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Amorim, Leila Denise Alves Ferreira, Rosemeire L. Fiaccone, Carlos Antônio S. T. Santos, Tereza Nadya dos Santos, Lia Terezinha L. P. de Moraes, Nelson F. Oliveira, Silvano O. Barbosa, et al. "Structural equation modeling in epidemiology." Cadernos de Saúde Pública 26, no. 12 (December 2010): 2251–62. http://dx.doi.org/10.1590/s0102-311x2010001200004.
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Structural equation modeling (SEM) is an important statistical tool for evaluating complex relations in several research areas. In epidemiology, the use and discussion of SEM have been limited thus far. This article presents basic principles and concepts in SEM, including an application using epidemiological data analysis from a study on the determinants of cognitive development in young children, considering constructs related to organization of the child's home environment, parenting style, and the child's health status. The relations between the constructs and cognitive development were measured. The results showed a positive association between psychosocial stimulus at home and cognitive development in young children. The article presents the contributions by SEM to epidemiology, highlighting the need for an a priori theoretical model for improving the study of epidemiological questions from a new perspective.
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Delmaar, C., H. Bremmer, and I. Tuinman. "Experimental Validation of the Consumer Exposure Modeling Tool ConsExpo." Epidemiology 17, Suppl (November 2006): S182. http://dx.doi.org/10.1097/00001648-200611001-00460.
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Bell, Michelle. "AIR QUALITY MODELING AS A TOOL FOR HUMAN HEALTH RESEARCH." Epidemiology 15, no. 4 (July 2004): S152. http://dx.doi.org/10.1097/00001648-200407000-00397.
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Kolesnichenko, Olga, Igor Nakonechniy, and Yuriy Kolesnichenko. "From digital to quantum epidemiology: The Quantum Data Lake concept for big data related to viral infectious diseases." Global Health Economics and Sustainability 2, no. 1 (March 20, 2024): 2148. http://dx.doi.org/10.36922/ghes.2148.
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The development of quantum epidemiology represents the next anticipated phase in epidemiology transformation, driven by the emergence of new quantum technologies. Epidemiology is currently transitioning into the digital era and undergoing a paradigm shift from a data-driven to a value-driven strategy. Epidemiology data are characterized by uncertainty, multidimensionality, and disconnection, thereby correlating with the preferential quantum approach for data exposition, value creation, and modeling. Examples of such complex epidemiology data include the data on DNA viruses with associated symptoms and diseases. The Quantum Data Lake concept is proposed and consists of several layers and quantum tools, including Robson semantic triples, Quantum Universal Exchange Language, Hyperbolic Dirac Net, “quantum ribosome” structure, quantum random access memory, teleportation, Quantum Query Language, non-Hermitian gates, and tensor networks (e.g., matrix product state, projected entangled pair state, and multiscale entanglement renormalization ansatz [MERA]), alongside PT-symmetry properties. PT-symmetry can serve as an intuitive modeling tool, and PT-symmetry breaking can detect the hidden shift in the information that is permanently updated in the Data Lake. The computational output is presented as PT-symmetry gain/loss equilibrium breaking in the form of a complex number, i.e., two possible variants of epidemic modeling. For MERA, non-Hermiticity with spontaneous PT-symmetry breaking can theoretically appear as a violation of the entanglement monotonicity and local entanglement gain, leading to a non-reversible character of the coarse-graining transformation. The duality of PT-symmetry equilibrium breaking can be compared to, for example, the estimation of the best and worst scenarios simultaneously, or the gain of entanglement can display a significant correlation between some studied parameters embedded into the data. The fundamental difference between digital and quantum epidemiology is the implementation of quantum logic and reliance on a quantum theory.
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Azimaee, Parisa, Mohammad Jafari Jozani, and Yaser Maddahi. "Calibration of surgical tools using multilevel modeling with LINEX loss function: Theory and experiment." Statistical Methods in Medical Research 30, no. 6 (April 13, 2021): 1523–37. http://dx.doi.org/10.1177/09622802211003620.
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Quantifying the tool–tissue interaction forces in surgery can be used in the training process of novice surgeons to help them better handle surgical tools and avoid exerting excessive forces. A significant challenge concerns the development of proper statistical learning techniques to model the relationship between the true force exerted on the tissue and several outputs read from sensors mounted on the surgical tools. We propose a nonparametric bootstrap technique and a Bayesian multilevel modeling methodology to estimate the true forces. We use the linear exponential loss function to asymmetrically penalize the over and underestimation of the applied forces to the tissue. We incorporate the direction of the force as a group factor in our analysis. A weighted approach is used to account for the nonhomogeneity of read voltages from the surgical tool. Our proposed Bayesian multilevel models provide estimates that are more accurate than those under the maximum likelihood and restricted maximum likelihood approaches. Moreover, confidence bounds are much narrower and the biases and root mean squared errors are significantly smaller in our multilevel models with the linear exponential loss function.
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Limburg, Hans, and Jan E. E. Keunen. "Blindness and low vision in The Netherlands from 2000 to 2020—modeling as a tool for focused intervention." Ophthalmic Epidemiology 16, no. 6 (December 2009): 362–69. http://dx.doi.org/10.3109/09286580903312251.
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Casado-Vara, Roberto, Marcos Severt, Antonio Díaz-Longueira, Ángel Martín del Rey, and Jose Luis Calvo-Rolle. "Dynamic Malware Mitigation Strategies for IoT Networks: A Mathematical Epidemiology Approach." Mathematics 12, no. 2 (January 12, 2024): 250. http://dx.doi.org/10.3390/math12020250.
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With the progress and evolution of the IoT, which has resulted in a rise in both the number of devices and their applications, there is a growing number of malware attacks with higher complexity. Countering the spread of malware in IoT networks is a vital aspect of cybersecurity, where mathematical modeling has proven to be a potent tool. In this study, we suggest an approach to enhance IoT security by installing security updates on IoT nodes. The proposed method employs a physically informed neural network to estimate parameters related to malware propagation. A numerical case study is conducted to evaluate the effectiveness of the mitigation strategy, and novel metrics are presented to test its efficacy. The findings suggest that the mitigation tactic involving the selection of nodes based on network characteristics is more effective than random node selection.
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Ben-Hassen, Céline, Catherine Helmer, Claudine Berr, and Hélène Jacqmin-Gadda. "Five-Year Dynamic Prediction of Dementia Using Repeated Measures of Cognitive Tests and a Dependency Scale." American Journal of Epidemiology 191, no. 3 (November 9, 2021): 453–64. http://dx.doi.org/10.1093/aje/kwab269.
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Abstract The progression of dementia prevalence over the years and the lack of efficient treatments to stop or reverse the cognitive decline make dementia a major public health challenge in the developed world. Identifying people at high risk of developing dementia could improve the treatment of these patients and help select the target population for preventive clinical trials. We used joint modeling to build a dynamic prediction tool of dementia based on the change over time of 2 neurocognitive tests (the Mini-Mental State Examination and the Isaacs Set Tests) as well as an autonomy scale (the Instrumental Activities of Daily Living). The model was estimated with data from the French cohort Personnes Agées QUID (1988–2015) and validated both by cross-validation and externally with data from the French Three City cohort (1999–2018). We evaluated its predictive abilities through area under the receiver operating characteristics curve and Brier score, accounting for right censoring and competing risk of death, and obtained an average area under the curve value of 0.95 for the risk of dementia in the next 5 or 10 years. This tool is able to discriminate a high-risk group of people from the rest of the population. This could be of help in clinical practice and research.
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Kunicki, Zachary J., Meghan L. Smith, and Eleanor J. Murray. "A Primer on Structural Equation Model Diagrams and Directed Acyclic Graphs: When and How to Use Each in Psychological and Epidemiological Research." Advances in Methods and Practices in Psychological Science 6, no. 2 (April 2023): 251524592311560. http://dx.doi.org/10.1177/25152459231156085.
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Many psychological researchers use some form of a visual diagram in their research processes. Model diagrams used with structural equation models (SEMs) and causal directed acyclic graphs (DAGs) can guide causal-inference research. SEM diagrams and DAGs share visual similarities, often leading researchers familiar with one to wonder how the other differs. This article is intended to serve as a guide for researchers in the psychological sciences and psychiatric epidemiology on the distinctions between these methods. We offer high-level overviews of SEMs and causal DAGs using a guiding example. We then compare and contrast the two methodologies and describe when each would be used. In brief, SEM diagrams are both a conceptual and statistical tool in which a model is drawn and then tested, whereas causal DAGs are exclusively conceptual tools used to help guide researchers in developing an analytic strategy and interpreting results. Causal DAGs are explicitly tools for causal inference, whereas the results of a SEM are only sometimes interpreted causally. A DAG may be thought of as a “qualitative schematic” for some SEMs, whereas SEMs may be thought of as an “algebraic system” for a causal DAG. As psychology begins to adopt more causal-modeling concepts and psychiatric epidemiology begins to adopt more latent-variable concepts, the ability of researchers to understand and possibly combine both of these tools is valuable. Using an applied example, we provide sample analyses, code, and write-ups for both SEM and causal DAG approaches.
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Oleson, Jacob J., Joseph E. Cavanaugh, J. Bruce Tomblin, Elizabeth Walker, and Camille Dunn. "Combining growth curves when a longitudinal study switches measurement tools." Statistical Methods in Medical Research 25, no. 6 (July 11, 2016): 2925–38. http://dx.doi.org/10.1177/0962280214534588.
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When longitudinal studies are performed to investigate the growth of traits in children, the measurement tool being used to quantify the trait may need to change as the subjects’ age throughout the study. Changing the measurement tool at some point in the longitudinal study makes the analysis of that growth challenging which, in turn, makes it difficult to determine what other factors influence the growth rate. We developed a Bayesian hierarchical modeling framework that relates the growth curves per individual for each of the different measurement tools and allows for covariates to influence the shapes of the curves by borrowing strength across curves. The method is motivated by and demonstrated by speech perception outcome measurements of children who were implanted with cochlear implants. Researchers are interested in assessing the impact of age at implantation and comparing the growth rates of children who are implanted under the age of two versus those implanted between the ages of two and four.
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Helikumi, Mlyashimbi, and Steady Mushayabasa. "Dog screening as a novel complementary guinea worm disease control tool to mitigate persistence in Chad: A modeling study." Parasite Epidemiology and Control 23 (November 2023): e00328. http://dx.doi.org/10.1016/j.parepi.2023.e00328.
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Lodise, Thomas P., Peggy S. McKinnon, and Michael Rybak. "Prediction Model to Identify Patients WithStaphylococcus aureusBacteremia at Risk for Methicillin Resistance." Infection Control & Hospital Epidemiology 24, no. 9 (September 2003): 655–61. http://dx.doi.org/10.1086/502269.
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AbstractObjectives:To identify institution-specific risk factors for MRSA bacteremia and develop an objective mechanism to estimate the probability of methicillin resistance in a given patient withStaphylococcus aureusbacteremia (SAB).Design:A cohort study was performed to identify institution-specific risk factors for MRSA. Logistic regression was used to model the likelihood of MRSA A stepwise approach was employed to derive a parsimonious model. The MRSA prediction tool was developed from the final model.Setting:A 279-bed, level 1 trauma center.Patients:Between January 1, 1999, and June 30, 2001, 494 patients with clinically significant episodes of SAB were identified.Results:The MRSA rate was 45.5%. Of 18 characteristics included in the logistic regression, the only independent features for MRSA were prior antibiotic exposure (OR, 9.2; CI95, 4.8 to 17.9), hospital onset (OR, 3.0; CI95, 1.9 to 4.9), history of hospitalization (OR, 2.5; CI95, 1.5 to 3.8), and presence of decubitus ulcers (OR, 2.5; CI95, 1.2 to 4.9). The prediction tool was derived from the final model, which was shown to accurately reflect the actual MRSA distribution in the cohort.Conclusion:Through multivariate modeling techniques, we were able to identify the most important determinants of MRSA at our institution and develop a tool to predict the probability of methicillin resistance in a patient with SAB. This knowledge can be used to guide empiric antibiotic selection. In the era of antibiotic resistance, such tools are essential to prevent indiscriminate antibiotic use and preserve the longevity of current antimicrobials.
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Mony, Vidya, Kevin Hultquist, and Supriya Narasimhan. "Financial and Mortality Modeling as a Tool to Present Infection Prevention Data: What a SIR of 1.2 Means for the Hospital." Infection Control & Hospital Epidemiology 41, S1 (October 2020): s64—s65. http://dx.doi.org/10.1017/ice.2020.550.
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Background: Presenting to hospital leadership is an annual requirement of many infection prevention (IP) programs. Most presentations include current statistical data of hospital-acquired infections (HAIs) and whether the hospital has met its goals according to the National Healthcare Safety Network (NHSN) criteria. We presented HAI data in a novel way, with financial and mortality modeling, to show the impact of IP interventions to leadership not attuned to NHSN metrics. Method: We looked at 4 HAIs, their trends, and their effect on our hospital, Santa Clara Valley Medical Center (SCVMC). To estimate the impact of specific HAIs, we used 2 metrics derived from a meta-analysis by the US Department of Health and Human Services (HHS): excess mortality and excess cost. Excess mortality is defined as the difference between the underlying population mortality and the affected population mortality expressed as deaths per 1,000 population. Excess cost is defined as the additional cost introduced per patient with a specific HAI versus a similarly admitted patient without that HAI. HHS data were multiplied by the number of HAI events at SCVMC to generate estimates. Result: In our presentation, we elucidated a previously unseen cost savings and decreased mortality with 2 HAIs, central-line–associated blood stream infections (CLABSIs) and catheter associated urinary tract infections (CAUTIs), which were below NHSN targets due to IP-led interventions. We then showed 2 other HAIs, Clostridium difficile infection (CDI) and surgical site infections (SSIs), which did not meet our expected NHSN and institutional goals and were estimated to increase costs and potential mortalities in the upcoming year. We argued that proactive monies directed toward expanding our IP program and HAI mitigation efforts would cost a fraction of the impending healthcare expenditures as predicted by the model. Conclusion: By applying financial and mortality modeling, we helped our leadership perceive the concrete effect of IP-led interventions versus presenting abstract NHSN metrics. We also emphasized that without proactive leadership investment, we would continue to overspend healthcare dollars while not meeting our goals. This format of presentation gave us critical leverage to advocate for and successfully expand our IP department. Further SHEA-led cost-analysis modeling and education are needed to help IP departments promote their efforts in an effective manner.Funding: NoneDisclosures: None
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Namba, Takanori, Masaki Ueno, Gen Inoue, Takayuki Imura, Wataru Saito, Toshiyuki Nakazawa, Masayuki Miyagi, Eiki Shirasawa, Osamu Takahashi, and Masashi Takaso. "Prediction tool for high risk of surgical site infection in spinal surgery." Infection Control & Hospital Epidemiology 41, no. 7 (April 24, 2020): 799–804. http://dx.doi.org/10.1017/ice.2020.107.
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AbstractObjective:The incidence of surgical site infection (SSI) is higher in spinal surgeries than in general orthopedic operations. In this study, we aimed to develop a scoring system with reduced health care costs for detecting spinal surgery patients at high risk for SSI.Design:Retrospective cohort study.Patients:In total, 824 patients who underwent spinal surgery at 2 university hospitals from September 2005 to May 2011.Methods:We reviewed the medical records of 824 patients, and we examined 19 risk factors to identify high-risk patients. After narrowing down the variables by univariate analysis, multiple logistic analysis was performed for factors with P values <.2, using SSI as a dependent variable. Only factors that showed P values <.05 were included in the final models, and each factor was scored based on the β coefficient values obtained. The clinical prediction rules were thereby prepared.Results:“Emergency operation,” “blood loss >400 mL,” “presence of diabetes,” “presence of skin disease,” and “total serum albumin value <3.2 g/dL” were detected by multivariable modeling and were incorporated into the risk scores. Applying these 5 independent predictive factors, we were able to predict the infection incidence after spinal surgery.Conclusions:Our present study could aid physicians in making decisions regarding prevention strategies in patients undergoing spinal surgery. Stratification of risks employing this scoring system will facilitate the identification of patients most likely to benefit from complex, time-consuming and expensive infection prevention strategies, thereby possibly reducing healthcare costs.
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Jayasekera, Jinani, Joseph A. Sparano, Young Chandler, Claudine Isaacs, Allison W. Kurian, Lawrence H. Kushi, Suzanne C. O'Neill, Clyde B. Schechter, and Jeanne S. Mandelblatt. "A simulation model-based clinical decision tool to guide personalized treatment based on individual characteristics: Does 21-gene recurrence score assay testing change decisions?" Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e12507-e12507. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e12507.
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e12507 Background: There is a need for web-based decision tools that integrate clinicopathologic features and genomic information to guide breast cancer therapy for women with node-negative, hormone receptor positive, HER2 negative (“early-stage”) breast cancer. We developed a novel simulation model-based clinical decision tool that provides prognostic estimates of treatment outcomes based on age, tumor size, grade, and comorbidities with and without 21-gene recurrence scores (RS). Methods: We adapted an extant breast cancer simulation model developed within the NCI-funded Cancer Intervention and Surveillance Modeling Network (CISNET) to derive estimates for the 10-year risks of distant recurrence, breast cancer-specific mortality, other cause mortality and life-years gained with endocrine vs. chemo-endocrine therapy for individual women based on their age, tumor size, grade, and comorbidity-level with and without RS test results. The model used an empiric Bayesian analytical approach to combine information from clinical trials, registry and claims data to provide individual estimates. External validation of the model was performed by comparing model-based breast cancer mortality rates and observed rates in the Surveillance Epidemiology and End Results (SEER) registry. Results: Several exemplar profiles were selected to illustrate the clinical utility of the decision tool. For example, the absolute chemotherapy benefit for 10-year distant recurrence risk and life-years gained, without RS testing, and the outcomes if a woman got tested and had a RS 16-20 are provided below for a 40-44-year-old woman and a 65–69-year-old woman diagnosed with a small (≤2cm), intermediate grade tumor and mild comorbidities. Conclusions: Simulation modeling is useful for creating clinical decision tools to support shared decision making for early-stage breast cancer treatment.[Table: see text]
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Musulin, Jelena, Sandi Baressi Šegota, Daniel Štifanić, Ivan Lorencin, Nikola Anđelić, Tijana Šušteršič, Anđela Blagojević, Nenad Filipović, Tomislav Ćabov, and Elitza Markova-Car. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review." International Journal of Environmental Research and Public Health 18, no. 8 (April 18, 2021): 4287. http://dx.doi.org/10.3390/ijerph18084287.
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COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in the daily lives of billions of people worldwide. Therefore, many efforts have been made by researchers across the globe in the attempt of determining the models of COVID-19 spread. The objectives of this review are to analyze some of the open-access datasets mostly used in research in the field of COVID-19 regression modeling as well as present current literature based on Artificial Intelligence (AI) methods for regression tasks, like disease spread. Moreover, we discuss the applicability of Machine Learning (ML) and Evolutionary Computing (EC) methods that have focused on regressing epidemiology curves of COVID-19, and provide an overview of the usefulness of existing models in specific areas. An electronic literature search of the various databases was conducted to develop a comprehensive review of the latest AI-based approaches for modeling the spread of COVID-19. Finally, a conclusion is drawn from the observation of reviewed papers that AI-based algorithms have a clear application in COVID-19 epidemiological spread modeling and may be a crucial tool in the combat against coming pandemics.
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Dai, Fuqiang, Hao Liu, Xia Zhang, and Qing Li. "Exploring the Emerging Trends of Spatial Epidemiology: A Scientometric Analysis Based on CiteSpace." SAGE Open 11, no. 4 (October 2021): 215824402110587. http://dx.doi.org/10.1177/21582440211058719.
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Infectious diseases are common challenges faced by people around the world, which jeopardize public health, as well as human well-being in various aspects of social and economic development. Although much progress has been made in spatial epidemiology, there is still very little scientific understanding of knowledge domain mapping with scientometric analysis. Based on a total of 4,552 literature records collected from the Web of Science Core Collection™, quantitative changes, research frontiers, research hotspots, and collaboration networks were analyzed by CiteSpace. The results show that both total publications and sum of times cited per year exhibit a rapid development trend in recent decades. The USA, England, and France are highly active in the field. The network of documents co-citation analysis is validated with almost same importance of documents, and primary research frontiers are landscape genetics, modeling and spatial analysis, and tropical diseases. The clustering of the keywords co-occurrence analysis network is heterogeneous and highly reliable, and research hotspots are related to phoma stem canker, vector preference, and aerosol chemical component. Scholars in the field of spatial epidemiology are closely connected, and they have been in a stable cooperative network, as well as institutions. Overall, scientometric analysis based on CiteSpace provides a sound tool to better understand the frontiers, hotspots, and emerging trends in the research domain of spatial epidemiology.
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Siegel, Erin M., Cornelia M. Ulrich, and David Shibata. "Risk Stratification for Early-onset Colorectal Cancer Screening: Are We Ready for Implementation?" Cancer Prevention Research 16, no. 9 (September 1, 2023): 479–81. http://dx.doi.org/10.1158/1940-6207.capr-23-0239.
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Abstract Early-onset colorectal cancer (EOCRC) is increasing at alarming rates and identifying risk factors is a high priority. There is a need to develop risk stratification approaches for colorectal cancer screening among younger populations. Although there is a growing body of evidence identifying risk factors for EOCRC, including the report by Imperiale and colleagues in this issue, risk stratification for EOCRC screening has not been implemented into practice. This publication highlights how essential it is to bring research findings into practice and bridge the gaps between developing risk prediction modeling in epidemiology and implementation science. While encouraging, we are still a long way off from having a clinically applicable risk prediction tool. See related article by Imperiale et al., p. 513
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Mattos, Thalita B., Larissa Avila Matos, and Victor H. Lachos. "A semiparametric mixed-effects model for censored longitudinal data." Statistical Methods in Medical Research 30, no. 12 (October 18, 2021): 2582–603. http://dx.doi.org/10.1177/09622802211046387.
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In longitudinal studies involving laboratory-based outcomes, repeated measurements can be censored due to assay detection limits. Linear mixed-effects (LMEs) models are a powerful tool to model the relationship between a response variable and covariates in longitudinal studies. However, the linear parametric form of linear mixed-effect models is often too restrictive to characterize the complex relationship between a response variable and covariates. More general and robust modeling tools, such as nonparametric and semiparametric regression models, have become increasingly popular in the last decade. In this article, we use semiparametric mixed models to analyze censored longitudinal data with irregularly observed repeated measures. The proposed model extends the censored linear mixed-effect model and provides more flexible modeling schemes by allowing the time effect to vary nonparametrically over time. We develop an Expectation-Maximization (EM) algorithm for maximum penalized likelihood estimation of model parameters and the nonparametric component. Further, as a byproduct of the EM algorithm, the smoothing parameter is estimated using a modified linear mixed-effects model, which is faster than alternative methods such as the restricted maximum likelihood approach. Finally, the performance of the proposed approaches is evaluated through extensive simulation studies as well as applications to data sets from acquired immune deficiency syndrome studies.
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Schell, Robert C., Bennett Allen, William C. Goedel, Benjamin D. Hallowell, Rachel Scagos, Yu Li, Maxwell S. Krieger, et al. "Identifying Predictors of Opioid Overdose Death at a Neighborhood Level With Machine Learning." American Journal of Epidemiology 191, no. 3 (November 23, 2021): 526–33. http://dx.doi.org/10.1093/aje/kwab279.
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Abstract Predictors of opioid overdose death in neighborhoods are important to identify, both to understand characteristics of high-risk areas and to prioritize limited prevention and intervention resources. Machine learning methods could serve as a valuable tool for identifying neighborhood-level predictors. We examined statewide data on opioid overdose death from Rhode Island (log-transformed rates for 2016–2019) and 203 covariates from the American Community Survey for 742 US Census block groups. The analysis included a least absolute shrinkage and selection operator (LASSO) algorithm followed by variable importance rankings from a random forest algorithm. We employed double cross-validation, with 10 folds in the inner loop to train the model and 4 outer folds to assess predictive performance. The ranked variables included a range of dimensions of socioeconomic status, including education, income and wealth, residential stability, race/ethnicity, social isolation, and occupational status. The R2 value of the model on testing data was 0.17. While many predictors of overdose death were in established domains (education, income, occupation), we also identified novel domains (residential stability, racial/ethnic distribution, and social isolation). Predictive modeling with machine learning can identify new neighborhood-level predictors of overdose in the continually evolving opioid epidemic and anticipate the neighborhoods at high risk of overdose mortality.
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Paris, Donna Marie, Rachel Renee Slaymaker, Heather Ann Guest, and Amy Christine Kalb. "Interprofessional Simulation as an Educational Tool to Assess Cultural Competence Among Health Professions Students." Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare 18, no. 3 (March 28, 2022): 163–71. http://dx.doi.org/10.1097/sih.0000000000000655.
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Cerdá, Magdalena, Mohammad S. Jalali, Ava D. Hamilton, Catherine DiGennaro, Ayaz Hyder, Julian Santaella-Tenorio, Navdep Kaur, Christina Wang, and Katherine M. Keyes. "A Systematic Review of Simulation Models to Track and Address the Opioid Crisis." Epidemiologic Reviews 43, no. 1 (2021): 147–65. http://dx.doi.org/10.1093/epirev/mxab013.
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Abstract The opioid overdose crisis is driven by an intersecting set of social, structural, and economic forces. Simulation models are a tool to help us understand and address thiscomplex, dynamic, and nonlinear social phenomenon. We conducted a systematic review of the literature on simulation models of opioid use and overdose up to September 2019. We extracted modeling types, target populations, interventions, and findings; created a database of model parameters used for model calibration; and evaluated study transparency and reproducibility. Of the 1,398 articles screened, we identified 88 eligible articles. The most frequent types of models were compartmental (36%), Markov (20%), system dynamics (16%), and agent-based models (16%). Intervention cost-effectiveness was evaluated in 40% of the studies, and 39% focused on services for people with opioid use disorder (OUD). In 61% of the eligible articles, authors discussed calibrating their models to empirical data, and in 31%, validation approaches used in the modeling process were discussed. From the 63 studies that provided model parameters, we extracted the data sources on opioid use, OUD, OUD treatment, cessation or relapse, emergency medical services, and death parameters. From this database, potential model inputs can be identified and models can be compared with prior work. Simulation models should be used to tackle key methodological challenges, including the potential for bias in the choice of parameter inputs, investment in model calibration and validation, and transparency in the assumptions and mechanics of simulation models to facilitate reproducibility.
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John, Goldin, Nikhil Shri Sahajpal, Ashis K. Mondal, Sudha Ananth, Colin Williams, Alka Chaubey, Amyn M. Rojiani, and Ravindra Kolhe. "Next-Generation Sequencing (NGS) in COVID-19: A Tool for SARS-CoV-2 Diagnosis, Monitoring New Strains and Phylodynamic Modeling in Molecular Epidemiology." Current Issues in Molecular Biology 43, no. 2 (July 30, 2021): 845–67. http://dx.doi.org/10.3390/cimb43020061.
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This review discusses the current testing methodologies for COVID-19 diagnosis and explores next-generation sequencing (NGS) technology for the detection of SARS-CoV-2 and monitoring phylogenetic evolution in the current COVID-19 pandemic. The review addresses the development, fundamentals, assay quality control and bioinformatics processing of the NGS data. This article provides a comprehensive review of the obstacles and opportunities facing the application of NGS technologies for the diagnosis, surveillance, and study of SARS-CoV-2 and other infectious diseases. Further, we have contemplated the opportunities and challenges inherent in the adoption of NGS technology as a diagnostic test with real-world examples of its utility in the fight against COVID-19.
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Savi, Merveille Koissi, Akash Yadav, Wanrong Zhang, Navin Vembar, Andrew Schroeder, Satchit Balsari, Caroline O. Buckee, Salil Vadhan, and Nishant Kishore. "A standardised differential privacy framework for epidemiological modeling with mobile phone data." PLOS Digital Health 2, no. 10 (October 27, 2023): e0000233. http://dx.doi.org/10.1371/journal.pdig.0000233.
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During the COVID-19 pandemic, the use of mobile phone data for monitoring human mobility patterns has become increasingly common, both to study the impact of travel restrictions on population movement and epidemiological modeling. Despite the importance of these data, the use of location information to guide public policy can raise issues of privacy and ethical use. Studies have shown that simple aggregation does not protect the privacy of an individual, and there are no universal standards for aggregation that guarantee anonymity. Newer methods, such as differential privacy, can provide statistically verifiable protection against identifiability but have been largely untested as inputs for compartment models used in infectious disease epidemiology. Our study examines the application of differential privacy as an anonymisation tool in epidemiological models, studying the impact of adding quantifiable statistical noise to mobile phone-based location data on the bias of ten common epidemiological metrics. We find that many epidemiological metrics are preserved and remain close to their non-private values when the true noise state is less than 20, in a count transition matrix, which corresponds to a privacy-less parameter ϵ = 0.05 per release. We show that differential privacy offers a robust approach to preserving individual privacy in mobility data while providing useful population-level insights for public health. Importantly, we have built a modular software pipeline to facilitate the replication and expansion of our framework.
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Feigin, Valery L., George A. Mensah, Bo Norrving, Christopher J. L. Murray, and Gregory A. Roth. "Atlas of the Global Burden of Stroke (1990-2013): The GBD 2013 Study." Neuroepidemiology 45, no. 3 (2015): 230–36. http://dx.doi.org/10.1159/000441106.
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Background: World mapping is an important tool to visualize stroke burden and its trends in various regions and countries. Objectives: To show geographic patterns of incidence, prevalence, mortality, disability-adjusted life years (DALYs) and years lived with disability (YLDs) and their trends for ischemic stroke and hemorrhagic stroke in the world for 1990-2013. Methodology: Stroke incidence, prevalence, mortality, DALYs and YLDs were estimated following the general approach of the Global Burden of Disease (GBD) 2010 with several important improvements in methods. Data were updated for mortality (through April 2014) and stroke incidence, prevalence, case fatality and severity through 2013. Death was estimated using an ensemble modeling approach. A new software package, DisMod-MR 2.0, was used as part of a custom modeling process to estimate YLDs. All rates were age-standardized to new GBD estimates of global population. All estimates have been computed with 95% uncertainty intervals. Results: Age-standardized incidence, mortality, prevalence and DALYs/YLDs declined over the period from 1990 to 2013. However, the absolute number of people affected by stroke has substantially increased across all countries in the world over the same time period, suggesting that the global stroke burden continues to increase. There were significant geographical (country and regional) differences in stroke burden in the world, with the majority of the burden borne by low- and middle-income countries. Conclusions: Global burden of stroke has continued to increase in spite of dramatic declines in age-standardized incidence, prevalence, mortality rates and disability. Population growth and aging have played an important role in the observed increase in stroke burden.
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Baptista-Leite, Ricardo, Henrique Lopes, Björn Vandewalle, Jorge Félix, Diogo Franco, Timo Clemens, and Helmut Brand. "Epidemiological Modeling of the Impact of Public Health Policies on Hepatitis C: Protocol for a Gamification Tool Targeting Microelimination." JMIR Research Protocols 12 (September 25, 2023): e38521. http://dx.doi.org/10.2196/38521.
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Background Hepatitis C is a disease with a strong social component, as its main transmission route is via blood, making it associated with lifestyle. Therefore, it is suitable to be worked on from the perspective of public health policy, which still has a lot of room to explore and improve, contrary to diagnoses and treatments, which are already very refined and effective. Objective An interactive gamified policy tool, designated as Let’s End HepC (LEHC), was created to understand the impact of policies related to hepatitis C on the disease’s epidemiology on a yearly basis until 2030. Methods To this end, an innovative epidemiological model was developed, integrating Markov chains to model the natural history of the disease and adaptive conjoint analysis to reflect the degree of application of each of the 24 public health policies included in the model. This double imputation model makes it possible to assess a set of indicators such as liver transplant, incidence, and deaths year by year until 2030 in different risk groups. Populations at a higher risk were integrated into the model to understand the specific epidemiological dynamics within the total population of each country and within segments that comprise people who have received blood products, prisoners, people who inject drugs, people infected through vertical transmission, and the remaining population. Results The model has already been applied to a group of countries, and studies in 5 of these countries have already been concluded, showing results very close to those obtained through other forms of evaluation. Conclusions The LEHC model allows the simulation of different degrees of implementation of each policy and thus the verification of its epidemiological impact on each studied population. The gamification feature allows assessing the adequate fulfillment of the World Health Organization goals for the elimination of hepatitis C by 2030. LEHC supports health decision makers and people who practice patient advocacy in making decisions based on science, and because LEHC is democratically shared, it ends up contributing to the increase of citizenship in health. International Registered Report Identifier (IRRID) RR1-10.2196/38521
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Liu, Xiaochen, Zhan Tian, Laixiang Sun, Junguo Liu, Wei Wu, Hanqing Xu, Landong Sun, and Chunfang Wang. "Mitigating heat-related mortality risk in Shanghai, China: system dynamics modeling simulations." Environmental Geochemistry and Health 42, no. 10 (April 29, 2020): 3171–84. http://dx.doi.org/10.1007/s10653-020-00556-9.
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Abstract Numerous studies in epidemiology, meteorology, and climate change research have demonstrated a significant association between abnormal ambient temperature and mortality. However, there is a shortage of research attention to a systematic assessment of potential mitigation measures which could effectively reduce the heat-related morbidity and mortality risks. This study first illustrates a conceptualization of a systems analysis version of urban framework for climate service (UFCS). It then constructs a system dynamics (SD) model for the UFCS and employs this model to quantify the impacts of heat waves on public health system in Shanghai and to evaluate the performances of two mitigation measures in the context of a real heat wave event in July 2013 in the city. Simulation results show that in comparison with the baseline without mitigation measures, if the hospital system could prepare 20% of beds available for emergency response to heat waves once receiving the warning in advance, the number of daily deaths could be reduced by 40–60 (15.8–19.5%) on the 2 days of day 7 and day 8; if increasing the minimum living allowance of 790 RMB/month in 2013 by 20%, the number of daily deaths could be reduced by 50–70 (17.7–21.9%) on the 2 days of day 8 and day 12. This tool can help policy makers systematically evaluate adaptation and mitigation options based on performance assessment, thus strengthening urban resilience to changing climate.
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Octaria, Rany, Samuel Cincotta, Jessica Healy, Camden Gowler, Prabasaj Paul, Maroya Walters, and Rachel Slayton. "An interactive patient transfer network and model visualization tool for multidrug-resistant organism prevention strategies." Antimicrobial Stewardship & Healthcare Epidemiology 3, S2 (June 2023): s120—s122. http://dx.doi.org/10.1017/ash.2023.403.
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Background: The CDC’s new Public Health Strategies to Prevent the Spread of Novel and Targeted Multidrug-Resistant Organisms (MDROs) were informed by mathematical models that assessed the impact of implementing preventive strategies directed at a subset of healthcare facilities characterized as influential or highly connected based on their predicted role in the regional spread of MDROs. We developed an interactive tool to communicate mathematical modeling results and visualize the regional patient transfer network for public health departments and healthcare facilities to assist in planning and implementing prevention strategies. Methods: An interactive RShiny application is currently hosted in the CDC network and is accessible to external partners through the Secure Access Management Services (SAMS). Patient transfer volumes (direct and indirect, that is, with up to 30 days in the community between admissions) were estimated from the CMS fee-for-service claims data from 2019. The spread of a carbapenem-resistant Enterobacterales (CRE)–like MDROs within a US state was simulated using a deterministic model with susceptible and infectious compartments in the community and healthcare facilities interconnected through patient transfers. Individuals determined to be infectious through admission screening, point-prevalence surveys (PPSs), or notified from interfacility communication were assigned lower transmissibility if enhanced infection prevention and control practices were in place at a facility. Results: The application consists of 4 interactive tabs. Users can visualize the statewide patient-sharing network for any US state and select territories in the first tab (Fig. 1). A feature allows users to highlight a facility of interest and display downstream or upstream facilities that received or sent transfers from the facility of interest, respectively. A second tab lists influential facilities to aid in prioritizing screening and prevention activities. A third tab lists all facilities in the state in descending order of their dispersal rate (ie, the rate at which patients are shared downstream to other facilities), which can help identify highly connected facilities. In the fourth tab, an interactive graph displays the predicted reduction of MDRO prevalence given a range of intervention scenarios (Fig. 2). Conclusions: Our RShiny application, which can be accessed by public health partners, can assist healthcare facilities and public health departments in planning and tailoring MDRO prevention activity bundles.Disclosures: None
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Yu, QinQin, Scott W. Olesen, Claire Duvallet, and Yonatan H. Grad. "Assessment of sewer connectivity in the United States and its implications for equity in wastewater-based epidemiology." PLOS Global Public Health 4, no. 4 (April 17, 2024): e0003039. http://dx.doi.org/10.1371/journal.pgph.0003039.
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Wastewater-based epidemiology is a promising public health tool that can yield a more representative view of the population than case reporting. However, only about 80% of the U.S. population is connected to public sewers, and the characteristics of populations missed by wastewater-based epidemiology are unclear. To address this gap, we used publicly available datasets to assess sewer connectivity in the U.S. by location, demographic groups, and economic groups. Data from the U.S. Census’ American Housing Survey revealed that sewer connectivity was lower than average when the head of household was American Indian and Alaskan Native, White, non-Hispanic, older, and for larger households and those with higher income, but smaller geographic scales revealed local variations from this national connectivity pattern. For example, data from the U.S. Environmental Protection Agency showed that sewer connectivity was positively correlated with income in Minnesota, Florida, and California. Data from the U.S. Census’ American Community Survey and Environmental Protection Agency also revealed geographic areas with low sewer connectivity, such as Alaska, the Navajo Nation, Minnesota, Michigan, and Florida. However, with the exception of the U.S. Census data, there were inconsistencies across datasets. Using mathematical modeling to assess the impact of wastewater sampling inequities on inferences about epidemic trajectory at a local scale, we found that in some situations, even weak connections between communities may allow wastewater monitoring in one community to serve as a reliable proxy for an interacting community with no wastewater monitoring, when cases are widespread. A systematic, rigorous assessment of sewer connectivity will be important for ensuring an equitable and informed implementation of wastewater-based epidemiology as a public health monitoring system.
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Perron, Jarrad, and Ji Hyun Ko. "Review of Quantitative Methods for the Detection of Alzheimer’s Disease with Positron Emission Tomography." Applied Sciences 12, no. 22 (November 11, 2022): 11463. http://dx.doi.org/10.3390/app122211463.
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The dementia spectrum is a broad range of disorders with complex diagnosis, pathophysiology, and a limited set of treatment options, where the most common variety is Alzheimer’s disease (AD). Positron emission tomography (PET) has become a valuable tool for the detection of AD; however, following the results of post-mortem studies, AD diagnosis has modest sensitivity and specificity at best. It remains common practice that readings of these images are performed by a physician’s subjective impressions of the spatial pattern of tracer uptake, and so quantitative methods based on established biomarkers have had little penetration into clinical practice. The present study is a review of the data-driven methods available for molecular neuroimaging studies (fluorodeoxyglucose-/amyloid-/tau-PET), with emphasis on the use of machine/deep learning as quantitative tools complementing the specialist in detecting AD. This work is divided into two broad parts. The first covers the epidemiology and pathology of AD, followed by a review of the role of PET imaging and tracers for AD detection. The second presents quantitative methods used in the literature for detecting AD, including the general linear model and statistical parametric mapping, 3D stereotactic surface projection, principal component analysis, scaled subprofile modeling, support vector machines, and neural networks.
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Miranda, Marie Lynn, Rashida Callender, Joally M. Canales, Elena Craft, Katherine B. Ensor, Max Grossman, Loren Hopkins, Jocelyn Johnston, Umair Shah, and Joshua Tootoo. "The Texas flood registry: a flexible tool for environmental and public health practitioners and researchers." Journal of Exposure Science & Environmental Epidemiology 31, no. 5 (June 26, 2021): 823–31. http://dx.doi.org/10.1038/s41370-021-00347-z.
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Abstract Background Making landfall in Rockport, Texas in August 2017, Hurricane Harvey resulted in unprecedented flooding, displacing tens of thousands of people, and creating environmental hazards and exposures for many more. Objective We describe a collaborative project to establish the Texas Flood Registry to track the health and housing impacts of major flooding events. Methods Those who enroll in the registry answer retrospective questions regarding the impact of storms on their health and housing status. We recruit both those who did and did not flood during storm events to enable key comparisons. We leverage partnerships with multiple local health departments, community groups, and media outlets to recruit broadly. We performed a preliminary analysis using multivariable logistic regression and a binomial Bayesian conditional autoregressive (CAR) spatial model. Results We find that those whose homes flooded, or who came into direct skin contact with flood water, are more likely to experience a series of self-reported health effects. Median household income is inversely related to adverse health effects, and spatial analysis provides important insights within the modeling approach. Significance Global climate change is likely to increase the number and intensity of rainfall events, resulting in additional health burdens. Population-level data on the health and housing impacts of major flooding events is imperative in preparing for our planet’s future.
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Abougarair, Ahmed J., and Shada E. Elwefati. "Identification and Control of Epidemic Disease Based Neural Networks and Optimization Technique." International Journal of Robotics and Control Systems 3, no. 4 (October 15, 2023): 780–803. http://dx.doi.org/10.31763/ijrcs.v3i4.1151.
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Developing effective strategies to contain the spread of infectious diseases, particularly in the case of rapidly evolving outbreaks like COVID-19, remains a pressing challenge. The Susceptible-Infected-Recovery (SIR) model, a fundamental tool in epidemiology, offers insights into disease dynamics. The SIR system exhibits complex nonlinear relationships between the input variables (e.g., population, infection rate, recovery rate) and the output variables (e.g., the number of infected individuals over time). We employ Recurrent Neural Networks (RNNs) to model the SIR system due to their ability to capture sequential dependencies and handle time-series data effectively. RNNs, with their ability to model nonlinear functions, can capture these intricate relationships, enabling accurate predictions and understanding of the dynamics of the system. Additionally, we apply the Pontryagin Minimum Principle (PMP) based different control strategies to formulate an optimal control approach aimed at maximizing the recovery rate while minimizing the number of affected individuals and achieving a balance between minimizing costs and satisfying constraints. This can include optimizing vaccination strategies, quarantine measures, treatment allocation, and resource allocation. The findings of this research indicate that the proposed modeling and control approach shows potential for a comprehensive analysis of viral spread, providing valuable insights and strategies for disease management on a global level. By integrating epidemiological modeling with intelligent control techniques, we contribute to the ongoing efforts aimed at combating infectious diseases on a larger scale.
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Selim, Abdelfattah, Ameer Megahed, Sahar Kandeel, Abdullah D. Alanazi, and Hamdan I. Almohammed. "Determination of Seroprevalence of Contagious Caprine Pleuropneumonia and Associated Risk Factors in Goats and Sheep Using Classification and Regression Tree." Animals 11, no. 4 (April 19, 2021): 1165. http://dx.doi.org/10.3390/ani11041165.
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Classification and Regression Tree (CART) analysis is a potentially powerful tool for identifying risk factors associated with contagious caprine pleuropneumonia (CCPP) and the important interactions between them. Our objective was therefore to determine the seroprevalence and identify the risk factors associated with CCPP using CART data mining modeling in the most densely sheep- and goat-populated governorates. A cross-sectional study was conducted on 620 animals (390 sheep, 230 goats) distributed over four governorates in the Nile Delta of Egypt in 2019. The randomly selected sheep and goats from different geographical study areas were serologically tested for CCPP, and the animals’ information was obtained from flock men and farm owners. Six variables (geographic location, species, flock size, age, gender, and communal feeding and watering) were used for risk analysis. Multiple stepwise logistic regression and CART modeling were used for data analysis. A total of 124 (20%) serum samples were serologically positive for CCPP. The highest prevalence of CCPP was between aged animals (>4 y; 48.7%) raised in a flock size ≥200 (100%) having communal feeding and watering (28.2%). Based on logistic regression modeling (area under the curve, AUC = 0.89; 95% CI 0.86 to 0.91), communal feeding and watering showed the highest prevalence odds ratios (POR) of CCPP (POR = 3.7, 95% CI 1.9 to 7.3), followed by age (POR = 2.1, 95% CI 1.6 to 2.8) and flock size (POR = 1.1, 95% CI 1.0 to 1.2). However, higher-accuracy CART modeling (AUC = 0.92, 95% CI 0.90 to 0.95) showed that a flock size >100 animals is the most important risk factor (importance score = 8.9), followed by age >4 y (5.3) followed by communal feeding and watering (3.1). Our results strongly suggest that the CCPP is most likely to be found in animals raised in a flock size >100 animals and with age >4 y having communal feeding and watering. Additionally, sheep seem to have an important role in the CCPP epidemiology. The CART data mining modeling showed better accuracy than the traditional logistic regression.
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Zuanetti, Daiane A., Júlia M. Pavan Soler, José E. Krieger, and Luis A. Milan. "Bayesian diagnostic analysis for quantitative trait loci mapping." Statistical Methods in Medical Research 29, no. 8 (November 29, 2019): 2238–49. http://dx.doi.org/10.1177/0962280219888950.
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QTL mapping is an important tool for identifying regions in chromosomes which are relevant to explain a response of interest. It is a special case of the regression model where an unknown number of missing (non-observable) covariates is involved leading to a complex variable selection procedure. Although several methods have been proposed to identify QTLs and to estimate parameters in the associated model, minimum attention has been devoted to the estimated model adequacy. In this paper, we present an overview of a few methods for residual and diagnostic analysis in the context of Bayesian regression modeling and adapt them to work with QTL mapping. The motivation of this study is to identify QTLs associated with the blood pressure of F2 rats and check the fitted model adequacy.
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Morokhovets, H. Yu, and I. P. Kaidashev. "A MATHEMATICAL MODEL FOR PROGNOSIS OF THE COVID-19 INCIDENCE IN UKRAINE USING GOOGLE TRENDS RESOURCES IN REAL-TIME AND FOR THE FUTURE PERIOD." Medical and Ecological Problems 26, no. 3-4 (August 31, 2022): 3–10. http://dx.doi.org/10.31718/mep.2022.26.3-4.01.
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Digital epidemiology resources are actively used for the timely response of the health care system to the emergence and spread of diseases. Analytical methods applicable to time series of data are used for detailed analysis of seasonal fluctuations of infectious diseases. Together with the Google Trends (GT) tool, such methods allow modeling the dynamics of diseases in real-time and for future periods. Given that the COVID-19 pandemic is still at an early stage of development, new methods of epidemiological surveillance of the disease will be able to ensure a timely response of the health care system to it. The aim of this research is to study the use of GT resources to build a mathematical model for the prognosis of the COVID-19 incidence in Ukraine in real time and for future periods. Materials and methods. In the course of the study, we used the GT tool to search Google queries “ковід, ковид, COVID-19” (KKC). Data on morbidity in Ukraine were obtained using the web resource: https://index.minfin.com.ua/ua/reference/coronavirus/ukraine/. Excel, Eviews, and StatPlus software packages were used to analyze time series, construct periodograms, correlograms, and mathematical models. The mathematical model of morbidity dynamics was built based on statistical exponential smoothing. Results. As Cyrillic equivalents of the term COVID-19, Ukrainians use the queries “кові(и)д”. Correlograms of KKC requests and actual incidence show seasonal fluctuations of the same frequency, and singular spectral analysis revealed statistically significant peaks. Based on statistical exponential smoothing, a prognostic model for the incidence of COVID-19 for 2022-2024 was built, which is reliable according to the criteria of accuracy and the results of the Dickey-Fuller test. Conclusions. The GT tool is a reliable source of data for studying the dynamics of the spread of COVID-19. Together with the use of additive time series models, it allows for a real-time reliable prognosis of the development of the disease. The presented approach to modeling the dynamics of the spread of COVID-19 can be used to track outbreaks of the disease and respond promptly to them both on a national and local scale.
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Alsanosy, Rashad. "Prevalence, Knowledge, Attitude, and Predictors of Waterpipe Smoking among School Adolescents in Saudi Arabia." Global Health 2022 (September 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/1902829.
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This cross-sectional study was designed to investigate the prevalence, knowledge, attitude, and predictors of waterpipe (WP) smoking among intermediate and secondary school adolescents in the Kingdom of Saudi Arabia (KSA). A self-administered anonymous questionnaire was used to collect data on demography, WP smoking status and patterns, the Arabic version of the Global Youth Tobacco Survey tool, and instruments to assess knowledge and attitude towards WP smoking. The Patient Health Questionnaire (PHQ-9) was also used. Descriptive and inferential statistical techniques were used. Modeling of WP smoking behavior was conducted using logistic regression. A total of 639 male students participated in this study. The prevalence of current WP and cigarette smokers were 17.7% and 14.6%, respectively. Out of the total population, 47.8% of students have the misconception that WP smoking is less harmful than cigarettes. A significant association ( P < 0.05) of some demographic factors (age, school stage, residence, and parents’ educational level) on WP smoking status was observed. Pleasure, socializing, and happiness represented the primary motives for initiating WP smoking. The majority of students had misconceptions about WP’s health effects. More than 50% believed that smoking WP could ease anxiety, cause less harm, and has less addictive properties compared to cigarettes. Modeling suggested that the most significant predictors of WP smoking were cigarette smoking, depression, and the attitude index. Current findings warrant further research and official health programs to promote educational initiatives regarding WP smoking.
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Brown, Tim, Wiwat Peerapatanapokin, Nalyn Siripong, and Robert Puckett. "The AIDS Epidemic Model 2023 for Estimating HIV Trends and Transmission Dynamics in Asian Epidemic Settings." JAIDS Journal of Acquired Immune Deficiency Syndromes 95, no. 1S (January 1, 2024): e13-e23. http://dx.doi.org/10.1097/qai.0000000000003319.
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Background: Thirteen Asian countries use the AIDS Epidemic Model (AEM) as their HIV model of choice. This article describes AEM, its inputs, and its application to national modeling. Setting: AEM is an incidence tool used by Spectrum for the Joint United Nations Programme on HIV/AIDS global estimates process. Methods: AEM simulates transmission of HIV among key populations (KPs) using measured trends in risk behaviors. The inputs, structure and calculations, interface, and outputs of AEM are described. The AEM process includes (1) collating and synthesizing data on KP risk behaviors, epidemiology, and size to produce model input trends; (2) calibrating the model to observed HIV prevalence; (3) extracting outputs by KP to describe epidemic dynamics and assist in improving responses; and (4) importing AEM incidence into Spectrum for global estimates. Recent changes to better align AEM mortality with Spectrum and add preexposure prophylaxis are described. Results: The application of AEM in Thailand is presented, describing the outputs and uses in-country. AEM replicated observed epidemiological trends when given observed behavioral inputs. The strengths and limitations of AEM are presented and used to inform thoughts on future directions for global models. Conclusions: AEM captures regional HIV epidemiology well and continues to evolve to meet country and global process needs. The addition of time-varying mortality and progression parameters has improved the alignment of the key population compartmental model of AEM with the age–sex-structured national model of Spectrum. Many of the features of AEM, including tracking the sources of infections over time, should be incorporated in future global efforts to build more generalizable models to guide policy and programs.
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Messina, Alexis, Michael Schyns, Björn-Olav Dozo, Vincent Denoël, Romain Van Hulle, Anne-Marie Etienne, Stéphanie Delroisse, et al. "Developing a Video Game as an Awareness and Research Tool Based on SARS-CoV-2 Epidemiological Dynamics and Motivational Perspectives." Transboundary and Emerging Diseases 2023 (February 24, 2023): 1–11. http://dx.doi.org/10.1155/2023/8205408.
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In mid-2020, the University of Liège (ULiège, Belgium) commissioned the ULiège Video Game Research Laboratory (Liège Game Lab) and the AR/VR Lab of the HEC-Management School of ULiège to create a serious game to raise awareness of preventive measures for its university community. This project has its origins in two objectives of the institutional policy of ULiège in response to the crisis caused by SARS-CoV-2 to raise awareness among community members of various preventive actions that can reduce the spread of the virus and to inform about the emergence and progression of a pandemic. After almost two years of design, the project resulted in the creation of SARS Wars, a decision-making management game for browsers and smartphones. This article presents the creative process of the game, specifically the integration of an adapted SEIR (susceptible-exposed-infectious-recovered) model, as well as the modeling of intercompartmental circulation dynamics in the game’s algorithm, and the various limitations observed regarding the game’s original missions and possibilities for future work. The SARS-CoV-2 video game project may be considered an innovative way to translate epidemiology into a language that can be used in the scope of citizen sciences. On the one hand, it provides an engaging tool and encourages active participation of the audience. On the other hand, it allows us to have a better understanding of the dynamic changes of a pandemic or an epidemic (crisis preparedness, monitoring, and control) and to anticipate potential consequences in the given parameters at set time (emerging risk identification), while offering insights for impact on some parameters on motivation (social science aspect).
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Alsulami, Samirah Hameed, Faisal Yasin, Zeeshan Afzal, and Maryam Shahid. "Efficient Solutions with the LRPS Method for Non-Linear Fractional Order Tuberculosis Models." Trends in Sciences 21, no. 5 (March 1, 2024): 7379. http://dx.doi.org/10.48048/tis.2024.7379.
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In this research article, we present a novel Non-Linear Fractional Order Tuberculosis mathematical model (NLFOTB) and introduce an efficient technique to obtain its solution. Fractional Order Models (FOMs) have garnered significant attention in contemporary research due to their widespread applicability. We address the challenge of solving the coupled Initial Value Problems (IVPs) associated with NLFOTB models by utilizing the groundbreaking LRPS method, which combines the RPS approach with the Laplace transform operator. This innovative approach generates approximate solutions in rapidly converging series forms, offering enhanced efficiency and reduced computational effort compared to conventional methods. Through the implementation of the LRPS method, we successfully derive an approximate solution for the NLFOTB model, contributing significantly to the field. Furthermore, our proposed approach demonstrates its efficacy in accurately capturing the dynamics of Tuberculosis (TB) through extensive computations and graphical representations, contributing to a deeper understanding of TB dynamics within a mathematical framework. Additionally, the LRPS method shows promise in tackling real world problems involving differential equations of various orders. Future investigations can extend the application of the LRPS method to explore other Fractional Order Models, further validating its effectiveness in a wide range of epidemic scenarios. Consequently, our study not only provides valuable insights into Tuberculosis dynamics but also introduces a powerful computational tool applicable to various practical problems in diverse disciplines, making a substantial contribution to the field of mathematical modeling and computation. HIGHLIGHTS · The comprehensive study highlights the LRPS strategy’s efficiency and accuracy in approximating solutions for fractional order equations. The research demonstrates its capability to predict compartmental behavior accurately within the specified range · The discussions presented in the article significantly contribute to the field of epidemiology by introducing and showcasing the LRPS approach’s effectiveness. As a valuable tool for investigating and validating epidemic models, the LRPS method offers improved efficiency and convenience, thereby enhancing the understanding of disease dynamics · The researchers anticipate that their findings will inspire further exploration and utilization of the LRPS technique in solving nonlinear models. This, in turn, is expected to contribute to advancements in the broader field of epidemiology, fostering continued innovation and development GRAPHICAL ABSTRACT
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Gressani, Oswaldo, Jacco Wallinga, Christian L. Althaus, Niel Hens, and Christel Faes. "EpiLPS: A fast and flexible Bayesian tool for estimation of the time-varying reproduction number." PLOS Computational Biology 18, no. 10 (October 10, 2022): e1010618. http://dx.doi.org/10.1371/journal.pcbi.1010618.
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In infectious disease epidemiology, the instantaneous reproduction number R t is a time-varying parameter defined as the average number of secondary infections generated by an infected individual at time t. It is therefore a crucial epidemiological statistic that assists public health decision makers in the management of an epidemic. We present a new Bayesian tool (EpiLPS) for robust estimation of the time-varying reproduction number. The proposed methodology smooths the epidemic curve and allows to obtain (approximate) point estimates and credible intervals of R t by employing the renewal equation, using Bayesian P-splines coupled with Laplace approximations of the conditional posterior of the spline vector. Two alternative approaches for inference are presented: (1) an approach based on a maximum a posteriori argument for the model hyperparameters, delivering estimates of R t in only a few seconds; and (2) an approach based on a Markov chain Monte Carlo (MCMC) scheme with underlying Langevin dynamics for efficient sampling of the posterior target distribution. Case counts per unit of time are assumed to follow a negative binomial distribution to account for potential overdispersion in the data that would not be captured by a classic Poisson model. Furthermore, after smoothing the epidemic curve, a “plug-in’’ estimate of the reproduction number can be obtained from the renewal equation yielding a closed form expression of R t as a function of the spline parameters. The approach is extremely fast and free of arbitrary smoothing assumptions. EpiLPS is applied on data of SARS-CoV-1 in Hong-Kong (2003), influenza A H1N1 (2009) in the USA and on the SARS-CoV-2 pandemic (2020-2021) for Belgium, Portugal, Denmark and France.
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Snider, Natalie G., Theresa Hastert, Ed Peters, Elena M. Stoffel, Laura Rozek, Ann Schwartz, and Kristen Purrington. "Abstract PR019: Evaluating the role of ambient air pollution in racial disparities of colorectal cancer incidence and survival in metropolitan Detroit." Cancer Epidemiology, Biomarkers & Prevention 32, no. 12_Supplement (December 1, 2023): PR019. http://dx.doi.org/10.1158/1538-7755.disp23-pr019.
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Abstract Colorectal cancer (CRC) is the third most diagnosed cancer in the United States and the third leading cause of cancer-related deaths. Moreover, Non-Hispanic Black (NHB) individuals have the highest rates of CRC incidence and mortality in the United States. We have become increasingly aware of the potential impact of social determinants of health (SDOH) on colorectal physiology and carcinogenesis. Other than individual and neighborhood level socioeconomic status (SES), neighborhood quality can also affect an individual’s risk of exposure to harmful environmental contaminants including ambient air pollution. Moreover, as NHB individuals are more likely to live in areas of lower area-level SES they experience these effects even further. We sought to understand whether higher exposure rates to fine particulate matter (PM2.5) played a role in racial differences in CRC incidence and survival in metropolitan Detroit. To do this, we obtained data from the Environmental Protection Agency (EPA) Environmental Justice Mapping and Screening Tool through the Georgetown University Environmental Impact Data Collective (EIDC) and patient data from the Metropolitan Detroit Cancer Surveillance System (MDCSS) registry. Incidence rates were calculated using population data from the American Community Survey. Associations between PM2.5 exposure and CRC incidence were estimated using Poisson modeling. Data were obtained for NHB (n=3778) and Non-Hispanic White (NHW) (n=9727) individuals diagnosed with invasive CRC between 2010 and 2019 in Wayne, Oakland, and Macomb counties, including their geocoded census tract at diagnosis as well as demographic and clinical features. Vital status was reported as months from the date of diagnosis to either death or last contact. PM2.5 metrics were converted to the national percentile of PM2.5 in the air utilizing the Bayesian space-time downscaling fusion model for derived estimates of air quality at the census tract level. Survival was assessed using Cox Proportional Hazards modeling. We found that an increase in the PM2.5 percentile increased the risk for CRC overall for NHB and NHW patients (Relative Risk [RR] = 1.80, 95% Confidence Interval [CI] = 1.55 - 2.08, p-value <0.0001). This association was even stronger among NHB patients (RR = 7.94, 95% CI = 5.45 - 11.52, p-value < 0.0001), but was not statistically significant among NHW patients (RR = 1.12, 95% CI = 0.95 – 1.32, p-value = 0.16). PM2.5 exposure was not associated with survival overall or stratified by race. We conclude that ambient air pollution, particularly PM2.5, increases the risk of CRC cancer and disproportionately affects NHB patients but may not play a role in mortality risk. These results highlight the importance of environmental justice research in biomedical research and the need for a better understanding of the etiology of CRC within and among differing genetic ancestries and races by evaluating the genetic and epigenetic differences in patient tumor samples. Citation Format: Natalie G. Snider, Theresa Hastert, Ed Peters, Elena M. Stoffel, Laura Rozek, Ann Schwartz, Kristen Purrington. Evaluating the role of ambient air pollution in racial disparities of colorectal cancer incidence and survival in metropolitan Detroit [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr PR019.
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Lin, Tsung-I., and Wan-Lun Wang. "Multivariate-t linear mixed models with censored responses, intermittent missing values and heavy tails." Statistical Methods in Medical Research 29, no. 5 (June 26, 2019): 1288–304. http://dx.doi.org/10.1177/0962280219857103.
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Multivariate longitudinal data arisen in medical studies often exhibit complex features such as censored responses, intermittent missing values, and atypical or outlying observations. The multivariate- t linear mixed model (MtLMM) has been recognized as a powerful tool for robust modeling of multivariate longitudinal data in the presence of potential outliers or fat-tailed noises. This paper presents a generalization of MtLMM, called the MtLMM-CM, to properly adjust for censorship due to detection limits of the assay and missingness embodied within multiple outcome variables recorded at irregular occasions. An expectation conditional maximization either (ECME) algorithm is developed to compute parameter estimates using the maximum likelihood (ML) approach. The asymptotic standard errors of the ML estimators of fixed effects are obtained by inverting the empirical information matrix according to Louis' method. The techniques for the estimation of random effects and imputation of missing responses are also investigated. The proposed methodology is illustrated on two real-world examples from HIV-AIDS studies and a simulation study under a variety of scenarios.
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Bottai, Matteo, and Giovanna Cilluffo. "Nonlinear parametric quantile models." Statistical Methods in Medical Research 29, no. 12 (July 19, 2020): 3757–69. http://dx.doi.org/10.1177/0962280220941159.
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Quantile regression is widely used to estimate conditional quantiles of an outcome variable of interest given covariates. This method can estimate one quantile at a time without imposing any constraints on the quantile process other than the linear combination of covariates and parameters specified by the regression model. While this is a flexible modeling tool, it generally yields erratic estimates of conditional quantiles and regression coefficients. Recently, parametric models for the regression coefficients have been proposed that can help balance bias and sampling variability. So far, however, only models that are linear in the parameters and covariates have been explored. This paper presents the general case of nonlinear parametric quantile models. These can be nonlinear with respect to the parameters, the covariates, or both. Some important features and asymptotic properties of the proposed estimator are described, and its finite-sample behavior is assessed in a simulation study. Nonlinear parametric quantile models are applied to estimate extreme quantiles of longitudinal measures of respiratory mechanics in asthmatic children from an epidemiological study and to evaluate a dose–response relationship in a toxicological laboratory experiment.
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Ackley, Sarah F., Justin Lessler, and M. Maria Glymour. "Dynamical Modeling as a Tool for Inferring Causation." American Journal of Epidemiology, August 27, 2021. http://dx.doi.org/10.1093/aje/kwab222.
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Abstract Dynamical models, commonly used in infectious disease epidemiology, are formal mathematical representations of time-changing systems or processes. For many chronic disease epidemiologists, the link between dynamical models and predominant causal inference paradigms is unclear. This commentary explains the use of dynamical models for representing causal systems and the relevance of dynamical models for causal inference. In certain simple settings, dynamical modeling and conventional statistical methods (e.g., regression-based methods) are equivalent, but dynamical modeling has advantages over conventional statistical methods for many causal inference problems. Dynamical models can be used to transparently encode complex biological knowledge, interference and spillover, effect modification, and variables that influence each other in continuous time. As our knowledge of biological and social systems and access to computational resources increases, there will be a growing utility for a variety of mathematical modeling tools in epidemiology.
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Meza, Rafael, and Jihyoun Jeon. "Mechanistic and biologically based models in epidemiology; a powerful underutilized tool." American Journal of Epidemiology, June 1, 2022. http://dx.doi.org/10.1093/aje/kwac099.
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Abstract Mechanistic and biologically based mathematical models of chronic and behavioral disease processes aim to capture the main mechanistic or biological features of the disease development, and to connect these with epidemiological outcomes. These approaches have a long history in epidemiological research and are complementary to traditional epidemiological or statistical approaches to investigate the role of risk factor exposures on disease risk. In the article by Simonetto et al. (Am J Epidemiol. XXXX;XXX(XX):XXXX–XXXX)), the authors present a mechanistic, process-oriented, model to investigate the role of smoking, hypertension and dyslipidemia on the development of atherosclerotic lesions and their progression to myocardial infarction (MI). Their approach builds on and brings to cardiovascular disease the ideas and perspectives of earlier mechanistic and biologically based models for the epidemiology of cancer and other chronic diseases, providing important insights into the mechanisms and epidemiology of smoking related MI. We argue that although mechanistic modeling approaches have demonstrated their value and place in epidemiology, they are highly underutilized. We call for efforts to grow mechanistic and biologically based modeling research, expertise and awareness in epidemiology, including the development of training and collaboration opportunities to attract more students and researchers from STEM areas into the epidemiology field.
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Cárdenas, Pablo, Vladimir Corredor, and Mauricio Santos-Vega. "Genomic epidemiological models describe pathogen evolution across fitness valleys." Science Advances 8, no. 28 (July 15, 2022). http://dx.doi.org/10.1126/sciadv.abo0173.
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Genomics is fundamentally changing epidemiological research. However, systematically exploring hypotheses in pathogen evolution requires new modeling tools. Models intertwining pathogen epidemiology and genomic evolution can help understand processes such as the emergence of novel pathogen genotypes with higher transmissibility or resistance to treatment. In this work, we present Opqua, a flexible simulation framework that explicitly links epidemiology to sequence evolution and selection. We use Opqua to study determinants of evolution across fitness valleys. We confirm that competition can limit evolution in high-transmission environments and find that low transmission, host mobility, and complex pathogen life cycles facilitate reaching new adaptive peaks through population bottlenecks and decoupling of selective pressures. The results show the potential of genomic epidemiological modeling as a tool in infectious disease research.
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Castagno, Paolo, Simone Pernice, Gianni Ghetti, Massimiliano Povero, Lorenzo Pradelli, Daniela Paolotti, Gianfranco Balbo, Matteo Sereno, and Marco Beccuti. "A computational framework for modeling and studying pertussis epidemiology and vaccination." BMC Bioinformatics 21, S8 (September 2020). http://dx.doi.org/10.1186/s12859-020-03648-6.
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Abstract Background Emerging and re-emerging infectious diseases such as Zika, SARS, ncovid19 and Pertussis, pose a compelling challenge for epidemiologists due to their significant impact on global public health. In this context, computational models and computer simulations are one of the available research tools that epidemiologists can exploit to better understand the spreading characteristics of these diseases and to decide on vaccination policies, human interaction controls, and other social measures to counter, mitigate or simply delay the spread of the infectious diseases. Nevertheless, the construction of mathematical models for these diseases and their solutions remain a challenging tasks due to the fact that little effort has been devoted to the definition of a general framework easily accessible even by researchers without advanced modelling and mathematical skills. Results In this paper we describe a new general modeling framework to study epidemiological systems, whose novelties and strengths are: (1) the use of a graphical formalism to simplify the model creation phase; (2) the implementation of an R package providing a friendly interface to access the analysis techniques implemented in the framework; (3) a high level of portability and reproducibility granted by the containerization of all analysis techniques implemented in the framework; (4) a well-defined schema and related infrastructure to allow users to easily integrate their own analysis workflow in the framework. Then, the effectiveness of this framework is showed through a case of study in which we investigate the pertussis epidemiology in Italy. Conclusions We propose a new general modeling framework for the analysis of epidemiological systems, which exploits Petri Net graphical formalism, R environment, and Docker containerization to derive a tool easily accessible by any researcher even without advanced mathematical and computational skills. Moreover, the framework was implemented following the guidelines defined by Reproducible Bioinformatics Project so it guarantees reproducible analysis and makes simple the developed of new user-defined workflows.
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Amer, Ahmed Noby, Ahmed Gaballah, Rasha Emad, Abeer Ghazal, and Nancy Attia. "Molecular Epidemiology of HIV-1 virus in Egypt: A major change in the circulating subtypes." Current HIV Research 19 (August 5, 2021). http://dx.doi.org/10.2174/1570162x19666210805091742.
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Background: Human immunodeficiency virus type 1 (HIV-1) is characterized by high genetic diversity due to its high-mutation and recombination rates. Although, there is an increasing prevalence of circulating recombinant forms (CRFs) worldwide. Subtype B is still recognized as the predominant subtype in the Middle East and North Africa (MENA) region. There is a limited sampling of HIV in this region due to its low prevalence. The main purpose of this study is to provide a summary of the current status of the resident HIV subtypes and their distribution among Egyptian patients. Methodology: Forty-five HIV-1 patients were included in this study. Partial pol gene covering the protease (PR) and reverse transcriptase (RT) was successfully amplified in 21 HIV patients using nested PCR of cDNA of the viral genomic RNA, then sequenced. The sequence data were used for viral HIV-1 subtyping by 5 online subtyping tools: NCBI viral genotyping tool, Stanford University HIV database (HIVDB) subtyping program, REGA tool, Context-based modeling for expeditious typing (COMET) tool, and Recombinant identification program (RIP) tool. The final subtype assignment was based on molecular phylogenetic analysis. Results: Unexpectedly, non-B subtypes are dominating with the most common circulating one is CRF02_AG (57.1%) followed by subtype B (14.3%), subtype BG recombinant (9.5%), CRF35_AD (9.5%), subtype A1 and CRF06_cpx (4.8 % each). Conclusion: To the best of our knowledge, this is the first study to tackle HIV-1 subtyping among the group of HIV-1 patients in Egypt. CRF02_AG is the most prevalent subtype in Egypt.
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Mistry, Dina, Maria Litvinova, Ana Pastore y Piontti, Matteo Chinazzi, Laura Fumanelli, Marcelo F. C. Gomes, Syed A. Haque, et al. "Inferring high-resolution human mixing patterns for disease modeling." Nature Communications 12, no. 1 (January 12, 2021). http://dx.doi.org/10.1038/s41467-020-20544-y.
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AbstractMathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for realism in addressing complex public health questions is, however, calling for accurate models of the human contact patterns that govern the disease transmission processes. Here we present a data-driven approach to generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features. We produce age-stratified contact matrices for 35 countries, including 277 sub-national administratvie regions of 8 of those countries, covering approximately 3.5 billion people and reflecting the high degree of cultural and societal diversity of the focus countries. We use the derived contact matrices to model the spread of airborne infectious diseases and show that sub-national heterogeneities in human mixing patterns have a marked impact on epidemic indicators such as the reproduction number and overall attack rate of epidemics of the same etiology. The contact patterns derived here are made publicly available as a modeling tool to study the impact of socio-economic differences and demographic heterogeneities across populations on the epidemiology of infectious diseases.
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Tolksdorf, Johanna, Michael W. Kattan, Stephen A. Boorjian, Stephen J. Freedland, Karim Saba, Cedric Poyet, Lourdes Guerrios, et al. "Multi-cohort modeling strategies for scalable globally accessible prostate cancer risk tools." BMC Medical Research Methodology 19, no. 1 (October 15, 2019). http://dx.doi.org/10.1186/s12874-019-0839-0.
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Abstract Background Online clinical risk prediction tools built on data from multiple cohorts are increasingly being utilized for contemporary doctor-patient decision-making and validation. This report outlines a comprehensive data science strategy for building such tools with application to the Prostate Biopsy Collaborative Group prostate cancer risk prediction tool. Methods We created models for high-grade prostate cancer risk using six established risk factors. The data comprised 8492 prostate biopsies collected from ten institutions, 2 in Europe and 8 across North America. We calculated area under the receiver operating characteristic curve (AUC) for discrimination, the Hosmer-Lemeshow test statistic (HLS) for calibration and the clinical net benefit at risk threshold 15%. We implemented several internal cross-validation schemes to assess the influence of modeling method and individual cohort on validation performance. Results High-grade disease prevalence ranged from 18% in Zurich (1863 biopsies) to 39% in UT Health San Antonio (899 biopsies). Visualization revealed outliers in terms of risk factors, including San Juan VA (51% abnormal digital rectal exam), Durham VA (63% African American), and Zurich (2.8% family history). Exclusion of any cohort did not significantly affect the AUC or HLS, nor did the choice of prediction model (pooled, random-effects, meta-analysis). Excluding the lowest-prevalence Zurich cohort from training sets did not statistically significantly change the validation metrics for any of the individual cohorts, except for Sunnybrook, where the effect on the AUC was minimal. Therefore the final multivariable logistic model was built by pooling the data from all cohorts using logistic regression. Higher prostate-specific antigen and age, abnormal digital rectal exam, African ancestry and a family history of prostate cancer increased risk of high-grade prostate cancer, while a history of a prior negative prostate biopsy decreased risk (all p-values < 0.004). Conclusions We have outlined a multi-cohort model-building internal validation strategy for developing globally accessible and scalable risk prediction tools.