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Prevalence and epidemiological characteristics of COVID-19 after one year of pandemic in Jakarta and neighbouring areas, Indonesia: A single center study.

Wuryantari SetiadiIsmail Ekoprayitno RoziDodi SafariWa Ode Dwi DaningratEdison JoharBenediktus YohanFrilasita Aisyah YudhaputriKarina Dian LestariSukma OktavianthiKhin Saw Aye MyintSafarina G MalikAmin Soebandrionull null
Published in: PloS one (2022)
We determined the prevalence and epidemiological characteristics of COVID-19 in Jakarta and neighboring areas, Indonesia from March 2020 to February 2021, based on nasopharyngeal/oropharyngeal (NP/OP) swab specimens that were tested at the Eijkman Institute for Molecular Biology, Jakarta. NP/OP swab specimens were collected from COVID-19 suspects or individuals in contact tracing programs from primary healthcare centers (PHC) and hospitals. The specimens were screened for the SARS-CoV-2 by qRT-PCR. Demography data and clinical symptoms were collected using national standardized laboratory form. Of 64,364 specimens, 10,130 (15.7%) were confirmed positive for SARS-CoV-2, with the peak prevalence of infection in March 2020 (26.3%) follow by in January 2021 (23.9%) and February 2021 (21.8%). We found that the positivity rate of the specimens from Jakarta, West Java, and Banten was 16.3%, 13.3%, and 16.8%, respectively. Positivity rate was higher in specimens from hospitals (16.9%) than PHC (9.4%). Of the positive specimens, 29.6% were from individuals aged >60 years old, followed by individuals aged 41-60 years old (24.2%). Among symptomatic cases of SARS-CoV-2, the most common symptoms were cough, fever, and a combination of both cough & fever. In conclusion, this study illustrates the prevalence and epidemiological characteristics from one COVID-19 diagnostic center in Jakarta and neighbouring areas in Indonesia.
Keyphrases
  • sars cov
  • respiratory syndrome coronavirus
  • healthcare
  • coronavirus disease
  • risk factors
  • fine needle aspiration
  • public health
  • machine learning
  • sleep quality
  • physical activity
  • big data
  • deep learning