curiosity and anxiety and remind us that we can prevent disease by protecting

Model-guided suggestions for targeted surveillance based on cattle shipments in the U.S.

Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22-34% of imported cattle while surveillance at 50 counties is predicted to sample 43%-61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets - Interstate Certificates of Veterinary Inspection and brand inspection data - to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable.

Eutrophication and predator presence overrule the effects of temperature on mosquito survival and development

Adequate predictions of mosquito-borne disease risk require an understanding of the relevant drivers governing mosquito populations. Since previous studies have focused mainly on the role of temperature, here we assessed the effects of other important ecological variables (predation, nutrient availability, presence of conspecifics) in conjunction with the role of temperature on mosquito life history parameters. We carried out two mesocosm experiments with the common brown house mosquito, Culex pipiens, a confirmed vector for West Nile Virus, Usutu and Sindbis, and a controphic species; the harlequin fly, Chironomus riparius. The first experiment quantified interactions between predation by Notonecta glauca L. (Hemiptera: Notonectidae) and temperature on adult emergence. The second experiment quantified interactions between nutrient additions and temperature on larval mortality and adult emergence. Results indicate that 1) irrespective of temperature, predator presence decreased mosquito larval survival and adult emergence by 20–50%, 2) nutrient additions led to a 3-4-fold increase in mosquito adult emergence and a 2-day decrease in development time across all temperature treatments, 3) neither predation, nutrient additions nor temperature had strong effects on the emergence and development rate of controphic Ch. riparius. Our study suggests that, in addition to of effects of temperature, ecological bottom-up (eutrophication) and top-down (predation) drivers can have strong effects on mosquito life history parameters. Current approaches to predicting mosquito-borne disease risk rely on large-scale proxies of mosquito population dynamics, such as temperature, vegetation characteristics and precipitation. Local scale management actions, however, will require understanding of the relevant top-down and bottom-up drivers of mosquito populations.