Evaluating bias in electronic health record data: using agent-based models to examine whether geographic disparities in community-acquired Methicillin-resistant Staphylococcus aureus are due to differential healthcare-seeking behaviors.

Electronic health records (EHR) are increasingly used in public health research. However, biases may exist when using EHR due to whether someone is captured in the data. Assessing the impact of bias in generating disparities identified with EHR data is difficult because information about healthcare-seeking behaviors is not included in the record.

PHARMACOKINETICS OF CEFTAZIDIME IN SIGNAL CRAYFISH () FOLLOWING A SINGLE DOSE.

Crustaceans are housed in zoos and aquariums and have also gained importance in the private sector and food industry. Shell lesions are common and often attributed to bacterial infections. However, few controlled studies have been performed evaluating antibiotics in crustaceans.

Evaluation of the efficacy of a live Escherichia coli biotherapeutic product (asymptomatic bacteriuria E. coli 212).

Background: Recurrent bacterial cystitis, often referred to as recurrent urinary tract infection (UTI), can be difficult to manage and alternative treatments are needed.

Hypothesis/objective: Intravesicular administration of asymptomatic bacteriuria (ASB) E. coli 212 will not be inferior to antimicrobial treatment for the management of recurrent UTI in dogs.

Movement-driven modelling reveals new patterns in disease transmission networks.

Interspecific interactions are highly relevant in the potential transmission of shared pathogens in multi-host systems. In recent decades, several technologies have been developed to study pathogen transmission, such as proximity loggers, GPS tracking devices and/or camera traps. Despite the diversity of methods aimed at detecting contacts, the analysis of transmission risk is often reduced to contact rates and the probability of transmission given the contact.

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.

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.

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.

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.

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.

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.

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.

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.