Design and Feasibility Study of the Mobile Application StopTheSpread

Designing a rabies control mobile application for a community-based rabies surveillance system during the COVID-19 pandemic in Bali, Indonesia

Background and Aim: Rabies remains a public health concern in Indonesia, and the coronavirus disease (COVID-19) pandemic has stymied rabies prevention and control efforts. There is a need to transform the rabies program to be adaptable to pandemic situations to improve program coverage on dog vaccination and rabies surveillance. This study aimed to create a rabies control (RaCon) mobile application for a community-based rabies surveillance system during COVID-19 in Bali, Indonesia. Materials and Methods: We employ the Design Science Research methodology. Surveillance officers, veterinarians, community leaders, outreach workers, and dog owners participated in a series of offline in-depth interviews and focus group discussions. The RaCon prototype was evaluated using the Post-Study System Usability Questionnaire (PSSUQ) framework, which included the system's usefulness, information quality, and interface quality. In this study, we used both a qualitative (n=50) and quantitative (n=342) approach. Results: According to the findings of this study, integrating public health and animal health into the rabies surveillance system are critical to supporting the One Health approach and encouraging community engagement in rabies programs. The RaCon prototype is expected to include features such as pet ownership, case report, news and announcements, nearest vet, health information, outbreak radar, emergency call, and app feedback. The RaCon prototype passed both qualitative and quantitative evaluations, indicating that it could be used to support the rabies surveillance system, particularly in the COVID-19 situation. Conclusion: The RaCon prototype was accepted by the users and got positive feedback in terms of the system's usefulness, information quality, and interface quality dimension. As a result, this prototype has the potential to be integrated into the rabies surveillance system in Bali, particularly to strengthen the community-based rabies surveillance system. Even though this prototype received positive feedback, this study focuses solely on the design development and evaluation of its user interface. As a result, further development is required before incorporating RaCon into the rabies prevention and control program.

Scalable and distributed strategies for socially distanced human mobility

COVID-19 is a global health crisis that has caused ripples in every aspect of human life. Amid widespread vaccinations testing, manufacture and distribution efforts, nations still rely on human mobility restrictions to mitigate infection and death tolls. New waves of infection in many nations, indecisiveness on the efficacy of existing vaccinations, and emerging strains of the virus call for intelligent mobility policies that utilize contact pattern and epidemiological data to check contagion. Our earlier work leveraged network science principles to design social distancing optimization approaches that show promise in slowing infection spread however, they prove to be computationally prohibitive and require complete knowledge of the social network. In this work, we present scalable and distributed versions of the optimization approaches based on Markov Chain Monte Carlo Gibbs sampling and grid-based spatial parallelization that tackle both the challenges faced by the optimization strategies. We perform extensive simulation experiments to show the ability of the proposed strategies to meet necessary network science measures and yield performance comparable to the optimal counterpart, while exhibiting significant speed-up. We study the scalability of the proposed strategies as well as their performance in realistic scenarios when a fraction of the population temporarily flouts the location recommendations.

Leveraging Network Science for Social Distancing to Curb Pandemic Spread

COVID-19 has irreversibly upended the course of human life and compelled countries to invoke national emergencies and strict public guidelines. As the scientific community is in the early stages of rigorous clinical testing to come up with effective vaccination measures, the world is still heavily reliant on social distancing to curb the rapid spread and mortality rates. In this work, we present three optimization strategies to guide human mobility and restrict contact of susceptible and infective individuals. The proposed strategies rely on well-studied concepts of network science, such as clustering and homophily, as well as two different scenarios of the SEIRD epidemic model. We also propose a new metric, called contagion potential, to gauge the infectivity of individuals in a social setting. Our extensive simulation experiments show that the recommended mobility approaches slow down spread considerably when compared against several standard human mobility models. Finally, as a case study of the mobility strategies, we introduce a mobile application, MyCovid, that provides periodic location recommendations to the registered app users.