Apple-shaped obesity; a risky soil for cytokine-accelerated severity in COVID-19 (preprint)

Obesity is one of the most significant risk factors for the deterioration and mortality associated with COVID-19 [1]. A certain proportion of COVID-19 patients experience marked elevations of inflammatory mediators, termed “cytokine storm”, resulting in the deterioration of the respiratory condition [2,3]. In the present study, we elucidate that the high visceral adipose tissue (VAT) burden was more closely related to accelerated inflammatory responses and the mortality of Japanese COVID-19 patients than other obesity-associated markers, including body mass index (BMI). To explore a novel stratification of COVID-19 patients, we infected mouse-adapted SARS-CoV-2 in several obese mice, revealing that VAT-dominant ob/ob mice and diet-induced obesity obese mice died after infection with low-titer mouse-adapted SARS-CoV-2 virus due to the subsequent cytokine storm, whereas none of the subcutaneous adipose tissue (SAT) dominant db/db mice or control lean wild-type mice died. SARS-CoV-2 genome and proteins were more abundant in the lungs of ob/ob mice, engulfed in macrophages, resulting in increased production of inflammatory cytokine represented by IL-6. As well as the anti-IL-6 treatment, the prevention of obesity by leptin administration improved the survival of SARS-CoV-2 infected ob/ob mice by reducing the viral protein burden and excessive immune responses.

Estimating infection-related human mobility networks based on time series data of COVID-19 infection in Japan (preprint)

Comprehensive and evidence-based countermeasures against emerging infectious diseases have become increas-ingly important in recent years. COVID-19 and many other infectious diseases are spread by human movement and contact, but complex transportation networks in 21 century make it difficult to predict disease spread in rapidly changing situations. It is especially challenging to estimate the network of infection transmission in the countries that the traffic and human movement data infrastructure is not yet developed. In this study, we devised a method to estimate the network of transmission of COVID-19 from the time series data of its infection and applied it to determine its spread across areas in Japan. We incorporated the effects of soft lockdowns, such as the declaration of a state of emergency, and changes in the infection network due to government-sponsored travel promotion, and predicted the spread of infection using the Tokyo Olympics as a model. The models used in this study are available online, and our data-driven infection network models are scalable, whether it be at the level of a city, town, country, or continent, and applicable anywhere in the world, as long as the time-series data of infections per region is available. These estimations of effective distance and the depiction of infectious disease networks based on actual infection data are expected to be useful in devising data-driven countermeasures against emerging infectious diseases worldwide.