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Additional file 1. The Chinese Centers for Disease Control and Prevention (CDC) Etiology of Diarrhea Surveillance Study Team.

Additional file 2. Supplementary methods.

Additional file 1. Supplementary tables and figures.

Additional file 1: Figure S1. The distribution of all 272 entry-exit ports in Chinese mainland. Figure S2. The flow chart on the data extraction process and criteria of imported infections in mainland of China, 2014–2018. Figure S3. The classification of infectious diseases at ports of Chinese mainland. Figure S4. Report card of infectious diseases of the People’s Republic of China. Figure S5. The disease spectrum of during-travel infectious diseases (A) and post-travel infectious diseases (B) in relation with the inbound provinces in Chinese mainland China, 2014–2018. Table S1. The travel reason for during-travel cases of five type of diseases in Chinese mainland, 2014–2018.

Additional file 1: Fig S1. Geographical distributions of onset-to-diagnosis interval, severe rate (SR) and case fatality rate (CFR). Fig S2. Frequency distribution of onset-to-diagnosis interval of confirmed COVID-19 cases by regions and epidemic periods. Fig S3: The onset-to-diagnosis interval-related COVID-19 disease severe rate and case fatality rate stratified by regions. Fig S4. The odds ratio and attributable fraction of COVID-19 disease severe rate resulted from onset-to-diagnosis intervals in Wuhan and outside Wuhan. Fig S5. The odds ratio and attributable fraction of COVID-19 case fatality rate resulted from onset-to-diagnosis intervals in Wuhan and outside Wuhan. Table S1. The subgroup analysis on the onset-to-diagnosis interval of patients with COVID-19 for age, sex, geographic region and epidemic period by multivariate linear regression. Table S2. Odds ratio of onset-to-diagnosis interval for the risk of severe COVID-19 by multivariate logistic regression model. Table S3. Attributable fraction of onset-to-diagnosis interval for the risk of severe COVID-19. Table S4. Odds ratio of onset-to-diagnosis interval for the risk of fatal COVID-19 by multivariate logistic regression model. Table S5. Attributable fraction of onset-to-diagnosis interval for the risk of fatal COVID-19. Table S6. The predicted and actual case number of severe and death cases according to the different cutoff value of onset-to-diagnosis interval.

Additional file 1. Additional information includes Methods, 14 Figures, 6 Tables and References. Table S1. The specific references for all 89 Bunyavirales viruses in China and Rift Valley fever virus in Africa and the Arabian Peninsula. Table S2. Search terminology used for the systematic review. Table S3. Information of explanatory covariates sources in this study. Table S4. Description of 42 potential explanatory covariates used in the modelling efforts. Table S5. Clinical characteristics of human cases infections with different viruses (species) belonging to the order Bunyavirales in China. Table S6. The model-predicted areas and population sizes at risk by monthly for RVFV occurrence in China. Fig. S1. The flow diagram of data collection for Rift Valley fever virus. Fig. S2. Recorded and predicted risk distribution of H. asiaticum presence in China. Fig. S3. Correlation matrix for 36 covariates in CCHFV model. Fig. S4. Correlation matrix for 18 covariates in RVFV model. Fig. S5. Distribution of the first identification site of 89 Bunyavirales viruses in China. Fig. S6. Geographical distributions of Bunyavirales viruses detected in animals. Fig. S7. The relationship matrix of Bunyavirales virus species and involved animals. Fig. S8. ROC curves and AUC values of CCHFV model. Fig. S9. The marginal effect of explanatory covariates for CCHFV occurrence risk in the model. Fig. S10. Recorded distribution of RVFV occurrence in Africa and the Arabian Peninsula. Fig. S11. ROC curves and AUC values of RVFV model. Fig. S12. The marginal effect of explanatory covariates for RVFV occurrence risk in the model. Fig. S13. Mean environmental suitability and monthly imported risk for RVFV from January to June in China. Fig. S14. Mean environmental suitability and monthly imported risk for RVFV from July to December in China.

Background To combat the COVID-19 pandemic, nonpharmaceutical interventions (NPI) were implemented worldwide, which impacted a broad spectrum of acute respiratory infections (ARI). Methods Etiologically diagnostic data from 142 559 cases with ARIs, who were tested for eight viral pathogens (influenza virus, IFV; respiratory syncytial virus, RSV; human parainfluenza virus, HPIV; human adenovirus; human metapneumovirus; human coronavirus, HCoV; human bocavirus, HBoV, and human rhinovirus, HRV) between 2012 and 2021, were analyzed to assess the changes of respiratory infections in China during the first COVID-19 pandemic year compared to pre-pandemic years. Results Test positive rates of all respiratory viruses decreased during 2020, compared to the average levels during 2012−2019, with changes ranging from -17·2% for RSV to -87·6% for IFV. Sharp decreases mostly occurred between February and August when massive NPIs remained active, although HRV rebounded to the historical level during the summer. While IFV and HMPV were consistently suppressed year round, RSV, HPIV, HCoV, HRV HBov resurged and went beyond historical levels during September, 2020−January, 2021, after NPIs were largely relaxed and schools reopened. Resurgence was more prominent among children younger than 18 years and in Northern China. These observations remain valid after accounting for seasonality and long-term trend of each virus. Conclusions Activities of respiratory viral infections were reduced substantially in the early phases of the COVID-19 pandemic, and massive NPIs were likely the main driver. Lifting of NPIs can lead to resurgence of viral infections, particularly in children.

Additional file 1: Fig. S1. Location of the study sites, Shangcheng County in Henan Province, and the villages for sampling. The left panel marked the study site of Shangcheng County in Henan Province, the county with the highest SFTS incidence in China. The zoomed map marked the selected villages where the sampling was performed. TM, Tumiao village in Wangqiao town; ZW, Zhaowan village in Wanggang town; PT, Pingtang village in Nianyushan town; HF, Hongfan village Guanmiao town; LTQ, Longtouqiao village in Hefengqiao town. The average annual incidence of each town was marked in red circle and number. Table S1. Seroepidemiological questionnaires of SFTS in epidemic area. Table S2. Demographic and epidemiological characteristics of participants in the current study. Table S3. The risk factors for anti-SFTSV seropositive response for IgG antibody at three sample points by univariate analysis. Table S4. The risk factors for anti-SFTSV seropositive response of IgG antibody at three sample points. Table S5. The risk factor analysis for anti-SFTSV IgG antibodies seropositive response among the whole population. Table S6. The risk factors for anti-SFTSV seropositive response for NAb at three sample points by univariate analysis. Table S7. The risk factors for anti-SFTSV seropositive response for NAb by logistic regression model. Table S8. The risk factor analysis for anti-SFTSV NAb seropositive response among the adjusted population. Table S9. The proportion of the IgG antibody titer in relate to Nab. Table S10. The risk factors for anti-SFTSV seropositive response of IgG antibody for different ages people by univariate analysis.

Additional file 1: Additional tables and figures.

Additional file 1: Figure S1. Geographical distribution of case fatality rate of the COVID-19 patients based on the surveillance database in the mainland of China as of 29 March 2020. Table S1. The hospitalized case fatality rates of COVID-19 patients in related provinces. Table S2. The mortality rate and hospitalized case fatality rate of COVID-19 in China. Table S3. The hospitalized case fatality rate and relative risk for death outcome of COVID-19 patients by location, age, and sex. Table S4. The risk factor of fatal outcome among confirmed patients with SARS-COV-2 infection in the case-control study. Table S5. The cumulative incidence probability of death over time for the COVID-19 patients. Table S6. Clinical course of fatal cases and the factors associated with death among confirmed patients with SARS-COV-2 infection. Table S7. Comparison on the characteristics of fatal cases with SARS-COV-2 infection between severe and mild cases as of 29 March 2020.

Additional file 1: Supplementary Table 1. Variance inflation factor (VIF) of each model. Supplementary Table 2. Comparison of demographic, clinical and epidemiological characteristics between COVID-19 imported cases from Hubei with an incubation period of ���14 and > 14 days. Supplementary Table 3. Factors associated with incubation period among 2341 imported cases from Hubei using accelerated failure time model. Supplementary Table 4. Factors affecting the severity of COVID-19 among 2337 imported cases from Hubei using Logistic regression model. Supplementary Table 5. Multivariate logistic regression model for local cases and imported cases after excluding outliers. Supplementary Table 6. Factors associated with the transmissibility of COVID-19 among 1035 primary imported cases from Hubei using Poisson regression model. Supplementary Figure 1. The temporal pattern of reporting date of individual COVID-19 cases in the aggregated database, compared with national surveillance data outside Hubei province in China. Supplementary Figure 2. Estimated distributions of the incubation period COVID-19 local cases based on Weibull parametric model. Supplementary Figure 3. Estimated distributions of the incubation period COVID-19 local cases based on log-normal parametric model. Supplementary Figure 4. Estimated distributions of the incubation period COVID-19 local cases based on log-logistic parametric model. Supplementary Figure 5. Estimated distributions of the incubation period COVID-19 imported cases from Hubei based on Gamma parametric model. Supplementary Figure 6. Estimated distributions of the incubation period COVID-19 imported cases from Hubei based on Weibull parametric model. Supplementary Figure 7. Estimated distributions of the incubation period COVID-19 imported cases from Hubei based on log-normal parametric model. Supplementary Figure 8. Estimated distributions of the incubation period COVID-19 imported cases from Hubei based on log-logistic parametric model. Supplementary Figure 9. The incubation period in different populations profiled along epidemic period among imported cases from Hubei.

Additional file 1: Table 1. Demographics and clinical characteristics of COVID-19 patients between SVS and LVS groups. Table 2. The association between delayed time to SARS-CoV-2 RNA negativity and symptoms on admission for COVID-19 patients. Table 3. Outbreak kinetics of SARS-CoV-2 viral loads level over three outbreak stages expressed as N and ORF Ct value for COVID-19 patients. Figure 1. IgM and IgG antibody titers compared between patients with high vs. low viral loads based on N gene (A and C) and ORF gene (B and D).