Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level

Ramírez‐Aldana, Ricardo; Gómez-Verján, Juan Carlos; Bello‐Chavolla, Omar Yaxmehen

doi:10.1101/2020.04.19.20071605

Cited by 15 publications

(15 citation statements)

References 33 publications

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“…Urban population percentage was found to be proportionally related to COVID-19 incidence rates which implies urbanization paves the way to increase the likelihood of transmission of COVID-19. High urban population leads to increase movement and activities of people in high-density urban area (Hamidi et al, 2020; Ramírez-Aldana, Gomez-Verjan, & Bello-Chavolla, 2020). The higher the population density is, the more likelihood to come in proximate contact between an infector and infectee (You, Wu, & Guo, 2020).…”

Section: Discussionmentioning

confidence: 99%

GIS-based spatial modeling to identify factors affecting COVID-19 incidence rates in Bangladesh

Rahman

Zafri

Ashik

et al. 2020

Preprint

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The outbreak of the COVID-19 pandemic is an unprecedented shock throughout the world which leads to generate a massive social, human, and economic crisis. However, there is a lack of research on geographic modeling of COVID-19 as well as identification of contributory factors affecting the COVID-19 in the context of developing countries. To fulfill the gap, this study aimed to identify the potential factors affecting the COVID-19 incidence rates at the district-level in Bangladesh using spatial regression model (SRM). Therefore, data related to 32 demographic, economic, weather, built environment, health, and facilities related factors were collected and analyzed to explain the spatial variability of this disease incidence. Three global (Ordinary least squares (OLS), spatial lag model (SLM) and spatial error model (SEM)) and one local (geographically weighted regression (GWR)) SRMs were developed in this study. The results of the models showed that four factors significantly affected the COVID-19 incidence rates in Bangladesh. Those four factors are urban population percentage, monthly consumption, number of health workers, and distance from the capital. Among the four developed models, the GWR model performed the best in explaining the variation of COVID-19 incidence rates across Bangladesh with a R square value of 78.6%. Findings from this research offer a better insight into the COVID-19 situation and would help to develop policies aimed to prevent the future epidemic crisis.

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Section: Discussionmentioning

confidence: 99%

GIS-based spatial modeling to identify factors affecting COVID-19 incidence rates in Bangladesh

Rahman

Zafri

Ashik

et al. 2020

Preprint

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“…Socio-economic and environmental aspects including population density, urban and rural settings, education level, lifestyle, the size of household and homeowners and climate conditions have been identified to affect the risk of catching the virus [8]. Similarly, several factors including age, urbanization, distance between cities and provinces, mean ambient temperature and level of literacy rate were spatially considered for the COVID-19 outbreak in Iran [9]. Boulos et al (2020), worked on understanding widespread of SARS-CoV-2 around the world through geographical tracking and mapping [4].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020)

Shariati

Mesgari

Kasraee

et al. 2020

J Environ Health Sci Engineer

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Understanding the spatial distribution of coronavirus disease 2019 (COVID-19) cases can provide valuable information to anticipate the world outbreaks and in turn improve public health policies. In this study, the cumulative incidence rate (CIR) and cumulative mortality rate (CMR) of all countries affected by the new corona outbreak were calculated at the end of March and April, 2020. Prior to the implementation of hot spot analysis, the spatial autocorrelation results of CIR were obtained. Hot spot analysis and Anselin Local Moran's I indices were then applied to accurately locate high and low-risk clusters of COVID-19 globally. San Marino and Italy revealed the highest CMR by the end of March, though Belgium took the place of Italy as of 30th April. At the end of the research period (by 30th April), the CIR showed obvious spatial clustering. Accordingly, southern, northern and western Europe were detected in the high-high clusters demonstrating an increased risk of COVID-19 in these regions and also the surrounding areas. Countries of northern Africa exhibited a clustering of hot spots, with a confidence level above 95%, even though these areas assigned low CIR values. The hot spots accounted for nearly 70% of CIR. Furthermore, analysis of clusters and outliers demonstrated that these countries are situated in the low-high outlier pattern. Most of the surveyed countries that exhibited clustering of high values (hot spot) with a confidence level of 99% (by 31st March) and 95% (by 30th April) were dedicated higher CIR values. In conclusion, hot spot analysis coupled with Anselin local Moran's I provides a scrupulous and objective approach to determine the locations of statistically significant clusters of COVID-19 cases shedding light on the high-risk districts.

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“…Literature supports that some certain studies were carried out over different parts of the world regarding the analysis of COVID-19 spatially (Zhou et al 2020 ; Fan et al 2020 , Gatto et al 2020 , Murugesan et al 2020 ; Kamel-Boulos and Geraghty 2020 ; Meyer et al 2017 ; Mo et al 2020 ; Roy et al 2020 ). Particularly, a comprehensive study on the nature of spatial spreading, geospatial pattern of transmission, vulnerability, and risk level along with hot spots analysis of COVID-19 were not widely analyzed (Ramírez-Aldana et al 2020 ; Huang et al 2020 ; Jia et al 2020 ; Kang et al 2020 ; Melin et al 2020 ; Ghosh et al 2020 ; Mishra et al 2020 ; Acharya and Porwal 2020 ). Conversely, in case of Bangladesh, in-depth study of COVID-19 using geospatial approach was very rare (Sakamoto et al 2020 ; Masrur et al 2020 ) and which were available mostly epidemiological, temporal, and modelling based and some of those mainly highlighted the impacts of COVID-19 on the economy, society, health, and environment (Anwar et al 2020 ; Bodrud-Doza 2020 ; Hossain et al 2020 ; Jahan et al 2020 ; Khan et al 2020a , b ; Khan et al 2020a ; Zabir et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Geospatial modelling on the spread and dynamics of 154 day outbreak of the novel coronavirus (COVID-19) pandemic in Bangladesh towards vulnerability zoning and management approaches

Rahman

Islam

2020

Model. Earth Syst. Environ.

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The novel COVID-19 is a worldwide transmitted pandemic and has received global attention. Since there is no effective medication yet, to minimize and control the transmission of the COVID-19, non-pharmaceutical interventions (NPIs) are followed globally. However, for the implementation of needful NPIs through effective management strategies and planning, space-time-based information on the nature, magnitude, pattern of transmission, hotspots, the potential risk factors, vulnerability, and risk level of the pandemic are important. Hence, this study was an attempt to in-depth assess and analyze the COVID-19 outbreak and transmission dynamics through space and time in Bangladesh using 154 day real-time epidemiological data series. District-level data were analyzed for the geospatial analysis and modelling using GIS. Getis-Ord Gi* statistics was applied for the hotspot analysis, and on the other hand, the analytical hierarchy process-based weighted sum method (AHP-WSM) was used for the modelling of vulnerability zoning of COVID-19. In Bangladesh, the status of the pandemic COVID-19 still is in exposure level. Disease transmitted at a high rate (20.37%), and doubling time of the cases were 11 days (latest week of the study period). The fatality rate was comparatively low (1.3%), and the recovery rate was about 57.50%. Geospatial analysis exhibits the disease propagates from the central parts, and Dhaka was the most exposed district followed by Chattogram, Narayanganj, Cumilla, and Bogra. A single strong clustering pattern in the central part, which spread out mainly to the southeastern part, was identified as a prime hotspot in both the cases and deaths distributions. Additionally, potential linkages between the transmission of disease and the selected factors that gear up the spreading of the disease were identified. The central, eastern, and southeastern parts were recognized as high vulnerable zone, and conversely, the western, southwestern , northwestern , and northeastern parts as medium vulnerable zone. The vulnerable zoning exercise made it possible to identify vulnerable areas with the different magnitude that require urgent intervention through proper management and action plan, and accordingly, comprehensive management strategies were anticipated. Thus, this study will be a useful guide towards understanding the space-time-based investigations and vulnerable area delineation of the COVID-19 and assist to formulate an effective management action plan to reduce and control the disease propagation and impacts. By appropriate adjustment of some factors with local relevance, COVID-19 vulnerability zoning derived here can be applied to other regions, and generally can be used for any other infectious disease. This method was applied at a regional scale, but the availability of larger scale data of the determining factors could be applied in small areas too, and accordingly, management strategies can be formulated.

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Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level

Abstract: 2 6

Cited by 15 publications

References 33 publications

GIS-based spatial modeling to identify factors affecting COVID-19 incidence rates in Bangladesh

GIS-based spatial modeling to identify factors affecting COVID-19 incidence rates in Bangladesh

Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020)

Geospatial modelling on the spread and dynamics of 154 day outbreak of the novel coronavirus (COVID-19) pandemic in Bangladesh towards vulnerability zoning and management approaches

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