| scholarly article | Q13442814 |
| P819 | ADS bibcode | 2020Sci...368..493K |
| P8150 | COVIDWHO ID | covidwho-18400 |
| P6179 | Dimensions Publication ID | 1125900460 |
| P356 | DOI | 10.1126/SCIENCE.ABB4218 |
| P932 | PMC publication ID | 7146642 |
| P698 | PubMed publication ID | 32213647 |
| P8299 | Semantic Scholar corpus ID | 214681593 |
| P50 | author | Alessandro Vespignani | Q4716441 |
| Christopher Dye | Q5112251 | ||
| John Brownstein | Q26704483 | ||
| Nuno Rodrigues Faria | Q29397358 | ||
| Oliver G. Pybus | Q32632869 | ||
| David M Pigott | Q42305842 | ||
| Louis du Plessis | Q55455541 | ||
| Huaiyu Tian | Q56383824 | ||
| Bernardo Gutierrez | Q58211795 | ||
| Moritz U. G. Kraemer | Q60609353 | ||
| Chieh-Hsi Wu | Q63922271 | ||
| Ruoran Li | Q84849773 | ||
| Samuel V Scarpino | Q87279309 | ||
| William P. Hanage | Q89637656 | ||
| Brennan Klein | Q95597722 | ||
| P2093 | author name string | Chia-Hung Yang | |
| Maylis Layan | |||
| Open COVID-19 Data Working Group | |||
| P2860 | cites work | Factors that make an infectious disease outbreak controllable | Q24568031 |
| Middle East respiratory syndrome coronavirus: quantification of the extent of the epidemic, surveillance biases, and transmissibility | Q28660530 | ||
| Regularization Paths for Generalized Linear Models via Coordinate Descent | Q29614491 | ||
| Incubation periods of acute respiratory viral infections: a systematic review. | Q30376497 | ||
| Multiscale, resurgent epidemics in a hierarchical metapopulation model | Q33913909 | ||
| Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia | Q84108313 | ||
| Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study | Q84096797 | ||
| The chi-square test of independence | Q37508430 | ||
| Temporally Varying Relative Risks for Infectious Diseases: Implications for Infectious Disease Control | Q38982515 | ||
| Genomic and epidemiological monitoring of yellow fever virus transmission potential | Q56341716 | ||
| Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus | Q62485348 | ||
| A database of geopositioned Middle East Respiratory Syndrome Coronavirus occurrences | Q84316161 | ||
| Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020 | Q84634494 | ||
| A Novel Coronavirus from Patients with Pneumonia in China, 2019 | Q86729469 | ||
| COVID-19 control in China during mass population movements at New Year | Q87281381 | ||
| Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020 | Q87410200 | ||
| [The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China] | Q87461543 | ||
| Risk for Transportation of 2019 Novel Coronavirus Disease from Wuhan to Other Cities in China | Q87461585 | ||
| Open access epidemiological data from the COVID-19 outbreak | Q89000034 | ||
| Reporting, Epidemic Growth, and Reproduction Numbers for the 2019 Novel Coronavirus (2019-nCoV) Epidemic | Q89548150 | ||
| Quantifying the association between domestic travel and the exportation of novel coronavirus (2019-nCoV) cases from Wuhan, China in 2020: a correlational analysis | Q89812925 | ||
| Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention | Q89855853 | ||
| Epidemiological data from the COVID-19 outbreak, real-time case information | Q90633218 | ||
| An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China | Q90802726 | ||
| P433 | issue | 6490 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | COVID-19 | Q84263196 |
| SARS-CoV-2 | Q82069695 | ||
| P304 | page(s) | 493-497 | |
| P577 | publication date | 2020-03-25 | |
| P1433 | published in | Science | Q192864 |
| P1476 | title | The effect of human mobility and control measures on the COVID-19 epidemic in China | |
| P478 | volume | 368 |
| Q94946379 | "Pairing assistance": the effective way to solve the breakdown of health services system caused by COVID-19 pandemic |
| Q96124508 | A data-driven network model for the emerging COVID-19 epidemics in Wuhan, Toronto and Italy |
| Q98568574 | A retrospective analysis of the dynamic transmission routes of the COVID-19 in mainland China |
| Q101565506 | A warm summer is unlikely to stop transmission of COVID-19 naturally |
| Q99407571 | Abrupt decline in tropospheric nitrogen dioxide over China after the outbreak of COVID-19 |
| Q99634999 | Acceptance of COVID-19 Vaccination during the COVID-19 Pandemic in China |
| Q100525954 | After the lockdown: simulating mobility, public health and economic recovery scenarios |
| Q101564432 | Air Quality Response in China Linked to the 2019 Novel Coronavirus (COVID-19) Lockdown |
| Q99584421 | Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK |
| Q98663412 | Applications of predictive modelling early in the COVID-19 epidemic |
| Q96033630 | Appropriate arrangement of cancer treatment after COVID-19 epidemic peaks in China |
| Q97073447 | Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study |
| Q92118706 | Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China |
| Q98665471 | Automated and partly automated contact tracing: a systematic review to inform the control of COVID-19 |
| Q91822635 | Beware of the second wave of COVID-19 |
| Q95298084 | COVID-19 outbreak reproduction number estimations and forecasting in Marche, Italy |
| Q97533980 | COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown |
| Q95603693 | COVID-19 spreading in Rio de Janeiro, Brazil: do the policies of social isolation really work? |
| Q92038327 | COVID-19 travel restrictions and the International Health Regulations - call for an open debate on easing of travel restrictions |
| Q100464089 | Changing travel patterns in China during the early stages of the COVID-19 pandemic |
| Q88975944 | Close contacts and household transmission of SARS-CoV-2 in China: a content analysis based on local Heath Commissions' public disclosures. |
| Q94585603 | Coast-to-Coast Spread of SARS-CoV-2 during the Early Epidemic in the United States |
| Q88979202 | Coast-to-coast spread of SARS-CoV-2 in the United States revealed by genomic epidemiology |
| Q102052586 | Community Movement and COVID-19: A global study using Google's Community Mobility Reports |
| Q95605988 | Cooperative virus propagation underlies COVID-19 transmission dynamics |
| Q99202304 | Coupled human-environment system amid COVID-19 crisis: A conceptual model to understand the nexus |
| Q100455175 | Crowding and the shape of COVID-19 epidemics |
| Q101476896 | Daily CO2 emission reduction indicates the control of activities to contain COVID-19 in China |
| Q98281098 | Digital technologies in the public-health response to COVID-19 |
| Q94941433 | Does lockdown reduce air pollution? Evidence from 44 cities in northern China |
| Q94513306 | Effect of non-pharmaceutical interventions to contain COVID-19 in China |
| Q98291569 | Effects of policies and containment measures on control of COVID-19 epidemic in Chongqing |
| Q98184366 | Epidemiological and clinical characteristics of the COVID-19 epidemic in Brazil |
| Q94568467 | Estimated Demand for US Hospital Inpatient and Intensive Care Unit Beds for Patients With COVID-19 Based on Comparisons With Wuhan and Guangzhou, China |
| Q94670243 | Estimating the burden of SARS-CoV-2 in France |
| Q88978543 | Estimating the number of undetected COVID-19 cases exported internationally from all of China |
| Q94487210 | Feasibility of controlling COVID-19 - Authors' reply |
| Q94487226 | Genomic Epidemiology of SARS-CoV-2 in Guangdong Province, China |
| Q101133136 | Genomic surveillance of COVID-19 cases in Beijing |
| Q98577454 | Global and local mobility as a barometer for COVID-19 dynamics |
| Q96647489 | Growth Rate and Acceleration Analysis of the COVID-19 Pandemic Reveals the Effect of Public Health Measures in Real Time |
| Q104509823 | Impact of Extreme Hot Climate on COVID-19 Outbreak in India |
| Q98163653 | Impact of lockdown on COVID-19 epidemic in Île-de-France and possible exit strategies |
| Q99240760 | Influenza activity during the outbreak of coronavirus disease 2019 in mainland China |
| Q95327910 | Mapping global variation in human mobility |
| Q96769016 | Mask wearing in pre-symptomatic patients prevents SARS-CoV-2 transmission: An epidemiological analysis |
| Q94551085 | Modeling shield immunity to reduce COVID-19 epidemic spread |
| Q95606617 | Modeling the impact of human mobility and travel restrictions on the potential spread of SARS-CoV-2 in Taiwan |
| Q95601898 | Modeling the impact of social distancing, testing, contact tracing and household quarantine on second-wave scenarios of the COVID-19 epidemic |
| Q96952365 | Modeling, state estimation, and optimal control for the US COVID-19 outbreak |
| Q98222311 | Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19 |
| Q95608586 | Moving Beyond a Peak Mentality: Plateaus, Shoulders, Oscillations and Other 'Anomalous' Behavior-Driven Shapes in COVID-19 Outbreaks |
| Q102053780 | Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic |
| Q99633616 | Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development |
| Q100945879 | Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic |
| Q92058348 | Nowruz travelers and the COVID-19 pandemic in Iran |
| Q99605603 | Pandæsim: An Epidemic Spreading Stochastic Simulator |
| Q99578548 | Parallel Minority Game and it's application in movement optimization during an epidemic |
| Q99550986 | Political partisanship influences behavioral responses to governors' recommendations for COVID-19 prevention in the United States |
| Q98733600 | Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing |
| Q102152186 | Preparing for a future COVID-19 wave: insights and limitations from a data-driven evaluation of non-pharmaceutical interventions in Germany |
| Q91932812 | Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period |
| Q102075511 | Ranking the effectiveness of worldwide COVID-19 government interventions |
| Q102388786 | Response2covid19, a dataset of governments' responses to COVID-19 all around the world |
| Q98657460 | Sharing patient-level real-time COVID-19 data |
| Q95618700 | Significant Relaxation of SARS-CoV-2-Targeted Non-Pharmaceutical Interventions Will Result in Profound Mortality: A New York State Modelling Study |
| Q99731720 | Status and influential factors of anxiety depression and insomnia symptoms in the work resumption period of COVID-19 epidemic: A multicenter cross-sectional study |
| Q104509833 | The Coupled Impact of Emergency Responses and Population Flows on the COVID-19 Pandemic in China |
| Q96226390 | The effect of large-scale anti-contagion policies on the COVID-19 pandemic |
| Q98568283 | The effect of travel restrictions on the geographical spread of COVID-19 between large cities in China: a modelling study |
| Q99629322 | The good, the bad and the ugly of COVID-19 lockdown effects on wildlife conservation: Insights from the first European locked down country |
| Q95602605 | The intensity of COVID-19 outbreaks is modulated by SARS-CoV-2 free-living survival and environmental transmission |
| Q98170180 | The macroecology of the COVID-19 pandemic in the Anthropocene |
| Q102108868 | The scales of human mobility |
| Q99637425 | The unintended consequences of inconsistent pandemic control policies |
| Q100395018 | The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology |
| Q97636635 | Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example |
| Q103028724 | Variation in microparasite free-living survival and indirect transmission can modulate the intensity of emerging outbreaks |
| Q109944804 | What human mobility data tell us about COVID-19 spread |
Search more.