Identifying key indicators of job loss trends during COVID-19 and beyond.

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.

Towards Dynamic lockdown strategies controlling pandemic spread under healthcare resource budget.

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.

Recreational and philanthropic sectors are the worst-hit US industries in the COVID-19 aftermath.

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.