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.

The Food-Trade Associated Spreading of COVID-19 Outbreak from a Contaminated Super Wholesale Food Market in Beijing (preprint)

Background: The re-emerging outbreak of COVID-19 in Beijing China in summer of 2020 was originated from a contaminated super food wholesale market. The transmission mechanism was analyzed. Methods: We hypothesized that the Xinfadi outbreak was associated with activities of food-trade. Therefore, all the confirmed cases were divided into groups of sellers, buyer, seller transmitted and buyer transmitted. Data for each case were georeferenced and aggregated to the 500m-spaced hexagon grids using geodata and base maps, road networks, urban points of interest from OpenStreetMap. The Xinfadi-related trade activity data were derived using Python crawling scripts. The spatial association of the outbreak was studied with Moran’s I statistic method and a Susceptible-Infected-Recovered (SIR) coupled Agent-Based Model (ABM). Findings: Of 335 cases reported, 177 (45·3%) were sellers who worked for the Market, 83 (23·7%) were buyers who visited the Market, and 74 were transmitted by either of the infected buyers or sellers. The Market was the outbreak center, which were spreading along the urban rapid transit lines. The areas with a high incidence were concentrated across neighborhoods in the southwest of Beijing's Fifth Ring Road and the west section of the Fourth Ring of southwestern Beijing, and the west portion of Fuxing Road. The highest number of seller transmission hubs were located in Market neighborhood, however the buyer transmission hubs extended to cover more than three different districts. Our SIR-ABM model analysis suggested that the trade-distancing strategy effectively reduced the R0. The retail shops closure strategy reduced nearly half number of visitors to market. The Buy-local policy option reduced more than 70% infection in total. Interpretation: The Xinfadi outbreak was associated contaminated super food market by people's movements for food-trade, including their interactions in related activities. Therefore, the retails closures and buy-local policy could reduce size of the outbreak and prevent possible outbreak in future.Funding Statement: Ministry of Science and Technology China (Grant number 2018ZX10712001-017 and 2018ZX10712001-018), Chinese Academy of Medical Sciences (2018RU010), Chinese Academy (2020-XZ-37) and the National Natural Science Foundation of China (71603253 and 72074209).Declaration of Interests: All authors declare no competing interests.