The status of coinfection during the national outbreak of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)Omicron BA.5.2 or BF.7 in China in the winter of 2022,which is suspected to contribute substantially...The status of coinfection during the national outbreak of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)Omicron BA.5.2 or BF.7 in China in the winter of 2022,which is suspected to contribute substantially to the overloaded severe cases,needs to be investigated.We analyzed the coinfection status of 385 severe patients infected with the Omicron variant in Guangzhou using metagenomic sequencing.We found that 317(82.3%)patients were coinfected with at least one additional pathogen(s),including bacteria(58.7%),fungi(27.1%)and viruses(73.5%).Pseudomonas aeruginosa(P.aeruginosa)(24.2%),Staphylococcus aureus(S.aureus)(14.0%),andKlebsiella pneumoniae(K.pneumonia)(13.4%)ranked as the top three coinfected bacteria.Aspergillus fumigatus(A.fumigatus)(39.5%),Pneumocystis jirovecii(P.jirovecii)(24.4%)andCanidia albicans(C.albicans)(22.1%)were the top three coinfected fungi.Epstein-Barr virus(EBV)(63.1%),Human herpesvirus 7(HHV-7)(34.8%),and Herpes simplex virus 1(HSV-1)(32.6%)were the top three coinfected viruses.Of note,the detection of multiple coinfections of potential pathogenic bacteria,fungi,and viruses,despite lacking consistent patterns,highlighted a complicated synergistic contribution to disease severity.Our study presents the most comprehensive spectrum of bacterial,fungal,and viral coinfections in Omicron-associated severe coronavirus disease 2019(COVID-19),implying that the coinfection of conditional pathogens might synergistically deteriorate the Omicron infection outcomes.展开更多
The main epidemiological features such as basic reproduction number,effective reproduction number and sensitivity analysis were extensively discussed for multi-age groups SEIHR model in this study.Firstly,by using of ...The main epidemiological features such as basic reproduction number,effective reproduction number and sensitivity analysis were extensively discussed for multi-age groups SEIHR model in this study.Firstly,by using of the next generation matrix method,basic reproduction number R0 of the total population was estimated as 1.57 using parameter values of four age groups of Fuzhou COVID-19 large wave.Given age group k,the values of R_(0k)(age group k to age group k),the values of R_(o)^(k)(an infected of age group k to the total population)and the values of R_(o)^(k)>R_(0k)>R_(o)^(k)(an infected of the total population to age group k)were also estimated,in which the explorations of the impacts of age groups revealed that the relationship was valid.Then,the fluctuating tendencies of effective reproduction number Rt were demonstrated by using two approaches(the surveillance data and the SEIHR model)for Fuzhou COVID-19 large wave,during which high-risk group(G4 group)mainly contributed the infection scale due to high susceptibility to infection and high risks to basic diseases.Further,the sensitivity analysis using two approaches(the sensitivity index and the PRCC values)revealed that susceptibility to infection of age groups played the vital roles,while the numerical simulation showed that infection scale varied with the changes of social contacts of age groups.The results of this study claimed that the high-risk group out of the total population was concerned by the local government with the highest susceptibility to infection against COVID-19.Conclusions This study verified that the partition structure of age groups of the total population,the susceptibility to infection of age groups,the social contacts among age groups were the important contributors of infection scale.The less social contacts and adequate hospital beds for high-risk group were profitable to control the spread of COVID-19.To avoid the emergence of medical runs against new variant in the future,the policymakers from local government were suggested to decline social contacts when hospital beds were limited.展开更多
基金supported by the National Key R&D Program of China(No.2023YFC3041500 and 2023YFC3041700)the Key-Area R&D Program of Guangdong Province(No.2022B1111020002)+1 种基金the R&D Program of Guangzhou Laboratory(No.SRPG23-001)the Guangzhou Scienceand Technology Planning Project(No.202103000026 and 202201020316).
文摘The status of coinfection during the national outbreak of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)Omicron BA.5.2 or BF.7 in China in the winter of 2022,which is suspected to contribute substantially to the overloaded severe cases,needs to be investigated.We analyzed the coinfection status of 385 severe patients infected with the Omicron variant in Guangzhou using metagenomic sequencing.We found that 317(82.3%)patients were coinfected with at least one additional pathogen(s),including bacteria(58.7%),fungi(27.1%)and viruses(73.5%).Pseudomonas aeruginosa(P.aeruginosa)(24.2%),Staphylococcus aureus(S.aureus)(14.0%),andKlebsiella pneumoniae(K.pneumonia)(13.4%)ranked as the top three coinfected bacteria.Aspergillus fumigatus(A.fumigatus)(39.5%),Pneumocystis jirovecii(P.jirovecii)(24.4%)andCanidia albicans(C.albicans)(22.1%)were the top three coinfected fungi.Epstein-Barr virus(EBV)(63.1%),Human herpesvirus 7(HHV-7)(34.8%),and Herpes simplex virus 1(HSV-1)(32.6%)were the top three coinfected viruses.Of note,the detection of multiple coinfections of potential pathogenic bacteria,fungi,and viruses,despite lacking consistent patterns,highlighted a complicated synergistic contribution to disease severity.Our study presents the most comprehensive spectrum of bacterial,fungal,and viral coinfections in Omicron-associated severe coronavirus disease 2019(COVID-19),implying that the coinfection of conditional pathogens might synergistically deteriorate the Omicron infection outcomes.
基金This study received the supports from Special Projects of the Central Government Guiding Local Science and Technology Development(2021L3018)Natural Science Foundation of Fujian Province of China(2021J01621)+3 种基金Consultancy Project by the Chinese Academy of Engineering(2022-JB-06)National Natural Science Foundation of China(12231012)Royal Society of Edinburgh(RSE1832)Engineering and Physical Sciences Research Council(EP/W522521/1).
文摘The main epidemiological features such as basic reproduction number,effective reproduction number and sensitivity analysis were extensively discussed for multi-age groups SEIHR model in this study.Firstly,by using of the next generation matrix method,basic reproduction number R0 of the total population was estimated as 1.57 using parameter values of four age groups of Fuzhou COVID-19 large wave.Given age group k,the values of R_(0k)(age group k to age group k),the values of R_(o)^(k)(an infected of age group k to the total population)and the values of R_(o)^(k)>R_(0k)>R_(o)^(k)(an infected of the total population to age group k)were also estimated,in which the explorations of the impacts of age groups revealed that the relationship was valid.Then,the fluctuating tendencies of effective reproduction number Rt were demonstrated by using two approaches(the surveillance data and the SEIHR model)for Fuzhou COVID-19 large wave,during which high-risk group(G4 group)mainly contributed the infection scale due to high susceptibility to infection and high risks to basic diseases.Further,the sensitivity analysis using two approaches(the sensitivity index and the PRCC values)revealed that susceptibility to infection of age groups played the vital roles,while the numerical simulation showed that infection scale varied with the changes of social contacts of age groups.The results of this study claimed that the high-risk group out of the total population was concerned by the local government with the highest susceptibility to infection against COVID-19.Conclusions This study verified that the partition structure of age groups of the total population,the susceptibility to infection of age groups,the social contacts among age groups were the important contributors of infection scale.The less social contacts and adequate hospital beds for high-risk group were profitable to control the spread of COVID-19.To avoid the emergence of medical runs against new variant in the future,the policymakers from local government were suggested to decline social contacts when hospital beds were limited.
