Time-analysis of COVID-19 dispersion among health care workers and the general population
Leme, P. A. F.; Jalalizadeh, M.; Dionato, F. A. V.; Buosi, K.; Dal Col, L. S. B.; Giacomelli, C. F.; Ferrari, K. L.; Pagliarone, A. C.; Gon, L. M.; Maia, C. L.; Esfahani, A. A.; Reis, L. O.
Show abstract
IntroductionHeath care workers with direct (HCW-D) or indirect (HCW-A) patient contact represent 4.2% to 17.8% of COVID-19 cases. We evaluate the temporal COVID-19 infection behavior among HCW-D, HCW-A, and non-HCW. MethodsFrom February 2020 to April 2021, trained nurses recorded age, gender, occupation, and symptoms in a COVID-19 testing outpatient health center. We allocated data into weekly time fractals and calculated the proportion of COVID-19 positive among HCW vs. non-HCW and incorporated an ARFIMA model (traditionally used in weather forecast) to predict future cases of COVID-19. ResultsAmong 8,998 COVID-19 RT-PCR tests, 3,462 (42%) patients were HCW-D, and 933 (11%) were HCW-A. Overall, 1,914 (21.3%) returned positive, representing 27%, 25% and 19% of HCW-D, HCW-A and non-HCW, respectively. HCW-D or HCW-A were significantly more likely to test positive for COVID-19 than non-HCW (OR=1.5, p<0.0001). The percentage of positive to negative test results remained steady over time. In the positive cases, the percentage of HCW to non-HCW declined significantly over time (Mann-Kendal trend test: tau=-0.58, p<0.0001). Our ARFIMA model showed a long-memory infection pattern in the occurrence of new COVID-19 cases lasting for months. Average error was 1.9 cases per week comparing predicted to actual values three months later (May-July 2021). ConclusionHCW have a sustained 50% higher risk of COVID-19 positivity in the pandemic. Time-series analysis showed a long-memory infection pattern with virus spread mainly among HCWs before the general population. The tool http://wdchealth.covid-map.com/shiny/covid-map/ will be updated according to population previous infection and vaccination impact.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Risk perceptions and preventive practices of COVID-19 among healthcare professionals in public hospitals in Ethiopia 95%
- Serological prevalence of SARS-CoV-2 infection and associated factors in health care workers in a “non-COVID” hospital in Mexico City 95%
- Risk perceptions and preventive practices of COVID-19 among healthcare professionals in public hospitals in Ethiopia 95%
Similar papers in this journal
- Uncovering COVID-19 Transmission Tree: Identifying Traced and Untraced Infections in an Infection Network 94%
- COVID-19 critical care simulations: An international cross-sectional survey 94%
- Spread of infection and treatment interruption among Japanese workers during the COVID-19 pandemic: a cross-sectional study 94%
Similar papers in this journal
Similar papers in this journal
- COVID-19 seroprevalence among healthcare workers of a large COVID Hospital in Rome reveals strengths and limits of two different serological tests 94%
- Half Year Longitudinal Seroprevalence of SARS-CoV-2-antibodies and Rule Compliance in German Hospital Employees 94%
- Predicting mortality, duration of treatment, pulmonary embolism and required ceiling of ventilatory support for COVID-19 inpatients: A Machine-Learning Approach 93%
Similar papers in this journal
- Impact of COVID-19 Outbreak on Healthcare Workers in a Tertiary Healthcare Center in India - A cross sectional study 95%
- Estimating the risk of incident SARS-CoV-2 infection among healthcare workers in quarantine hospitals: the Egyptian example 94%
- Yet another lockdown? A large-scale study on people’s unwillingness to be confined during the first 5 months of the COVID-19 pandemic in Spain 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.