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

Isaza, Vahid; Parizadi, Taher; Isazade, Esmail

doi:10.1007/s41324-022-00488-9

Cited by 6 publications

(4 citation statements)

References 41 publications

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“…The study identify a strong correlation between the COVID-19 variables and the population density [16]. The authors in [17] utilized three geographical models to analyze and evaluate COVID-19 pandemic. Geographic Weight Regression (GWR),Getis-OrdGi*(G-i-star) statistics, and Moran auto-correlation spatial analysis are carried out on data of confirmed cases, deaths, recoveries, and number of hospital beds.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting High-Risk Individuals in Disease Outbreaks Using Fuzzy Logic-Based Contact Tracing Model.

Aklah,

Al-Safi,

Abdali

2023

Preprint

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Contact tracing plays a crucial role in identifying exposed individuals at high risk of infection during disease outbreaks. In this paper, we propose a fuzzy logic-based contact tracing model for predicting high-risk exposed individuals in disease outbreaks. The model utilizes various input parameters, including distance, overlap time, visiting time lag, incubation time, and facility size, to assess the risk of infection. Through the application of fuzzy logic, the model enables the modeling of complex relationships and uncertainties associated with these input parameters. We evaluated the model using simulated data, demonstrating its effectiveness in identifying individuals at different levels of risk. The evaluation includes partial input evaluation, and comprehensive inputs assessment to assess the impact of each parameter on the risk of infection. The results highlight the importance of considering multiple factors in contact tracing and provide insights into the key parameters that significantly influence the risk assessment. The proposed model has the potential to assist public health authorities in making informed decisions and implementing targeted interventions to mitigate the spread of diseases in outbreak situations. Moreover, it helps to alleviate unnecessary fear among individuals who are less likely to have been infected.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting High-Risk Individuals in Disease Outbreaks Using Fuzzy Logic-Based Contact Tracing Model.

Aklah,

Al-Safi,

Abdali

2023

Preprint

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“…In certain instances, spatial-based surveys have been undertaken to venture into the field and amass comprehensive data on the pandemic's dynamics. These efforts aim to provide invaluable information that can serve as a lifeline in our battle against infectious diseases, including the formidable adversary that is COVID-19 (Isaza et al, 2023). In this regard, and despite significant advancements in the field of disease management, infectious diseases remain a crucial concern in epidemiology and public health.…”

Section: Introductionmentioning

confidence: 99%

“…The spatial distribution patterns of COVID-19 in Iran have been analyzed in several studies (including Isaza et al, 2023;Nojomi et al, 2021;Sharifi et al, 2022;Raoofi et al, 2020). Rahnama and Bazargan (2020) relied on ArcGIS software and spatial selfcorrelation to analyze the data of individuals diagnosed with COVID-19 between 22 February 2020 and 22 March 2020 (21,638 cases) and found that the provinces of Qom, Mazandaran, Gilan, Qazvin, Isfahan, Semnan, Markazi, and Yazd were situated within the HH cluster.…”

Section: Introductionmentioning

confidence: 99%

Territorial patterns of COVID-19 in Iran

Aryan Kya

2024

ASPAL

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Motives: Spatial analysis has become an essential tool in understanding the underlying factors that contribute to the distribution of viral pandemics, diseases, injuries, and mortality patterns. By visualizing geographical data in spatial maps, researchers can identify local distribution patterns and potential drivers behind these patterns. In health and medical sciences, there has been a growing recognition that spatial analysis and mapping techniques are helpful in addressing various challenges related to the allocation of healthcare resource in both urban and rural areas. Aim: The objective of this study was to analyze the spatial distribution pattern of the COVID-19 pandemic and the Index of Proximity Distribution (IPD) across 31 provinces of Iran between February 2019 and February 2023. A two-stage sampling method combining convenience and cluster sampling was used to examine COVID-19 distribution patterns in 31 provinces of Iran between 22 February 2020 and 22 February 2023. COVID-19 and IPD data were collected as part of this panel study. Data were analyzed using t-tests, chi-square tests, and analysis of variance (ANOVA) in SPSS version 28 (α = 0.05). Subsequently, daily COVID-19 infection data for each province in the analyzed period were processed in ArcGIS software, and the spatial distribution pattern of the pandemic in Iran were visualized by point density analysis. Standard distance and standard deviation ellipse techniques were employed to assess the density or dispersion of infected individuals and to determine the spatial distribution pattern of COVID-19 in Iran. A spatial autocorrelation (Moran’s I) analysis was conducted to identify the spatial distribution pattern of COVID-19 in Iran. Additionally, distance-based spatial autocorrelation was used to examine the prevalence of COVID-19 infection across Iranian provinces. In a grouping analysis, 31 Iranian provinces were classified into five groups based on the number of COVID-19 cases, and spatial statistics were used to examine the prevalence of COVID-19 within each group. A hot spot analysis and a standard distance (SD) analysis were conducted to explore spatial correlations in the number of individuals affected by COVID-19 in each province. Results: Based on the Moran index, a random spatial pattern with a Z-Score of 1.485 was identified in March 2019, whereas a clustered distribution of COVID-19 with a Z-Score of 3.039 was determined in February 2023. The distance-based spatial autocorrelation analysis revealed a positive value of the Moran index (0.136627) at a distance of 383.3 kilometers from Tehran, which points to positive spatial autocorrelation and a higher number of COVID-19 cases in nearby regions. Conversely, the Moran index assumed a negative value of 0.040246 at a distance of 726.6 kilometers from Tehran, which suggests that the number of pandemic cases decreased over distance from Tehran. Moreover, based on the results of the hot spot analysis, Tehran province was identified as a hot cluster with a higher prevalence of COVID-19 cases in that region. In contrast, Bushehr province was classified as a cold cluster with a lower prevalence of COVID-19 cases in comparison with the surrounding regions. These findings provide valuable insights into the spatial distribution and clustering of COVID-19 cases in Iran. The shift from a random spatial pattern in 2019 to clustered distribution in 2023 indicates that the pandemic spread rate increased over time. The positive spatial autocorrelation near Tehran highlights the role of proximity and population movement in the transmission of the virus. Furthermore, the identification of hot spots and cold spots in a country can inform targeted interventions and resource allocation to effectively manage and control the pandemic. Overall, this study demonstrates the value of spatial analysis in identifying the spatial distribution patterns and the dynamics of the COVID-19 pandemic in Iran. The integration of spatial analysis techniques with epidemiological data contributes to a better understanding of spatial-temporal patterns, facilitates effective public health responses and resource allocation strategies. These findings contribute to the growing body of knowledge on the spatial epidemiology of COVID-19 and can aid in informing future preparedness and response efforts in Iran and other regions that face similar challenges.

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“…According to the previous studies, Hamadan province was recognized as high-risk areas among its neighboring provinces (i.e., Lorestan, Kurdistan, Kermanshah, Markazi, Zanjan and Qazvin) [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

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

Soltanian,

Ahmaddoost-razdari,

Mahjub

et al. 2023

BMC Public Health

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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.

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

Cited by 6 publications

References 41 publications

Predicting High-Risk Individuals in Disease Outbreaks Using Fuzzy Logic-Based Contact Tracing Model.

Predicting High-Risk Individuals in Disease Outbreaks Using Fuzzy Logic-Based Contact Tracing Model.

Territorial patterns of COVID-19 in Iran

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

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