Your browser doesn't support javascript.
Response to COVID-19 in Cyprus: Policy changes and epidemic trends.
Kakoullis, Loukas; Eliades, Elias; Papachristodoulou, Eleni; Parperis, Konstantinos; Chra, Paraskevi; Constantinidou, Anastasia; Chatzittofis, Andreas; Sampsonas, Fotios; Panos, George.
  • Kakoullis L; Department of Respiratory Medicine, University of Patras General Hospital, Patras, Greece.
  • Eliades E; Department of Health Sciences, University of Ulster, Belfast, UK.
  • Papachristodoulou E; Department of Respiratory Medicine, University of Patras General Hospital, Patras, Greece.
  • Parperis K; Department of Internal Medicine, University of Cyprus Medical School, Nicosia, Cyprus.
  • Chra P; Department of Medicine, Division of Rheumatology, University of Arizona College of Medicine, Phoenix, AZ, USA.
  • Constantinidou A; Department of Microbiology, Evangelismos Hospital, Athens, Greece.
  • Chatzittofis A; Department of Internal Medicine, University of Cyprus Medical School, Nicosia, Cyprus.
  • Sampsonas F; Department of Psychiatry, University of Cyprus Medical School, Nicosia, Cyprus.
  • Panos G; Department of Respiratory Medicine, University of Patras General Hospital, Patras, Greece.
Int J Clin Pract ; 75(4): e13944, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-991414
ABSTRACT

OBJECTIVES:

In late July, Cyprus experienced the second epidemic wave of COVID-19. We present the steps taken by the government and evaluate their effect on epidemic trends. MATERIALS Cyprus Press and Information Office data were analysed. Using an R-based forecasting program, two models were created to predict cases up to 01/09/2020 Model 1, which utilised data up to 09/06/2020, when airports reopened to foreign travelers with COVID-19 screening; and Model 2, which utilised data until 24/06/2020, when screening for passengers from low-transmission countries was discontinued.

RESULTS:

PIO data revealed no significant policy changes between 24/06/2020 and 31/07/2020. Prediction models were robust and accurate (Model 1, R2  = 0.999, P < .001; Model 2, R2  = 0.998, P < .001). By August 30th, recorded cases exceeded those predicted by Model 1 by 24.47% and by Model 2 by 20.95%, with P values <.001 for both cases.

CONCLUSIONS:

The significant difference between recorded cases and those projected by Models 1 and 2 suggests that changes in epidemic trends may have been associated with policy changes after their respective dates. Discontinuation of major restrictions such as airport reopening, can destabilise the control of the epidemic, and may concomitantly necessitate a reevaluation of the current epidemic status. In the face of an evolving situation such as the COVID-19 pandemic, states are forced to balance the imposing of restrictions against their impact on the economy.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Public Policy / Pandemics / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: Europa Language: English Journal: Int J Clin Pract Journal subject: Medicine Year: 2021 Document Type: Article Affiliation country: Ijcp.13944

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Public Policy / Pandemics / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: Europa Language: English Journal: Int J Clin Pract Journal subject: Medicine Year: 2021 Document Type: Article Affiliation country: Ijcp.13944