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1.
Transbound Emerg Dis ; 2022 Apr 02.
Article in English | MEDLINE | ID: covidwho-1774901

ABSTRACT

The ongoing enzootic circulation of the Middle East respiratory syndrome coronavirus (MERS-CoV) in the Middle East and North Africa is increasingly raising the concern about the possibility of its recombination with other human-adapted coronaviruses, particularly the pandemic SARS-CoV-2. We aim to provide an updated picture about ecological niches of MERS-CoV and associated socio-environmental drivers. Based on 356 confirmed MERS cases with animal contact reported to the WHO and 63 records of animal infections collected from the literature as of 30 May 2020, we assessed ecological niches of MERS-CoV using an ensemble model integrating three machine learning algorithms. With a high predictive accuracy (area under receiver operating characteristic curve = 91.66% in test data), the ensemble model estimated that ecologically suitable areas span over the Middle East, South Asia and the whole North Africa, much wider than the range of reported locally infected MERS cases and test-positive animal samples. Ecological suitability for MERS-CoV was significantly associated with high levels of bareland coverage (relative contribution = 30.06%), population density (7.28%), average temperature (6.48%) and camel density (6.20%). Future surveillance and intervention programs should target the high-risk populations and regions informed by updated quantitative analyses.

2.
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
3.
BMC Infect Dis ; 21(1): 452, 2021 May 19.
Article in English | MEDLINE | ID: covidwho-1236546

ABSTRACT

BACKGROUND: COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. METHODS: An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. RESULTS: Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019-13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21-12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14-4.98) for moderate-selenium-deficient cities and 3.06 (1.49-6.27) for severe-selenium-deficient cities. CONCLUSIONS: Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.


Subject(s)
COVID-19/diagnosis , Selenium/analysis , COVID-19/mortality , COVID-19/virology , China/epidemiology , Crops, Agricultural/chemistry , Humans , Micronutrients/analysis , SARS-CoV-2/isolation & purification , Selenium/deficiency , Soil/chemistry , Survival Analysis
4.
Infect Dis Poverty ; 10(1): 66, 2021 May 08.
Article in English | MEDLINE | ID: covidwho-1220374

ABSTRACT

BACKGROUND: The ongoing transmission of the Middle East respiratory syndrome coronavirus (MERS-CoV) in the Middle East and its expansion to other regions are raising concerns of a potential pandemic. An in-depth analysis about both population and molecular epidemiology of this pathogen is needed. METHODS: MERS cases reported globally as of June 2020 were collected mainly from World Health Organization official reports, supplemented by other reliable sources. Determinants for case fatality and spatial diffusion of MERS were assessed with Logistic regressions and Cox proportional hazard models, respectively. Phylogenetic and phylogeographic analyses were performed to examine the evolution and migration history of MERS-CoV. RESULTS: A total of 2562 confirmed MERS cases with 150 case clusters were reported with a case fatality rate of 32.7% (95% CI: 30.9‒34.6%). Saudi Arabia accounted for 83.6% of the cases. Age of ≥ 65 years old, underlying conditions and ≥ 5 days delay in diagnosis were independent risk factors for death. However, a history of animal contact was associated with a higher risk (adjusted OR = 2.97, 95% CI: 1.10-7.98) among female cases < 65 years but with a lower risk (adjusted OR = 0.31, 95% CI: 0.18-0.51) among male cases ≥ 65 years old. Diffusion of the disease was fastest from its origin in Saudi Arabia to the east, and was primarily driven by the transportation network. The most recent sub-clade C5.1 (since 2013) was associated with non-synonymous mutations and a higher mortality rate. Phylogeographic analyses pointed to Riyadh of Saudi Arabia and Abu Dhabi of the United Arab Emirates as the hubs for both local and international spread of MERS-CoV. CONCLUSIONS: MERS-CoV remains primarily locally transmitted in the Middle East, with opportunistic exportation to other continents and a potential of causing transmission clusters of human cases. Animal contact is associated with a higher risk of death, but the association differs by age and sex. Transportation network is the leading driver for the spatial diffusion of the disease. These findings how this pathogen spread are helpful for targeting public health surveillance and interventions to control endemics and to prevent a potential pandemic.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Adult , Aged , Animals , Evolution, Molecular , Female , Humans , Logistic Models , Male , Middle Aged , Middle East Respiratory Syndrome Coronavirus/isolation & purification , Molecular Epidemiology , Mortality , Phylogeny , Saudi Arabia/epidemiology , Survival Analysis , Zoonoses/epidemiology , Zoonoses/virology
5.
Euro Surveill ; 25(40)2020 10.
Article in English | MEDLINE | ID: covidwho-841040

ABSTRACT

BackgroundThe natural history of disease in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remained obscure during the early pandemic.AimOur objective was to estimate epidemiological parameters of coronavirus disease (COVID-19) and assess the relative infectivity of the incubation period.MethodsWe estimated the distributions of four epidemiological parameters of SARS-CoV-2 transmission using a large database of COVID-19 cases and potential transmission pairs of cases, and assessed their heterogeneity by demographics, epidemic phase and geographical region. We further calculated the time of peak infectivity and quantified the proportion of secondary infections during the incubation period.ResultsThe median incubation period was 7.2 (95% confidence interval (CI): 6.9‒7.5) days. The median serial and generation intervals were similar, 4.7 (95% CI: 4.2‒5.3) and 4.6 (95% CI: 4.2‒5.1) days, respectively. Paediatric cases < 18 years had a longer incubation period than adult age groups (p = 0.007). The median incubation period increased from 4.4 days before 25 January to 11.5 days after 31 January (p < 0.001), whereas the median serial (generation) interval contracted from 5.9 (4.8) days before 25 January to 3.4 (3.7) days after. The median time from symptom onset to discharge was also shortened from 18.3 before 22 January to 14.1 days after. Peak infectivity occurred 1 day before symptom onset on average, and the incubation period accounted for 70% of transmission.ConclusionThe high infectivity during the incubation period led to short generation and serial intervals, necessitating aggressive control measures such as early case finding and quarantine of close contacts.


Subject(s)
Coronavirus Infections/transmission , Coronavirus/pathogenicity , Infectious Disease Incubation Period , Pneumonia, Viral/transmission , Adolescent , Adult , Age Distribution , Aged , Aged, 80 and over , Betacoronavirus , COVID-19 , Child , Child, Preschool , China/epidemiology , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Epidemiologic Studies , Female , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , SARS-CoV-2 , Young Adult
6.
Lancet Reg Health West Pac ; 2: 100020, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-747797

ABSTRACT

BACKGROUND: Before effective vaccines become widely available, sufficient understanding of the impacts of climate, human movement and non-pharmaceutical interventions on the transmissibility of COVID-19 is needed but still lacking. METHODS: We collected by crowdsourcing a database of 11 003 COVID-19 cases from 305 cities outside Hubei Province from December 31, 2019 to April 27, 2020. We estimated the daily effective reproduction numbers (Rt ) of COVID-19 in 41 cities where the crowdsourced case data are comparable to the official surveillance data. The impacts of meteorological variables, human movement indices and nonpharmaceutical emergency responses on Rt were evaluated with generalized estimation equation models. FINDINGS: The median Rt was 0•46 (IQR: 0•37-0•87) in the northern cities, higher than 0•20 (IQR: 0•09-0•52) in the southern cities (p=0•004). A higher local transmissibility of COVID-19 was associated with a low temperature, a relative humidity near 70-75%, and higher intracity and intercity human movement. An increase in temperature from 0℃ to 20℃ would reduce Rt by 30% (95 CI 10-46%). A further increase to 30℃ would result in another 17% (95% CI 5-27%) reduction. An increase in relative humidity from 40% to 75% would raise the transmissibility by 47% (95% CI 9-97%), but a further increase to 90% would reduce the transmissibility by 12% (95% CI 4-19%). The decrease in intracity human movement as a part of the highest-level emergency response in China reduced the transmissibility by 36% (95% CI 27-44%), compared to 5% (95% CI 1-9%) for restricting intercity transport. Other nonpharmaceutical interventions further reduced Rt by 39% (95% CI 31-47%). INTERPRETATION: Climate can affect the transmission of COVID-19 where effective interventions are implemented. Restrictions on intracity human movement may be needed in places where other nonpharmaceutical interventions are unable to mitigate local transmission. FUNDING: China Mega-Project on Infectious Disease Prevention; U.S. National Institutes of Health and National Science Foundation.

7.
Lancet Infect Dis ; 20(10): 1141-1150, 2020 10.
Article in English | MEDLINE | ID: covidwho-601834

ABSTRACT

BACKGROUND: As of June 8, 2020, the global reported number of COVID-19 cases had reached more than 7 million with over 400 000 deaths. The household transmissibility of the causative pathogen, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remains unclear. We aimed to estimate the secondary attack rate of SARS-CoV-2 among household and non-household close contacts in Guangzhou, China, using a statistical transmission model. METHODS: In this retrospective cohort study, we used a comprehensive contact tracing dataset from the Guangzhou Center for Disease Control and Prevention to estimate the secondary attack rate of COVID-19 (defined as the probability that an infected individual will transmit the disease to a susceptible individual) among household and non-household contacts, using a statistical transmission model. We considered two alternative definitions of household contacts in the analysis: individuals who were either family members or close relatives, such as parents and parents-in-law, regardless of residential address, and individuals living at the same address regardless of relationship. We assessed the demographic determinants of transmissibility and the infectivity of COVID-19 cases during their incubation period. FINDINGS: Between Jan 7, 2020, and Feb 18, 2020, we traced 195 unrelated close contact groups (215 primary cases, 134 secondary or tertiary cases, and 1964 uninfected close contacts). By identifying households from these groups, assuming a mean incubation period of 5 days, a maximum infectious period of 13 days, and no case isolation, the estimated secondary attack rate among household contacts was 12·4% (95% CI 9·8-15·4) when household contacts were defined on the basis of close relatives and 17·1% (13·3-21·8) when household contacts were defined on the basis of residential address. Compared with the oldest age group (≥60 years), the risk of household infection was lower in the youngest age group (<20 years; odds ratio [OR] 0·23 [95% CI 0·11-0·46]) and among adults aged 20-59 years (OR 0·64 [95% CI 0·43-0·97]). Our results suggest greater infectivity during the incubation period than during the symptomatic period, although differences were not statistically significant (OR 0·61 [95% CI 0·27-1·38]). The estimated local reproductive number (R) based on observed contact frequencies of primary cases was 0·5 (95% CI 0·41-0·62) in Guangzhou. The projected local R, had there been no isolation of cases or quarantine of their contacts, was 0·6 (95% CI 0·49-0·74) when household was defined on the basis of close relatives. INTERPRETATION: SARS-CoV-2 is more transmissible in households than SARS-CoV and Middle East respiratory syndrome coronavirus. Older individuals (aged ≥60 years) are the most susceptible to household transmission of SARS-CoV-2. In addition to case finding and isolation, timely tracing and quarantine of close contacts should be implemented to prevent onward transmission during the viral incubation period. FUNDING: US National Institutes of Health, Science and Technology Plan Project of Guangzhou, Project for Key Medicine Discipline Construction of Guangzhou Municipality, Key Research and Development Program of China.


Subject(s)
Contact Tracing , Coronavirus Infections/transmission , Family Characteristics , Pneumonia, Viral/transmission , Adult , Asymptomatic Infections/epidemiology , Basic Reproduction Number , Betacoronavirus , COVID-19 , China/epidemiology , Contact Tracing/statistics & numerical data , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Female , Humans , Incidence , Male , Middle Aged , Models, Theoretical , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Quarantine , Retrospective Studies , Risk Factors , SARS-CoV-2 , Young Adult
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