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Background Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the number of cases of coronavirus disease (COVID-19) in the United States has exponentially increased. Identifying and monitoring individuals with COVID-19 and individuals who have been exposed to the disease is critical to prevent transmission. Traditional contact tracing mechanisms are not structured on the scale needed to address this pandemic. As businesses reopen, institutions and agencies not traditionally engaged in disease prevention are being tasked with ensuring public safety. Systems to support organizations facing these new challenges are critically needed. Most currently available symptom trackers use a direct-to-consumer approach and use personal identifiers, which raises privacy concerns. Objective Our aim was to develop a monitoring and reporting system for COVID-19 to support institutions conducting monitoring activities without compromising privacy. Methods Our multidisciplinary team designed a symptom tracking system after consultation with experts. The system was designed in the Georgetown University AvesTerra knowledge management environment, which supports data integration and synthesis to identify actionable events and maintain privacy. We conducted a beta test for functionality among consenting Georgetown University medical students. Results The symptom tracker system was designed based on guiding principles developed during peer consultations. Institutions are provided access to the system through an efficient onboarding process that uses clickwrap technology to document agreement to limited terms of use to rapidly enable free access. Institutions provide their constituents with a unique identifier to enter data through a web-based user interface to collect vetted symptoms as well as clinical and epidemiologic data. The website also provides individuals with educational information through links to the COVID-19 prevention recommendations from the US Centers for Disease Control and Prevention. Safety features include instructions for people with new or worsening symptoms to seek care. No personal identifiers are collected in the system. The reporter mechanism safeguards data access so that institutions can only access their own data, and it provides institutions with on-demand access to the data entered by their constituents, organized in summary reports that highlight actionable data. Development of the system began on March 15, 2020, and it was launched on March 20, 2020. In the beta test, 48 Georgetown University School of Medicine students or their social contacts entered data into the system from March 31 to April 5, 2020. One of the 48 users (2%) reported active COVID-19 infection and had no symptoms by the end of the monitoring period. No other participants reported symptoms. Only data with the unique entity identifier for our beta test were generated in our summary reports. Conclusions This system harnesses insights into privacy and data sharing to avoid regulatory and legal hurdles to rapid adaption by entities tasked with maintaining public safety. Our pilot study demonstrated feasibility and ease of use. Refinements based on feedback from early adapters included release of a Spanish language version. These systems provide technological advances to complement the traditional contact tracing and digital tracing applications being implemented to limit SARS-CoV-2 transmission during reopening.

A menacing context has emerged when a dread threat persists and requires a community to reorganize its life to help mitigate consequences of threat. This article explores how menacing context links drivers of forced migration, the perception of threat among local families and domestic decision-making about remaining in place, fleeing or combinations of both. Employing a coding scheme based on dread threat theory, this article illustrates through case studies of a cholera epidemic, total war setting and a complex situation with infectious disease, civil strife and drought threats how to transform qualitative data from ethnographic, autobiographical and journalistic sources into a quantitative measurement scale of local perception of threat for use in formal modelling, forecasting and potentially enhanced humanitarian responses to mass displacement.

BACKGROUND The emergence of SARS-CoV-2 and the resulting COVID-19 pandemic has led to tremendous strain on institutions/agencies working to prevent viral transmission.1,2 Up to a fifth of individuals with COVID-19 infection require hospitalization, but those with milder symptoms convalesce isolated at home.3 Furthermore, individuals exposed to COVID-19 cases may be monitored on home quarantine for up to fourteen days.4,5 Current symptom monitoring systems are resource intensive, using telephone, tele-video or text-messaging. Monitoring a multitude of individuals can quickly exceed institutional capacity. Our multidisciplinary team implemented privacy-assured systems using the Georgetown University’s AvesTerra framework for privacy-assured technology for HIV surveillance,6 to design a user-friendly system to efficiently track symptoms associated with COVID-19 infection. OBJECTIVE We conducted a beta-test of the system to test the functionality of the system and reporter functions that aim to provide institutions and agencies to identify individuals with changing health status who may be able to come off isolation or quarantine. METHODS The Georgetown University COVID-19 Symptom Tracker (www.covidgu.org) captures key risk factors and symptoms for remote self-monitoring of COVID-19 patients and exposed individuals on home isolation or quarantine. Self-reported temperature, cough, sore throat, shortness of breath or other symptoms commonly associated with COVID-19 are entered via a simple web interface using a unique identification number without personal identifiers. We beta-tested the COVID-19 Symptom Tracker under a protocol deemed exempt by the Georgetown University Institutional Review Board (IRB). Georgetown University medical students were invited to participate by email, with a link to a Qualtrics survey used to describe the project, provide instructions, and document consent. A random unique identifier (ID) number was directly generated in Qualtrics for consenting individuals. Participants were asked to enter data twice daily for three days. An aggregate summary report was downloaded by the research team. No personal identifiers were available to the study team. RESULTS Results A total of forty-eight users participated in beta-testing conducted between March 31 and April 5, 2020 (Table 1). One individual reported active COVID-19 infection, and forty-seven individuals were not infected. On the last day of monitoring, the individual with COVID-19 infection was asymptomatic. None of the forty-seven other participants reported symptoms concerning for COVID-19 infection. By the April 5, thirty-eight of the forty-seven individuals had completed three days of reporting and were no longer submitting reports. CONCLUSIONS We designed and deployed a user-friendly and scalable rapid response system to efficiently monitor individuals with or exposed to COVID-19 while maintaining privacy. The system is designed to accommodate data from millions of unique individuals. Reports can be automatically generated by agencies/institutions that include only their own institution-specific data. Our beta-test demonstrates feasibility and functionality of the Symptom Tracker and Reporter. Actionable information such as the ID number alert for the person whose symptoms have resolved or an individual with new symptoms would provide data to inform individual and institutional decision making. Information on the individuals who have not submitted data for the past twenty four hours is also generated, and allows institutions to identify subgroups of individuals who may need to be contacted. Possible explanations for this including non-compliance with requests for entering symptoms, completion of the recommended quarantine/isolation period that would thus be an alert to remove the isolation/quarantine order, or possibly due to worsening symptoms that have led to hospitalization. We designed the system with a streamlined onboarding process and without inclusion of personal identifiers to facilitate widespread use. Standard procedures to execute data use agreements as is required when identifiers are included are arduous and impractical during this public health emergency. Further, there may be reluctance by the individual to use a system that captures personal identifiers. In Figure 1, we describe the process for institutions/agencies to gain access to the Symptom Tracker by agreeing to the terms of use outlined in Table 2. Participating institutions/agencies are provided with a Reporter executable file and unique authorization code to access data linked to their own entity. Institutions/agencies maintain access to the unique identifiers assigned to individuals they ask to enter data into the system, thereby maintaining privacy and confidentiality from our development team and other system users. The Symptom Tracker is now freely-available to support COVID-19 contact tracing and monitoring. A safety feature alerts individuals who report new or worsening symptoms to contact their healthcare providers. The system also allows prospective monitoring of research cohorts to identify incidence and duration of COVID-19 infection while maintaining confidentiality. Additional modifications have since been implemented including a Spanish language version of the website. There are no geographic limitations to the use of this system, and hence could be implemented in any environment where is internet access is available. CLINICALTRIAL N/A

Introduction: Improving the healthcare system is a major public health challenge. Collaborative learning health systems (CLHS) -network organizations that allow all healthcare stakeholders to collaborate at scale -are a promising response. However, we know little about CLHS mechanisms of actions, nor how to optimize CLHS performance. Agent-based models (ABM) have been used to study a variety of complex systems. We translate the conceptual underpinnings of a CLHS to a computational model and demonstrate initial computational and face validity.Methods: CLHSs are organized to allow stakeholders (patients and families, clinicians, researchers) to collaborate, at scale, in the production and distribution of information, knowledge, and know-how for improvement. We build up a CLHS ABM from a population of patient-and doctor-agents, assign them characteristics, and set them into interaction, resulting in engagement, information, and knowledge to facilitate optimal treatment selection. To assess computational and face validity, we vary a single parameter -the degree to which patients influence other patients -and trace its effects on patient engagement, shared knowledge, and outcomes. Results:The CLHS ABM, developed in Python and using the open-source modeling framework Mesa, is delivered as a web application. The model is simulated on a cloud server and the user interface is a web browser using Python and Plotly Dash. Holding all other parameters steady, when patient influence increases, the overall patient population activation increases, leading to an increase in shared knowledge, and higher median patient outcomes. Conclusions:We present the first theoretically-derived computational model of CLHSs, demonstrating initial computational and face validity. These preliminary results suggest that modeling CLHSs using an ABM is feasible and potentially valid. A well-developed and validated computational model of the health system may have profound effects on understanding mechanisms of action, potential intervention targets, and ultimately translation to improved outcomes.