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1.
IJID Reg ; 3: 150-156, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1757417

ABSTRACT

Objective: The aim of this study was to determine current and previous SARS-COV-2 infection, and describe risk factors associated with seropositivity, among HCWs and hospital staff between June and October of 2020. Methodology: Data from the day of enrollment for a prospective cohort study were analyzed to determine point prevalence and seroprevalence of SARS-CoV-2 infection in HCWs and hospital staff of a university hospital in Colombia. Respiratory samples were collected to perform RT-PCR tests, along with blood samples to measure SARS-CoV-2 IgM and IgG antibodies. Data on nosocomial and community risk factors for infection were also collected and analyzed. Findings: 420 HCWs and hospital staff members were included. The seroprevalence at baseline was 23.2%, of which 10.7% had only IgM antibodies, 0.7% had IgG, and 11.7% had IgM and IgG. The prevalence of acute SARS-CoV-2 infection was 1.9%. Being a nurse assistant was significantly associated with seropositivity when compared with all other job duties (PR 2.39, 95% CI 1.27-3.65, p = 0.01). Conclusions: Overall SARS-CoV-2 prevalence was 1.9% and seroprevalence was 23.15%. Nurse assistants, medical doctors or students, and laboratory workers had a higher possibility of being SARS-CoV-2 seropositive.

2.
SSRN;
Preprint in English | SSRN | ID: ppcovidwho-325818

ABSTRACT

Introduction: Healthcare workers (HCWs) have been severely affected by the COVID-19 pandemic. Multiple risk factors have been described in HCWs, including lack of personal protective equipment (PPE), workplace setting, and profession. Screening of HCWs aims to reduce the rate of transmission to patients and colleagues. This study aims to determine current and previous SARS-COV-2 infection and describe risk factors associated with seropositivity among HCWs between June and October of 2020. Methodology: We analyzed data from the day of enrollment of a prospective cohort study, to determine point prevalence and seroprevalence of SARS-CoV-2 infection in HCWs of a university hospital in Colombia. We collected respiratory samples to perform RT-PCR tests and blood samples to measure SARS-CoV-2 IgM and IgG antibodies. We collected and analyzed data on nosocomial and community risk factors for infection. Findings: 420 hospital staff members were included. The seroprevalence at baseline was 23·2%. Of which 10·7% had only IgM, 0·7% had IgG, and 11·7% had IgM and IgG antibodies. The prevalence of acute SARS-CoV-2 infection was 1·9%. Nurse assistants, medical doctors and students, and laboratory workers were more likely to be seropositive than professional nurses (PR 2·21 95% CI [1·10-3·52];PR 2·18, 95% CI [1·06-3·52];PR 2·21, 95% CI [1·02-3·63], respectively). Interpretation: Overall SARS-CoV-2 prevalence was 1·9% and seroprevalence was 23·15%. Nurse assistants, medical doctors or students, or laboratory workers had a higher possibility of being SARS-CoV-2 seropositive.

3.
Travel Med Infect Dis ; 40: 101988, 2021.
Article in English | MEDLINE | ID: covidwho-1071979

ABSTRACT

BACKGROUND: The outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that was first detected in the city of Wuhan, China has now spread to every inhabitable continent, but now the attention has shifted from China to other epicentres. This study explored early assessment of the influence of spatial proximities and travel patterns from Italy on the further spread of SARS-CoV-2 worldwide. METHODS: Using data on the number of confirmed cases of COVID-19 and air travel data between countries, we applied a stochastic meta-population model to estimate the global spread of COVID-19. Pearson's correlation, semi-variogram, and Moran's Index were used to examine the association and spatial autocorrelation between the number of COVID-19 cases and travel influx (and arrival time) from the source country. RESULTS: We found significant negative association between disease arrival time and number of cases imported from Italy (r = -0.43, p = 0.004) and significant positive association between the number of COVID-19 cases and daily travel influx from Italy (r = 0.39, p = 0.011). Using bivariate Moran's Index analysis, we found evidence of spatial interaction between COVID-19 cases and travel influx (Moran's I = 0.340). Asia-Pacific region is at higher/extreme risk of disease importation from the Chinese epicentre, whereas the rest of Europe, South-America and Africa are more at risk from the Italian epicentre. CONCLUSION: We showed that as the epicentre changes, the dynamics of SARS-CoV-2 spread change to reflect spatial proximities.


Subject(s)
COVID-19/epidemiology , Communicable Diseases, Imported/epidemiology , Models, Statistical , Air Travel/statistics & numerical data , China/epidemiology , Humans , Italy/epidemiology , Population Surveillance , Risk , SARS-CoV-2/isolation & purification , Travel/statistics & numerical data
4.
Paediatr Respir Rev ; 35: 64-69, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-608740

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that was declared a pandemic by the World Health Organization on 11th March, 2020. Response to this ongoing pandemic requires extensive collaboration across the scientific community in an attempt to contain its impact and limit further transmission. Mathematical modelling has been at the forefront of these response efforts by: (1) providing initial estimates of the SARS-CoV-2 reproduction rate, R0 (of approximately 2-3); (2) updating these estimates following the implementation of various interventions (with significantly reduced, often sub-critical, transmission rates); (3) assessing the potential for global spread before significant case numbers had been reported internationally; and (4) quantifying the expected disease severity and burden of COVID-19, indicating that the likely true infection rate is often orders of magnitude greater than estimates based on confirmed case counts alone. In this review, we highlight the critical role played by mathematical modelling to understand COVID-19 thus far, the challenges posed by data availability and uncertainty, and the continuing utility of modelling-based approaches to guide decision making and inform the public health response. †Unless otherwise stated, all bracketed error margins correspond to the 95% credible interval (CrI) for reported estimates.


Subject(s)
Coronavirus Infections/epidemiology , Decision Making , Models, Theoretical , Pneumonia, Viral/epidemiology , Public Health , Betacoronavirus , COVID-19 , Coronavirus Infections/physiopathology , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Data Collection , Humans , Pandemics/prevention & control , Pneumonia, Viral/physiopathology , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , SARS-CoV-2 , Severity of Illness Index
5.
Paediatr Respir Rev ; 35: 57-60, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-603916

ABSTRACT

Models have played an important role in policy development to address the COVID-19 outbreak from its emergence in China to the current global pandemic. Early projections of international spread influenced travel restrictions and border closures. Model projections based on the virus's infectiousness demonstrated its pandemic potential, which guided the global response to and prepared countries for increases in hospitalisations and deaths. Tracking the impact of distancing and movement policies and behaviour changes has been critical in evaluating these decisions. Models have provided insights into the epidemiological differences between higher and lower income countries, as well as vulnerable population groups within countries to help design fit-for-purpose policies. Economic evaluation and policies have combined epidemic models and traditional economic models to address the economic consequences of COVID-19, which have informed policy calls for easing restrictions. Social contact and mobility models have allowed evaluation of the pathways to safely relax mobility restrictions and distancing measures. Finally, models can consider future end-game scenarios, including how suppression can be achieved and the impact of different vaccination strategies.


Subject(s)
Coronavirus Infections/epidemiology , Health Policy , Models, Theoretical , Pneumonia, Viral/epidemiology , Policy Making , Betacoronavirus , COVID-19 , COVID-19 Vaccines , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Developing Countries , Epidemiologic Methods , Humans , Models, Economic , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , Public Health , Public Policy , SARS-CoV-2 , Travel , Viral Vaccines/therapeutic use
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