Community-acquired Staphylococcus aureus skin and soft tissue infection risk assessment using hotspot analysis and risk maps: the case of California emergency departments.

BACKGROUND: Community-acquired Staphylococcus aureus (CA-Sa) skin and soft tissue infections (SSTIs) are historically associated with densely populated urban areas experiencing high poverty rates, intravenous drug use, and homelessness. However, the epidemiology of CA-Sa SSTIs in the United States has been poorly understood since the plateau of the Community-acquired Methicillin-resistant Staphylococcus aureus epidemic in 2010. This study examines the spatial variation of CA-Sa SSTIs in a large, geographically heterogeneous population and identifies neighborhood characteristics associated with increased infection risk. METHODS: Using a unique neighborhood boundary, California Medical Service Study Areas, a hotspot analysis, and estimates of neighborhood infection risk ratios were conducted for all CA-Sa SSTIs presented in non-Federal California emergency departments between 2016 and 2019. A Bayesian Poisson regression model evaluated the association between neighborhood-level infection risk and population structure, neighborhood poverty rates, and being a healthcare shortage area. RESULTS: Emergency departments in more rural and mountainous parts of California experienced a higher burden of CA-Sa SSTIs between 2016 and 2019. Neighborhoods with high infection rates were more likely to have a high percentage of adults living below the federal poverty level and be a designated healthcare shortage area. Measures of population structure were not associated with infection risk in California neighborhoods. CONCLUSIONS: Our results highlight a potential change in the epidemiology of CA-Sa SSTIs in California emergency departments. Future studies should investigate the CA-Sa burden in other geographies to identify whether this shift in epidemiology holds across other states and populations. Further, a more thorough evaluation of potential mechanisms for the clustering of infections seen across California neighborhoods is needed.

Antimicrobial Susceptibility in Respiratory Pathogens and Farm and Animal Variables in Weaned California Dairy Heifers: Logistic Regression and Bayesian Network Analyses.

Weaned dairy heifers are a relatively understudied production group. Bovine respiratory disease (BRD) is the most common cause of antimicrobial drug (AMD) use, morbidity, and mortality in this production group. The study of antimicrobial resistance (AMR) is complicated because many variables that may affect AMR are related. This study generates hypotheses regarding the farm- and animal-level variables (e.g., vaccination, lane cleaning, and AMD use practices) that may be associated with AMR in respiratory isolates from weaned dairy heifers. A cross-sectional study was performed using survey data and respiratory isolates (Pasteurella multocida, Mannheimia haemolytica, and Histophilus somni) collected from 341 weaned dairy heifers on six farms in California. Logistic regression and Bayesian network analyses were used to evaluate the associations between farm- and animal-level variables with minimum inhibitory concentration (MIC) classification of respiratory isolates against 11 AMDs. Farm-level variables associated with MIC classification of respiratory isolates included the number of source farms of a calf-rearing facility, whether the farm practiced onsite milking, the use of lagoon water for flush lane cleaning, and respiratory and pinkeye vaccination practices. Animal-level variables associated with a MIC classification included whether the calf was BRD-score-positive and time since the last phenicol treatment.

Surveillance of Flea-Borne Typhus in California, 2011-2019.

Flea-borne typhus (FBT), also referred to as murine typhus, is an acute febrile disease in humans caused by the bacteria Rickettsia typhi. Currently, cases of FBT are reported for public health surveillance purposes (i.e., to detect incidence and outbreaks) in a few U.S. states. In California, healthcare providers and testing laboratories are mandated to report to their respective local public health jurisdictions whenever R. typhi or antibodies reactive to R. typhi are detected in a patient, who then report cases to state health department. In this study, we characterize the epidemiology of flea-borne typhus cases in California from 2011 to 2019. A total of 881 cases were reported during this period, with most cases reported among residents of Los Angeles and Orange Counties (97%). Demographics, animal exposures, and clinical courses for case patients were summarized. Additionally, spatiotemporal cluster analyses pointed to five areas in southern California with persistent FBT transmission.

Prevalence of African animal trypanosomiasis among livestock and domestic animals in Uganda: a systematic review and meta-regression analysis from 1980 to 2022.

African animal trypanosomiasis (AAT) is one of the major constraints to animal health and production in sub-Saharan Africa. To inform AAT control in Uganda and help advance along the progressive control pathway (PCP), we characterized AAT prevalence among eight host species in Uganda and explored factors that influence the prevalence variation between studies. We retrieved AAT prevalence publications (n = 2232) for Uganda (1980-2022) from five life sciences databases, focusing on studies specifying AAT detection methods, sample size, and the number of trypanosome-positive animals. Following PRISMA guidelines, we included 56 publications, and evaluated publication bias by the Luis Furuya-Kanamori (LFK) index. National AAT prevalence under DNA diagnostic methods for cattle, sheep and goats was 22.15%, 8.51% and 13.88%, respectively. Under DNA diagnostic methods, T. vivax was the most common Trypanosoma sp. in cattle (6.15%, 95% CI: 2.91-10.45) while T. brucei was most common among small ruminants (goats: 8.78%, 95% CI: 1.90-19.88, and sheep: 8.23%, 95% CI: 4.74-12.50, respectively). Northern and Eastern regions accounted for the highest AAT prevalence. Despite the limitations of this study (i.e., quality of reviewed studies, underrepresentation of districts/regions), we provide insights that could be used for better control of AAT in Uganda and identify knowledge gaps that need to be addressed to support the progressive control of AAT at country level and other regional endemic countries with similar AAT eco-epidemiology.

Infection prediction in swine populations with machine learning.

The pork industry is an essential part of the global food system, providing a significant source of protein for people around the world. A major factor restraining productivity and compromising animal wellbeing in the pork industry is disease outbreaks in pigs throughout the production process: widespread outbreaks can lead to losses as high as 10% of the U.S. pig population in extreme years. In this study, we present a machine learning model to predict the emergence of infection in swine production systems throughout the production process on a daily basis, a potential precursor to outbreaks whose detection is vital for disease prevention and mitigation. We determine features that provide the most value in predicting infection, which include nearby farm density, historical test rates, piglet inventory, feed consumption during the gestation period, and wind speed and direction. We utilize these features to produce a generalizable machine learning model, evaluate the model's ability to predict outbreaks both seven and 30 days in advance, allowing for early warning of disease infection, and evaluate our model on two swine production systems and analyze the effects of data availability and data granularity in the context of our two swine systems with different volumes of data. Our results demonstrate good ability to predict infection in both systems with a balanced accuracy of [Formula: see text] on any disease in the first system and balanced accuracies (average prediction accuracy on positive and negative samples) of [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] on porcine reproductive and respiratory syndrome, porcine epidemic diarrhea virus, influenza A virus, and Mycoplasma hyopneumoniae in the second system, respectively, using the six most important predictors in all cases. These models provide daily infection probabilities that can be used by veterinarians and other stakeholders as a benchmark to more timely support preventive and control strategies on farms.

Risk mapping and risk factors analysis of rabies in livestock in Bangladesh using national-level passive surveillance data.

Rabies is a major zoonotic disease around the world, causing significant mortality to both humans and animals, especially in low- and middle-income countries. In Bangladesh, rabies is transmitted mostly by the bite of infected dogs and jackals to humans and domestic livestock, causing severe economic losses and public health hazards. Our study analyzed national passive surveillance data of veterinary hospital-reported rabies cases in cattle, buffalo, sheep, and goats from 2015 to 2017 in all 64 districts of Bangladesh. We used a zero-inflated negative binomial regression model to identify the main environmental and socio-economic risk factors associated with rabies occurrence in livestock, and we used model results to generate risk maps. Our study revealed that monsoon precipitation (RR=1.28, p-value=0.043) was positively associated with rabies cases in livestock, and the percentage of adults who have completed university education was also a significant predictor (RR=0.58, p-value<0.001) likely suggesting that districts with higher education levels tended to have a lower reporting of rabies cases in livestock. The standardized incidence ratio maps and predicted relative risk maps revealed a high risk of rabies cases in southeast areas in Bangladesh. We recommend implementing risk-based vaccination strategies in dogs and jackals in those high-risk areas before monsoon to reduce the burden of rabies cases in domestic ruminants and humans in Bangladesh.

Evaluation of the application of sequence data to the identification of outbreaks of disease using anomaly detection methods.

Anomaly detection methods have a great potential to assist the detection of diseases in animal production systems. We used sequence data of Porcine Reproductive and Respiratory Syndrome (PRRS) to define the emergence of new strains at the farm level. We evaluated the performance of 24 anomaly detection methods based on machine learning, regression, time series techniques and control charts to identify outbreaks in time series of new strains and compared the best methods using different time series: PCR positives, PCR requests and laboratory requests. We introduced synthetic outbreaks of different size and calculated the probability of detection of outbreaks (POD), sensitivity (Se), probability of detection of outbreaks in the first week of appearance (POD1w) and background alarm rate (BAR). The use of time series of new strains from sequence data outperformed the other types of data but POD, Se, POD1w were only high when outbreaks were large. The methods based on Long Short-Term Memory (LSTM) and Bayesian approaches presented the best performance. Using anomaly detection methods with sequence data may help to identify the emergency of cases in multiple farms, but more work is required to improve the detection with time series of high variability. Our results suggest a promising application of sequence data for early detection of diseases at a production system level. This may provide a simple way to extract additional value from routine laboratory analysis. Next steps should include validation of this approach in different settings and with different diseases.

Quantitative risk assessment of African swine fever introduction into Spain by legal import of swine products.

African swine fever (ASF) is currently threatening the global swine industry. Its unstoppable global spread poses a serious risk to Spain, one of the world's leading producers. Over the past years, there has been an increased global burden of ASF not only in swine but also swine products. Unfortunately, many pigs are not diagnosed before slaughter and their products are used for human consumption. These ASF-contaminated products are only a source for new ASF outbreaks when they are consumed by domestic pigs or wild boar, which may happen either by swill feeding or landfill access. This study presents a quantitative stochastic risk assessment model for the introduction of ASF into Spain via the legal import of swine products, specifically pork and pork products. Entry assessment, exposure assessment, consequence assessment and risk estimation were carried out. The results suggest an annual probability of ASF introduction into Spain of 1.74 × 10-4, the highest risk being represented by Hungary, Portugal, and Poland. Monthly risk distribution is homogeneously distributed throughout the year. Illegal trade and pork product movement for own consumption (e.g., air and ship passenger luggage) have not been taken into account due to the lack of available, accredited data sources. This limitation may have influenced the model's outcomes and, the risk of introduction might be higher than that estimated. Nevertheless, the results presented herein would contribute to allocating resources to areas at higher risk, improving prevention and control strategies and, ultimately, would help reduce the risk of ASF introduction into Spain.

Quantitative Risk Assessment of Oocyst Versus Bradyzoite Foodborne Transmission of Toxoplasma gondii in Brazil.

Toxoplasma gondii is a globally distributed zoonotic protozoan parasite. Infection with T. gondii can cause congenital toxoplasmosis in developing fetuses and acute outbreaks in the general population, and the disease burden is especially high in South America. Prior studies found that the environmental stage of T. gondii, oocysts, is an important source of infection in Brazil; however, no studies have quantified this risk relative to other parasite stages. We developed a Bayesian quantitative risk assessment (QRA) to estimate the relative attribution of the two primary parasite stages (bradyzoite and oocyst) that can be transmitted in foods to people in Brazil. Oocyst contamination in fruits and greens contributed significantly more to overall estimated T. gondii infections than bradyzoite-contaminated foods (beef, pork, poultry). In sensitivity analysis, treatment, i.e., cooking temperature for meat and washing efficiency for produce, most strongly affected the estimated toxoplasmosis incidence rate. Due to the lack of regional food contamination prevalence data and the high level of uncertainty in many model parameters, this analysis provides an initial estimate of the relative importance of food products. Important knowledge gaps for oocyst-borne infections were identified and can drive future studies to improve risk assessments and effective policy actions to reduce human toxoplasmosis in Brazil.

A Bayesian multilevel analysis exploring population-level effects mediating the relationship between area-level poverty and community-acquired Methicillin-resistant Staphylococcus aureus (CA-MRSA) infection across California communities.

Poverty is an often-cited driver of health disparities, and associations between poverty and community-acquired Methicillin-resistant Staphylococcus aureus (CA-MRSA) infection are well documented. However, the pathways through which poverty influences infection have not been thoroughly examined. This project aims to identify mediating variables, or mechanisms, explaining why area-level poverty is associated with CA-MRSA infection in Californians. Bayesian multilevel models accounting for spatial confounding were developed to test whether the association between area-level poverty and CA-MRSA infection is mediated by living in a primary care shortage area (HCSA), living near an adult correctional facility, and residential environmental degradation. The association between area-level poverty and CA-MRSA infection can be partially explained by spatial autocorrelation, living in an HCSA, and environmental degradation in the neighborhood. Combined, the mediators explain approximately 6% of the odds of CA-MRSA infection for individuals living in neighborhoods with high poverty rates and 50% of the statistical association between area-level poverty and CA-MRSA infection. The statistical association between area-level poverty and infection was completely explained by the mediators for individuals living in neighborhoods with low poverty rates.

Predicting antimicrobial resistance of bacterial pathogens using time series analysis.

Antimicrobial resistance (AMR) is arguably one of the major health and economic challenges in our society. A key aspect of tackling AMR is rapid and accurate detection of the emergence and spread of AMR in food animal production, which requires routine AMR surveillance. However, AMR detection can be expensive and time-consuming considering the growth rate of the bacteria and the most commonly used analytical procedures, such as Minimum Inhibitory Concentration (MIC) testing. To mitigate this issue, we utilized machine learning to predict the future AMR burden of bacterial pathogens. We collected pathogen and antimicrobial data from >600 farms in the United States from 2010 to 2021 to generate AMR time series data. Our prediction focused on five bacterial pathogens (Escherichia coli, Streptococcus suis, Salmonella sp., Pasteurella multocida, and Bordetella bronchiseptica). We found that Seasonal Auto-Regressive Integrated Moving Average (SARIMA) outperformed five baselines, including Auto-Regressive Moving Average (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA). We hope this study provides valuable tools to predict the AMR burden not only of the pathogens assessed in this study but also of other bacterial pathogens.

Impact of sensor data pre-processing strategies and selection of machine learning algorithm on the prediction of metritis events in dairy cattle.

With all the sensor data currently generated at high frequency in dairy farms, there is potential for earlier diagnosis of postpartum diseases compared with traditional monitoring methodologies. Our objectives were 1) to compare the impact of sensor data pre-processing on classifier performance by using multiple time windows before a given metritis event, while considering other cow-level factors and farm-scheduled activities; 2) to compare the performance of random forest (RF), k-nearest neighbors (k-NN), and support vector machine (SVM) classifiers at different decision thresholds using different number of past observations (time-lags) for the detection of behavioral patterns associated with changes in metritis scores; and 3) to compare classifier performance between each one of the five behaviors registered every hour by an ear-tag 3-axis accelerometer (CowManager, Agis Autimatisering, Harmelen, Netherlands). A total of 239 metritis events were created by comparing metritis scores between two consecutive clinical evaluations from cows that were retrospectively selected from a dataset containing sensor data and health information during the first 21 days postpartum from June 2014 to May 2017. Hourly sensor data classified by the accelerometer as either ruminating, eating, not active (including both standing or lying), and two different levels of activity (active and high activity) behaviors corresponding to the 3 days before each metritis event were aggregated every 24-, 12-, 6-, and 3-hour time windows. Multiple time-lags were also used to determine the optimal number of past observations needed for optimal classification. Similarly, different decision thresholds were compared in terms of model performance. Depending on the classifier, algorithm hyperparameters were optimized using grid search (RF, k-NN, SVM) and random search (RF). All behaviors changed throughout the study period and showed distinct daily patterns. From the three algorithms, RF had the highest F1 score followed by k-NN and SVM. Furthermore, sensor data aggregated every 6- or 12-h time windows had the best model performance at multiple time-lags. We concluded that the data from the first 3 days post-partum should be discarded when studying metritis, and either one of the five behaviors measured with CowManager could be used when predicting metritis when sensor data were aggregated every 6- or 12-hour time windows, and using time-lags corresponding to 2-3 days before a given event, depending on the time window used. This study shows how to maximize sensor data in their potential for disease prediction, enhancing the performance of algorithms used in machine learning.

An Updated Review of Ornithodoros Ticks as Reservoirs of African Swine Fever in Sub-Saharan Africa and Madagascar.

This updated review provides an overview of the available information on Ornithodoros ticks as reservoirs and biological vectors of the ASF virus in Africa and Indian Ocean islands in order to update the current knowledge in this field, inclusive of an overview of available methods to investigate the presence of ticks in the natural environment and in domestic pig premises. In addition, it highlights the major areas of research that require attention in order to guide future investigations and fill knowledge gaps. The available information suggests that current knowledge is clearly insufficient to develop risk-based control and prevention strategies, which should be based on a sound understanding of genotype distribution and the potential for spillover from the source population. Studies on tick biology in the natural and domestic cycle, including genetics and systematics, represent another important knowledge gap. Considering the rapidly changing dynamics affecting the African continent (demographic growth, agricultural expansion, habitat transformation), anthropogenic factors influencing tick population distribution and ASF virus (ASFV) evolution in Africa are anticipated and have been recorded in southern Africa. This dynamic context, together with the current global trends of ASFV dissemination, highlights the need to prioritize further investigation on the acarological aspects linked with ASF ecology and evolution.

A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms.

Antimicrobial resistance (AMR) is one of the major challenges of the century and should be addressed with a One Health approach. This study aimed to develop a tool that can provide a better understanding of AMR patterns and improve management practices in swine production systems to reduce its spread between farms. We generated similarity networks based on the phenotypic AMR pattern for each farm with information on important bacterial pathogens for swine farming based on the Euclidean distance. We included seven pathogens: Actinobacillus suis, Bordetella bronchiseptica, Escherichia coli, Glaesserella parasuis, Pasteurella multocida, Salmonella spp., and Streptococcus suis; and up to seventeen antibiotics from ten classes. A threshold criterion was developed to reduce the density of the networks and generate communities based on their AMR profiles. A total of 479 farms were included in the study although not all bacteria information was available on each farm. We observed significant differences in the morphology, number of nodes and characteristics of pathogen networks, as well as in the number of communities and susceptibility profiles of the pathogens to different antimicrobial drugs. The methodology presented here could be a useful tool to improve health management, biosecurity measures and prioritize interventions to reduce AMR spread in swine farming.

Assessment of the knowledge and behavior of backyard and small-scale producers in California regarding disease prevention, biosecurity practices and antibiotics use.

The number and popularity of backyard poultry and livestock farming have rapidly increased in California as well as other states in the United States following consumers' preference for local and organic products in the last few years. This study aimed to investigate current on-farm management and farmers' understanding of Veterinary Feed Directive (VFD) and California Senate Bill (SB) 27 implications for disease prevention, biosecurity procedures, and antimicrobial use in small-scale and backyard farms in California. The survey consisted of 38 questions. The responses of 242 backyard and small-scale livestock owners were investigated in this study. Descriptive statistics summarized survey responses, and multivariable logistic regression evaluated the association of antibiotics purchase and use, and the impact of VFD and SB27 on antibiotic use with demographics and on-farm management. Backyard and small-scale farmers in California mostly raised chickens or small ruminants with small herd sizes kept for personal use. Antibiotics were generally used for individual treatment of a sick animal with the guidance of a veterinarian. VFD and SB27 implementation promoted the judicious use of antibiotics, specifically, by enhancing the relationship between backyard and small-scale farmers with veterinarians and treating fewer animals with antibiotics under veterinary oversight. Therefore, better access to veterinary service in backyard and small-scale farms will improve the farmer's knowledge of good husbandry practices with judicious antimicrobial use in livestock and finally contribute to reducing the risk of antimicrobial resistance in California.

Prevalence and geographic distribution of Babesia conradae and detection of Babesia vogeli in free-ranging California coyotes (Canis latrans).

Babesia species are intraerythrocytic piroplasms that can result in disease characterized by hemolytic anemia and thrombocytopenia. Of the 5 species that are known to infect canids in the United States, Babesia conradae is most frequently diagnosed in California, and Babesia vogeli is prevalent in the US. Despite the recent re-emergence of B. conradae, the mechanism of transmission is not known. Coyotes (Canis latrans) have been a proposed reservoir of disease, and previous work has shown that dogs with known aggressive interactions with coyotes are at greater risk for infection. This study aimed to determine the prevalence of B. conradae in wild coyote populations in California to assess the viability of coyotes as a potential source of infection for domestic dogs. Four hundred and sixty-one splenic samples were obtained during post-mortem examination of coyote carcasses from Southern California, Fresno, and Hopland. Demographic data including age, sex, cause of death, and urbanity were collected for each coyote. DNA was extracted from samples and amplified using real-time PCR with primers specific for the B. conradae ITS-2 gene. The 18S gene was amplified and sequenced using conventional PCR primers specific to the Babesia genus from any coyotes positive for B. conradae. In total, 22 coyotes tested positive for B. conradae in Fresno (n = 15), Orange (n = 4), San Bernardino (n = 1), and Los Angeles counties (n = 1) with an overall prevalence of 4.8%. Coyotes from Fresno (P<.01) and rural coyotes (P<.01) were significantly more likely to be infected with B. conradae. Ten of 14 samples sequenced were 99-100% homologous to B. conradae, and 4 samples were 100% homologous with B. vogeli DNA indicating co-infection with both pathogens. This study demonstrates that coyotes can become infected and harbor B. conradae and B. vogeli and should be investigated as a possible source of infection in domestic dogs.

Application of Bayesian Regression for the Identification of a Catchment Area for Cancer Cases in Dogs and Cats.

Research on cancer in dogs and cats, among other diseases, finds an important source of information in registry data collected from hospitals. These sources have proved to be decisive in establishing incidences and identifying temporal patterns and risk factors. However, the attendance of patients is not random, so the correct delimitation of the hospital catchment area (CA) as well as the identification of the factors influencing its shape is relevant to prevent possible biases in posterior inferences. Despite this, there is a lack of data-driven approaches in veterinary epidemiology to establish CA. Therefore, our aim here was to apply a Bayesian method to estimate the CA of a hospital. We obtained cancer (n = 27,390) and visit (n = 232,014) registries of dogs and cats attending the Veterinary Medical Teaching Hospital of the University of California, Davis from 2000 to 2019 with 2,707 census tracts (CTs) of 40 neighboring counties. We ran hierarchical Bayesian models with different likelihood distributions to define CA for cancer cases and visits based on the exceedance probabilities for CT random effects, adjusting for species and period (2000-2004, 2005-2009, 2010-2014, and 2015-2019). The identified CAs of cancer cases and visits represented 75.4 and 83.1% of the records, respectively, including only 34.6 and 39.3% of the CT in the study area. The models detected variation by species (higher number of records in dogs) and period. We also found that distance to hospital and average household income were important predictors of the inclusion of a CT in the CA. Our results show that the application of this methodology is useful for obtaining data-driven CA and evaluating the factors that influence and predict data collection. Therefore, this could be useful to improve the accuracy of analysis and inferences based on registry data.

Network analysis of live pig movements in North Macedonia: Pathways for disease spread.

Globalization of trade, and the interconnectivity of animal production systems, continues to challenge efforts to control disease. A better understanding of trade networks supports development of more effective strategies for mitigation for transboundary diseases like African swine fever (ASF), classical swine fever (CSF), and foot-and-mouth disease (FMD). North Macedonia, bordered to the north and east by countries with ongoing ASF outbreaks, recently reported its first incursion of ASF. This study aimed to describe the distribution of pigs and pig farms in North Macedonia, and to characterize the live pig movement network. Network analyses on movement data from 2017 to 2019 were performed for each year separately, and consistently described weakly connected components with a few primary hubs that most nodes shipped to. In 2019, the network demonstrated a marked decrease in betweenness and increase in communities. Most shipments occurred within 50 km, with movements <6 km being the most common (22.5%). Nodes with the highest indegree and outdegree were consistent across years, despite a large turnover among smallholder farms. Movements to slaughterhouses predominated (85.6%), with movements between farms (5.4%) and movements to market (5.8%) playing a lesser role. This description of North Macedonia's live pig movement network should enable implementation of more efficient and cost-effective mitigation efforts strategies in country, and inform targeted educational outreach, and provide data for future disease modeling, in the region.

In vitro susceptibility testing for the emerging pathogenic mould Veronaea botryosa and pharmacokinetic parameters of natamycin in white sturgeon (Acipenser transmontanus).

Systemic phaeohyphomycosis caused by the dematiaceous mould Veronaea botryosa is an important emergent disease affecting captive sturgeons (Acipenser spp.). The disease, colloquially known as "fluid belly," causes morbidity and mortality in adult animals resulting in significant economic losses to the aquaculture industry. Advancements in therapeutic and prophylactic protocols have been partially hampered by the lack of basic protocols to grow and manipulate the fungus in the laboratory. In this study, microbroth kinetic protocols were established to analyse V. botryosa growth in seven nutrient media at different temperatures. Generated area under the curve (AUC) indicates that potato flake dextrose broth (PFD-B) and Sabouraud dextrose broth (SD-B) incubated at 25°C provided the greatest growth. The generated protocol was then used to test the susceptibility of V. botryosa isolates to natamycin, a macrolide polyene antifungal agent used as a food preservative. SD-B and RPMI with l-glutamine (+RPMI-B) containing different concentrations of natamycin were inoculated with V. botryosa conidia and the generated growth curves were compared using cubic smoothing spline model. The non-inhibitory concentration and minimal inhibitory concentration (MIC; decrease of AUC by 90% compared with control) were determined to be <1 µg/mL and 16 µg/mL of natamycin in SD-B media. To gain an understanding of the tissue distribution of natamycin in white sturgeon, pharmacokinetics was tested. Based on pharmacokinetic parameters determined in this study and targeting a blood concentration >16 µg/mL for 24 h, an intravenous dose >1 g/kg would be needed, making the use of this drug unrealistic. The information presented in this study can be used to investigate susceptibility of pathogenic fungus to antimicrobials and disinfectants as well as support future therapeutic protocols against emerging fungal diseases like fluid belly.