The multi-dimensional challenges of controlling respiratory virus transmission in indoor spaces: Insights from the linkage of a microscopic pedestrian simulation and SARS-CoV-2 transmission model.

SARS-CoV-2 transmission in indoor spaces, where most infection events occur, depends on the types and duration of human interactions, among others. Understanding how these human behaviours interface with virus characteristics to drive pathogen transmission and dictate the outcomes of non-pharmaceutical interventions is important for the informed and safe use of indoor spaces. To better understand these complex interactions, we developed the Pedestrian Dynamics-Virus Spread model (PeDViS), an individual-based model that combines pedestrian behaviour models with virus spread models incorporating direct and indirect transmission routes. We explored the relationships between virus exposure and the duration, distance, respiratory behaviour, and environment in which interactions between infected and uninfected individuals took place and compared this to benchmark 'at risk' interactions (1.5 metres for 15 minutes). When considering aerosol transmission, individuals adhering to distancing measures may be at risk due to the buildup of airborne virus in the environment when infected individuals spend prolonged time indoors. In our restaurant case, guests seated at tables near infected individuals were at limited risk of infection but could, particularly in poorly ventilated places, experience risks that surpass that of benchmark interactions. Combining interventions that target different transmission routes can aid in accumulating impact, for instance by combining ventilation with face masks. The impact of such combined interventions depends on the relative importance of transmission routes, which is hard to disentangle and highly context dependent. This uncertainty should be considered when assessing transmission risks upon different types of human interactions in indoor spaces. We illustrated the multi-dimensionality of indoor SARS-CoV-2 transmission that emerges from the interplay of human behaviour and the spread of respiratory viruses. A modelling strategy that incorporates this in risk assessments can help inform policy makers and citizens on the safe use of indoor spaces with varying inter-human interactions.

Habitat loss exacerbates pathogen spread: An Agent-based model of avian influenza infection in migratory waterfowl.

Habitat availability determines the distribution of migratory waterfowl along their flyway, which further influences the transmission and spatial spread of avian influenza viruses (AIVs). The extensive habitat loss in the East Asian-Australasian Flyway (EAAF) may have potentially altered the virus spread and transmission, but those consequences are rarely studied. We constructed 6 fall migration networks that differed in their level of habitat loss, wherein an increase in habitat loss resulted in smaller networks with fewer sites and links. We integrated an agent-based model and a susceptible-infected-recovered model to simulate waterfowl migration and AIV transmission. We found that extensive habitat loss in the EAAF can 1) relocate the outbreaks northwards, responding to the distribution changes of wintering waterfowl geese, 2) increase the outbreak risk in remaining sites due to larger goose congregations, and 3) facilitate AIV transmission in the migratory population. In addition, our modeling output was in line with the predictions from the concept of "migratory escape", i.e., the migration allows the geese to "escape" from the location where infection risk is high, affecting the pattern of infection prevalence in the waterfowl population. Our modeling shed light on the potential consequences of habitat loss in spreading and transmitting AIV at the flyway scale and suggested the driving mechanisms behind these effects, indicating the importance of conservation in changing spatial and temporal patterns of AIV outbreaks.

Mass Mortality Caused by Highly Pathogenic Influenza A(H5N1) Virus in Sandwich Terns, the Netherlands, 2022.

We collected data on mass mortality in Sandwich terns (Thalasseus sandvicensis) during the 2022 breeding season in the Netherlands. Mortality was associated with at least 2 variants of highly pathogenic avian influenza A(H5N1) virus clade 2.3.4.4b. We report on carcass removal efforts relative to survival in colonies. Mitigation strategies urgently require structured research.

The PB1 gene from H9N2 avian influenza virus showed high compatibility and increased mutation rate after reassorting with a human H1N1 influenza virus.

BACKGROUND: Reassortment between human and avian influenza viruses (AIV) may result in novel viruses with new characteristics that may threaten human health when causing the next flu pandemic. A particular risk may be posed by avian influenza viruses of subtype H9N2 that are currently massively circulating in domestic poultry in Asia and have been shown to infect humans. In this study, we investigate the characteristics and compatibility of a human H1N1 virus with avian H9N2 derived genes. METHODS: The polymerase activity of the viral ribonucleoprotein (RNP) complex as combinations of polymerase-related gene segments derived from different reassortment events was tested in luciferase reporter assays. Reassortant viruses were generated by reverse genetics. Gene segments of the human WSN-H1N1 virus (A/WSN/1933) were replaced by gene segments of the avian A2093-H9N2 virus (A/chicken/Jiangsu/A2093/2011), which were both the Hemagglutinin (HA) and Neuraminidase (NA) gene segments in combination with one of the genes involved in the RNP complex (either PB2, PB1, PA or NP). The growth kinetics and virulence of reassortant viruses were tested on cell lines and mice. The reassortant viruses were then passaged for five generations in MDCK cells and mice lungs. The HA gene of progeny viruses from different passaging paths was analyzed using Next-Generation Sequencing (NGS). RESULTS: We discovered that the avian PB1 gene of H9N2 increased the polymerase activity of the RNP complex in backbone of H1N1. Reassortant viruses were able to replicate in MDCK and DF1 cells and mice. Analysis of the NGS data showed a higher substitution rate for the PB1-reassortant virus. In particular, for the PB1-reassortant virus, increased virulence for mice was measured by increased body weight loss after infection in mice. CONCLUSIONS: The higher polymerase activity and increased mutation frequency measured for the PB1-reassortant virus suggests that the avian PB1 gene of H9N2 may drive the evolution and adaptation of reassortant viruses to the human host. This study provides novel insights in the characteristics of viruses that may arise by reassortment of human and avian influenza viruses. Surveillance for infections with H9N2 viruses and the emergence of the reassortant viruses in humans is important for pandemic preparedness.

Can breeders prevent pathogen adaptation when selecting for increased resistance to infectious diseases?

BACKGROUND: Recent research shows that genetic selection has high potential to reduce the prevalence of infectious diseases in livestock. However, like all interventions that target infectious diseases, genetic selection of livestock can exert selection pressure on pathogen populations. Such selection on the pathogen may lead to escape strategies and reduce the effect of selection of livestock for disease resistance. Thus, to successfully breed livestock for lower disease prevalence, it is essential to develop strategies that prevent the invasion of pathogen mutants that escape host resistance. Here we investigate the conditions under which such "escape mutants" can replace wild-type pathogens in a closed livestock population using a mathematical model of disease transmission. RESULTS: Assuming a single gene that confers sufficient resistance, results show that genetic selection for resistance in livestock typically leads to an "invasion window" within which an escape mutant of the pathogen can invade. The bounds of the invasion window are determined by the frequency of resistant hosts in the population. The lower bound occurs when the escape mutant has an advantage over the wild-type pathogen in the population. The upper bound occurs when local eradication of the pathogen is expected. The invasion window is smallest when host resistance is strong and when infection with the wild-type pathogen provides cross immunity to infection with the escape mutant. CONCLUSIONS: To minimise opportunities for pathogens to adapt, under the assumptions of our model, the aim of disease control through genetic selection should be to achieve herd-level eradication of the infection faster than the rate of emergence of escape mutants of the pathogen. Especially for microparasitic infections, this could be achieved by placing animals into herds according to their genetic resistance, such that these herds stay completely out of the invasion window. In contrast to classical breeding theory, our model suggests that multi-trait selection with gradual improvement of each trait of the breeding goal might not be the best strategy when resistance to infectious disease is part of the breeding goal. Temporally, combining genetic selection with other interventions helps to make the invasion window smaller, and thereby reduces the risk of invasion of escape mutants.

On the origin of the genetic variation in infectious disease prevalence: Genetic analysis of disease status versus infections for Digital Dermatitis in Dutch dairy cattle.

The purpose of this study was to investigate the origin of the genetic variation in the prevalence of bovine digital dermatitis (DD) by comparing a genetic analysis of infection events to a genetic analysis of disease status. DD is an important endemic infectious disease affecting the claws of cattle. For disease status, we analysed binary data on individual disease status (0,1; indicating being free versus infected), whereas for infections, we analysed binary data on disease transmission events (1,0; indicating becoming infected or not). The analyses of the two traits were compared using cross-validation. The analysis of disease status captures a combination of genetic variation in disease susceptibility and the ability of individuals to recover, whereas the analysis of infections captures genetic variation in susceptibility only. Estimated genetic variances for both traits indicated substantial genetic variation. The GEBV for disease status and infections correlated with only 0.60, indicating that both models indeed capture distinct information. Together, these results suggest the presence of genetic variation not only in disease susceptibility, but also in the ability of individuals to recover from DD. We argue that the presence of genetic variation in recovery implies that breeders should distinguish between infected individuals versus infectious individuals. This is because epidemiological theory shows that selection for recovery is effective only when it targets recovery from being infectious.

Effects of migration network configuration and migration synchrony on infection prevalence in geese.

Migration can influence dynamics of pathogen-host interactions. However, it is not clearly known how migration pattern, in terms of the configuration of the migration network and the synchrony of migration, affects infection prevalence. We therefore applied a discrete-time SIR model, integrating environmental transmission and migration, to various migration networks, including networks with serial, parallel, or both serial and parallel stopover sites, and with various levels of migration synchrony. We applied the model to the infection of avian influenza virus in a migratory geese population. In a network with only serial stopover sites, increasing the number of stopover sites reduced infection prevalence, because with every new stopover site, the amount of virus in the environment was lower than that in the previous stopover site, thereby reducing the exposure of the migratory population. In a network with parallel stopover sites, both increasing the number and earlier appearance of the stopover sites led to an earlier peak of infection prevalence in the migratory population, because the migratory population is exposed to larger total amount of virus in the environment, speeding-up the infection accumulation. Furthermore, higher migration synchrony reduced the average number of cumulative infection, because the majority of the population can fly to a new stopover site where the amount of virus is still relatively low and has not been increased due to virus shedding of infected birds. Our simulations indicate that a migration pattern with multiple serial stopover sites and with highly synchronized migration reduces the infection prevalence.

Quantifying transmission of Mycobacterium avium subsp. paratuberculosis among group-housed dairy calves.

Johne's disease (JD) is a chronic enteritis caused by Mycobacterium avium subsp. paratuberculosis (MAP), with control primarily aimed at preventing new infections among calves. The aim of the current study was to quantify calf-to-calf transmission of MAP among penmates in an experimental trial. Newborn Holstein bull calves (n = 32) were allocated into pens of 4, with 2 inoculated (IN) calves and 2 calves that were contact exposed (CE). Calves were group-housed for 3 months, with frequent collection of fecal and blood samples and tissue collection after euthanasia. The basic reproduction ratio (R0) was estimated using a final size (FS) model with a susceptible-infected model, based on INF-γ ELISA and tissue culture followed by qPCR. In addition, the transmission rate parameter (ß) for new shedding events was estimated using a general linearized method (GLM) model with a susceptible-infected-susceptible model based on culture, followed by qPCR, of fecal samples collected during group housing. The R0 was derived for IN and CE calves separately, due to a difference in susceptibility, as well as differences in duration of shedding events. Based on the FS model, interferon-γ results from blood samples resulted in a R 0 IG of 0.90 (0.24, 2.59) and tissue culture resulted in a R 0 T of 1.36 (0.45, 3.94). Based on the GLM model, the R0 for CE calves to begin shedding (R 0 CE ) was 3.24 (1.14, 7.41). We concluded that transmission of MAP infection between penmates occurred and that transmission among calves may be an important cause of persistent MAP infection on dairy farms that is currently uncontrolled for in current JD control programs.

Genetic parameters and genomic breeding values for digital dermatitis in Holstein Friesian dairy cattle: host susceptibility, infectivity and the basic reproduction ratio.

BACKGROUND: For infectious diseases, the probability that an animal gets infected depends on its own susceptibility, and on the number of infectious herd mates and their infectivity. Together with the duration of the infectious period, susceptibility and infectivity determine the basic reproduction ratio of the disease ([Formula: see text]). [Formula: see text] is the average number of secondary cases caused by a typical infectious individual in an otherwise uninfected population. An infectious disease dies out when [Formula: see text] is lower than 1. Thus, breeding strategies that aim at reducing disease prevalence should focus on reducing [Formula: see text], preferably to a value lower than 1. In animal breeding, however, [Formula: see text] has received little attention. Here, we estimate the additive genetic variance in host susceptibility, host infectivity, and [Formula: see text] for the endemic claw disease digital dermatitis (DD) in Holstein Friesian dairy cattle, and estimate genomic breeding values (GEBV) for these traits. We recorded DD disease status of both hind claws of 1513 cows from 12 Dutch dairy farms, every 2 weeks, 11 times. The genotype data consisted of 75,904 single nucleotide polymorphisms (SNPs) for 1401 of the cows. We modelled the probability that a cow got infected between recordings, and compared four generalized linear mixed models. All models included a genetic effect for susceptibility; Models 2 and 4 also included a genetic effect for infectivity, while Models 1 and 2 included a farm*period interaction. We corrected for variation in exposure to infectious herd mates via an offset. RESULTS: GEBV for [Formula: see text] from the model that included genetic effects for susceptibility only had an accuracy of ~ 0.39 based on cross-validation between farms, which is very high given the limited amount of data and the complexity of the trait. Models with a genetic effect for infectivity showed a larger bias, but also a slightly higher accuracy of GEBV. Additive genetic standard deviation for [Formula: see text] was large, i.e. ~ 1.17, while the mean [Formula: see text] was 2.36. CONCLUSIONS: GEBV for [Formula: see text] showed substantial variation. The mean [Formula: see text] was only about one genetic standard deviation greater than 1. These results suggest that lowering DD prevalence by selective breeding is promising.

Derivation of the economic value of R0 for macroparasitic diseases and application to sea lice in salmon.

BACKGROUND: Macroparasites, such as ticks, lice, and helminths, are a concern in livestock and aquaculture production, and can be controlled by genetic improvement of the host population. Genetic improvement should aim at reducing the rate at which parasites spread across the farmed population. This rate is determined by the basic reproduction ratio, i.e. [Formula: see text], which is the appropriate breeding goal trait. This study aims at providing a method to derive the economic value of [Formula: see text]. METHODS: Costs of a disease are the sum of production losses and expenditures on disease control. Genetic improvement of [Formula: see text] lowers the loss-expenditure frontier. Its economic effect depends on whether the management strategy is optimized or not. The economic value may be derived either from the reduction in losses with constant expenditures or from the reduction in expenditures with constant losses. RESULTS: When [Formula: see text] ≤ 1, the economic value of a further reduction is zero because there is no risk of a major epidemic. When [Formula: see text] > 1 and management is optimized, the economic value increases with decreasing values of [Formula: see text], because both the mean number of parasites per host and frequency of treatments decrease at an increasing rate when [Formula: see text] decreases. When [Formula: see text] > 1 and management is not optimized, the economic value depends on whether genetic improvement is used for reducing expenditures or losses. For sea lice in salmon, the economic value depends on a reduction in expenditures with constant losses, and is estimated to be 0.065€/unit [Formula: see text]/kg production. DISCUSSION: Response to selection for measures of disease prevalence cannot be predicted from quantitative genetic theory alone. Moreover, many studies fail to address the issue of whether genetic improvement results in reduced losses or expenditures. Using [Formula: see text] as the breeding goal trait, weighed by its appropriate economic value, avoids these issues. CONCLUSION: When management is optimized, the economic value increases with decreasing values of [Formula: see text] (until the threshold of [Formula: see text], where it drops to zero). When management is not optimized, the economic value depends on whether genetic improvement is used for reduced expenditures or production losses. For sea lice in salmon, the economic value is estimated to be 0.065 €/unit [Formula: see text]/kg production.

A model to estimate effects of SNPs on host susceptibility and infectivity for an endemic infectious disease.

BACKGROUND: Infectious diseases in farm animals affect animal health, decrease animal welfare and can affect human health. Selection and breeding of host individuals with desirable traits regarding infectious diseases can help to fight disease transmission, which is affected by two types of (genetic) traits: host susceptibility and host infectivity. Quantitative genetic studies on infectious diseases generally connect an individual's disease status to its own genotype, and therefore capture genetic effects on susceptibility only. However, they usually ignore variation in exposure to infectious herd mates, which may limit the accuracy of estimates of genetic effects on susceptibility. Moreover, genetic effects on infectivity will exist as well. Thus, to design optimal breeding strategies, it is essential that genetic effects on infectivity are quantified. Given the potential importance of genetic effects on infectivity, we set out to develop a model to estimate the effect of single nucleotide polymorphisms (SNPs) on both host susceptibility and host infectivity. To evaluate the quality of the resulting SNP effect estimates, we simulated an endemic disease in 10 groups of 100 individuals, and recorded time-series data on individual disease status. We quantified bias and precision of the estimates for different sizes of SNP effects, and identified the optimum recording interval when the number of records is limited. RESULTS: We present a generalized linear mixed model to estimate the effect of SNPs on both host susceptibility and host infectivity. SNP effects were on average slightly underestimated, i.e. estimates were conservative. Estimates were less precise for infectivity than for susceptibility. Given our sample size, the power to estimate SNP effects for susceptibility was 100% for differences between genotypes of a factor 1.56 or more, and was higher than 60% for infectivity for differences between genotypes of a factor 4 or more. When disease status was recorded 11 times on each animal, the optimal recording interval was 25 to 50% of the average infectious period. CONCLUSIONS: Our model was able to estimate genetic effects on susceptibility and infectivity. In future genome-wide association studies, it may serve as a starting point to identify genes that affect disease transmission and disease prevalence.

Small distances can keep bacteria at bay for days.

Transmission of pathogens between spatially separated hosts, i.e., indirect transmission, is a commonly encountered phenomenon important for epidemic pathogen spread. The routes of indirect transmission often remain untraced, making it difficult to develop control strategies. Here we used a tailor-made design to study indirect transmission experimentally, using two different zoonotic bacteria in broilers. Previous experiments using a single bacterial species yielded a delay in the onset of transmission, which we hypothesized to result from the interplay between diffusive motion of infectious material and decay of infectivity in the environment. Indeed, a mathematical model of diffusive pathogen transfer predicts a delay in transmission that depends both on the distance between hosts and on the magnitude of the pathogen decay rate. Our experiments, carried out with two bacterial species with very different decay rates in the environment, confirm the difference in transmission delay predicted by the model. These results imply that for control of an infectious agent, the time between the distant exposure and the infection event is important. To illustrate how this can work we analyzed data observed on the spread of vancomycin-resistant Enterococcus in an intensive care unit. Indeed, a delayed vancomycin-resistant Enterococcus transmission component was identified in these data, and this component disappeared in a study period in which the environment was thoroughly cleaned. Therefore, we suggest that the impact of control strategies against indirect transmission can be assessed using our model by estimating the control measures' effects on the diffusion coefficient and the pathogen decay rate.

Quantification of transmission of foot-and-mouth disease virus caused by an environment contaminated with secretions and excretions from infected calves.

Foot-and-mouth disease virus (FMDV) infected animals can contaminate the environment with their secretions and excretions. To quantify the contribution of a contaminated environment to the transmission of FMDV, this study used calves that were not vaccinated and calves that were vaccinated 1 week prior to inoculation with the virus in direct and indirect contact experiments. In direct contact experiments, contact calves were exposed to inoculated calves in the same room. In indirect contact experiments, contact calves were housed in rooms that previously had held inoculated calves for three days (either from 0 to 3 or from 3 to 6 days post inoculation). Secretions and excretions from all calves were tested for the presence of FMDV by virus isolation; the results were used to quantify FMDV transmission. This was done using a generalized linear model based on a 2 route (2R, i.e. direct contact and environment) SIR model that included information on FMDV survival in the environment. The study shows that roughly 44% of transmission occurs via the environment, as indicated by the reproduction ratio R0(2R)environment that equalled 2.0, whereas the sum of R0(2R)contact and R0(2R)environment equalled 4.6. Because vaccination 1 week prior to inoculation of the calves conferred protective immunity against FMDV infection, no transmission rate parameters could be estimated from the experiments with vaccinated calves. We conclude that a contaminated environment contributes considerably to the transmission of FMDV therefore that hygiene measures can play a crucial role in FMD control.

Role of the Environment in the Transmission of Antimicrobial Resistance to Humans: A Review.

To establish a possible role for the natural environment in the transmission of clinically relevant AMR bacteria to humans, a literature review was conducted to systematically collect and categorize evidence for human exposure to extended-spectrum ß-lactamase-producing Enterobacteriaceae, methicillin-resistant Staphylococcus aureus, and vancomycin-resistant Enterococcus spp. in the environment. In total, 239 datasets adhered to inclusion criteria. AMR bacteria were detected at exposure-relevant sites (35/38), including recreational areas, drinking water, ambient air, and shellfish, and in fresh produce (8/16). More datasets were available for environmental compartments (139/157), including wildlife, water, soil, and air/dust. Quantitative data from exposure-relevant sites (6/35) and environmental compartments (11/139) were scarce. AMR bacteria were detected in the contamination sources (66/66) wastewater and manure, and molecular data supporting their transmission from wastewater to the environment (1/66) were found. The abundance of AMR bacteria at exposure-relevant sites suggests risk for human exposure. Of publications pertaining to both environmental and human isolates, however, only one compared isolates from samples that had a clear spatial and temporal relationship, and no direct evidence was found for transmission to humans through the environment. To what extent the environment, compared to the clinical and veterinary domains, contributes to human exposure needs to be quantified. AMR bacteria in the environment, including sites relevant for human exposure, originate from contamination sources. Intervention strategies targeted at these sources could therefore limit emission of AMR bacteria to the environment.

Genetic analysis of infectious diseases: estimating gene effects for susceptibility and infectivity.

BACKGROUND: Genetic selection of livestock against infectious diseases can complement existing interventions to control infectious diseases. Most genetic approaches that aim at reducing disease prevalence assume that individual disease status (infected/not-infected) is solely a function of its susceptibility to a particular pathogen. However, individual infectivity also affects the risk and prevalence of an infection in a population. Variation in susceptibility and infectivity between hosts affects transmission of an infection in the population, which is usually measured by the value of the basic reproduction ratio R 0 . R 0 is an important epidemiological parameter that determines the risk and prevalence of infectious diseases. An individual's breeding value for R 0 is a function of its genes that influence both susceptibility and infectivity. Thus, to estimate the effects of genes on R 0 , we need to estimate the effects of genes on individual susceptibility and infectivity. To that end, we developed a generalized linear model (GLM) to estimate relative effects of genes for susceptibility and infectivity. A simulation was performed to investigate bias and precision of the estimates, the effect of R 0 , the size of the effects of genes for susceptibility and infectivity, and relatedness among group mates on bias and precision. We considered two bi-allelic loci that affect, respectively, the individuals' susceptibility only and individuals' infectivity only. RESULTS: A GLM with complementary log-log link function can be used to estimate the relative effects of genes on the individual's susceptibility and infectivity. The model was developed from an equation that describes the probability of an individual to become infected as a function of its own susceptibility genotype and infectivity genotypes of all its infected group mates. Results show that bias is smaller when R 0 ranges approximately from 1.8 to 3.1 and relatedness among group mates is higher. With larger effects, both absolute and relative standard deviations become clearly smaller, but the relative bias remains the same. CONCLUSIONS: We developed a GLM to estimate the relative effect of genes that affect individual susceptibility and infectivity. This model can be used in genome-wide association studies that aim at identifying genes that influence the prevalence of infectious diseases.

Estimation of the transmission of foot-and-mouth disease virus from infected sheep to cattle.

The quantitative role of sheep in the transmission of foot-and-mouth disease virus (FMDV) is not well known. To estimate the role of sheep in the transmission of FMDV, a direct contact transmission experiment with 10 groups of animals each consisting of 2 infected lambs and 1 contact calf was performed. Secretions and excretions (oral swabs, blood, urine, faeces and probang samples) from all animals were tested for the presence of FMDV by virus isolation (VI) and/or RT-PCR. Serum was tested for the presence of antibodies against FMDV. To estimate FMDV transmission, the VI, RT-PCR and serology results were used. The partial reproduction ratio R0p i.e. the average number of new infections caused by one infected sheep introduced into a population of susceptible cattle, was estimated using either data of the whole infection chain of the experimental epidemics (the transient state method) or the final sizes of the experimental epidemics (the final size method). Using the transient state method, R0p was estimated as 1.0 (95% CI 0.2 - 6.0) using virus isolation results and 1.4 (95% CI 0.3 - 8.0) using RT-PCR results. Using the final size method, R0p was estimated as 0.9 (95% CI 0.2 - 3.0). Finally, R0p was compared to the R0's obtained in previous transmission studies with sheep or cattle only. This comparison showed that the infectivity of sheep is lower than that of cattle and that sheep and cattle are similarly susceptible to FMD. These results indicate that in a mixed population of sheep and cattle, sheep play a more limited role in the transmission of FMDV than cattle.

Airborne Microorganisms From Livestock Production Systems and Their Relation to Dust.

Large amounts of airborne microorganisms are emitted from livestock production. These emitted microorganisms may associate with dust, and are suspected to pose a risk of airborne infection to humans in vicinity and to animals on other farms. However, the extent to which airborne transmission may play a role in the epidemic, and how dust acts as a carrier of microorganisms in the transmission processes is unknown. The authors present the current knowledge of the entire process of airborne transmission of microorganisms-from suspension and transportation until deposition and infection-and their relation to dust. The sampling and the mitigation techniques of airborne microorganisms and dust in livestock production systems are introduced as well.

BCG vaccination reduces bovine tuberculosis transmission, improving prospects for elimination.

Bacillus Calmette-Guérin (BCG) is a routinely used vaccine for protecting children against Mycobacterium tuberculosis that comprises attenuated Mycobacterium bovis. BCG can also be used to protect livestock against M. bovis; however, its effectiveness has not been quantified for this use. We performed a natural transmission experiment to directly estimate the rate of transmission to and from vaccinated and unvaccinated calves over a 1-year exposure period. The results show a higher indirect efficacy of BCG to reduce transmission from vaccinated animals that subsequently become infected [74%; 95% credible interval (CrI): 46 to 98%] compared with direct protection against infection (58%; 95% CrI: 34 to 73%) and an estimated total efficacy of 89% (95% CrI: 74 to 96%). A mechanistic transmission model of bovine tuberculosis (bTB) spread within the Ethiopian dairy sector was developed and showed how the prospects for elimination may be enabled by routine BCG vaccination of cattle.

Survival of heterophyid metacercaria in common carp (Cyprinus carpio).

Heterophyidae are small intestinal trematodes that infect vertebrates worldwide. Common carp (Cyprinus carpio) is one of the most preferred freshwater fish species by consumers in Asia, the region where fish-borne trematodes like Heterophyidae are most prevalent. How long Heterophyidae survive in common carp is unknown. The objective of this study was to quantify survival of Heterophyidae in common carp after experimental exposure. Fish of 0.18 g were either used as controls or exposed to 250 heterophyid cercaria for 24 h. Control fish did not become infected. Percentage infection of exposed fish at 0-2 (n = 53), >2-10 (n = 15), >10-20 (n = 11), and >20-27 (n = 33) weeks post exposure was 98, 80, 100, and 100 % respectively. The number of metacercaria per fish did not significantly decrease (P = 0.19) during 27 weeks after exposure: exp [3.6200-0.0193 × weeks post exposure]. All developed metacercaria were identified as Haplorchis spp. It was concluded that Heterophyidae may persist in carp for a long time, implying that harvestable carp are a risk to human health.

A novel method to jointly estimate transmission rate and decay rate parameters in environmental transmission models.

In environmental transmission, pathogens transfer from one individual to another via the environment. It is a common transmission mechanism in a wide range of host-pathogen systems. Incorporating environmental transmission in dynamic transmission models is crucial for gauging the effect of interventions, as extrapolating model results to new situations is only valid when the mechanisms are modelled correctly. The challenge in environmental transmission models lies in not jointly identifiable parameters for pathogen shedding, decay, and transmission dynamics. To solve this unidentifiability issue, we present a stochastic environmental transmission model with a novel scaling method for shedding rate parameter and a novel estimation method that distinguishes transmission rate and decay rate parameters. The core of our scaling and estimation method is calculating exposure and relating exposure to infection risks. By scaling shedding rate parameter, we standardize exposure to pathogens contributed by one infectious individual present during one time interval to one. The standardized exposure leads to a standard definition of transmission rate parameter applicable to scenarios with different decay rate parameters. Hence, we unify direct transmission (large decay rate) and environmental transmission in a continuous manner. More importantly, our exposure-based estimation method can correctly estimate back the transmission rate and the decay rate parameters, while the commonly used trajectory-based method failed. The reason is that exposure-based method gives the correct weight to infection data from previous observation periods. The correct estimation from exposure-based method will lead to more reliable predictions of intervention impact. Using the effect of disinfection as an example, we show how incorrectly estimated parameters may lead to incorrect conclusions about the effectiveness of interventions. This illustrates the importance of correct estimation of transmission rate and decay rate parameters for extrapolating environmental transmission models and predicting intervention effects.