Stringent non-pharmaceutical interventions increased the incidence of drunk driving-related road traffic crashes (preprint)

Drunk driving is one of the leading causes of traffic deaths in China. Although the non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic resulted in dramatic reductions in transport and mobility in 2020, to date, little is known about how drunk driving and related traffic crashes varied with the NPIs. We created a high-resolution and comprehensive drunk driving dataset. Based on 836,194 drunk driving in 335 cities in China from 2016 to 2020, we employ the causality models to examine and quantify trends in overall and subgroup drunk driving and related traffic crashes road traffic mortality throughout 2020. Subgroup analyses were done by place (urban and rural), sex, employment, education level, age group, geographical location (road, city, province and region), and by type of motor vehicle. Despite the marked reductions in drunk driving and related traffic crashes in 2020, the incidence of the induced traffic crashes increased during the stringent NPI period. Substantial differences persist across populations, locations and motor vehicles. Drunk driving occurred in the morning, at western China and rural catchment areas as well as drunk drivers aged 18–30, with high education background or white-collars have more declines. The drunk drivers aged 30–50, with high education levels, white-collar, female, and occurred at night, in the middle east China and urban areas have a higher incidence of traffic crash compared with other subgroups especially in Stage I. These comparable findings can inform decision-makers in planning precisely targeted interventions for cracking down on drunk driving during the events like public health arenas.

Asymptomatic SARS-CoV-2 infection and the demography of COVID-19 (preprint)

Asymptomatic individuals carrying SARS-CoV-2 can transmit the virus and contribute to outbreaks of COVID-19, but it is not yet clear how the proportion of asymptomatic infections varies by age and geographic location. Here we use detailed surveillance data gathered during COVID-19 resurgences in six cities of China at the beginning of 2021 to investigate this question. Data were collected by multiple rounds of city-wide PCR test with detailed contact tracing, where each patient was monitored for symptoms through the whole course of infection. We find that the proportion of asymptomatic infections declines with age (coefficient =-0.006, P<0.01), falling from 56% in age group 0-9 years to 12% in age group >60 years. Using an age-stratified compartment model, we show that this age-dependent asymptomatic pattern together with the age distribution of overall cases can explain most of the geographic differences in reported asymptomatic proportions. Combined with demography and contact matrices from other countries worldwide, we estimate that a maximum of 22%-55% of SARS-CoV-2 infections would come from asymptomatic cases in an uncontrolled epidemic based on asymptomatic proportions in China. Our analysis suggests that flare-ups of COVID-19 are likely if only adults are vaccinated and that surveillance and possibly control measures among children will be still needed in the future to contain epidemic resurgence.

Early Detection of COVID-19 Epidemic Using Search Engine Query Data (preprint)

Background: Early warnings of emerging infectious disease are crucial to prevent epidemics. However, in the early stage of the COVID-19 pandemic, traditional infectious disease surveillance failed to deliver a warning alert. The aim of this work is to develop search-engine-based surveillance methods for the early warning and prediction of COVID-19 outbreaks. Methods: By using more than 444 million Baidu search queries from China as training set, we collected 32 keywords from the Baidu Search Index that may related to COVID-19 outbreak from 18 December 2019 to 11 February 2020. The Beijing Xinfadi outbreak from 30 May 2020 to 30 July 2020 was used as independent test set. A multiple linear regression was applied to model the relationship between the daily query frequencies of keywords and the daily new cases. Findings: Our results show that 11 keywords in search queries were highly correlated to the daily numbers of confirmed cases (r =0.96, P <0.01). An abnormal initial peak (1.46 times the normal volume) in queries appeared on 31 December 2019, which could have served as an early warning signal for an outbreak. Of particular concern, on this day, the volume of the query “Wuhan Seafood Market” increased by over 240 times (from 10 to 2410), the volume of the query “Wuhan outbreak” increased by over 622 times (from 7 to 4359), and 17.5% of China’s query volume originated from Hubei Province, 51.15% of which was from Wuhan city. The quantitative model using four keywords (“Epidemic”, “Masks”, “Coronavirus” and “Clustered pneumonia”) successfully predicted the daily numbers of cases for the next two days, and detected an early signal during the Beijing Xinfadi outbreak (R2 =0.80). Interpretation: Our study demonstrates the ability of search engine query data to detect COVID-19 outbreaks, and suggests that abnormalities in query volume can serve as early warning signals.

A follow-up study shows no new infections caused by patients with repeat positive of COVID-19 in Wuhan (preprint)

BackgroundIt has been reported that a few recovered COVID-19 patients could suffer repeat positive, testing positive for the SARS-CoV-2 virus again after they were discharged from hospital. Understanding the epidemiological characteristics of patients with repeat positive is vital in preventing a second wave of COVID-19. MethodsIn this study, the epidemiological and clinical features for 20,280 COVID-19 patients from multiple centers between 31 December 2019 and 4 August 2020 in Wuhan were collected and followed. In addition, the RT-qPCR testing results for 4,079 individuals who had close contact with the patients suffering repeat positive were also obtained. Results2,466 (12.16%) of 20,280 patients presented with a repeat positive of SARS-CoV-2 after they were discharged from hospital. 4,079 individuals had close contact with them. The PCR result were negative for the 4,079 individuals. ConclusionsBy a follow-up study in Wuhan, we show the basic characteristics of patients with repeat positive and no new infections caused by patients with repeat positive of COVID-19.

Risk of COVID-19 is associated with long-term exposure to air pollution (preprint)

High risk of severe disease of COVID-19 has been associated with patients with chronic obstructive pulmonary disease, cardiovascular disease or hypertension, and long-term exposure to PM2.5 has been associated with COVID-19 mortality. We collate individual level data of confirmed COVID-19 cases during the first wave of the epidemic in mainland China by March 6, 2020. We pair these data with a mobile phone dataset, covering human movements from Wuhan before the travel ban and inner-city movements during the time of emergency response from 324 cities in China. Adjusting for socio-economic factors, an increase of 10 g/m3 in NO2 or PM2.5 was found to be associated with a 22.41% (95%CI: 7.28%-39.89%) or 15.35% (95%CI: 5.60%-25.98%) increase in the number of COVID-19 cases, and a 19.20% (95%CI: 4.03%-36.59%) or 9.61% (95%CI: 0.12%-20.01%) increase in severe infection, respectively. Our results highlight the importance of air quality improvements to health benefits.

Crowding and the epidemic intensity of COVID-19 transmission (preprint)

The COVID-19 pandemic is straining public health systems worldwide and major non-pharmaceutical interventions have been implemented to slow its spread. During the initial phase of the outbreak the spread was primarily determined by human mobility. Yet empirical evidence on the effect of key geographic factors on local epidemic spread is lacking. We analyse highly-resolved spatial variables for cities in China together with case count data in order to investigate the role of climate, urbanization, and variation in interventions across China. Here we show that the epidemic intensity of COVID-19 is strongly shaped by crowding, such that epidemics in dense cities are more spread out through time, and denser cities have larger total incidence. Observed differences in epidemic intensity are well captured by a metapopulation model of COVID-19 that explicitly accounts for spatial hierarchies. Densely-populated cities worldwide may experience more prolonged epidemics. Whilst stringent interventions can shorten the time length of these local epidemics, although these may be difficult to implement in many affected settings.

The effect of human mobility and control measures on the COVID-19 epidemic in China (preprint)

The ongoing COVID-19 outbreak has expanded rapidly throughout China. Major behavioral, clinical, and state interventions are underway currently to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, have affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was well explained by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases are still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China have substantially mitigated the spread of COVID-19.

Early evaluation of the Wuhan City travel restrictions in response to the 2019 novel coronavirus outbreak (preprint)

Respiratory illness caused by a novel coronavirus (COVID-19) appeared in China during December 2019. Attempting to contain infection, China banned travel to and from Wuhan city on 23 January and implemented a national emergency response. Here we evaluate the spread and control of the epidemic based on a unique synthesis of data including case reports, human movement and public health interventions. The Wuhan shutdown slowed the dispersal of infection to other cities by an estimated 2.91 days (95%CI: 2.54-3.29), delaying epidemic growth elsewhere in China. Other cities that implemented control measures pre-emptively reported 33.3% (11.1-44.4%) fewer cases in the first week of their outbreaks (13.0; 7.1-18.8) compared with cities that started control later (20.6; 14.5-26.8). Among interventions investigated here, the most effective were suspending intra-city public transport, closing entertainment venues and banning public gatherings. The national emergency response delayed the growth and limited the size of the COVID-19 epidemic and, by 19 February (day 50), had averted hundreds of thousands of cases across China.