An agent-based model of spread of a pandemic with validation using COVID-19 data from New York State.

We introduce a novel agent based model where each agent carries an effective viral load that captures the instantaneous state of infection of the agent. We simulate the spread of a pandemic and subsequently validate it by using publicly available COVID-19 data. Our simulation tracks the temporal evolution of a virtual city or community of agents in terms of contracting infection, recovering asymptomatically, or getting hospitalized. The virtual community is divided into family groups with 2-6 individuals in each group. Agents interact with other agents in virtual public places like at grocery stores, on public transportation and in offices. We initially seed the virtual community with a very small number of infected individuals and then monitor the disease spread and hospitalization over a period of fifty days, which is a reasonable time-frame for the initial spread of a pandemic. An uninfected or asymptomatic agent is randomly selected from a random family group in each simulation step for visiting a random public space. Subsequently, an uninfected agent contracts infection if the public place is occupied by other infected agents. We have calibrated our simulation rounds according to the size of the population of the virtual community for simulating realistic exposure of agents to a contagion. Our simulation results are consistent with the publicly available hospitalization and ICU patient data from three distinct regions of varying sizes in New York state. Our model can predict the trend in epidemic spread and hospitalization from a set of simple parameters and could be potentially useful in predicting the disease evolution based on available data and observations about public behavior. Our simulations suggest that relaxing the social distancing measures may increase the hospitalization numbers by some 30% or more.

Mapping Information Needs of Patients With Sexually Transmitted Infections Using Web-Based Data Sources: Grounded Theory Investigation.

BACKGROUND: According to the World health organization (WHO), more than 1 million sexually transmitted infections (STIs) are acquired each day across the world. The incidence rates of STIs in the United States are at a record high for the fourth consecutive year. Owing to the stigma associated with the incidence of STI, there is a general reluctance to seek information in person. Instead, web-based information sources remain the primary avenues of information-seeking. However, these sources are designed without a comprehensive understanding of the information needs of individuals who have contracted STIs. OBJECTIVE: This study aimed to investigate the information needs of individuals who have or suspect they have contracted an STI. A better understanding of their information needs can drive the design of more effective digital interventions. METHODS: This is a qualitative and analytical study of 549 transcripts (consisting of queries posted over the last 10 years) from web-based forums of the American Sexual Health Association (ASHA), which allows patients, volunteers, and health care providers connect anonymously. The analysis follows a grounded theory (GT) approach with multiple coding stages to uncover categories and themes. RESULTS: Three categories of information needs emerged. The first two, clinical and logistical, are similar to other contexts. However, our analysis shows that there is a significant need for the last category-psychosocial information. Approximately 59% of instances are linked to concerns such as confusion, discretion, remorse, and others. These needs vary across the stages of a patient's journey from symptom manifestation to treatment maintenance. CONCLUSIONS: Responding to the needs of individuals who have or suspect they have contracted an STI requires compassionate and personalized responses (beyond factual clinical and logistical information). Web-based forums provide anonymity but do not adequately incorporate mechanisms, practices, or incentives to respond to diverse psychosocial concerns. Innovative approaches to add such support can make the digital interventions more effective for this group of individuals.

A seq2seq model to forecast the COVID-19 cases, deaths and reproductive R numbers in US counties.

The global pandemic of coronavirus disease 2019 (COVID-19) has killed almost two million people worldwide and over 400 thousand in the United States (US). As the pandemic evolves, informed policy-making and strategic resource allocation relies on accurate forecasts. To predict the spread of the virus within US counties, we curated an array of county-level demographic and COVID-19-relevant health risk factors. In combination with the county-level case and death numbers curated by John Hopkins university, we developed a forecasting model using deep learning (DL). We implemented an autoencoder-based Seq2Seq model with gated recurrent units (GRUs) in the deep recurrent layers. We trained the model to predict future incident cases, deaths and the reproductive number, R For most counties, it makes accurate predictions of new incident cases, deaths and R values, up to 30 days in the future. Our framework can also be used to predict other targets that are useful indices for policymaking, for example hospitalization or the occupancy of intensive care units. Our DL framework is publicly available on GitHub and can be adapted for other indices of the COVID-19 spread. We hope that our forecasts and model can help local governments in the continued fight against COVID-19.

A seq2seq model to forecast the COVID-19 cases, deaths and reproductive R numbers in US counties.

The global pandemic of coronavirus disease 2019 (COVID-19) has killed almost two million people worldwide and over 400 thousand in the United States (US). As the pandemic evolves, informed policy-making and strategic resource allocation relies on accurate forecasts. To predict the spread of the virus within US counties, we curated an array of county-level demographic and COVID-19-relevant health risk factors. In combination with the county-level case and death numbers curated by John Hopkins university, we developed a forecasting model using deep learning (DL). We implemented an autoencoder-based Seq2Seq model with gated recurrent units (GRUs) in the deep recurrent layers. We trained the model to predict future incident cases, deaths and the reproductive number, R. For most counties, it makes accurate predictions of new incident cases, deaths and R values, up to 30 days in the future. Our framework can also be used to predict other targets that are useful indices for policymaking, for example hospitalization or the occupancy of intensive care units. Our DL framework is publicly available on GitHub and can be adapted for other indices of the COVID-19 spread. We hope that our forecasts and model can help local governments in the continued fight against COVID-19.

A comparative analysis of moral principles and behavioral norms in eight ethical codes relevant to health sciences librarianship, medical informatics, and the health professions.

OBJECTIVE: Based on the authors' shared interest in the interprofessional challenges surrounding health information management, this study explores the degree to which librarians, informatics professionals, and core health professionals in medicine, nursing, and public health share common ethical behavior norms grounded in moral principles. METHODS: Using the "Principlism" framework from a widely cited textbook of biomedical ethics, the authors analyze the statements in the ethical codes for associations of librarians (Medical Library Association [MLA], American Library Association, and Special Libraries Association), informatics professionals (American Medical Informatics Association [AMIA] and American Health Information Management Association), and core health professionals (American Medical Association, American Nurses Association, and American Public Health Association). This analysis focuses on whether and how the statements in these eight codes specify core moral norms (Autonomy, Beneficence, Non-Maleficence, and Justice), core behavioral norms (Veracity, Privacy, Confidentiality, and Fidelity), and other norms that are empirically derived from the code statements. RESULTS: These eight ethical codes share a large number of common behavioral norms based most frequently on the principle of Beneficence, then on Autonomy and Justice, but rarely on Non-Maleficence. The MLA and AMIA codes share the largest number of common behavioral norms, and these two associations also share many norms with the other six associations. IMPLICATIONS: The shared core of behavioral norms among these professions, all grounded in core moral principles, point to many opportunities for building effective interprofessional communication and collaboration regarding the development, management, and use of health information resources and technologies.

Challenges in ethics, safety, best practices, and oversight regarding HIT vendors, their customers, and patients: a report of an AMIA special task force.

The current commercial health information technology (HIT) arena encompasses a number of competing firms that provide electronic health applications to hospitals, clinical practices, and other healthcare-related entities. Such applications collect, store, and analyze patient information. Some vendors incorporate contract language whereby purchasers of HIT systems, such as hospitals and clinics, must indemnify vendors for malpractice or personal injury claims, even if those events are not caused or fostered by the purchasers. Some vendors require contract clauses that force HIT system purchasers to adopt vendor-defined policies that prevent the disclosure of errors, bugs, design flaws, and other HIT-software-related hazards. To address this issue, the AMIA Board of Directors appointed a Task Force to provide an analysis and insights. Task Force findings and recommendations include: patient safety should trump all other values; corporate concerns about liability and intellectual property ownership may be valid but should not over-ride all other considerations; transparency and a commitment to patient safety should govern vendor contracts; institutions are duty-bound to provide ethics education to purchasers and users, and should commit publicly to standards of corporate conduct; and vendors, system purchasers, and users should encourage and assist in each others' efforts to adopt best practices. Finally, the HIT community should re-examine whether and how regulation of electronic health applications could foster improved care, public health, and patient safety.

The impact of electronic medical record systems on outpatient workflows: a longitudinal evaluation of its workflow effects.

CONTEXT: The promise of the electronic medical record (EMR) lies in its ability to reduce the costs of health care delivery and improve the overall quality of care--a promise that is realized through major changes in workflows within the health care organization. Yet little systematic information exists about the workflow effects of EMRs. Moreover, some of the research to-date points to reduced satisfaction among physicians after implementation of the EMR and increased time, i.e., negative workflow effects. A better understanding of the impact of the EMR on workflows is, hence, vital to understanding what the technology really does offer that is new and unique. OBJECTIVE: (i) To empirically develop a physician centric conceptual model of the workflow effects of EMRs; (ii) To use the model to understand the antecedents to the physicians' workflow expectation from the new EMR; (iii) To track physicians' satisfaction overtime, 3 months and 20 months after implementation of the EMR; (iv) To explore the impact of technology learning curves on physicians' reported satisfaction levels. DESIGN: The current research uses the mixed-method technique of concept mapping to empirically develop the conceptual model of an EMR's workflow effects. The model is then used within a controlled study to track physician expectations from a new EMR system as well as their assessments of the EMR's performance 3 months and 20 months after implementation. SETTING: The research tracks the actual implementation of a new EMR within the outpatient clinics of a large northeastern research hospital. PARTICIPANTS: The pre-implementation survey netted 20 physician responses; post-implementation Time 1 survey netted 22 responses, and Time 2 survey netted 26 physician responses. INTERVENTION: The implementation of the actual EMR served as the intervention. Since the study was conducted within the same setting and tracked a homogenous group of respondents, the overall study design ensured against extraneous influences on the results. MAIN OUTCOME MEASURES: Outcome measures were derived empirically from the conceptual model. They included 85 items that measured physician perceptions of the EMR's workflow effect on the following eight issues: (1) administration, (2) efficiency in patient processing, (3) basic clinical processes, (4) documentation of patient encounter, (5) economic challenges and reimbursement, (6) technical issues, (7) patient safety and care, and (8) communication and confidentiality. The items were used to track expectations prior to implementation and they served as retrospective measures of satisfaction with the EMR in post-implementation Time 1 and Time 2. RESULTS: The findings suggest that physicians conceptualize EMRs as an incremental extension of older computerized provider order entries (CPOEs) rather than as a new innovation. The EMRs major functional advantages are seen to be very similar to, if not the same as, those of CPOEs. Technology learning curves play a statistically significant though minor role in shaping physician perceptions. CONCLUSIONS: The physicians' expectations from the EMR are based on their prior beliefs rather than on a rational evaluation of the EMR's fit, functionality, or performance. Their decision regarding the usefulness of the EMR is made very early, within the first few months of use of the EMR. These early perceptions then remain stable and become the lens through which subsequent experience with the EMR is interpreted. The findings suggest a need for communication based interventions aimed at explaining the value, fit, and usefulness of EMRs to physicians early in the pre- and immediate post-EMR implementation stages.

A code of professional ethical conduct for the American Medical Informatics Association: an AMIA Board of Directors approved white paper.

The AMIA Board of Directors has decided to periodically publish AMIA's Code of Professional Ethical Conduct for its members in the Journal of the American Medical Informatics Association. The Code also will be available on the AMIA Web site at www.amia.org as it continues to evolve in response to feedback from the AMIA membership. The AMIA Board acknowledges the continuing work and dedication of the AMIA Ethics Committee. AMIA is the copyright holder of this work.

New technique (the NOW test) for rapid detection of Streptococcus pneumoniae in the nasopharynx.

Although the NOW test was originally introduced as a rapid pneumococcal antigen detection test for use with urine samples, it was successfully adapted to nasopharyngeal samples in the present study. The sensitivity, specificity, positive predictive value, and negative predictive value of the test were 92.2, 97.7, 95.9, and 95.5%, respectively. These results demonstrate that nasopharyngeal colonization with Streptococcus pneumoniae can be documented within 15 min of sample collection.

Urinary excretion of pneumococcal cell wall polysaccharide in children.

The urinary excretion of the cell wall polysaccharide of Streptococcus pneumoniae was studied in 92 children with the NOW test. Cell wall polysaccharide was detected in 65% of pneumococcal carriers and in 10% of noncarriers. Excretion rates were similar in healthy children and in children with acute otitis media. The high rate of antigen excretion among nonill carriers suggests that colonization is a major source of urinary antigen in children.