Showing papers by "Lin Wang published in 2020"

Serial Interval of COVID-19 among Publicly Reported Confirmed Cases.

Abstract: We estimate the distribution of serial intervals for 468 confirmed cases of coronavirus disease reported in China as of February 8, 2020. The mean interval was 3.96 days (95% CI 3.53-4.39 days), SD 4.75 days (95% CI 4.46-5.07 days); 12.6% of case reports indicated presymptomatic transmission.

Age-specific mortality and immunity patterns of SARS-CoV-2 infection in 45 countries

Abstract: The number of COVID-19 deaths is often used as a key indicator of SARS-CoV-2 epidemic size. 42 However, heterogeneous burdens in nursing homes and variable reporting of deaths in elderly 43 individuals can hamper comparisons of deaths and the number of infections associated with them 44 across countries. Using age-specific death data from 45 countries, we find that relative differences 45 in the number of deaths by age amongst individuals aged

Epidemiological data from the COVID-19 outbreak, real-time case information.

Abstract: Cases of a novel coronavirus were first reported in Wuhan, Hubei province, China, in December 2019 and have since spread across the world. Epidemiological studies have indicated human-to-human transmission in China and elsewhere. To aid the analysis and tracking of the COVID-19 epidemic we collected and curated individual-level data from national, provincial, and municipal health reports, as well as additional information from online reports. All data are geo-coded and, where available, include symptoms, key dates (date of onset, admission, and confirmation), and travel history. The generation of detailed, real-time, and robust data for emerging disease outbreaks is important and can help to generate robust evidence that will support and inform public health decision making.

Risk for Transportation of Coronavirus Disease from Wuhan to Other Cities in China.

Abstract: On January 23, 2020, China quarantined Wuhan to contain coronavirus disease (COVID-19). We estimated the probability of transportation of COVID-19 from Wuhan to 369 other cities in China before the quarantine. Expected COVID-19 risk is >50% in 130 (95% CI 89-190) cities and >99% in the 4 largest metropolitan areas.

Serial interval of SARS-CoV-2 was shortened over time by nonpharmaceutical interventions.

Abstract: Studies of novel coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have reported varying estimates of epidemiological parameters, including serial interval distributions-i.e., the time between illness onset in successive cases in a transmission chain-and reproduction numbers. By compiling a line-list database of transmission pairs in mainland China, we show that mean serial intervals of COVID-19 shortened substantially from 7.8 to 2.6 days within a month (9 January to 13 February 2020). This change was driven by enhanced nonpharmaceutical interventions, particularly case isolation. We also show that using real-time estimation of serial intervals allowing for variation over time provides more accurate estimates of reproduction numbers than using conventionally fixed serial interval distributions. These findings could improve our ability to assess transmission dynamics, forecast future incidence, and estimate the impact of control measures.

Risk for Transportation of 2019 Novel Coronavirus Disease from Wuhan to Other Cities in China

Abstract: On January 23, 2020, China quarantined Wuhan to contain 2019 novel coronavirus disease (COVID-19) We estimated the probability of transportation of COVID-19 from Wuhan to 369 other cities in China before the quarantine Expected COVID-19 risk is >50% in 130 (95% CI 89-190) cities and >99% in the 4 largest metropolitan areas

The serial interval of COVID-19 from publicly reported confirmed cases

Abstract: We estimate the distribution of serial intervals for 468 confirmed cases of COVID-19 reported in 93 Chinese cities by February 8, 2020. The mean and standard deviation are 3.96 (95% CI 3.53-4.39) and 4.75 (95% CI 4.46-5.07) days, respectively, with 12.6% of reports indicating pre-symptomatic transmission.

Optimization of identifiability for efficient community detection

Abstract: Many physical and social systems are best described by networks. And the structural properties of these networks often critically determine the properties and function of the resulting mathematical models. An important method to infer the correlations between topology and function is the detection of community structure, which plays a key role in the analysis, design, and optimization of many complex systems. The nonnegative matrix factorization has been used prolifically to that effect in recent years, although it cannot guarantee balanced partitions, and it also does not allow a proactive computation of the number of communities in a network. This indicates that the nonnegative matrix factorization does not satisfy all the nonnegative low-rank approximation conditions. Here we show how to resolve this important open problem by optimizing the identifiability of community structure. We propose a new form of nonnegative matrix decomposition and a probabilistic surrogate learning function that can be solved according to the majorization–minimization principle. Extensive in silico tests on artificial and real-world data demonstrate the efficient performance in community detection, regardless of the size and complexity of the network.

Reconstruction of Transmission Pairs for Novel Coronavirus Disease 2019 (COVID-19) in Mainland China: Estimation of Superspreading Events, Serial Interval, and Hazard of Infection.

Abstract: BACKGROUND: Knowledge on the epidemiological features and transmission patterns of 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 1,407 transmission pairs that formed 643 transmission clusters in mainland China was reconstructed from 9,120 COVID-19 confirmed cases reported during January 15 - February 29, 2020 Statistical model fittings were used to identify the super-spreaders and estimate serial interval distributions Age and gender-stratified hazard of infection were estimated for household versus non-household transmissions RESULTS: There were 34 primary cases identified as super-spreaders, with 5 super-spreading events occurred within households Mean and standard deviation of serial intervals were estimated as 50 (95% CrI: 44, 55) and 52 (95% CrI: 49, 57) days for household transmissions and 52 (95% CrI: 46, 58) and 53 (95% CrI: 49, 57) days for non-household transmissions, respectively Hazard of being infected outside of households is higher for age between 18 and 64 years, whereas hazard of being infected within households is higher for young and old people CONCLUSIONS: Non-negligible frequency of super-spreading 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 non-pharmaceutical interventions to mitigate this pandemic

Effects of Proactive Social Distancing on COVID-19 Outbreaks in 58 Cities, China.

Abstract: Cities across China implemented stringent social distancing measures in early 2020 to curb coronavirus disease outbreaks. We estimated the speed with which these measures contained transmission in cities. A 1-day delay in implementing social distancing resulted in a containment delay of 2.41 (95% CI 0.97-3.86) days.

Household transmissions of SARS-CoV-2 in the time of unprecedented travel lockdown in China.

Abstract: Question: What are the characteristics of household and social transmissions of COVID-19 areas outside of epidemic centers? Findings: Based on 1,407 COVID-19 reported infection events in China outside of Hubei Province between 20 January and 19 February 2020, we estimate the distribution of secondary infection sizes, frequency of super spreading events, serial intervals and age-stratified hazard of infection. Young and older people have higher risks of being infected with households while males 65+ of age are responsible for a disproportionate number of household infections. Meaning: This report is the first large-scale analysis of the household and social transmission events in the COVID-19 pandemic.

COVID-19 serial interval estimates based on confirmed cases in public reports from 86 Chinese cities

Abstract: As a novel coronavirus (COVID-19) continues to spread widely and claim lives worldwide, its transmission characteristics remain uncertain. Here, we present and analyze the serial intervals-the time period between the onset of symptoms in an index (infector) case and the onset of symptoms in a secondary (infectee) case-of 339 confirmed cases of COVID-19 identified from 264 cities in mainland China prior to February 19, 2020. Here, we provide the complete dataset in both English and Chinese to support further COVID-19 research and modeling efforts.

Evolution of effective serial interval of SARS-CoV-2 by non-pharmaceutical interventions

Abstract: Studies of novel coronavirus disease (COVID-19) have reported varying estimates of epidemiological parameters such as serial intervals and reproduction numbers. By compiling a unique line-list database of transmission pairs in mainland China, we demonstrated that serial intervals of COVID-19 have shortened substantially from a mean of 7.8 days to 2.6 days within a month. This change is driven by enhanced non-pharmaceutical interventions, in particular case isolation. We also demonstrated that using real-time estimation of serial intervals allowing for variation over time would provide more accurate estimates of reproduction numbers, than by using conventional definition of fixed serial interval distributions. These findings are essential to improve the assessment of transmission dynamics, forecasting future incidence, and estimating the impact of control measures.

Proactive social distancing mitigates COVID-19 outbreaks within a month across 58 mainland China cities.

Abstract: In early 2020, cities across China enacted strict social distancing measures to contain emerging coronavirus (COVID-19) outbreaks. We estimated the speed with which these measures contained community transmission in each of 58 Chinese cities. On average, containment was achieved 7.83 days (SD 6.79 days) after the implementation of social distancing interventions, with an average reduction in the reproduction number (Rt) of 54.3% (SD 17.6%) over that time period. A single day delay in the implementation of social distancing led to a 2.41 (95% CI: 0.97, 3.86) day delay in containment. Swift social distancing interventions may thus achieve rapid containment of newly emerging COVID-19 outbreaks.

CoVID-19 in Singapore: Impact of Contact Tracing and Self-awareness on Healthcare Demand

Abstract: Background. A great concern around the globe now is to mitigate the COVID-19 pandemic via contact tracing. Analyzing the control strategies during the first five months of 2020 in Singapore is important to estimate the effectiveness of contacting tracing measures. Methods. We developed a mathematical model to simulate the COVID-19 epidemic in Singapore, with local cases stratified into 5 categories according to the conditions of contact tracing and self-awareness. Key parameters of each category were estimated from local surveillance data. We also simulated a set of possible scenarios to predict the effects of contact tracing and self-awareness for the following month. Findings. During January 23 - March 16, 2020, the success probabilities of contact tracing and self-awareness were estimated to be 31% (95% CI 28%-33%) and 54% (95% CI 51%-57%), respectively. During March 17 - April 7, 2020, several social distancing measures (e.g., limiting mass gathering) were introduced in Singapore, which, however, were estimated with minor contribution to reduce the non-tracing reproduction number per local case (R_(l,2)). If contact tracing and self-awareness cannot be further improved, we predict that the COVID-19 epidemic will continue to spread in Singapore if R_(l,2)≥1.5. Conclusion. Contact tracing and self-awareness can mitigate the COVID-19 transmission, and can be one of the key strategies to ensure a sustainable reopening after lifting the lockdown.