Risk score models for urinary tract infection hospitalization.

Annually, urinary tract infections (UTIs) affect over a hundred million people worldwide. Early detection of high-risk individuals can help prevent hospitalization for UTIs, which imposes significant economic and social burden on patients and caregivers. We present two methods to generate risk score models for UTI hospitalization. We utilize a sample of patients from the insurance claims data provided by the Centers for Medicare and Medicaid Services to develop and validate the proposed methods. Our dataset encompasses a wide range of features, such as demographics, medical history, and healthcare utilization of the patients along with provider quality metrics and community-based metrics. The proposed methods scale and round the coefficients of an underlying logistic regression model to create scoring tables. We present computational experiments to evaluate the prediction performance of both models. We also discuss different features of these models with respect to their impact on interpretability. Our findings emphasize the effectiveness of risk score models as practical tools for identifying high-risk patients and provide a quantitative assessment of the significance of various risk factors in UTI hospitalizations such as admission to ICU in the last 3 months, cognitive disorders and low inpatient, outpatient and carrier costs in the last 6 months.

Quantifying association and disparities between diabetes complications and COVID-19 outcomes: A retrospective study using electronic health records.

BACKGROUND: Despite established relationships between diabetic status and an increased risk for COVID-19 severe outcomes, there is a limited number of studies examining the relationships between diabetes complications and COVID-19-related risks. We use the Adapted Diabetes Complications Severity Index to define seven diabetes complications. We aim to understand the risk for COVID-19 infection, hospitalization, mortality, and longer length of stay of diabetes patients with complications. METHODS: We perform a retrospective case-control study using Electronic Health Records (EHRs) to measure differences in the risks for COVID-19 severe outcomes amongst those with diabetes complications. Using multiple logistic regression, we calculate adjusted odds ratios (OR) for COVID-19 infection, hospitalization, and in-hospital mortality of the case group (patients with diabetes complications) compared to a control group (patients without diabetes). We also calculate adjusted mean difference in length of stay between the case and control groups using multiple linear regression. RESULTS: Adjusting demographics and comorbidities, diabetes patients with renal complications have the highest odds for COVID-19 infection (OR = 1.85, 95% CI = [1.71, 1.99]) while those with metabolic complications have the highest odds for COVID-19 hospitalization (OR = 5.58, 95% CI = [3.54, 8.77]) and in-hospital mortality (OR = 2.41, 95% CI = [1.35, 4.31]). The adjusted mean difference (MD) of hospital length-of-stay for diabetes patients, especially those with cardiovascular (MD = 0.94, 95% CI = [0.17, 1.71]) or peripheral vascular (MD = 1.72, 95% CI = [0.84, 2.60]) complications, is significantly higher than non-diabetes patients. African American patients have higher odds for COVID-19 infection (OR = 1.79, 95% CI = [1.66, 1.92]) and hospitalization (OR = 1.62, 95% CI = [1.39, 1.90]) than White patients in the general diabetes population. However, White diabetes patients have higher odds for COVID-19 in-hospital mortality. Hispanic patients have higher odds for COVID-19 infection (OR = 2.86, 95% CI = [2.42, 3.38]) and shorter mean length of hospital stay than non-Hispanic patients in the general diabetes population. Although there is no significant difference in the odds for COVID-19 hospitalization and in-hospital mortality between Hispanic and non-Hispanic patients in the general diabetes population, Hispanic patients have higher odds for COVID-19 hospitalization (OR = 1.83, 95% CI = [1.16, 2.89]) and in-hospital mortality (OR = 3.69, 95% CI = [1.18, 11.50]) in the diabetes population with no complications. CONCLUSIONS: The presence of diabetes complications increases the risks of COVID-19 infection, hospitalization, and worse health outcomes with respect to in-hospital mortality and longer hospital length of stay. We show the presence of health disparities in COVID-19 outcomes across demographic groups in our diabetes population. One such disparity is that African American and Hispanic diabetes patients have higher odds of COVID-19 infection than White and Non-Hispanic diabetes patients, respectively. Furthermore, Hispanic patients might have less access to the hospital care compared to non-Hispanic patients when longer hospitalizations are needed due to their diabetes complications. Finally, diabetes complications, which are generally associated with worse COVID-19 outcomes, might be predominantly determining the COVID-19 severity in those infected patients resulting in less demographic differences in COVID-19 hospitalization and in-hospital mortality.

Disease Occurrence in- and the Transferal of Zoonotic Agents by North American Feedlot Cattle.

North America is a large producer of beef and contains approximately 12% of the world's cattle inventory. Feedlots are an integral part of modern cattle production in North America, producing a high-quality, wholesome protein food for humans. Cattle, during their final stage, are fed readily digestible high-energy density rations in feedlots. Cattle in feedlots are susceptible to certain zoonotic diseases that impact cattle health, growth performance, and carcass characteristics, as well as human health. Diseases are often transferred amongst pen-mates, but they can also originate from the environment and be spread by vectors or fomites. Pathogen carriage in the gastrointestinal tract of cattle often leads to direct or indirect contamination of foods and the feedlot environment. This leads to the recirculation of these pathogens that have fecal-oral transmission within a feedlot cattle population for an extended time. Salmonella, Shiga toxin-producing Escherichia coli, and Campylobacter are commonly associated with animal-derived foods and can be transferred to humans through several routes such as contact with infected cattle and the consumption of contaminated meat. Brucellosis, anthrax, and leptospirosis, significant but neglected zoonotic diseases with debilitating impacts on human and animal health, are also discussed.

Incidence of adductor tubercle tendinitis and its effect on clinical results in patients with genu varum undergoing high tibial osteotomy.

OBJECTIVE: This study aims to investigate the incidence of adductor tubercle tendinitis (ATT) and demonstrate the effects of intraoperative steroid injections on clinical outcomes in patients undergoing high tibial osteotomy (HTO) due to genu varum (GV). PATIENTS AND METHODS: Of 738 patients who underwent HTO due to GV between 2014-2021, 454 patients had accompanying preoperative ATT. Of these patients, 324 patients who presented to follow-ups and had adequate medical records were evaluated. Mean patient age was 52.6 years. Patients who received steroid injections to the adductor tubercle during HTO (Group I, n=182) and patients who did not receive injections (Group II, n=142) were compared. Univariate and multivariate logistic regression analyses were performed by evaluating preoperative and postoperative VAS scores and presence of ATT. RESULTS: Preoperative ATT sensitivity was present in 61% of the patients. ATT findings were significantly less in Group I compared to Group II in the early-term (1-3 months) (p=0.0001), while there was no significant difference in the late-term (6-12 months) (p=0.880). There was no statistically significant difference between the preoperative and postoperative VAS scores of the groups (p=0.0001). CONCLUSIONS: Stress and tendinitis often develop in the adductor muscle groups due to increased adductor moment in GV. We believe that tendinitis contributes to increased knee pain. HTO reduces the symptoms of tendinitis in the long term by decreasing the adductor moment, while intra-operative steroid injections contribute to relieving complaints related to tendinitis in the early term.

Bilateral orbital myositis in a patient with rheumatoid arthritis on treatment with tofacitinib: a report and review of literature.

Orbital myositis is a rare inflammatory condition affecting the extraocular muscles of the eyes. It has also been linked to systemic autoimmune diseases. We present a case of orbital myositis in a 57-year-old male undergoing treatment for rheumatoid arthritis (RA) with tofacitinib, a Janus kinase inhibitor (JAK). Prompt administration of intravenous steroids led to rapid symptom improvement. To date, only six published cases have documented the association between RA and orbital myositis. This is the first description of orbital myositis occurring during treatment with the anti-inflammatory drug tofacitinib, an increasingly used disease-modifying anti-rheumatic drug (DMARD). We review the literature and emphasize the importance of ongoing vigilance regarding adverse events linked to tofacitinib.

A case of bowel perforation secondary to burn conversion.

SUMMARY: Burn conversion is a process by which superficial partial-thickness burns spontaneously progress into deep partialthickness or full-thickness wounds. Factors that influence this process centre around poor perfusion which can be related to either too much or too little fluid resuscitation, infection, free radical damage, and metabolic or nutritional derangements. Therein lies the role of preventative strategies, i.e., adequate fluid resuscitation, prompt identification and management of sepsis, correction of electrolyte derangements and early institution of feeds. Prevention of burn conversion could prevent the need for surgical intervention and improve the morbidity and mortality of burns patients.

Delayed management of paediatric burn sepsis resulting in limb loss.

SUMMARY: Primary health care centres, community health centres and district hospitals often have medical staff that have minimal exposure to paediatric patients. This may contribute to the challenge of recognising a critically ill paediatric patient. It is already a difficult task as many clinicians are not comfortable or well equipped to manage burn patients, even in regional or tertiary facilities. Identification of the systemic inflammatory response syndrome (SIRS) versus sepsis is difficult in burns owing to the clinical presentation. Identifying the clinical signs determines the need for immediate treatment (i.e., fluid resuscitation) no matter the cause. Investigations will follow to determine the cause, further management and response to treatment. These two cases illustrate the deficit in skill and knowledge in the identification of the sick burninjured child. Although telemedicine has made large advances in allowing access to expert advice in remote locations, its usefulness is dependent on the clinical signs being identified and adequately portrayed to the expert. The way forward is better undergraduate and postgraduate training in this area with an emphasis on clinical acumen.

Septic shock prediction and knowledge discovery through temporal pattern mining.

Sepsis is the body's adverse response to infection which can lead to septic shock and eventually death if not treated in a timely manner. Analyzing patterns in sepsis patients' health status over time can help predict septic shock before its onset allowing healthcare providers to be more proactive. Temporal pattern mining methods can be used to identify trends in a patient's health status over time. If these methods return too many patterns, however, this can hinder knowledge discovery and practical implementation at the bedside in acute care settings. We propose a framework to find a small number of relevant temporal patterns in electronic health records for the early prediction of septic shock. Our framework consists of a temporal pattern mining method and three pattern selection techniques based on non-contrasted group support (PST1), contrasted group support (PST2), and model predictive power (PST3, PST4). We find that model-based feature selection approaches PST3 and PST4 yield the best prediction performance among these techniques. However, PST2 identifies more multi-state patterns with abnormal health states, which can give healthcare providers indicators of patient deterioration towards septic shock. Hence, from a knowledge discovery perspective, it may be worthwhile to sacrifice a small amount of prediction power for actionable patient health information through the implementation of PST2.

Nomogram predictive model for differentiation between renal oncocytoma and chromophobe renal cell carcinoma at multi-phasic CT: a retrospective study.

AIM: To develop a predictive model to discriminate renal oncocytoma (RO) from chromophobe renal carcinoma (chRCC) using multiphase computed tomography (CT). MATERIALS AND METHODS: Two hundred and five cases of renal tumours were analysed retrospectively regarding attenuation values during four CT phases, in addition to age, size, and sex. Then, logistic analysis was applied and a nomogram model developed to predict the most significant variables that can be used to differentiate between both tumour types. The cases were histopathologically proven as 81 cases of RO and 124 cases of chRCC. RESULTS: There was no association between the sex of the patient and the tumour types (p=0.41); however, there was a significant positive association between RO and the age of the patient (odds ratio 1.05; 95% confidence interval 1.02-1.08; p=0.001)) and a significant negative association between tumour size and RO (odds ratio 0.81; 95% confidence interval 73-90; p<0.001). There was a significant difference between tumour types in the contrast-enhanced phases. Logistic regression showed that absolute arterial enhancement (AAE) and absolute venous enhancement (AVE) are the most significant predictors for discriminating between tumour types. Combining these variables, size, AAE, and AVE were the best classifiers to discriminate between tumour types with an area under the curve of 0.90. A nomogram model was developed using these variables to predict RO probability in different case scenarios. CONCLUSION: The nomogram can predict the probability of RO from chRCC by using the best predictors, size, AAE, and AVE, with high accuracy.

The Antioxidant Effect of Natural Antimicrobials in Shrimp Primary Intestinal Cells Infected with Nematopsis messor.

Nematopsis messor infections severely impact on shrimp's health with devastating economic consequences on shrimp farming. In a shrimp primary intestinal cells (SGP) model of infection, a sub-inhibitory concentration (0.5%) of natural antimicrobials (Aq) was able to reduce the ability of N. messor to infect (p < 0.0001). To prevent N. messor infection of SGP cells, Aq inhibits host actin polymerization and restores tight junction integrity (TEER) and the expression of Zo-1 and occluding. The oxidative burst, caused by N. messor infection, is attenuated by Aq through the inhibition of NADPH-produced H2O2. Simultaneous to the reduction in H2O2 released, the activity of catalase (CAT) and superoxide dismutase (SOD) were also significantly increase (p < 0.0001). The antimicrobial mixture inactivates the ERK signal transduction pathway by tyrosine dephosphorylation and reduces the expression of DCR2, ALF-A, and ALF-C antimicrobial peptides. The observed in vitro results were also translated in vivo, whereby the use of a shrimp challenge test, we show that in N. messor infected shrimp the mortality rate was 68% compared to the Aq-treated group where the mortality rate was maintained at 14%. The significant increase in CAT and SOD activity in treated and infected shrimp suggested an in vivo antioxidant role for Aq. In conclusion, our study shows that Aq can efficiently reduce N. messor colonization of shrimp's intestinal cells in vitro and in vivo and the oxidative induced cellular damage, repairs epithelial integrity, and enhances gut immunity.

Amputations secondary to burn injuries in a resource-limited setting.

INTRODUCTION: Amputations are a devastating consequence of severe burns. Amputations in a resource-limited setting are challenging as rehabilitation services available to these patients are inconsistent and often fragmented. Epileptic patients are a particularly vulnerable group when it comes to burn-injuries and often sustain deeper burns. The aim of this study is to analyse amputations secondary to burn injuries. We seek to identify vulnerable groups as a means for advocacy efforts to reduce the devastation of an amputation secondary to a burn injury. This paper highlights the burden of these injuries on the healthcare system and emphasizes the need for additional trained therapists for the rehabilitation of these patients. METHODS: A retrospective database review was conducted. All burns admissions who underwent an amputation between 1 February 2016 and 31 January 2019 were considered. RESULTS: A total of 1575 patients were admitted during the study period. Fifty-four percent of the admissions were paediatric patients. The amputation rate in the paediatric population was 1.5% (13/850) while in the adult population it was 4.8% (35/724) . Most paediatric amputations were as a result of electrical injuries. Flame burns were most likely to result in amputations in the adult group and convulsions were the leading circumstance leading to the injury. There was no significant difference in sepsis or length of stay between the groups. There were no mortalities in the paediatric group but there was an 11% mortality rate in the adult group. CONCLUSION: The incidence of amputations in burns is low, however, it remains a devastating morbidity. Epileptics are a vulnerable group and these patients account for the most amputations among adult burns patients. Education interventions are needed regarding their diagnosis, administration of their medication and the importance of compliance. Advocacy efforts to ensure constant supply of anti-epileptic drugs at the clinics and other district level health facilities is also essential. Electrical injuries in children are not as common as hot water scalds, however, they are more likely to result in amputation. Communities need to be informed of the risk associated with illegal electrical connections and initiatives need to drive the safe provision of affordable electricity to these under-privileged, vulnerable communities.

Innovative Treatments Enhancing the Functionality of Gut Microbiota to Improve Quality and Microbiological Safety of Foods of Animal Origin.

The gastrointestinal tract, or gut, microbiota is a microbial community containing a variety of microorganisms colonizing throughout the gut that plays a crucial role in animal health, growth performance, and welfare. The gut microbiota is closely associated with the quality and microbiological safety of foods and food products originating from animals. The gut microbiota of the host can be modulated and enhanced in ways that improve the quality and safety of foods of animal origin. Probiotics-also known as direct-fed microbials-competitive exclusion cultures, prebiotics, and synbiotics have been utilized to achieve this goal. Reducing foodborne pathogen colonization in the gut prior to slaughter and enhancing the chemical, nutritional, or sensory characteristics of foods (e.g., meat, milk, and eggs) are two of many positive outcomes derived from the use of these competitive enhancement-based treatments in food-producing animals.

Malaria infection and associated risk factors in pregnant women attending antenatal care clinics in Al Jabalian Locality, White Nile state, Sudan.

Pregnant women are more susceptible to malaria which is associated with adverse effects on pregnancy. It is one of the leading causes of maternal mortality in Sudan. The main aim of this study was to determine the prevalence rate of malaria in pregnant women. This cross sectional descriptive study was carried out in Al Jabalian and Kenana hospitals, White Nile State, Sudan. The data of the present study has been collected from 400 Sudanese pregnant women, during a period extending from 16th July 2018 to 25th October 2018. The overall the prevalence of malaria was 38.5% (154), Plasmodium falciparum was only malaria parasite observed in all samples. From 154 pregnant women infected with malaria, the third trimester had higher prevalence 53.9% (83), followed by the second trimester 31.8% (49) and the first trimester was 14.3% (22), P<0.0001. The multigravida had high infection with prevalence of 54.5% (84), secondgravida was 24.7% (38) and primigravida was 20.8% (32), P<0.0001. Significant association was noticed between the malaria parasite infection and occupation, ANC attendance and utility of mosquito net, P-value 0.05, 0.0024, 0.0010, respectively. However, no significant association was observed with education level and malaria infection. The study was recommended to promote diagnosis during pregnancy, take anti-malarial medicine as routine care to pregnant women and improve environmental sanitation.

Mapping of critical events in disease progression through binary classification: Application to amyotrophic lateral sclerosis.

BACKGROUND: The progression of many degenerative diseases is tracked periodically using scales evaluating functionality in daily activities. Although estimating the timing of critical events (i.e., disease tollgates) during degenerative disease progression is desirable, the necessary data may not be readily available in scale records. Further, analysis of disease progression poses data challenges, such as censoring and misclassification errors, which need to be addressed to provide meaningful research findings and inform patients. METHODS: We developed a novel binary classification approach to map scale scores into disease tollgates to describe disease progression leveraging standard/modified Kaplan-Meier analyses. The approach is demonstrated by estimating progression pathways in amyotrophic lateral sclerosis (ALS). Tollgate-based ALS Staging System (TASS) specifies the critical events (i.e., tollgates) in ALS progression. We first developed a binary classification predicting whether each TASS tollgate was passed given the itemized ALSFRS-R scores using 514 ALS patients' data from Mayo Clinic-Rochester. Then, we utilized the binary classification to translate/map the ALSFRS-R data of 3,264 patients from the PRO-ACT database into TASS. We derived the time trajectories of ALS progression through tollgates from the augmented PRO-ACT data using Kaplan-Meier analyses. The effects of misclassification errors, condition-dependent dropouts, and censored data in trajectory estimations were evaluated with Interval Censored Kaplan Meier Analysis and Multistate Model for Panel Data. RESULTS: The approach using Mayo Clinic data accurately estimated tollgate-passed states of patients given their itemized ALSFRS-R scores (AUCs > 0.90). The tollgate time trajectories derived from the augmented PRO-ACT dataset provide valuable insights; we predicted that the majority of the ALS patients would have modified arm function (67%) and require assistive devices for walking (53%) by the second year after ALS onset. By the third year, most (74%) ALS patients would occasionally use a wheelchair, while 48% of the ALS patients would be wheelchair-dependent by the fourth year. Assistive speech devices and feeding tubes were needed in 49% and 30% of the patients by the third year after ALS onset, respectively. The onset body region alters some tollgate passage time estimations by 1-2 years. CONCLUSIONS: The estimated tollgate-based time trajectories inform patients and clinicians about prospective assistive device needs and life changes. More research is needed to personalize these estimations according to prognostic factors. Further, the approach can be leveraged in the progression of other diseases.

Prediction of Sepsis Related Mortality: An Optimization Approach.

Sepsis is a condition that progresses quickly and is a major cause of mortality in hospitalized patients. Data-driven diagnostic and therapeutic interventions are essential to ensure early diagnosis and appropriate care. The Sequential Organ Failure Assessment (SOFA) score is widely utilized in clinical practice to assess septic patients for organ dysfunction. The SOFA score uses points between 0 and 4 to quantify the level of dysfunction in six organ systems. These points are determined based on expert opinion and not informed by data, thus their usefulness can vary among different medical institutions depending on the targeted use. In this study, we propose multiple strategies to adjust the SOFA score using mixed-integer programming to improve the in-hospital mortality prediction of septic patients based on Electronic Health Records (EHRs). We use the same variables and threshold values of the original SOFA score in each strategy. Thus, the proposed approach takes advantage of optimization and data analysis while taking into account the medical expertise. Our results demonstrate a statistically significant improvement ( ) in the prediction of in-hospital mortality among patients susceptible to sepsis when implementing our proposed strategies. Area under the receiver operator curve (AUC) and accuracy values of 0.8928 and 0.8904 are achieved by optimizing the point values of the SOFA score.

The value of missing information in severity of illness score development.

OBJECTIVE: We aim to investigate the hypothesis that using information about which variables are missing along with appropriate imputation improves the performance of severity of illness scoring systems used to predict critical patient outcomes. STUDY DESIGN AND SETTING: We quantify the impact of missing and imputed variables on the performance of prediction models used in the development of a sepsis-related severity of illness scoring system. Electronic health records (EHR) data were compiled from Christiana Care Health System (CCHS) on 119,968 adult patients hospitalized between July 2013 and December 2015. Two outcomes of interest were considered for prediction: (1) first transfer to intensive care unit (ICU) and (2) in-hospital mortality. Five different prediction models were employed. Indicators were utilized in these prediction models to identify when variables were missing and imputed. RESULTS: We observed statistically significant gains in prediction performance when moving from models that did not indicate missing information to those that did. Moreover, this increase was higher in models that use summary variables as predictors compared to those that use all variables. CONCLUSION: When developing prediction models using longitudinal EHR data, researchers should explore the incorporation of indicators for missing variables along with appropriate imputation.

Tollgate-based progression pathways of ALS patients.

OBJECTIVE: To capture ALS progression in arm, leg, speech, swallowing, and breathing segments using a disease-specific staging system, namely tollgate-based ALS staging system (TASS), where tollgates refer to a set of critical clinical events including having slight weakness in arms, needing a wheelchair, needing a feeding tube, etc. METHODS: We compiled a longitudinal dataset from medical records including free-text clinical notes of 514 ALS patients from Mayo Clinic, Rochester-MN. We derived tollgate-based progression pathways of patients up to a 1-year period starting from the first clinic visit. We conducted Kaplan-Meier analyses to estimate the probability of passing each tollgate over time for each functional segment. RESULTS: At their first clinic visit, 93%, 77%, and 60% of patients displayed some level of limb, bulbar, and breathing weakness, respectively. The proportion of patients at milder tollgate levels (tollgate level < 2) was smaller for arm and leg segments (38% and 46%, respectively) compared to others (> 65%). Patients showed non-uniform TASS pathways, i.e., the likelihood of passing a tollgate differed based on the affected segments at the initial visit. For instance, stratified by impaired segments at the initial visit, patients with limb and breathing impairment were more likely (62%) to use bi-level positive airway pressure device in a year compared to those with bulbar and breathing impairment (26%). CONCLUSION: Using TASS, clinicians can inform ALS patients about their individualized likelihood of having critical disabilities and assistive-device needs (e.g., being dependent on wheelchair/ventilation, needing walker/wheelchair or communication devices), and help them better prepare for future.

Models for predicting the evolution of influenza to inform vaccine strain selection.

Influenza vaccine composition is reviewed before every flu season because influenza viruses constantly evolve through antigenic changes. To inform vaccine updates, laboratories that contribute to the World Health Organization Global Influenza Surveillance and Response System monitor the antigenic phenotypes of circulating viruses all year round. Vaccine strains are selected in anticipation of the upcoming influenza season to allow adequate time for production. A mismatch between vaccine strains and predominant strains in the flu season can significantly reduce vaccine effectiveness. Models for predicting the evolution of influenza based on the relationship of genetic mutations and antigenic characteristics of circulating viruses may inform vaccine strain selection decisions. We review the literature on state-of-the-art tools and prediction methodologies utilized in modeling the evolution of influenza to inform vaccine strain selection. We then discuss areas that are open for improvement and need further research.

Seroprevalence of Rhodococcus equi in horses in Israel.

Rhodococcus equi is a common cause of pneumonia in foals and has extensive clinical, economic and possibly zoonotic consequences. This bacterium survives well in the environment and may be considered as normal flora of adult horses. Certain strains of this bacterium are extremely virulent in foals, and early identification and intervention is crucial for prognosis. Rhodococcus equi is endemic in many parts of the world and occasionally isolated in Israel. This study was designed to evaluate R. equi seroprevalence in adult horses in Israel to indirectly indicate the potential level of exposure of susceptible foals. Sera were collected from 144 horses during spring 2011 and from 293 horses during fall 2014, and the presence of antibodies against virulent R. equi was detected by enzyme-linked immunosorbent assay. Equine seroprevalence of R. equi was found to be 7.6% in 2011 and 5.1% in 2014. Only one farm had seropositive horses in 2011, whereas several farms had seropositive horses in 2014. No significant risk factors for seropositivity were found. Rhodococcus equi appears to be endemic in Israel. This is the first survey of R. equi in Israel that provides information on the epidemiology of this important bacterium.

Deriving effective vaccine allocation strategies for pandemic influenza: Comparison of an agent-based simulation and a compartmental model.

Individuals are prioritized based on their risk profiles when allocating limited vaccine stocks during an influenza pandemic. Computationally expensive but realistic agent-based simulations and fast but stylized compartmental models are typically used to derive effective vaccine allocation strategies. A detailed comparison of these two approaches, however, is often omitted. We derive age-specific vaccine allocation strategies to mitigate a pandemic influenza outbreak in Seattle by applying derivative-free optimization to an agent-based simulation and also to a compartmental model. We compare the strategies derived by these two approaches under various infection aggressiveness and vaccine coverage scenarios. We observe that both approaches primarily vaccinate school children, however they may allocate the remaining vaccines in different ways. The vaccine allocation strategies derived by using the agent-based simulation are associated with up to 70% decrease in total cost and 34% reduction in the number of infections compared to the strategies derived by using the compartmental model. Nevertheless, the latter approach may still be competitive for very low and/or very high infection aggressiveness. Our results provide insights about potential differences between the vaccine allocation strategies derived by using agent-based simulations and those derived by using compartmental models.