Spatio-temporal analysis of the COVID-19 pandemic in Iran

Spatial analysis of COVID-19 incidence and mortality rates in northwest iran for future epidemic preparedness

The COVID-19 pandemic has underscored the critical need for effective public health strategies to combat infectious diseases. This study examines the epidemiological characteristics and spatial distribution of COVID-19 incidence and mortality in Zanjan Province, northwest Iran, to inform future epidemic preparedness. Using data from 39,739 hospitalized COVID-19 cases recorded between February 2020 and September 2021, sourced from the Medical Care Monitoring Center, we conducted descriptive and geospatial analyses. Demographic, clinical, and spatial variables were analyzed using logistic regression and advanced spatial techniques, including Kernel Density Estimation and Local Moran's I, to identify risk factors and disease hotspots. Results revealed that women accounted for 52% of cases, with higher incidence rates, while men exhibited higher mortality rates (7.86% vs. 7.80%). Urban areas, particularly the provincial capital, were identified as hotspots, with the highest patient density (20,384 cases per 10 km²). Comorbidities such as HIV/AIDS (OR: 4.85), chronic liver disease (OR: 3.6), chronic blood diseases (OR: 2.8), and cancer (OR: 2.5) significantly increased mortality risk, with ventilator use showing the highest odds ratio for death (OR = 91). Vaccination significantly reduced mortality, with fully vaccinated individuals experiencing a 6.3% mortality rate compared to 8.1% in unvaccinated individuals. Spatial analysis highlighted population density and mobility as key drivers of disease spread. These findings emphasize the importance of integrating spatial and epidemiological data to enhance pandemic preparedness. Targeted interventions in urban hotspots, early detection systems, and prioritizing vaccination for high-risk populations are critical for mitigating future outbreaks. This study provides a foundation for evidence-based public health strategies to strengthen global epidemic response and improve preparedness for future health crises.

Impact of COVID-19 Disease Control Committee (CDCC) policies on prevention of the disease using Bayes network inference in west of Iran

BACKGROUND

The start of the COVID-19 pandemic was an emergency situation that led each country to adopt specific regional strategies to control it. Given the spread of COVID-19 disease, it is crucial to evaluate which policy is more effective in reducing disease transmission. The purpose of this study was to determine the impact of policies made by COVID-19 Disease Control Committee (CDCC) to reduce the risk of the disease in Hamadan province.

METHODS

In the observational study, the data were extracted from three sources in Hamadan, west of Iran; first, the session reports of CDCC; second, information on periodic evaluations conducted by the primary health care directory in Hamadan from April to August 2021 and third, expert panel opinion. Bayes network analysis was used to determine the effect of each policy on mortality rate by GeNIe software version 2.2.

RESULTS

Among the policies adopted by CDCC in Hamadan, seven policies, i.e., vaccination, limiting gatherings, social distancing, wearing a mask, job closure, travel restriction, and personal hygiene had the most impact to prevent the spread of COVID-19, respectively. In this study, the prevalence of the disease was 17.64% with the implementation of these policies. Now, if all these policies are observed 30% more, the prevalence will decrease to 14.18%.

CONCLUSION

This study showed that if the seven policies (i.e., vaccination, limiting gatherings, social distancing, wearing a mask, job closure, travel restriction, and personal hygiene) are followed simultaneously in the community, the risk of contracting the disease will be greatly reduced. Therefore, in the pandemic of infectious diseases, such policies can help prevent the spread of the disease.