Background: Knowledge on the epidemiological features and transmission patterns of novel coronavirus disease (COVID-19) is accumulating. Detailed line-list data with household settings can advance the understanding of COVID-19 transmission dynamics.Methods: A unique database with detailed demographic characteristics, travel history, social relationships, and epidemiological timelines for 1407 transmission pairs that formed 643 transmission clusters in mainland China was reconstructed from 9120 COVID-19 confirmed cases reported during 15 January–29 February 2020. Statistical model fittings were used to identify the superspreading events and estimate serial interval distributions. Age- and sex-stratified hazards of infection were estimated for household vs nonhousehold transmissions.Results: There were 34 primary cases identified as superspreaders, with 5 superspreading events occurred within households. Mean and standard deviation of serial intervals were estimated as 5.0 (95% credible interval [CrI], 4.4–5.5) days and 5.2 (95% CrI, 4.9–5.7) days for household transmissions and 5.2 (95% CrI, 4.6–5.8) and 5.3 (95% CrI, 4.9–5.7) days for nonhousehold transmissions, respectively. The hazard of being infected outside of households is higher for people aged 18–64 years, whereas hazard of being infected within households is higher for young and old people.Conclusions: Nonnegligible frequency of superspreading events, short serial intervals, and a higher risk of being infected outside of households for male people of working age indicate a significant barrier to the identification and management of COVID-19 cases, which requires enhanced nonpharmaceutical interventions to mitigate this pandemic.

Cryptocurrencies gain trust in users by publicly disclosing the full creation and transaction history. In return, the transaction history faithfully records the whole spectrum of cryptocurrency user behaviors. This article analyzes and summarizes the existing research on knowledge discovery in the cryptocurrency transactions using data mining techniques. Specifically, we classify the existing research into three aspects, i.e., transaction tracings and blockchain address linking, the analyses of collective user behaviors, and the study of individual user behaviors. For each aspect, we present the problems, summarize the methodologies, and discuss major findings in the literature. Furthermore, an enumeration of transaction data parsing and visualization tools and services is also provided. Finally, we outline several gaps and trends for future investigation in this research area.

CryptoKitties was the first widely recognized blockchain game. Players could own, breed, and trade kitties, which are the only prop in the game. The game gained explosive growth upon its release but quickly collapsed in a short time. This study analyzes its entire player activity history for the first time in literature and tries to find the reasons for the rise and fall of this first blockchain game mania. First, we extracted the five million transaction records among 100 thousand addresses involved in CryptoKitties in the past three years. Based on the numbers of addresses involved in the game each day, we divide the game progress into four stages: the primer, the rise, the fall, and the serenity. We construct a temporal kitty ownership transfer network and analyze the varying network parameters in the four stages. We find that a large number of players poured in during the 10th and 18th days since the game release and quickly exited in the following month. Since then, a few big players have gradually dominated the game, concentrating the game resources. Through further analysis, we find that the main reason for the rapid increase in the game popularity was the increase of public attention by media outlets, while the reasons for the rapid decline in the game popularity include the oversupply of kitties, the decreasing of player income, a widening gap between the rich and poor players, and the limitations of blockchain systems. Based on these observations, we advise on the further blockchain game design: (1) to finely control the production of props and avoid an oversupply, (2) to balance the gaming cost and revenue and protect the enjoyment of players, (3) to narrow down the gap between rich and poor and create an equal gaming community, (4) to consider the limitations of blockchain systems in their game designs.

Professional association football is a game of talent. The success of a professional club hinges largely on its ability of assembling the best team. Building on a dataset of player transfer records among more than 400 clubs in 24 world-wide top class leagues from 2011 to 2015, this study aims to relate a club’s success to its activities in the player transfer market from a network perspective. We confirm that modern professional football is indeed a money game, in which larger investment spent on the acquisition of talented players generally yields better team performance. However, further investigation shows that professional football clubs can actually play different strategies in surviving or even excelling this game, and the success of strategies is strongly associated to their network properties in the football player transfer network.