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The management of diabetes mellitus focuses on close monitoring of a patient’s blood glucose level while the clinician experiments with a dosing strategy using clinical guidelines and his/her own experience. We propose a pharmacokinetic and pharmacodynamics model that characterizes the dose-response of patients receiving anti-diabetic drug therapy. We derive and establish a direct relationship between drug dosage and blood glucose level. This new drug-dose drug-effect model, combined with a linear disease progression model, is used to fit the patient’s daily self-monitored blood glucose (SMBG) data to obtain the personalized treatment effect for each patient. The model predicts the long-term drug effect using the prescribed dose, thus allowing for dose optimization. The model is evaluated on patients with gestational diabetes mellitus. SMBG data collected during the first month of treatment is used to train the model. The model is able to characterize the personalized dose-response and disease progression. Moreover, when compared to a descriptive autoregression model, our model gives a better long-term prediction of the drug effect on the trend of the blood glucose level. This mechanism-based treatment effect model utilizes daily recorded blood glucose data to estimate and predict a patient’s personalized dose-response and disease progression. Such evidence can be used by clinicians to individualize and optimize dose regimens to achieve better treatment outcomes.

In this paper, we couple a general-purpose infectious disease theory with a computational modeling framework to analyze strategies for avian influenza containment. We focus on virus transmission among domestic poultry populations to optimize and evaluate the effectiveness of three containment strategies and their combinations: reducing the contact rate among domestic birds, reducing the population of infected birds, and reducing the transportation of infected birds. We illustrate their usage during a two-wave avian flu outbreak in Nigeria. Our findings show that reducing contacts by 20% via cluster isolation early in the first wave can achieve containment rapidly. It also helps avert the second wave. Slaughtering infected birds is not as effective, requiring scheduled killings of over 80% of the poultry while failing to avert the second wave. This practice also risks damaging the local economy and potential secondary infections from the carcasses of infected birds. Reducing transportation between northern and southern Nigeria does not offer good containment since the disease spread began in both regions simultaneously. Reducing transportation has an impact when applied to neighboring regions and cities, or when the initial incidence of the disease is localized. Combination strategies prove to be the most practical and cost-effective to implement. The use of 3D-effectiveness visualized plots allows policymakers to evaluate multiple combination strategies and choose the one that optimizes containment while also adhering to budget constraints, resource availability, and management preference. The generalized mathematical theory and modeling framework is highly flexible and can be applied to other diseases, including those with multiple hosts, multiple species involvement, and across a broad array of heterogeneous regions.

In this study, we describe the development and use of a biological-behavior-intervention computational informatics framework that combines disease modelling for infectious virus with stratifications for social behavior and employment, and resource logistics. The framework incorporates heterogeneous group behavior and interaction dynamics, and optimizes intervention and resources for effective containment. We demonstrate its usage by analyzing and optimizing containment strategies for the 2014-2016 West Africa Ebola outbreak, and its implementation for responses to the 2020 COVID-19 pandemic in the United States. Our analysis shows that timely action within 1.5 months from the onset of confirmed cases can cut down 90% of overall infections and bring rapid containment within 6-8 months. The additional medical resources required are minor and would ensure proper treatment and quarantine of patients while reducing the risk of infections among healthcare workers. The benefit (in infection / death control) would be reduced by 10 to over 100 fold and time to containment would increase by 2-4 fold when intervention and medical resources are injected within 5 months. In contrast, the additional resources needed to bring down the overall infection in a delayed intervention are significant, with inferior results. The disease module can be tailored for different pathogens. It expands the well-used SEIR model to include social and intervention activities, asymptomatic and post-recovery transmission, hospitalization, outcome of recovery, and funeral events. The model also examines the transmission rate of health care workers and allows for heterogenous infection factors among different groups. It also captures time-variant human behavior during the horizon of the outbreak. The framework optimizes the intervention timeline and resource allocation during an infectious disease outbreak and offers insights on how resource availability in time and quantity can affect the disease trends and containment significantly. This can inform policy, disease management and resource allocation. While focusing on bed availability for quarantine and treatment appears to be simplistic, their necessity for Ebola responses cannot be overemphasized. We link these insights to a web-based tool to provide quick and intuitive observations for decision making and investigation of the disease outbreak situation. Subsequent use of the system to determine the optimal timing and effectiveness and tradeoffs analysis of various non-pharmaceutical intervention strategies for COVID-19 provide a foundation for policy makers to execute the first-step response. These results have been implemented on the ground since March 2020. The web-based tool pinpoints accurately the import of disease from global travels and associated disease spread and health burdens. This prospectively affirms the importance of such a real-time computational system, and its availability before onset of a pandemic.

This study investigates the safety and efficacy of a large-dose, needle-based epidural technique in obstetric anesthesia. The technique differs from a standard, catheter-based approach in that the anesthetic dose is administered through an epidural needle prior to insertion of the epidural catheter. Using a data-driven informatics and machine learning approach, our findings show that the needle-based technique is faster and more dose-effective in achieving sensory level. We also find that injecting large doses in the epidural space through the epidural needle is safe, with complication rates similar to those reported in published literature for catheter-based technique. Further, machine learning reveals that if the needle dose is kept under 18 ml, the resulting hypotension rate will be significantly lower than published results. The machine learning framework can predict the incidence of hypotension with 85% accuracy. The findings from this investigation facilitate delivery improvement and establish an improved clinical practice guideline for training and for dissemination of safe practice.

The Zika virus (ZIKV) outbreak in South American countries and its potential association with microcephaly in newborns and Guillain-Barré Syndrome led the World Health Organization to declare a Public Health Emergency of International Concern. To understand the ZIKV disease dynamics and evaluate the effectiveness of different containment strategies, we propose a compartmental model with a vector-host structure for ZIKV. The model utilizes logistic growth in human population and dynamic growth in vector population. Using this model, we derive the basic reproduction number to gain insight on containment strategies. We contrast the impact and influence of different parameters on the virus trend and outbreak spread. We also evaluate different containment strategies and their combination effects to achieve early containment by minimizing total infections. This result can help decision makers select and invest in the strategies most effective to combat the infection spread. The decision-support tool demonstrates the importance of "digital disease surveillance" in response to waves of epidemics including ZIKV, Dengue, Ebola and cholera.

This paper examines several different queuing models for intensive care units (ICU) and the effects on wait times, utilization, return rates, mortalities, and number of patients served. Five separate intensive care units at an urban hospital are analyzed and distributions are fitted for arrivals and service durations. A system-based simulation model is built to capture all possible cases of patient flow after ICU admission. These include mortalities and returns before and after hospital exits. Patients are grouped into 9 different classes that are categorized by severity and length of stay (LOS). Each queuing model varies by the policies that are permitted and by the order the patients are admitted. The first set of models does not prioritize patients, but examines the advantages of smoothing the operating schedule for elective surgeries. The second set analyzes the differences between prioritizing admissions by expected LOS or patient severity. The last set permits early ICU discharges and conservative and aggressive bumping policies are contrasted. It was found that prioritizing patients by severity considerably reduced delays for critical cases, but also increased the average waiting time for all patients. Aggressive bumping significantly raised the return and mortality rates, but more conservative methods balance quality and efficiency with lowered wait times without serious consequences.

The primary purpose of this study was to develop a clinical tool capable of identifying discriminatory characteristics that can predict patients who will return within 72 hours to the Pediatric emergency department (PED). We studied 66,861 patients who were discharged from the EDs during the period from May 1 2009 to December 31 2009. We used a classification model to predict return visits based on factors extracted from patient demographic information, chief complaint, diagnosis, treatment, and hospital real-time ED statistics census. We began with a large pool of potentially important factors, and used particle swarm optimization techniques for feature selection coupled with an optimization-based discriminant analysis model (DAMIP) to identify a classification rule with relatively small subsets of discriminatory factors that can be used to predict - with 80% accuracy or greater - return within 72 hours. The analysis involves using a subset of the patient cohort for training and establishment of the predictive rule, and blind predicting the return of the remaining patients. Good candidate factors for revisit prediction are obtained where the accuracy of cross validation and blind prediction are over 80%. Among the predictive rules, the most frequent discriminatory factors identified include diagnosis (97%), patient complaint (97%), and provider type (57%). There are significant differences in the readmission characteristics among different acuity levels. For Level 1 patients, critical readmission factors include patient complaint (57%), time when the patient arrived until he/she got an ED bed (64%), and type/number of providers (50%). For Level 4/5 patients, physician diagnosis (100%), patient complaint (99%), disposition type when patient arrives and leaves the ED (30%), and if patient has lab test (33%) appear to be significant. The model was demonstrated to be consistent and predictive across multiple PED sites.The resulting tool could enable ED staff and administrators to use patient specific values for each of a small number of discriminatory factors, and in return receive a prediction as to whether the patient will return to the ED within 72 hours. Our prediction accuracy can be as high as over 85%. This provides an opportunity for improving care and offering additional care or guidance to reduce ED readmission.

The First International Workshop on The Role and Impact of Mathematics in Medicine (RIMM) convened in Paris in June 2010. A broad range of researchers discussed the difficulties, challenges and opportunities faced by those wishing to see mathematical methods contribute to improved medical outcomes. Finding mechanisms for interdisciplinary meetings, developing a common language, staying focused on the medical problem at hand, deriving realistic mathematical solutions, obtaining high quality data and seeing things through “by the bedside” are some of the issues discussed by the participants.

As the volume and availability of biological databases continue widespread growth, it has become increasingly difficult for research scientists to identify all relevant information for biological entities of interest. Details of nucleotide sequences, gene expression, molecular interactions, and three-dimensional structures are maintained across many different databases. To retrieve all necessary information requires an integrated system that can query multiple databases with minimized overhead. This paper introduces a universal parser and relational schema translator that can be utilized for all NCBI databases in Abstract Syntax Notation (ASN.1). The data models for OMIM, Entrez-Gene, Pubmed, MMDB and GenBank have been successfully converted into relational databases and all are easily linkable helping to answer complex biological questions. These tools facilitate research scientists to locally integrate databases from NCBI without significant workload or development time.