BMC Public Health (Sep 2024)

Designing a model to estimate the burden of COVID-19 in Iran

  • Sadegh Ghazanfari,
  • Mohammad Meskarpour-Amiri,
  • Sayyed-Morteza Hosseini-Shokouh,
  • Ehsan Teymourzadeh,
  • Parisa Mehdizadeh,
  • Mahmood Salesi

DOI
https://doi.org/10.1186/s12889-024-19920-w
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 12

Abstract

Read online

Abstract The novel coronavirus disease 2019 (COVID-19) is the latest evidence of an epidemic disease resulting in an extraordinary number of infections and claimed several lives, along with extensive economic and social consequences. In response to the emergency situation, countries introduced different policies to address the situation, with different levels of efficacy. This paper outlines the protocol for developing a model to analyze the burden of COVID-19 in Iran and the effect of policies on the incidence and cumulative death of the disease. The importance of the model lies in the fact that no study, according to the authors’ best knowledge, tried to quantify the impact of the disease on Iran society and the impact of various implemented interventions on disease control. Based on a systematic review of COVID-19 prediction models and expert interviews, we developed a system dynamics model that not only includes an epidemic part but also considers the impact of various policies implemented by the Ministry of Health. The epidemic model estimates the incidence and mortality of COVID-19 in Iran. The model also intends to evaluate the effect of implemented policies on these outcomes. The model reflects the continuum of COVID-19 infection and care in Iran (of which some of its elements are unique) and key activities and decisions in delivering care. The model is calibrated and validated using data published by the Ministry of Health of Iran. Finally, the study aims to provide evidence of the impact of interventions intended to curb COVID-19 in Iran. Insights provided by the model will be necessary for controlling either future waves of the disease or similar future pandemics.

Keywords