Abstract The novel coronavirus (COVID‐19) has an enormous impact on the daily lives and health of people residing in more than 200 nations. This article proposes a deep learning‐based system for the rapid diagnosis of COVID‐19. Chest x‐ray radiograph images were used because recent findings revealed that these images contain salient features about COVID‐19 disease. Transfer learning was performed using different pre‐trained convolutional neural networks models for binary (normal and COVID‐19) and triple (normal, COVID‐19 and viral pneumonia) class problems. Deep features were extracted from a fully connected layer of the ResNET50v2 model and feature dimension was reduced through feature reduction methods. Feature fusion of feature sets reduced through analysis of variance (ANOVA) and mutual information feature selection (MIFS) was fed to Fine K‐nearest neighbour to perform binary classification. Similarly, serial feature fusion of MIFS and chi‐square features were utilized to train Medium Gaussian Support Vector Machines to distinguish normal, COVID‐19 and viral pneumonia cases. The proposed framework yielded accuracies of 99.5% for binary and 95.5% for triple class experiments. The proposed model shows better performance than the existing methods, and this research has the potential to assist medical professionals to enhance the diagnostic ability to detect coronavirus disease.

Model-driven policymaking for epidemic control is a challenging collaborative process. It begins when a team of public-health officials, epidemiologists, and economists construct a reasonably predictive disease model representative of the team’s region of interest as a function of its unique socio-economic and demographic characteristics. As the team considers possible interventions such as school closures, social distancing, vaccination drives, etc., they need to simultaneously model each intervention’s effect on disease spread and economic cost. The team then engages in an extensive what-if analysis process to determine a cost-effective policy: a schedule of when, where and how extensively each intervention should be applied. This policymaking process is often an iterative and laborious programming-intensive effort where parameters are introduced and refined, model and intervention behaviors are modified, and schedules changed. We have designed and developed EpiPolicy to support this effort.

Information dissemination using automated phone calls allows reaching low-literate and tech-naive populations. Open challenges include rapid verification of expected knowledge gaps in the community, dissemination of specific information to address these gaps, and follow-up measurement of knowledge retention. We report Sawaal, a voice-based telephone service that uses audio-quizzes to address these challenges. Sawaal allows its open community of users to post and attempt multiple-choice questions and to vote and comment on them. Sawaal spreads virally as users challenge friends to quiz competitions. Administrator-posted questions allow confirming specific knowledge gaps, spreading correct information and measuring knowledge retention via rephrased, repeated questions. In 14 weeks and with no advertisement, Sawaal reached 3,433 users (120,119 calls) in Pakistan, who contributed 13,276 questions that were attempted 455,158 times by 2,027 users. Knowledge retention remained significant for up to two weeks. Surveys revealed that 71% of the mostly low-literate, young, male users were blind.

Speech is more natural than text for a large part of the world including hard-to-reach populations (low-literate, poor, tech-novice, visually-impaired, marginalized) and oral cultures. Voice-based services over simple mobile phones are effective means to provide orality-driven social connectivity to such populations. We present Baang, a versatile and inclusive voice-based social platform that allows audio content creation and sharing among its open community of users. Within 8 months, Baang spread virally to 10,721 users (69% of them blind) who participated in 269,468 calls and shared their thoughts via 44,178 audio-posts, 343,542 votes, 124,389 audio-comments and 94,864 shares. We show that the ability to vote, comment and share leads to viral spread, deeper engagement, longer retention and emergence of true dialog among participants. Beyond connectivity, Baang provides its users with a voice and a social identity as well as means to share information and get community support.

Today's Internet scarcely resembles the mythological image of it as a fundamentally democratic system. Instead, users are at the whims of a small number of providers who control nearly everything about users' experiences on the Internet. In response, researchers and engineers have proposed, over the past decade, many systems to re-democratize the Internet, pushing control over data and systems back to the users. Yet nearly all such projects have failed. In this paper we explore why: what are the goals of such systems and what has caused them to run aground?

We explore how telephone-based services might be mass adopted by low-literate users in the developing world. We focus on speech and push-button dialog systems requiring neither literacy nor training. Building on the success of Polly, a simple telephone-based voice manipulation and forwarding system that was first tested in 2011, we report on its first large-scale sustained deployment. In 24/7 operation in Pakistan since May 9, 2012, as of mid-September Polly has spread to 85,000 users, engaging them in 495,000 interactions, and is continuing to spread to 1,000 new people daily. It has also attracted 27,000 people to a job search service, who in turn listened 279,000 times to job ads and forwarded them 22,000 times to their friends. We report users' activity over time and across demographics, analyze user behavior within several randomized controlled trials, and describe lessons learned regarding spread, scalability and sustainability of telephone-based speech-based services.