Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 12 de 12
Filter
1.
Clin Infect Dis ; 2021 Nov 12.
Article in English | MEDLINE | ID: covidwho-1758700

ABSTRACT

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.

2.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-311007

ABSTRACT

Background: Nationwide nonpharmaceutical interventions (NPI) were used to combat the novel coronavirus disease (COVID-19) during 2020 in the mainland of China. These NPIs have proven effective on mitigating the spread of COVID-19, but their broad impact on other diseases remains under-investigated. In this study, we aim to assess whether such broad impact exists on notifiable diseases in China.Methods: Weekly incidence and mortality data for 31 major notifiable infectious diseases at the province level were extracted from the China Information System for Disease Control and Prevention from 2014 to 2020. We assessed the impact of NPIs by contrasting the incidences of each infectious disease in predefined COVID-19 phases during 2020 to the average incidences in the corresponding time intervals during 2014-2019.Findings: We observed decreased incidences of most diseases during the phases after the lockdown of Wuhan. In general, respiratory diseases and gastrointestinal or enteroviral diseases were more affected than sexually transmitted or bloodborne diseases and vector-borne or zoonotic diseases. Seasonal flu and rubella were the most sensitive to the NPIs, with reductions of 67-99% in incidence rates throughout the NPI-implemented phases in China (Jan 27-Dec 31, 2020). Among gastrointestinal or enteroviral diseases, the hand, foot and mouth disease (HFMD) was subject to the largest declines during Jan 27-Aug 31, 2020, with >90% reduction in incidence rate. Phases with more stringent NPIs were associated with more reductions. Non-respiratory diseases, particularly HFMD, gonorrhea and brucellosis, rebounded towards the end of the year as the NPIs were relaxed.Interpretation: NPIs are broadly effective in containing infectious diseases. Less disruptive NPIs such as wearing face masks are still effective in mitigating respiratory diseases but are not adequate for containing non-respiratory diseases.Funding Statement: This work was supported by grants from the National Natural Science Funds [91846302, 81825019], the China Mega-Project on Infectious Disease Prevention [2018ZX10713001, 2018ZX10713002, 2018ZX10201001 and 2017ZX10103004], and the US National Institutes of Health [R56 AI148284].Declaration of Interests: All authors declare no competing interests.Ethics Approval Statement: Missing.

3.
Nat Commun ; 12(1): 6923, 2021 11 26.
Article in English | MEDLINE | ID: covidwho-1537314

ABSTRACT

Nationwide nonpharmaceutical interventions (NPIs) have been effective at mitigating the spread of the novel coronavirus disease (COVID-19), but their broad impact on other diseases remains under-investigated. Here we report an ecological analysis comparing the incidence of 31 major notifiable infectious diseases in China in 2020 to the average level during 2014-2019, controlling for temporal phases defined by NPI intensity levels. Respiratory diseases and gastrointestinal or enteroviral diseases declined more than sexually transmitted or bloodborne diseases and vector-borne or zoonotic diseases. Early pandemic phases with more stringent NPIs were associated with greater reductions in disease incidence. Non-respiratory diseases, such as hand, foot and mouth disease, rebounded substantially towards the end of the year 2020 as the NPIs were relaxed. Statistical modeling analyses confirm that strong NPIs were associated with a broad mitigation effect on communicable diseases, but resurgence of non-respiratory diseases should be expected when the NPIs, especially restrictions of human movement and gathering, become less stringent.


Subject(s)
Communicable Diseases/epidemiology , Disease Notification/statistics & numerical data , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/transmission , China/epidemiology , Communicable Disease Control , Communicable Diseases/classification , Communicable Diseases/transmission , Humans , Incidence , Models, Statistical , SARS-CoV-2
4.
Clin Infect Dis ; 2021 Nov 12.
Article in English | MEDLINE | ID: covidwho-1522157

ABSTRACT

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.

5.
Nat Commun ; 12(1): 5026, 2021 08 18.
Article in English | MEDLINE | ID: covidwho-1363491

ABSTRACT

Nationwide prospective surveillance of all-age patients with acute respiratory infections was conducted in China between 2009‒2019. Here we report the etiological and epidemiological features of the 231,107 eligible patients enrolled in this analysis. Children <5 years old and school-age children have the highest viral positivity rate (46.9%) and bacterial positivity rate (30.9%). Influenza virus, respiratory syncytial virus and human rhinovirus are the three leading viral pathogens with proportions of 28.5%, 16.8% and 16.7%, and Streptococcus pneumoniae, Mycoplasma pneumoniae and Klebsiella pneumoniae are the three leading bacterial pathogens (29.9%, 18.6% and 15.8%). Negative interactions between viruses and positive interactions between viral and bacterial pathogens are common. A Join-Point analysis reveals the age-specific positivity rate and how this varied for individual pathogens. These data indicate that differential priorities for diagnosis, prevention and control should be highlighted in terms of acute respiratory tract infection patients' demography, geographic locations and season of illness in China.


Subject(s)
Bacteria/isolation & purification , Bacterial Infections/microbiology , Respiratory Tract Infections/microbiology , Respiratory Tract Infections/virology , Virus Diseases/virology , Viruses/isolation & purification , Adolescent , Adult , Bacteria/classification , Bacteria/genetics , Bacterial Infections/epidemiology , Child , Child, Preschool , China/epidemiology , Female , Humans , Infant , Male , Prospective Studies , Respiratory Tract Infections/epidemiology , Seasons , Virus Diseases/epidemiology , Viruses/classification , Viruses/genetics , Young Adult
6.
Lancet Reg Health West Pac ; 16: 100268, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1415636

ABSTRACT

BACKGROUND: Non pharmaceutical interventions (NPI) including hand washing directives were implemented in China and worldwide to combat the COVID-19 pandemic, which are likely to have had impacted a broad spectrum of enteric pathogen infections. METHODS: Etiologically diagnostic data from 45 937 and 67 395 patients with acute diarrhea between 2012 and 2020, who were tested for seven viral pathogens and 13 bacteria respectively, were analyzed to assess the changes of enteric pathogen infections in China during the first COVID-19 pandemic year compared to pre-pandemic years. FINDINGS: Test positive rates of all enteric viruses decreased during 2020, compared to the average levels during 2012-2019, with a relative decrease of 71•75% for adenovirus, 58•76% for norovirus, 53•50% for rotavirus A, and 72•07% for the combination of other four uncommon viruses. In general, a larger reduction of positive rate in viruses was seen among adults than pediatric patients. A rebound of rotavirus A was seen after September 2020 in North China rather than South China. Test positive rates of bacteria decreased during 2020, compared to the average levels during 2012-2019, excepting for nontyphoidal Salmonella and Campylobacter coli with 66•53% and 90•48% increase respectively. This increase was larger for pediatric patients than for adult patients. INTERPRETATION: The activity of enteric pathogens changed profoundly alongside the NPIs implemented during the COVID-19 pandemic in China. Greater reductions of the test positive rates were found for almost all enteric viruses than for bacteria among acute diarrhea patients, with further large differences by age and geography. Lifting of NPIs will lead to resurgence of enteric pathogen infections, particularly in children whose immunity may not have been developed and/or waned. FUNDING: China Mega-Project on Infectious Disease Prevention; National Natural Science Funds.

8.
Infect Dis Poverty ; 10(1): 48, 2021 Apr 12.
Article in English | MEDLINE | ID: covidwho-1181127

ABSTRACT

BACKGROUND: COVID-19 has posed an enormous threat to public health around the world. Some severe and critical cases have bad prognoses and high case fatality rates, unraveling risk factors for severe COVID-19 are of significance for predicting and preventing illness progression, and reducing case fatality rates. Our study focused on analyzing characteristics of COVID-19 cases and exploring risk factors for developing severe COVID-19. METHODS: The data for this study was disease surveillance data on symptomatic cases of COVID-19 reported from 30 provinces in China between January 19 and March 9, 2020, which included demographics, dates of symptom onset, clinical manifestations at the time of diagnosis, laboratory findings, radiographic findings, underlying disease history, and exposure history. We grouped mild and moderate cases together as non-severe cases and categorized severe and critical cases together as severe cases. We compared characteristics of severe cases and non-severe cases of COVID-19 and explored risk factors for severity. RESULTS: The total number of cases were 12 647 with age from less than 1 year old to 99 years old. The severe cases were 1662 (13.1%), the median age of severe cases was 57 years [Inter-quartile range(IQR): 46-68] and the median age of non-severe cases was 43 years (IQR: 32-54). The risk factors for severe COVID-19 were being male [adjusted odds ratio (aOR) = 1.3, 95% CI: 1.2-1.5]; fever (aOR = 2.3, 95% CI: 2.0-2.7), cough (aOR = 1.4, 95% CI: 1.2-1.6), fatigue (aOR = 1.3, 95% CI: 1.2-1.5), and chronic kidney disease (aOR = 2.5, 95% CI: 1.4-4.6), hypertension (aOR = 1.5, 95% CI: 1.2-1.8) and diabetes (aOR = 1.96, 95% CI: 1.6-2.4). With the increase of age, risk for the severity was gradually higher [20-39 years (aOR = 3.9, 95% CI: 1.8-8.4), 40-59 years (aOR = 7.6, 95% CI: 3.6-16.3), ≥ 60 years (aOR = 20.4, 95% CI: 9.5-43.7)], and longer time from symtem onset to diagnosis [3-5 days (aOR = 1.4, 95% CI: 1.2-1.7), 6-8 days (aOR = 1.8, 95% CI: 1.5-2.1), ≥ 9 days(aOR = 1.9, 95% CI: 1.6-2.3)]. CONCLUSIONS: Our study showed the risk factors for developing severe COVID-19 with large sample size, which included being male, older age, fever, cough, fatigue, delayed diagnosis, hypertension, diabetes, chronic kidney diasease, early case identification and prompt medical care. Based on these factors, the severity of COVID-19 cases can be predicted. So cases with these risk factors should be paid more attention to prevent severity.


Subject(s)
Age Factors , COVID-19/epidemiology , Comorbidity , Severity of Illness Index , Sex Factors , Adolescent , Adult , Aged , Aged, 80 and over , Child , Child, Preschool , China/epidemiology , Early Diagnosis , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Risk Factors , Young Adult
9.
Eur Radiol ; 31(9): 7192-7201, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1141413

ABSTRACT

OBJECTIVES: An artificial intelligence model was adopted to identify mild COVID-19 pneumonia from computed tomography (CT) volumes, and its diagnostic performance was then evaluated. METHODS: In this retrospective multicenter study, an atrous convolution-based deep learning model was established for the computer-assisted diagnosis of mild COVID-19 pneumonia. The dataset included 2087 chest CT exams collected from four hospitals between 1 January 2019 and 31 May 2020. The true positive rate, true negative rate, receiver operating characteristic curve, area under the curve (AUC) and convolutional feature map were used to evaluate the model. RESULTS: The proposed deep learning model was trained on 1538 patients and tested on an independent testing cohort of 549 patients. The overall sensitivity was 91.5% (195/213; p < 0.001, 95% CI: 89.2-93.9%), the overall specificity was 90.5% (304/336; p < 0.001, 95% CI: 88.0-92.9%) and the general AUC value was 0.955 (p < 0.001). CONCLUSIONS: A deep learning model can accurately detect COVID-19 and serve as an important supplement to the COVID-19 reverse transcription-polymerase chain reaction (RT-PCR) test. KEY POINTS: • The implementation of a deep learning model to identify mild COVID-19 pneumonia was confirmed to be effective and feasible. • The strategy of using a binary code instead of the region of interest label to identify mild COVID-19 pneumonia was verified. • This AI model can assist in the early screening of COVID-19 without interfering with normal clinical examinations.


Subject(s)
COVID-19 , Deep Learning , Artificial Intelligence , Humans , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed
10.
Math Biosci Eng ; 17(4): 2936-2949, 2020 03 30.
Article in English | MEDLINE | ID: covidwho-806454

ABSTRACT

The coronavirus disease 2019 (COVID-2019), a newly emerging disease in China, posed a public health emergency of China. Wuhan is the most serious affected city. Some measures have been taken to control the transmission of COVID-19. From Jan. 23rd, 2020, gradually increasing medical resources (such as health workforce, protective clothing, essential medicines) were sent to Wuhan from other provinces, and the government has established the hospitals to quarantine and treat infected individuals. Under the condition of sufficient medical resources in Wuhan, late-stage of epidemic showed a downward trend. Assessing the effectiveness of medical resources is of great significance for the future response to similar disease. Based on the transmission mechanisms of COVID-19 and epidemic characteristics of Wuhan, by using time-dependent rates for some parameters, we establish a dynamical model to reflect the changes of medical resources on transmission of COVID-19 in Wuhan. Our model is applied to simulate the reported data on cumulative and new confirmed cases in Wuhan from Jan. 23rd to Mar. 6th, 2020. We estimate the basic reproduction number R0 = 2.71, which determines whether the disease will eventually die out or not under the absence of effective control measures. Moreover, we calculate the effective daily reproduction ratio Re(t), which is used to measure the 'daily reproduction number'. We obtain that Re(t) drops less than 1 since Feb. 8th. Our results show that delayed opening the 'Fire God Hill' hospital will greatly increase the magnitude of the outbreak. This shows that the government's timely establishment of hospitals and effective quarantine via quick detection prevent a larger outbreak.


Subject(s)
Betacoronavirus , Coronavirus Infections/transmission , Models, Biological , Pandemics , Pneumonia, Viral/transmission , Basic Reproduction Number/statistics & numerical data , COVID-19 , China/epidemiology , Computer Simulation , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Hospital Design and Construction , Hospitals , Humans , Mathematical Concepts , Pandemics/prevention & control , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Quarantine/statistics & numerical data , SARS-CoV-2 , Time Factors
11.
J Int Med Res ; 48(9): 300060520952256, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-760417

ABSTRACT

Since the outbreak of coronavirus disease 2019 (COVID-19) in December 2019, an epidemic has spread rapidly worldwide. COVID-19 is caused by the highly infectious severe acute respiratory syndrome coronavirus-2. A 42-year-old woman presented to hospital who was suffering from epigastric discomfort and dyspepsia for the past 5 days. Before the onset of symptoms, she was healthy, and had no travel history to Wuhan or contact with laboratory-confirmed COVID-19 cases. An examination showed chronic superficial gastritis with erosion and esophagitis. Enhanced magnetic resonance imaging of the abdomen showed a lesion in the right lower lobe of the lungs. Chest computed tomography showed multiple ground-glass opacity in the lungs. Reverse transcription-polymerase chain reaction was negative for severe acute respiratory syndrome coronavirus-2. There was no improvement after antibiotic treatment. Polymerase chain reaction performed 2 days later was positive and she was diagnosed with COVID-19. After several days of antiviral and symptomatic treatments, her symptoms improved and she was discharged. None of the medical staff were infected. Clinical manifestations of COVID-19 are nonspecific, making differentiating it from other diseases difficult. This case shows the sequence in which symptoms developed in a patient with COVID-19 with gastrointestinal symptoms as initial manifestations.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/complications , Gastrointestinal Diseases/diagnosis , Gastrointestinal Diseases/virology , Pneumonia, Viral/complications , Adult , COVID-19 , Coronavirus Infections/transmission , Coronavirus Infections/virology , Female , Gastrointestinal Diseases/pathology , Humans , Pandemics , Pneumonia, Viral/transmission , Pneumonia, Viral/virology , Prognosis , SARS-CoV-2
SELECTION OF CITATIONS
SEARCH DETAIL