An outbreak of acute respiratory disease caused by HAdV-55 in Beijing, China, 2020.

Human adenoviruses (HAdVs) can cause acute respiratory diseases (ARDs) worldwide, and HAdV-55 is a reemergent pathogen in recent years. In the study, we investigated an outbreak of ARD at a school due to HAdV-55 in Beijing, China, during the early outbreak of coronavirus disease 2019 (COVID-19). The epidemic prevention team was dispatched to the school to collect epidemiologic data and nasopharyngeal samples. Then, real-time reverse transcription polymerase chain reaction (PCR) and multiplex PCR assays were used to detect severe acute respiratory syndrome coronavirus 2 and other respiratory pathogens, respectively. One representative HAdV-55 isolate was selected and submitted for whole-genome sequencing using a MiSeq system and the whole-genome phylogenetic tree was conducted based on the maximum likelihood method. The outbreak lasted from January 27 to February 6, 2020, and 108 students developed fever, among whom 60 (55.56%) cases were diagnosed with HAdV-55 infection in the laboratory using real-time PCR and 56 cases were hospitalized. All the confirmed cases had a fever and 11 cases (18.33%) presented with a fever above 39°C. Other main clinical symptoms included sore throat (43.33%) and headache (43.33%). We obtained and assembled the full genome of one isolate, BJ-446, with 34 761 nucleotides in length. HAdV-55 isolate BJ-446 was 99.85% identical to strain QS-DLL, which was the first HAdV-55 strain in China isolated from an ARD outbreak in Shanxi in 2006. One and four amino acid mutations were observed in the hexon gene and the coding region of L2 pV 40.1 kDa protein, respectively. We identified the first HAdV-55 infection associated with the ARD outbreak in Beijing since the emergence of COVID-19. The study suggests that improved surveillance of HAdV is needed, although COVID-19 is still prevalent in the world.

Quantitative analysis of the impact of various urban socioeconomic indicators on search-engine-based estimation of COVID-19 prevalence.

Numerous studies have proposed search engine-based estimation of COVID-19 prevalence during the COVID-19 pandemic; however, their estimation models do not consider the impact of various urban socioeconomic indicators (USIs). This study quantitatively analysed the impact of various USIs on search engine-based estimation of COVID-19 prevalence using 15 USIs (including total population, gross regional product (GRP), and population density) from 369 cities in China. The results suggested that 13 USIs affected either the correlation (SC-corr) or time lag (SC-lag) between search engine query volume and new COVID-19 cases ( p <0.05). Total population and GRP impacted SC-corr considerably, with their correlation coefficients r for SC-corr being 0.65 and 0.59, respectively. Total population, GRP per capita, and proportion of the population with a high school diploma or higher had simultaneous positive impacts on SC-corr and SC-lag ( p <0.05); these three indicators explained 37-50% of the total variation in SC-corr and SC-lag. Estimations for different urban agglomerations revealed that the goodness of fit, R 2 , for search engine-based estimation was more than 0.6 only when total urban population, GRP per capita, and proportion of the population with a high school diploma or higher exceeded 11.08 million, 120,700, and 38.13%, respectively. A greater urban size indicated higher accuracy of search engine-based estimation of COVID-19 prevalence. Therefore, the accuracy and time lag for search engine-based estimation of infectious disease prevalence can be improved only when the total urban population, GRP per capita, and proportion of the population with a high school diploma or higher are greater than the aforementioned thresholds.

Pneumonia Patients Caused by Co-infection With SARS-CoV-2 and Human Adenovirus in China.

Objectives: To data, no patients with obvious epidemiological relationship co-infected with SARS-CoV-2 and other pathogens have been reported. Here, we investigated 10 patients caused by co-infection with SARS-CoV-2 and human adenovirus (HAdV), resulting in third-generation transmission. Materials and Methods: From Jan 15, 2020, we enrolled 10 patients with pneumonia in Hunan Province, China. Epidemiological, clinical, and laboratory investigation results from these patients were analyzed. An epidemiological investigation was performed to assess whether patient infections were linked using conventional methods and metagenomic sequencing. Results: The presence of co-infection with SARS-CoV-2 and HAdV was determined via RT-PCR and metagenomic sequencing. Phylogenetic analysis revealed that SARS-CoV-2 and HAdV genomes clustered together, with similar genetic relationships. The first patient likely became co-infected during meetings or travel in Wuhan. The patient transmitted the virus via dinners and meetings, which resulted in four second-generation cases. Then, a second-generation case transmitted the virus to her family members or relatives via presymptomatic transmission. Conclusions: This study described an example of co-infection with SARS-CoV-2 and HAdV in pneumonia patients, which caused third-generation cases and inter-regional transmission via meetings, household interactions, and dinner parties. We also observed the persistent and presymptomatic transmission of co-infection, which has the potential to make the continued control of the COVID-19 pandemic challenging. Continuous surveillance is needed to monitor the prevalence, infectivity, transmissibility, and pathogenicity of SARS-CoV-2 co-infection with other pathogens to evaluate its real risk.

Food-trade-associated COVID-19 outbreak from a contaminated wholesale food supermarket in Beijing.

The re-emerging outbreak of COVID-19 in Beijing, China, in the summer of 2020 originated from a SARS-CoV-2-infested wholesale food supermarket. We postulated that the Xinfadi market outbreak has links with food-trade activities. Our Susceptible to the disease, Infectious, and Recovered coupled Agent Based Modelling (SIR-ABM) analysis for studying the diffusion of SARS-CoV-2 particles suggested that the trade-distancing strategy effectively reduces the reproduction number (R0). The retail shop closure strategy reduced the number of visitors to the market by nearly half. In addition, the buy-local policy option reduced the infection by more than 70% in total. Therefore, retail closures and buy-local policies could serve as significantly effective strategies that have the potential to reduce the size of the outbreak and prevent probable outbreaks in the future.