An overview of Iran's actions in response to the COVID-19 pandemic and in building health system resilience.

This article is part of the Research Topic 'Health Systems Recovery in the Context of COVID-19 and Protracted Conflict'. The considerable human, social, and economic impacts of COVID-19 have demonstrated a global lack of health system resilience, highlighting gaps in health system capacities due to fragmented approaches to health system financing, planning, and implementation. One of the key actions for ensuring equitable essential health services in all countries in normal situations as well as emergencies is through strengthening the primary healthcare (PHC) system. In the context of the unfolding pandemic, the Iranian Ministry of Health and Medical Education (MoHME) undertook a variety of strategic actions to ensure the sustainability of health services during the current health emergency and to promote health system resilience against future shocks. Right after the Alma-Ata declaration in 1978, MoHME pursued the PHC philosophy incorporating the principles within the WHO health system framework and its six building blocks. In response to the evolving pandemic, MoHME put in place several interventions to ensure the maintenance of essential health services in addition to the provision of response. Some interventions were new, informed by global experience with COVID-19, while others leveraged existing strengths within the existing health system. Those were taking a whole-of-government approach; leveraging the PHC capacity; supporting the workforce; strengthening preparedness and response; improving access to medicines, vaccines, and health products; and leveraging the health information system into the pandemic response. Health system strengthening that promotes resilience is imperative for governments as health systems are fundamental to sustainable socioeconomic development. In recognition of this, the WHO Eastern Mediterranean Regional Office (EMRO) has recently outlined regional priorities for advancing universal health coverage (UHC) and ensuring health security. Iran's approach both prior to and during the pandemic is strongly aligned with those regional priorities, which are "primary health care-oriented models; enhancing health workforce; promoting equity; enabling environment for research; improving access to countermeasures; and fostering health system resilience."

Detection of influenza epidemics using hidden Markov and Serfling approaches.

OBJECTIVE: Detection of epidemics is a critical issue in epidemiology of infectious diseases which enable healthcare system to better control it. This study is devoted to investigating the 5-year trend in influenza and severe acute respiratory infection cases in Iran. The epidemics were also detected using the hidden Markov model (HMM) and Serfling model. STUDY DESIGN: In this study, we used SARI data reported in the World Health Organization (WHO) FluNet web-based tool from August 2011 to August 2016. METHODS: SARI data in Iran from August 2011 to August 2016 were used. We applied the HMM and Serfling model for indicating the two epidemic and non-epidemic phases. The registered outbreak activity recorded on the WHO website was used as the gold standard. The coefficient of determination was reported to compare the goodness of fit of the models. RESULTS: Serfling models modified by 30% and 35% of the data had a sensitivity of 91.67% and 95.83%, while for 15%, 20% and 25% were 70.83%, 79.17% and 83.33%, respectively. Sensitivity of HMM and autoregressive HMM (AHMM) was 66.67% and 92.86%. All fitted models have a specificity of over 96%. The R2 for HMM and AHMM was calculated 0.73 and 0.85, respectively, showing better fitness of these models, while R2 was around 50% for different types of Serfling models. CONCLUSIONS: Both modified Serfling and HMM were acceptable models in determining the epidemic points for the detection of weekly SARI. The AHMM had better fitness, higher detection power and more accurate detection of the incidence of epidemics than Serfling model and high sensitivity and specificity. In addition to AHMM, Serfling models with 30% and 35% modification can be used to detect epidemics due to approximately the same accuracy but the simplicity of the calculations.