A Covid-19 Tracker for Medical Front-liners

The management of Covid-19 affected patients is a very difficult task. The current healthcare system of India is not able to cope with the enormous flow of patients and is in a dire need for improvement. This implementation paper provides a system which will manage all the affected patients right from the time they are Covid-19 positive till the time they are treated and discharged. This paper includes all the technical details of a fully implemented healthcare management system which is a significant improvement in the current system. The proposed system is a cross platform multi user web app which can be used by multiple stakeholders to carry out smooth management of the patients. It consists of a lot of key features like dynamic location-wise patient status, an accurate tracking system of ambulances, a statistical trend analysis of patients and categorical report generation of patients. This system aims to help the medical Front-liners in efficient management of Covid-19 patients, and it is a common site for all the different health workers like field workers and medical officers to work togetherand fight against this deadly disease affecting our country. © 2022 IEEE.

The impact of socioeconomic status on telemedicine utilization during the COVID-19 pandemic among surgical clinics at an academic tertiary care center.

BACKGROUND: The COVID-19 pandemic caused many surgical providers to conduct outpatient evaluations using remote audiovisual conferencing technology (i.e., telemedicine) for the first time in 2020. We describe our year-long institutional experience with telemedicine in several general surgery clinics at an academic tertiary care center and examine the relationship between area-based socioeconomic measures and the likelihood of telemedicine participation. METHODS: We performed a retrospective review of our outpatient telemedicine utilization among four subspecialty clinics (including two acute care and two elective surgery clinics). Geocoding was used to link patient visit data to area-based socioeconomic measures and a multivariable analysis was performed to examine the relationship between socioeconomic indicators and patient participation in telemedicine. RESULTS: While total outpatient visits per month reached a nadir in April 2020 (65% decrease in patient visits when compared to January 2020), there was a sharp increase in telemedicine utilization during the same month (38% of all visits compared to 0.8% of all visits in the month prior). Higher rates of telemedicine utilization were observed in the two elective surgery clinics (61% and 54%) compared to the two acute care surgery clinics (14% and 9%). A multivariable analysis demonstrated a borderline-significant linear trend (p = 0.07) between decreasing socioeconomic status and decreasing odds of telemedicine participation among elective surgery visits. A sensitivity analysis to examine the reliability of this trend showed similar results. CONCLUSION: Telemedicine has many patient-centered benefits, and this study demonstrates that for certain elective subspecialty clinics, telemedicine may be utilized as the preferred method for surgical consultations. However, to ensure the equitable adoption and advancement of telemedicine services, healthcare providers will need to focus on mitigating the socioeconomic barriers to telemedicine participation.

A Covid-19 Vaccination Tracking and Control Platform in Santiago de Cali

The monitoring and control of epidemics is one of the most relevant topics in the field of smart health within smart cities. Smart health take advantage of a new generation of information technologies, such as big data, mobile internet, cloud computing and artificial intelligence, in order to transform the traditional medical system in a comprehensive way, making healthcare more efficient and personalized. From electronic Health records (EHR), diverse information about the epidemiological situation in institutions that provide health services can be extracted. This document describes the development of a platform to carry out the control and monitoring of vaccination process against Covid-19, which is based on cloud data storage technologies and make use of a existing platform designed for the registration of EHR emphasizing on data collection for structuring of epidemiological control strategies. The main goal is to identify and characterize patients who meet the prioritization criteria for Covid-19 vaccination according to stages defined by the Colombia Ministry of Health, execute the geocoding processes and identification of health conditions according to their previous EHR records, in order to accomplish an efficient and intelligent execution, monitoring and control of vaccination that impacts the epidemiological risk mitigation process. At the end of the document is described the use of the developed platform for the monitoring and control of the Covid-19 vaccination process in a Basic Health Services Unit called Medicips, which provides health services to approximately 90,000 people in the city of Santiago de Cali, Colombia. © 2022, Springer Nature Switzerland AG.

What Does Twitter Say About Self-Regulated Learning? Mapping Tweets From 2011 to 2021.

Social network services such as Twitter are important venues that can be used as rich data sources to mine public opinions about various topics. In this study, we used Twitter to collect data on one of the most growing theories in education, namely Self-Regulated Learning (SRL) and carry out further analysis to investigate What Twitter says about SRL? This work uses three main analysis methods, descriptive, topic modeling, and geocoding analysis. The searched and collected dataset consists of a large volume of relevant SRL tweets equal to 54,070 tweets between 2011 and 2021. The descriptive analysis uncovers a growing discussion on SRL on Twitter from 2011 till 2018 and then markedly decreased till the collection day. For topic modeling, the text mining technique of Latent Dirichlet allocation (LDA) was applied and revealed insights on computationally processed topics. Finally, the geocoding analysis uncovers a diverse community from all over the world, yet a higher density representation of users from the Global North was identified. Further implications are discussed in the paper.

Community predictors of COVID-19 cases and deaths in Massachusetts: Evaluating changes over time using geospatially refined data.

BACKGROUND: The COVID-19 pandemic has highlighted the need for targeted local interventions given substantial heterogeneity within cities and counties. Publicly available case data are typically aggregated to the city or county level to protect patient privacy, but more granular data are necessary to identify and act upon community-level risk factors that can change over time. METHODS: Individual COVID-19 case and mortality data from Massachusetts were geocoded to residential addresses and aggregated into two time periods: "Phase 1" (March-June 2020) and "Phase 2" (September 2020 to February 2021). Institutional cases associated with long-term care facilities, prisons, or homeless shelters were identified using address data and modeled separately. Census tract sociodemographic and occupational predictors were drawn from the 2015-2019 American Community Survey. We used mixed-effects negative binomial regression to estimate incidence rate ratios (IRRs), accounting for town-level spatial autocorrelation. RESULTS: Case incidence was elevated in census tracts with higher proportions of Black and Latinx residents, with larger associations in Phase 1 than Phase 2. Case incidence associated with proportion of essential workers was similarly elevated in both Phases. Mortality IRRs had differing patterns from case IRRs, decreasing less substantially between Phases for Black and Latinx populations and increasing between Phases for proportion of essential workers. Mortality models excluding institutional cases yielded stronger associations for age, race/ethnicity, and essential worker status. CONCLUSIONS: Geocoded home address data can allow for nuanced analyses of community disease patterns, identification of high-risk subgroups, and exclusion of institutional cases to comprehensively reflect community risk.

Spatio-temporal data visualization for monitoring of control measures in the prevention of the spread of COVID-19 in Bosnia and Herzegovina.

Aim The damage caused by the COVID-19 pandemic has made the prevention of its further spread at the top of the list of priorities of many governments and state institutions responsible for health and civil protection around the world. This prevention implies an effective system of epidemiological surveillance and the application of timely and effective control measures. This research focuses on the application of techniques for modelling and geovisualization of epidemic data with the aim of simple and fast communication of analytical results via geoportal. Methods The paper describes the approach applied through the project of establishing the epidemiological location-intelligence system for monitoring the effectiveness of control measures in preventing the spread of COVID-19 in Bosnia and Herzegovina. Results Epidemic data were processed and the results related to spatio-temporal analysis of the infection spread were presented by compartmental epidemic model, reproduction number R, epi-curve diagrams as well as choropleth maps for different levels of administrative units. Geovisualization of epidemic data enabled the release of numerous information from described models and indicators, providing easier visual communication of the spread of the disease and better recognition of its trend. Conclusion The approach involves the simultaneous application of epidemic models and epidemic data geovisualization, which allows a simple and rapid evaluation of the epidemic situation and the effects of control measures. This contributes to more informative decision-making related to control measures by suggesting their selective application at the local level.