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COVID-19 is one of the deadliest pandemics in modern human history that has killed nearly a million people and rapidly inundated the healthcare resources around the world. Current lockdown measures to curb infection spread are threatening to bring the world economy to a halt, necessitating dynamic lockdown policies that incorporate the healthcare resource budget of people in a zone. We conceive a dynamic pandemic lockdown strategy that employs reinforcement learning to modulate the zone mobility, while restricting the COVID-19 hospitalizations within its healthcare resource budget. We employ queueing theory to model the inflow and outflow of patients and validate the approach through extensive simulation on real demographic and epidemiological data from the boroughs of New York City. Our experiments demonstrate that this approach can not only adapt to the varying trends in contagion in a region by regulating its own lockdown level, but also manages the overheads associated with time-varying dynamic lockdown policies.

Wireless Sensor Networks (WSNs) enable promising solutions to large-scale industrial, medical and environmental tracking and monitoring applications. Software Defined Networking (SDN) is a new paradigm that decouples the network control and data forwarding functionalities and may potentially improve data sensing in a highly dynamic environment. The networking community is directing its efforts towards ensuring that the software defined WSNs (SD-WSN) carry out the data sensing even in a hostile environment characterized by node or link failures. In this work, we present an adaptive topology control strategy based on reinforcement learning for mobile SD-WSN. The approach employs the notion of statistically significant subgraphs, called motifs, that have been shown to render graph robustness to biological networks. Our simulation experiments on the map of New York City shows that this approach is capable of modulating network parameters to achieve varying goals such as high data delivery, low latency and energy efficiency.

There is a concerted effort to develop vaccines to combat the public health crisis caused by COVID-19. Health experts are raising questions concerning the clinical trials, price and distribution of the vaccines among the public. Policymakers must design regulations to allocate vaccines equitably based on epidemiological as well as economic considerations. Allocation strategies must factor in the extent and duration of immunity the vaccines yield from future infections. We present a time-varying linear optimization-based approach, which incorporates epidemiological factors, such as population density, susceptible count and infected ratio as well as transportation costs, to disseminate vaccines among zones. Our approach also employs an update rule on the epidemiological statistics spawning from the SEIRD model to learn the extent of immunity provided by the vaccines. Our experiments are performed on the map of New York State with realistic mobility using spatial and ordinary differential equation-based SEIRD models. We show that the proposed approach allocates vaccines optimally while preventing zones from resource starvation. It adapts its vaccine recommendation to meet the prespecified epidemiological and economic policy requirements of zones, and is highly amenable to accommodate multiple immunity ratios, vaccine types and the time-span of acquired vaccine immunity. Moreover, we demonstrate that one can learn the latent immunity ratios of the vaccines to make more informed recommendations with time. Our ordinary differential equation-based analysis on the real demographic and infection data of New York State show that a small fraction of zones tends to exhibit a high resource demand due to their vulnerability to the pandemic spread. Our vaccine allocation strategy incorporates infection ratio as one of the criteria and is applicable to conceive a drug allocation strategy. The results suggest that incorporating the vulnerability score of zones to the epidemic spread into vaccine allocation may enhance recommendations and aid policy making.

COVID-19, declared by the World Health Organization as a Public Health Emergency of International Concern, has claimed over 2.7 million lives worldwide. In the absence of vaccinations, social distancing and lockdowns emerged as the means to curb infection spread, with the downside of bringing the world economy to a standstill. In this work, we explore the epidemiological, socioeconomic and demographic factors affecting the unemployment rates of United States that may contribute towards policymaking to contain contagion and mortality while balancing the economy in the future. We identify the ethnic groups and job sectors that are affected by the pandemic and demonstrate that Gross Domestic Product (GDP), race, age group, lockdown severity and infected count are the key indicators of post-COVID job loss trends.

Lockdown measures to curb the spread of COVID-19 has brought the world economy on the brink of a recession. It is imperative that nations formulate administrative policies based on the changing economic landscape. In this work, we apply a statistical approach, called topic modeling, on text documents of job loss notices of 26 US states to identify the specific states and industrial sectors affected economically by this ongoing public health crisis. Our analysis reveals that there is a considerable incongruity in job loss patterns between the pre- and during-COVID timelines in several states and the recreational and philanthropic sectors register high job losses. It further shows that the interplay among several possible socioeconomic factors would lead to job losses in many sectors, while also creating new job opportunities in other sectors such as public service, pharmaceuticals and media, making the job loss trends a key indicator of the world economy. Finally, we compare the low income job loss rates against overall job losses due to COVID-19 in the US counties, and discuss the implications of press reports on reopening businesses and the unemployed workforce being absorbed by other sectors.