A Mobile App and Dashboard for Early Detection of Infectious Disease Outbreaks: Development Study

Prevention, diagnosis and pharmacological treatment of infections in pregnancy: The mobile app GAIA! for healthcare providers and patients

OBJECTIVE

To develop and assess the GAIA! app, designed to assist pregnant women and healthcare professionals in managing infectious diseases during pregnancy, and to bridge the information gap between health professionals and expectant mothers.

STUDY DESIGN

This collaborative initiative in Italy involved partnerships with the University of Florence, Careggi University Hospital, and other institutions. The app, built on the Ionic framework, is available on both Apple and Google App Stores. It offers two distinct modes: "healthcare providers" and "patients." Content for the app was derived from extensive literature reviews and clinical guidelines.

RESULTS

Since its August 2022 launch, the GAIA! app has garnered over 2,500 downloads, indicating its effectiveness and acceptance within the community. The app differentiates itself from others, such as the Sanford Guide, by focusing specifically on the needs of pregnant women. It ensures cross-platform compatibility, a user-friendly interface, and offline functionality.

CONCLUSIONS

The GAIA! app has successfully addressed a niche in infectious disease management for pregnant women, gaining significant traction within the community. While it has seen substantial success, challenges like continuous updates and potential language expansion remain. Future endeavors will address these challenges and further evaluate the app's impact on maternal and child health.

Towards malaria elimination: a reflection about digital notification modules to improve malaria cases notification speed and follow-up in the Brazilian Amazon region

BACKGROUND

Health information systems (HIS) are a pivotal element in epidemiological surveillance. In Brazil, malaria persists as a public health challenge, with 99% of its occurrences concentrated in the Amazon region, where cases are reported through the HIS Sivep-Malaria. Recent technological advancements indicate that case notifications can be expedited through more efficient systems with broader coverage. The objective of this study is to analyse opportunities for notification within Sivep-Malaria and explore the implementation of mobile electronic devices and applications to enhance the performance of malaria case notifications and use.

METHODS

This descriptive study analyses data on malaria-positive cases in the Brazilian Amazon from 2004 to 2022. Malaria Epidemiological Surveillance System (Sivep-Malaria) data were used. The Brazilian Amazon region area is approximately 5 million km2 across nine different states in Brazil. Data entry opportunities were assessed by considering the time difference between the 'date of data entry' and the 'date of notification.' Descriptive statistics, including analyses of means and medians, were conducted across the entire Amazon region, and for indigenous population villages and gold mining areas.

RESULTS

Between 2004 and 2022, 6,176,878 new malaria cases were recorded in Brazil. The average data entry opportunity throughout the period was 17.9 days, with a median of 8 days. The most frequently occurring value was 1 day, and 99% of all notifications were entered within 138 days, with 75.0% entered within 20 days after notification. The states with the poorest data entry opportunities were Roraima and Tocantins, with averages of 31.3 and 31.0 days, respectively. For indigenous population villages and gold mining areas, the median data entry opportunities were 23 and 15 days, respectively.

CONCLUSIONS

In malaria elimination, where surveillance is a primary strategy for evaluating each reported case, reducing notification time, enhancing data quality and being able to follow-up cases through computerized reports offer significant benefits for cases investigation. Technological improvements in Sivep-Malaria could yield substantial benefits for malaria control in Brazil, aiding the country in achieving disease elimination and fulfilling the Sustainable Development Goals.

Mpox-AISM: AI-mediated super monitoring for mpox and like-mpox

Swift and accurate diagnosis for earlier-stage monkeypox (mpox) patients is crucial to avoiding its spread. However, the similarities between common skin disorders and mpox and the need for professional diagnosis unavoidably impaired the diagnosis of earlier-stage mpox patients and contributed to mpox outbreak. To address the challenge, we proposed "Super Monitoring", a real-time visualization technique employing artificial intelligence (AI) and Internet technology to diagnose earlier-stage mpox cheaply, conveniently, and quickly. Concretely, AI-mediated "Super Monitoring" (mpox-AISM) integrates deep learning models, data augmentation, self-supervised learning, and cloud services. According to publicly accessible datasets, mpox-AISM's Precision, Recall, Specificity, and F1-score in diagnosing mpox reach 99.3%, 94.1%, 99.9%, and 96.6%, respectively, and it achieves 94.51% accuracy in diagnosing mpox, six like-mpox skin disorders, and normal skin. With the Internet and communication terminal, mpox-AISM has the potential to perform real-time and accurate diagnosis for earlier-stage mpox in real-world scenarios, thereby preventing mpox outbreak.

Developing public health surveillance dashboards: a scoping review on the design principles

BACKGROUND

Public Health Dashboards (PHDs) facilitate the monitoring and prediction of disease outbreaks by continuously monitoring the health status of the community. This study aimed to identify design principles and determinants for developing public health surveillance dashboards.

METHODOLOGY

This scoping review is based on Arksey and O'Malley's framework as included in JBI guidance. Four databases were used to review and present the proposed principles of designing PHDs: IEEE, PubMed, Web of Science, and Scopus. We considered articles published between January 1, 2010 and November 30, 2022. The final search of articles was done on November 30, 2022. Only articles in the English language were included. Qualitative synthesis and trend analysis were conducted.

RESULTS

Findings from sixty-seven articles out of 543 retrieved articles, which were eligible for analysis, indicate that most of the dashboards designed from 2020 onwards were at the national level for managing and monitoring COVID-19. Design principles for the public health dashboard were presented in five groups, i.e., considering aim and target users, appropriate content, interface, data analysis and presentation types, and infrastructure.

CONCLUSION

Effective and efficient use of dashboards in public health surveillance requires implementing design principles to improve the functionality of these systems in monitoring and decision-making. Considering user requirements, developing a robust infrastructure for improving data accessibility, developing, and applying Key Performance Indicators (KPIs) for data processing and reporting purposes, and designing interactive and intuitive interfaces are key for successful design and development.

Primary Care Physicians' and Patients' Perspectives on Equity and Health Security of Infectious Disease Digital Surveillance

PURPOSE

The coronavirus disease 2019 (COVID-19) pandemic facilitated the rapid development of digital detection surveillance (DDS) for outbreaks. This qualitative study examined how DDS for infectious diseases (ID) was perceived and experienced by primary care physicians and patients in order to highlight ethical considerations for promoting patients' autonomy and health care rights.

METHODS

In-depth interviews were conducted with a purposefully selected group of 16 primary care physicians and 24 of their patients. The group was reflective of a range of ages, educational attainment, and clinical experiences from urban areas in northern and southern China. Interviews were audio recorded, transcribed, and translated. Two researchers coded data and organized it into themes. A third researcher reviewed 15% of the data and discussed findings with the other researchers to assure accuracy.

RESULTS

Five themes were identified: ambiguity around the need for informed consent with usage of DDS; importance of autonomous decision making; potential for discrimination against vulnerable users of DDS for ID; risk of social inequity and disparate care outcomes; and authoritarian institutions' responsibility for maintaining health data security. The adoption of DDS meant some patients would be reluctant to go to the hospital for fear of either being discriminated against or forced into quarantine. Certain groups (older people and children) were thought to be vulnerable to DDS misappropriation.

CONCLUSIONS

These findings indicate the paramount importance of establishing national and international ethical frameworks for DDS implementation. Frameworks should guide all aspects of ID surveillance, addressing privacy protection and health security, and underscored by principles of social equity and accountability.Annals "Online First" article.

Mobile app to perform anonymized longitudinal studies in the context of COVID-19 adverse drug reaction monitoring, leveraging the citizenship engagement

BACKGROUND

Over the last few years, increasingly studies focused on the development of mobile apps as complementary tools to existing pharmacovigilance traditional surveillance systems for improving and facilitating adverse drug reactions reporting.

OBJECTIVE

In this study, we evaluated the potentiality of a new mobile app (vaxEffect@UniMiB) to perform longitudinal studies while preserving the anonymity of the respondents. We applied it to monitor the adverse drug reactions during COVID-19 vaccination campaign in a sample of Italian population.

METHODS

We administered vaxEffect@UniMiB to a convenience sample of academic subjects vaccinated at Milano-Bicocca University hub for COVID-19 during the Italian national vaccination campaign. vaxEffect@UniMiB was developed for both Android and iOS devices. The mobile app asks users to send their medical history and, upon every vaccine administration, their vaccination data and the adverse reactions that occurred within seven days after the vaccination, allowing the follow of reactions dynamic for each respondent. The app sends data over the web to an application server. The web server, along with receiving all user data, saves them in a SQL database server, and reminds patients to submit vaccine and adverse reactions' data by push notifications sent to the mobile app through Firebase Cloud Messaging. On initial startup of the app, a unique user identifier was generated for each respondent, so that its anonymity is completely ensured, while enabling longitudinal studies.

RESULTS

A total of 3712 people have been vaccinated during the first vaccination wave. A total of 2733 respondents between the ages of 19 and 80, coming from the University of Milano-Bicocca and the Politecnico of Milan, participated in the survey. Overall, we collected the information about vaccination and adverse reactions to the first vaccine dose for 2226 subjects (60.0% of vaccinated), to the second dose for 1610 subjects (43.3%), and, in a non-sponsored fashion, to the third dose for 169 individuals.

CONCLUSIONS

vaxEffect@UniMiB revealed to be the first attempt in performing longitudinal studies to monitor the same subject over time in terms of the reported ADRs after each vaccine administration, while guaranteeing at the same time complete anonymity of the subjects. A series of aspects contributed to a positive involvement from people in using this application to report their ADRs to vaccination: ease of use, availability from multiple platforms, anonymity of all the survey participants and protection of the submitted data and the healthcare workers' support.

CLINICALTRIAL

Environmental scan of COVID-19 infection dashboards in the Florida public school system

Public dashboards have been one of the most effective tools to provide critical information about COVID-19 cases during the pandemic. However, dashboards for COVID-19 that have not received a lot of scrutiny are those from the public school system. We conducted an environmental scan of published dashboards that report and track new COVID-19 infections in the Florida public school system. We found that thirty-four percent of counties do not provide any public dashboard, and there was significant heterogeneity in the data quality and framework of existing systems. There were poor interfaces without visual tools to trace the trend of COVID-19 cases in public schools and significant limitations for data extraction. Given these observations, it is impossible to conduct meaningful policy evaluations and proper surveillance. Additional work and oversight are needed to improve public data reported.