"What Do I Say? How Do I Say it?" Twitter as a Knowledge Dissemination Tool for Mental Health Research.

This study aims to generate evidence-based guidelines for researchers regarding how to effectively disseminate mental health research via Twitter. Three hundred mental health research Tweets posted from September 2018 to September 2019 were sampled from two large Australian organizations. Twenty-seven predictor variables were coded for each Tweet across five thematic categories: messaging; research area; mental health area; external networks; and media features. Regression analyses were conducted to determine associations with engagement outcomes of Favourites, Retweets, and Comments. Less than half (n = 10) of predictor variables passed validity tests. Notably, conclusions could not reliably be drawn on whether a Tweet featured evidence-based information. Tweets were significantly more likely to be Retweeted if they contained a hyperlink or multimedia. Tweets were significantly more likely to receive comments if they focused on a specific population group. These associations remain significant when controlling for organization. These findings indicate that researchers may be able to maximize engagement on Twitter by highlighting the population groups that the research applies to and enriching Tweets with multimedia content. In addition, care should be taken to ensure users can infer which messages are evidence-based. Guidelines and an accompanying resource are proposed.

Within-herd transmission of Mycoplasma bovis infections after initial detection in dairy cows.

Mycoplasma bovis outbreaks in cattle, including pathogen spread between age groups, are not well understood. Our objective was to estimate within-herd transmission across adult dairy cows, youngstock, and calves. Results from 3 tests (PCR, ELISA, and culture) per cow and 2 tests (PCR and ELISA) per youngstock and calf were used in an age-stratified susceptible-infected-removed/recovered (SIR) model to estimate within-herd transmission parameters, pathways, and potential effects of farm management practices. A cohort of adult cows, youngstock, and calves on 20 Dutch dairy farms with a clinical outbreak of M. bovis in adult cows were sampled, with collection of blood, conjunctival fluid, and milk from cows, and blood and conjunctival fluid from calves and youngstock, 5 times over a time span of 12 wk. Any individual with at least one positive laboratory test was considered M. bovis-positive. Transmission dynamics were modeled using an age-stratified SIR model featuring 3 age strata. Associations with farm management practices were explored using Fisher's exact tests and Poisson regression. Estimated transmission parameters were highly variable among herds and cattle age groups. Notably, transmission from cows to cows, youngstock, or to calves was associated with R-values ranging from 1.0 to 80 secondarily infected cows per herd, 1.2 to 38 secondarily infected youngstock per herd, and 0.1 to 91 secondarily infected calves per herd, respectively. In case of transmission from youngstock to youngstock, calves or to cows, R-values were 0.7 to 96 secondarily infected youngstock per herd, 1.1 to 76 secondarily infected calves per herd, and 0.1 to 107 secondarily infected cows per herd. For transmission from calves to calves, youngstock or to cows, R-values were 0.5 to 60 secondarily infected calves per herd, 1.1 to 41 secondarily infected youngstock per herd, and 0.1 to 47 secondarily infected cows per herd. Among on-farm transmission pathways, cow-to-youngstock, cow-to-calf, and cow-to-cow were identified as most significant contributors, with calf-to-calf and calf-to-youngstock also having noteworthy roles. Youngstock-to-youngstock was also implicated, albeit to a lesser extent. Whereas the primary focus was a clinical outbreak of M. bovis among adult dairy cows, it was evident that transmission extended to calves and youngstock, contributing to overall spread. Factors influencing transmission and specific transmission pathways were associated with internal biosecurity (separate caretakers for various age groups, number of people involved), external biosecurity (contractors, external employees), as well as indirect transmission routes (number of feed and water stations).

The Role of Pre-Contrast Sequences of an MRI-Guided Breast Biopsy in Localizing a Target.

Objective: This retrospective study aims to assess the role of pre-contrast sequences of an MRI-guided breast biopsy (MRIB) exam in confident and accurate lesion site localization based on tissue landmarks. Methods: The charts of all consecutives MRIB that were performed between January 2018 and December 2020 were reviewed. The images of the eligible exams were analyzed by 3 breast radiologists. Each radiologist independently attempted to identify lesion site on pre-contrast MRIB sequences, while blinded to the post-contrast MRIB images. Confidence levels (I-confident, II-not confident, and III-unknown) were assigned by each reviewer. A fourth radiologist assessed the accuracy (≤5 mm-accurate, >5 mm-inaccurate) in lesion site localization using the actual biopsied lesion site and the post-contrast MRIB images as reference. Descriptive statistics were used to calculate the percentage of confidence and accuracy categories for each reviewer, with Chi-square tests applied to analyze relationships between categorical variables. Results: There were 174 female patients with 181 lesions eligible for the trial. When the lesion site is confidently identified on the pre-contrast MRIB images (level 1 confidence), mean grade 1 accuracy was 93.8% (P < .001). Accuracy decreased with Level II and III confidence (55.3% and 34.2% respectively). Up to 61.4% improved accuracy was demonstrated when combining the performance of 2 reviewers. No correlation was found between breast density, lesion morphology, or biopsy positioning with confidence level or accuracy grade. Conclusion: Careful review of the pre-contrast MRIB images and familiarization with the surrounding tissue landmarks are important steps in confidently and accurately detecting lesion site.

An individual level infectious disease model in the presence of uncertainty from multiple, imperfect diagnostic tests.

Bayesian compartmental infectious disease models yield important inference on disease transmission by appropriately accounting for the dynamics and uncertainty of infection processes. In addition to estimating transition probabilities and reproductive numbers, these statistical models allow researchers to assess the probability of disease risk and quantify the effectiveness of interventions. These infectious disease models rely on data collected from all individuals classified as positive based on various diagnostic tests. In infectious disease testing, however, such procedures produce both false-positives and false-negatives at varying rates depending on the sensitivity and specificity of the diagnostic tests being used. We propose a novel Bayesian spatio-temporal infectious disease modeling framework that accounts for the additional uncertainty in the diagnostic testing and classification process that provides estimates of the important transmission dynamics of interest to researchers. The method is applied to data on the 2006 mumps epidemic in Iowa, in which over 6,000 suspected mumps cases were tested using a buccal or oral swab specimen, a urine specimen, and/or a blood specimen. Although all procedures are believed to have high specificities, the sensitivities can be low and vary depending on the timing of the test as well as the vaccination status of the individual being tested.

Clinical and Magnetic Resonance Imaging Radiomics-Based Survival Prediction in Glioblastoma Using Multiparametric Magnetic Resonance Imaging.

INTRODUCTION: Survival prediction in glioblastoma remains challenging, and identification of robust imaging markers could help with this relevant clinical problem. We evaluated multiparametric magnetic resonance imaging-derived radiomics to assess prediction of overall survival (OS) and progression-free survival (PFS). METHODOLOGY: A retrospective, institutional review board-approved study was performed. There were 93 eligible patients, of which 55 underwent gross tumor resection and chemoradiation (GTR-CR). Overall survival and PFS were assessed in the entire cohort and the GTR-CR cohort using multiple machine learning pipelines. A model based on multiple clinical variables was also developed. Survival prediction was assessed using the radiomics-only, clinical-only, and the radiomics and clinical combined models. RESULTS: For all patients combined, the clinical feature-derived model outperformed the best radiomics model for both OS (C-index, 0.706 vs 0.597; P < 0.0001) and PFS prediction (C-index, 0.675 vs 0.588; P < 0.001). Within the GTR-CR cohort, the radiomics model showed nonstatistically improved performance over the clinical model for predicting OS (C-index, 0.638 vs 0.588; P = 0.4). However, the radiomics model outperformed the clinical feature model for predicting PFS in GTR-CR cohort (C-index, 0.641 vs 0.550; P = 0.004). Combined clinical and radiomics model did not yield superior prediction when compared with the best model in each case. CONCLUSIONS: When considering all patients, regardless of therapy, the radiomics-derived prediction of OS and PFS is inferior to that from a model derived from clinical features alone. However, in patients with GTR-CR, radiomics-only model outperforms clinical feature-derived model for predicting PFS.

Incorporating infectious duration-dependent transmission into Bayesian epidemic models.

Compartmental models are commonly used to describe the spread of infectious diseases by estimating the probabilities of transitions between important disease states. A significant challenge in fitting Bayesian compartmental models lies in the need to estimate the duration of the infectious period, based on limited data providing only symptom onset date or another proxy for the start of infectiousness. Commonly, the exponential distribution is used to describe the infectious duration, an overly simplistic approach, which is not biologically plausible. More flexible distributions can be used, but parameter identifiability and computational cost can worsen for moderately sized or large epidemics. In this article, we present a novel approach, which considers a curve of transmissibility over a fixed infectious duration. The incorporation of infectious duration-dependent (IDD) transmissibility, which decays to zero during the infectious period, is biologically reasonable for many viral infections and fixing the length of the infectious period eases computational complexity in model fitting. Through simulation, we evaluate different functional forms of IDD transmissibility curves and show that the proposed approach offers improved estimation of the time-varying reproductive number. We illustrate the benefit of our approach through a new analysis of the 1995 outbreak of Ebola Virus Disease in the Democratic Republic of the Congo.

Prevalence and significance of incidental findings on computed tomography pulmonary angiograms: A retrospective cohort study.

OBJECTIVE: The purpose of this study was to analyze the prevalence and significance of incidental findings on computed tomography pulmonary angiography (CTPA) studies and to assess the diagnostic yield of CTPA in identifying an alternate diagnosis to pulmonary embolism (PE) on PE negative exams. METHODS: All patients who had a CTPA exam for PE evaluation between Jan 2016 and Dec 2018 with a negative PE result were included in the study. A total of 2083 patients were identified. We retrospectively queried the electronic medical record and the radiology report and recorded the following: Age, Sex, BMI, Patient location and Incidental findings. The incidental findings were classified into type 1 (Alternate diagnosis other than PE which could explain the patient's symptoms), type 2 (non-emergent findings which needed further work up) and type 3 findings (non-emergent findings which did not need further work up). Logistic regression analysis was performed to determine what factors affected the probability of finding a type 1 incidental (alternate diagnosis) or a type 2 incidental. RESULTS: 74.5% of the patients in our study had at least one incidental finding. Type 1 incidental findings (alternate diagnosis to PE) were found in 864 patients (41.5%). The most common type 1 finding was pneumonia followed by fluid overload. Male sex, increased age and lower BMI were significantly associated with increased odds of a type 1 incidental(p < 0.05). Similarly, all the patient locations had significantly different odds of finding a type-1 incidental, with ICU having the highest odds, followed by inpatient, ED and outpatient locations (p < 0.05). 563 patients (27%) had at least one type 2 incidental findings and the most common type 2 findings were progressive lung malignancy/ metastatic disease and new pulmonary nodule. Increased age was significantly associated with the probability of a type 2 finding (p < 0.05). CONCLUSIONS: CTPA may suggest an alternative diagnosis to pulmonary embolism in approximately 40% of the patients with a negative study. The probability of finding an alternate diagnosis (type 1 incidental) is higher in elderly patients and in patients referred from ICU and inpatient units.

Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques.

OBJECTIVES: Despite the robust diagnostic performance of MRI-based radiomic features for differentiating between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) reported on prior studies, the best sequence or a combination of sequences and model performance across various machine learning pipelines remain undefined. Herein, we compare the diagnostic performance of multiple radiomics-based models to differentiate GBM from PCNSL. METHODS: Our retrospective study included 94 patients (34 with PCNSL and 60 with GBM). Model performance was assessed using various MRI sequences across 45 possible model and feature selection combinations for nine different sequence permutations. Predictive performance was assessed using fivefold repeated cross-validation with five repeats. The best and worst performing models were compared to assess differences in performance. RESULTS: The predictive performance, both using individual and a combination of sequences, was fairly robust across multiple top performing models (AUC: 0.961-0.977) but did show considerable variation between the best and worst performing models. The top performing individual sequences had comparable performance to multiparametric models. The best prediction model in our study used a combination of ADC, FLAIR, and T1-CE achieving the highest AUC of 0.977, while the second ranked model used T1-CE and ADC, achieving a cross-validated AUC of 0.975. CONCLUSION: Radiomics-based predictive accuracy can vary considerably, based on the model and feature selection methods as well as the combination of sequences used. Also, models derived from limited sequences show performance comparable to those derived from all five sequences. KEY POINTS: • Radiomics-based diagnostic performance of various machine learning models for differentiating glioblastoma and PCNSL varies considerably. • ML models using limited or multiple MRI sequences can provide comparable performance, based on the chosen model. • Embedded feature selection models perform better than models using a priori feature reduction.

CARJ 2021: Year in Review.

The past year has been one of unprecedented challenge for the modern world and especially the medical profession. This review explores some of the most impactful topics published in the CARJ during the COVID-19 pandemic including physician wellbeing and burnout, patient safety, and technological innovations including dual energy CT, quantitative imaging and ultra-high frequency ultrasound. The impact of the COVID-19 pandemic on trainee education is discussed and evidence-based tips for providing value-added care are reviewed. Patient privacy considerations relevant to the development of artificial intelligence applications for medical imaging are explored. These publications in the CARJ demonstrate that although this year has brought adversity, it has also been a harbinger for new and exciting areas of focus in our field.

Cerebral computed tomographic angiography using third-generation reconstruction algorithm provides improved image quality with lower contrast and radiation dose.

PURPOSE: We hypothesized that cerebral CT angiogram performed using third-generation reconstruction algorithm and lower contrast dose-low-kVp technique (LD-CTA) will provide better image quality when compared with regular contrast dose CTA at 120 kVp using a sinogram-affirmed iterative reconstruction algorithm (ND-CTA). METHODS: Retrospective imaging review of 100 consecutive patients (50 each in LD- and ND-CTA groups). Two readers independently assessed the subjective image quality across multiple vascular segments on a Likert-like scale. Differences in contrast dose, CT dose index (CTDI), and dose length product (DLP) were compared using Mann-Whitney U test. Fisher's exact test was used to compare subjective image quality. Similarly, contrast- and signal-to-noise ratios (CNR and SNR) were compared in the mid-M1 MCA vessels bilaterally and the mid-basilar artery using Mann-Whitney U test. Interclass correlation coefficient (ICC) was calculated for the SNR/CNR values. RESULTS: Both observers showed excellent correlation in subjective image quality (mean percentage agreement of 95.2% for group 1 versus 89.2% for group 2). LD-CTA group showed better SNR and CNR (p < 0.0001) for both MCA vessels and the mid-basilar artery. Interclass correlation coefficient showed moderate correlation (0.51-0.63) between readers. LD-CTA group also used lower contrast (49 cc versus 97 cc in ND-CTA) and had lower radiation exposure (DLP/CTDI for both groups 268.3/80.7 vs 519.5/36.08, both < 0.0001). CONCLUSION: Next-generation reconstruction algorithm and low-kV scanning significantly improved image quality on cerebral CTA images despite lower contrast dose and, in addition, have lower radiation exposure.

Correlation of Values for Indocyanine Green Angiography in the Arterographically Normal Lower Extremity.

INTRODUCTION: Indocyanine green angiography (ICGA) is an assessment tool used to detect the degree of perfusion of tissue. This technology is being utilized in a variety of applications, including in the lower extremity. Principally, ICGA has been reported as a tool to determine pre-, intra-, or post-operative perfusion. The aim of this study is to determine objective values for ICGA in patients with arterographically normal lower extremities. MATERIALS AND METHODS: Prospectively, 20 patients were identified with no history of peripheral vascular disease who were scheduled to undergo distal subtraction angiography of the lower extremity. Based on the angiogram, 18 patients were deemed anatomically normal-a patent posterior tibial, anterior tibial, and peroneal artery with blood flow past the ankle into the foot. These patients then underwent ICGA on the dorsal and plantar aspects of the foot. The time to blush (seconds) and ingress and egress rates (units/second) were calculated. RESULTS: The mean time to blush for the dorsal aspect is 26.7s (standard deviation [SD]=22.16) and the median is 14s (range=4-81s) and for the plantar aspect is 9.33s (SD=18.64), and the median is 3.5s (range=1-68s). The region of interest (ROI) ingress rates for the dorsal foot is: mean 3.29 (SD=5.18) and median is 1.60 (range=0.2-21.7). The ROI egress rates for the dorsal foot is: mean 0.55 (SD=0.43) and median is 0.35 (range=0.3-1.40). The ROI ingress rates for the plantar foot is: mean 10.02 (SD=11.91) and median 2.60 (range=0.30-34.20). The ROI egress rates for the plantar foot is: mean 1.57 (SD=1.65) and median is 1.00 (range=0.10-5.70). CONCLUSIONS: This is the first study attempting to establish objective values for ICGA in the vascularly patent lower extremity.

Effects of Age, Acoustic Challenge, and Verbal Working Memory on Recall of Narrative Speech.

BACKGROUND/STUDY CONTEXT: A common goal during speech comprehension is to remember what we have heard. Encoding speech into long-term memory frequently requires processes such as verbal working memory that may also be involved in processing degraded speech. Here the authors tested whether young and older adult listeners' memory for short stories was worse when the stories were acoustically degraded, or whether the additional contextual support provided by a narrative would protect against these effects. METHODS: The authors tested 30 young adults (aged 18-28 years) and 30 older adults (aged 65-79 years) with good self-reported hearing. Participants heard short stories that were presented as normal (unprocessed) speech or acoustically degraded using a noise vocoding algorithm with 24 or 16 channels. The degraded stories were still fully intelligible. Following each story, participants were asked to repeat the story in as much detail as possible. Recall was scored using a modified idea unit scoring approach, which included separately scoring hierarchical levels of narrative detail. RESULTS: Memory for acoustically degraded stories was significantly worse than for normal stories at some levels of narrative detail. Older adults' memory for the stories was significantly worse overall, but there was no interaction between age and acoustic clarity or level of narrative detail. Verbal working memory (assessed by reading span) significantly correlated with recall accuracy for both young and older adults, whereas hearing ability (better ear pure tone average) did not. CONCLUSION: The present findings are consistent with a framework in which the additional cognitive demands caused by a degraded acoustic signal use resources that would otherwise be available for memory encoding for both young and older adults. Verbal working memory is a likely candidate for supporting both of these processes.

Potential Future Applications for Negative Pressure Wound Therapy and Installation Devices.

Negative-pressure wound therapy (NPWT) applies vacuum pressure to a wound bed sealed by an adhesive dressing to improve wound healing. A cleansing solution, often antibiotics or saline, may be instilled into the wound bed concurrently and removed via suction, thus enhancing the therapeutic effect. The therapeutic effect results from improved blood flow and removal of inflammatory factors. Since 1995, the FDA has approved NPWT for medical use. Since then, this technology has been applied to different types of wounds, including diabetic and decubitus ulcers and postsurgical incisional wounds. There are many applications for NPWT that remain to be explored. In this article, we postulate on novel and future uses for NPWT, including application in targeted drug delivery, stem cell therapy, and the prospect of combination with filtration devices, adaptable smart dressings, and remote monitoring.

The Orthopedic Upper Extremity Surgeon's Role in the Diagnosis and Treatment of Osteoporosis: Making Use of Opportunistic Imaging.

Osteoporosis is an underdiagnosed and undertreated public health issue that contributes to a high financial burden on the healthcare system and imposes significant morbidity and mortality on the patient population. Upper extremity orthopedic surgeons are in a unique position to diagnose osteoporosis prior to patients suffering a fragility fracture by using imaging that they already obtain in their current workflow. The use of X-rays and CT scans can effectively diagnose osteoporosis with high sensitivity and specificity. By incorporating these diagnostic methods into standard practice, upper extremity orthopedic surgeons can play a critical role in the early diagnosis and treatment of osteoporosis. This can prevent severe fractures, improve patient outcomes, and reduce the overall healthcare burden by initiating timely treatment and patient education. This review emphasizes the importance of opportunistic imaging in enhancing osteoporosis management and suggests that upper extremity surgeons can significantly and effectively contribute to primary and secondary fracture prevention.

Ruminant-dense environments increase risk of Shiga toxin-producing Escherichia coli independently of ruminant contact.

Cattle and other domestic ruminants are the primary reservoirs of O157 and non-O157 Shiga toxin-producing Escherichia coli (STEC). Living in areas with high ruminant density has been associated with excess risk of infection, which could be due to both direct ruminant contact and residual environmental risk, but the role of each is unclear. We investigated whether there is any meaningful risk to individuals living in ruminant-dense areas if they do not have direct contact with ruminants. Using a Bayesian spatial framework, we investigated the association between the density of ruminants on feedlots and STEC incidence in Minnesota from 2010 to 2019, stratified by serogroup and season, and adjusting for direct ruminant contact. For every additional head of cattle or sheep per 10 acres, the incidence of O157 STEC infection increased by 30% (IRR 1.30; 95% CrI 1.18, 1.42) or 135% (IRR 2.35; 95% CrI 1.14, 4.20), respectively, during the summer months. Sheep density was also associated with O157 STEC risk during winter (IRR 4.28; 95% CrI 1.40, 8.92). The risk of non-O157 STEC infection was only elevated in areas with goat operations during summer (IRR 19.6; 95% CrI 1.69, 78.8). STEC risk associated with ruminant density was independent of direct ruminant contact across serogroups and seasons. Our findings demonstrate that living in a ruminant-dense area increases an individual's risk of O157 and non-O157 STEC infection even without direct ruminant contact, indicating that prevention efforts need to extend to community strategies for averting indirect transmission from local ruminant populations. I mportance: STEC are zoonotic enteric bacteria responsible for 2.5 million illnesses each year. Infections in young children can be especially devastating, causing hemolytic uremic syndrome (HUS), a debilitating and sometimes fatal form of acute kidney injury. STEC's primary reservoirs are cattle and other domestic ruminants, and transmission can occur through food, water, animal contact, and person-to-person. Living near ruminants poses a significant risk of STEC infection; however, the proportion of that risk due to direct ruminant contact or other routes of transmission is unknown. Our research demonstrates that direct ruminant contact is a substantial risk irrespective of location, and that individuals living in ruminant-rich regions are at high risk of STEC infection regardless of whether they come into contact with ruminants. These findings indicate a need for multi-pronged prevention efforts that emphasize control of contamination in the environments surrounding ruminant populations, in addition to biosafety precautions when contacting ruminants directly.

Melanoma-Derived Extracellular Vesicles Induce CD36-Mediated Pre-Metastatic Niche.

CD36 expression in both immune and non-immune cells is known to be directly involved in cancer metastasis. Extracellular vesicles (EVs) secreted by malignant melanocytes play a vital role in developing tumor-promoting microenvironments, but it is unclear whether this is mediated through CD36. To understand the role of CD36 in melanoma, we first analyzed the SKCM dataset for clinical prognosis, evaluated the percentage of CD36 in lymphatic fluid-derived EVs (LEVs), and tested whether melanoma-derived EVs increase CD36 expression and induce M2-macrophage-like characteristics. Furthermore, we performed a multiplex immunofluorescence (MxIF) imaging analysis to evaluate the CD36 expression and its colocalization with various other cells in the lymph node (LN) of patients and control subjects. Our findings show that cutaneous melanoma patients have a worse clinical prognosis with high CD36 levels, and a higher percentage of CD36 in total LEVs were found at baseline in melanoma patients compared to control. We also found that monocytic and endothelial cells treated with melanoma EVs expressed more CD36 than untreated cells. Furthermore, melanoma-derived EVs can regulate immunosuppressive macrophage-like characteristics by upregulating CD36. The spatial imaging data show that cells in tumor-involved sentinel LNs exhibit a higher probability of CD36 expression than cells from control LNs, but this was not statistically significant. Conclusively, our findings demonstrated that CD36 plays a vital role in controlling the immunosuppressive microenvironment in the LN, which can promote the formation of a protumorigenic niche.

To invade or not to invade: two approaches to a prokaryotic predatory life cycle.

Bdellovibrio and like organisms (BALOs) are a group of Gram-negative bacterial predators that are defined as having a periplasmic life cycle, whereby the predator enters into the periplasm of a prey cell. Recently, a predator of Caulobacter crescentus with a novel epibiotic life cycle was identified as a new species - Bdellovibrio exovorus. Therefore, this raises the question as to what determines the type of life cycle of a predator. Six bacterial strains susceptible to predation by B. exovorus JSS were isolated from soil, sewage, and activated sludge. 16S rRNA gene sequence analysis revealed these prey cells to be Acinetobacter johnsonii, Acinetobacter junii, Aeromonas hydrophila, and Delftia acidovorans. The life cycle of B. exovorus was epibiotic on all these prey cells. Environmental samples were enriched with these prey cells; new BALOs were isolated and their life cycle assessed. All new isolates had a periplasmic life cycle. BALOs generally have diverse prey ranges, and thus, not all new prey cells could be used by each new predator. Overall, each prey cell was able to support the growth of predators with either life cycle. Therefore it was confirmed that it is the predator and not the prey that determines the type of life cycle.

Bayesian modeling of dynamic behavioral change during an epidemic.

For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in real-time. Behavioral change is often ignored in epidemic modeling efforts, making these models less useful than they could be. We address this by introducing a novel class of data-driven epidemic models which characterize and accurately estimate behavioral change. Our proposed model allows time-varying transmission to be captured by the level of "alarm" in the population, with alarm specified as a function of the past epidemic trajectory. We investigate the estimability of the population alarm across a wide range of scenarios, applying both parametric functions and non-parametric functions using splines and Gaussian processes. The model is set in the data-augmented Bayesian framework to allow estimation on partially observed epidemic data. The benefit and utility of the proposed approach is illustrated through applications to data from real epidemics.