Background: The COVID-19 pandemic has affected all sectors of the economy and society. To understand the impact of the pandemic on rms in China and suggest responding public policies, we investigated rms in Guangdong Province (a Province with the highest Gross Domestic Product in China). Methods: The survey sample included 524 rms in 15 cities in Guangdong Province. We chose these rms from list published by the government, considering the industrial characteristics of Guangdong province and rm size. The questionnaire comprised of four categories and included 17 questions was developed based on previous studies carried out by OECD. The executives of rms were contacted by phone or WeChat, and were invited to answer self-administered questionnaires through an on-line survey platform. The data was analyzed by SPSS. Results: The following ndings are worth to be noticed: (1) 48.7% of rms maintained stability, and 35.1% of the rms experienced a halt in operation or faced closure; (2) Nearly 70%-90% of the rms are or are willing to transform to online marketing, remote o ce work, and digital operations. (3) 46% of rms believe that there will be a certain loss this year, and 83.5% expected a decreasing trend of the city's GDP growth. Conclusions: rms in Guangdong Province have faced great challenges in the epidemic. The rms' production and operation activities are limited, and risks are faced. It is necessary to effectively implement supporting policies to profoundly lower production costs for rms, and help rms survive the di cult period, and even gradually transit to normal business operation status. Background The current COVID-19 is a rapidly evolving global challenge and like any pandemic, it weakens health systems, costs lives, and also poses great risks to the global economy and security [1, 2, 3, 4]. According to the data from WHO (World Health Organization) and Johns Hopkins University, till the middle of June 2020, the global COVID-19 pandemic has con rmed more than 7.5 million cases, causing nearly 420 thousand deaths in around 215 countries (https://www.arcgis.com/apps/opsdashboard/index.html). COVID-19 pandemic is a public health emergency. It's a sudden outbreak that causes or is likely to cause serious public health damages including major infectious diseases, mass unexplained diseases, major food and occupational poisonings or other serious public health issues [5]. Global economic growth is expected to decrease continually on account of the epidemic impact throughout the world [6, 7]. According to the Organization for Economic Cooperation and Development (OECD)'s forecast, the global GDP (Gross Domestic Product) growth rate will drop to 2.4% in 2020 [3]. The current continuous worldwide spread of COVID-19 has greatly increased the risk of uncertainty and global recession [8, 9][1]. Supply chain disruption, shrinking demand for consumption and investment, signi cant weakening of economic activities, and damaged market con dence have put more severe tests on the resilience of relevant economies, the level of...

Objectives:To estimate the basic reproduction number of the Wuhan novel coronavirus (2019-nCoV). Methods:Based on the susceptible-exposed-infected-removed (SEIR) compartment model and the assumption that the infectious cases with symptoms occurred before 26 January, 2020 are resulted from free propagation without intervention, we estimate the basic reproduction number of 2019-nCoV according to the reported confirmed cases and suspected cases, as well as the theoretical estimated number of infected cases by other research teams, together with some epidemiological determinants learned from the severe acute respiratory syndrome (SARS). Results:The basic reproduction number fall between 2.8 and 3.3 by using the real-time reports on the number of 2019-nCoV-infected cases from People's Daily in China and fall between 3.2 and 3.9 on the basis of the predicted number of infected cases from international colleagues. Conclusions:The early transmission ability of 2019-nCoV is close to or slightly higher than SARS.It is a controllable disease with moderate to high transmissibility. Timely and effective control measures are needed to prevent the further transmissions. K E Y W O R D S2019 novel coronavirus (2019-nCoV), basic reproduction number, epidemiology

Linus Bengtsson and colleagues examine the use of mobile phone positioning data to monitor population movements during disasters and outbreaks, finding that reports on population movements can be generated within twelve hours of receiving data.

Most severe disasters cause large population movements. These movements make it difficult for relief organizations to efficiently reach people in need. Understanding and predicting the locations of affected people during disasters is key to effective humanitarian relief operations and to long-term societal reconstruction. We collaborated with the largest mobile phone operator in Haiti (Digicel) and analyzed the movements of 1.9 million mobile phone users during the period from 42 d before, to 341 d after the devastating Haiti earthquake of January 12, 2010. Nineteen days after the earthquake, population movements had caused the population of the capital Port-au-Prince to decrease by an estimated 23%. Both the travel distances and size of people's movement trajectories grew after the earthquake. These findings, in combination with the disorder that was present after the disaster, suggest that people's movements would have become less predictable. Instead, the predictability of people's trajectories remained high and even increased slightly during the three-month period after the earthquake. Moreover, the destinations of people who left the capital during the first three weeks after the earthquake was highly correlated with their mobility patterns during normal times, and specifically with the locations in which people had significant social bonds. For the people who left Port-au-Prince, the duration of their stay outside the city, as well as the time for their return, all followed a skewed, fat-tailed distribution. The findings suggest that population movements during disasters may be significantly more predictable than previously thought.trajectory | human mobility | disaster informatics | disaster relief I n 2010, natural disasters displaced 42 million people, directly affected an estimated 217 million people, and resulted in USD 120 billion worth of damage (1, 2). The humanitarian response to natural disasters relies critically on data on the geographic distribution of affected people (3). During the early response phase, data on population distributions are fundamental to the delivery of water, food, and shelter, and to the creation of sampling frames for needs assessment surveys (4). During later stage reconstruction efforts, population distribution data is required for the allocation of schooling resources, delivery of seeds, construction of houses, and the like (5, 6).Despite a number of studies on human mobility patterns during small-scale, short-term emergencies such as crowd panics (7,8) and fires (9, 10), research on the dynamics of population mobility during large-scale disasters such as earthquakes, tsunamis, floods, and hurricanes has been limited (11). Existing research on population movements after large-scale disasters has been hampered by difficulties in collecting representative longitudinal data in places where infrastructure and social order have collapsed (12, 13), and where study populations are moving across vast geographical areas (14). Existing research has found that people displaced ...

In this study we analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile phone call data records. By measuring the uncertainties of movements using entropy, considering both the frequencies and temporal correlations of individual trajectories, we find that the theoretical maximum predictability is as high as 88%. To verify whether such a theoretical limit can be approached, we implement a series of Markov chain (MC) based models to predict the actual locations visited by each user. Results show that MC models can produce a prediction accuracy of 87% for stationary trajectories and 95% for non-stationary trajectories. Our findings indicate that human mobility is highly dependent on historical behaviors, and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy.

Effective response to infectious disease epidemics requires focused control measures in areas predicted to be at high risk of new outbreaks. We aimed to test whether mobile operator data could predict the early spatial evolution of the 2010 Haiti cholera epidemic. Daily case data were analysed for 78 study areas from October 16 to December 16, 2010. Movements of 2.9 million anonymous mobile phone SIM cards were used to create a national mobility network. Two gravity models of population mobility were implemented for comparison. Both were optimized based on the complete retrospective epidemic data, available only after the end of the epidemic spread. Risk of an area experiencing an outbreak within seven days showed strong dose-response relationship with the mobile phone-based infectious pressure estimates. The mobile phone-based model performed better (AUC 0.79) than the retrospectively optimized gravity models (AUC 0.66 and 0.74, respectively). Infectious pressure at outbreak onset was significantly correlated with reported cholera cases during the first ten days of the epidemic (p < 0.05). Mobile operator data is a highly promising data source for improving preparedness and response efforts during cholera outbreaks. Findings may be particularly important for containment efforts of emerging infectious diseases, including high-mortality influenza strains.

The ongoing rapid expansion of the Word Wide Web (WWW) greatly increases the information of effective transmission from heterogeneous individuals to various systems. Extensive research for information diffusion is introduced by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and empirical studies, unification and comparison of different theories and approaches are lacking, which impedes further advances. In this article, we review recent developments in information diffusion and discuss the major challenges. We compare and evaluate available models and algorithms to receptively investigate their physical roles and optimization designs. Potential impacts and future directions are discussed. We emphasize that information diffusion has great scientific depth and combines diverse research fields which makes it interesting for physicists as well as interdisciplinary researchers.

Objective: To estimate the potential risk and geographic range of Wuhan novel coronavirus (2019-nCoV) spread within and beyond China from