Changes in the spatial distribution of COVID-19 incidence in Italy using GIS-based maps

Martellucci, Cecilia Acuti; Sah, Ranjit; Rabaan, Ali A.; Dhama, Kuldeep; Casalone, Cristina; Arteaga‐Livias, Kovy; Sawano, Toyoaki; Ozaki, Akihiko; Bhandari, Divya; Higuchi, Asaka; Kotera, Yasuhiro; Fathah, Zareena; Roy, Namrata; Rahman, Mohammed Ateeq Ur; Tanimoto, Tetsuya; Rodríguez‐Morales, Alfonso J.

doi:10.1186/s12941-020-00373-z

Cited by 37 publications

(25 citation statements)

References 15 publications

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“…Regarding temporal distribution, times with high disease risk are also identified which gives crew to possible causes including, in particular, seasonal changes. A number of studies on spatial temporal distribution of COVID-19 have been conducted ( Chen et al, 2020 ; Yang et al, 2020 ; Gayawan et al, 2020 ; Martellucci et al, 2020 ; Adekunle et al, 2020 ; Arashi et al, 2020 ; Kumar et al, 2020 ; Briz-Redón & Serrano-Aroca, 2020 ; Diop et al, 2020 ; Jia et al, 2020 ; Xie et al, 2020 ; Likassa, 2020 ; Sarfo & Karuppannan, 2020 ; Daw et al, 2020 ; Ye & Hu, 2020 ). The majority of these though, have used the geographical information system (GIS) technology as compared to statistical modelling using spatial temporal models.…”

Section: Introductionmentioning

confidence: 99%

Spatial temporal distribution of COVID-19 risk during the early phase of the pandemic in Malawi

Ngwira¹,

Kumwenda²,

Munthali³

et al. 2021

PeerJ

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Background COVID-19 has been one of the greatest challenges the world has faced since the second world war. This study aimed at investigating the distribution of COVID-19 in both space and time in Malawi. Methods The study used publicly available data of COVID-19 cases for the period from 2 April 2020 to 28 October 2020. Semiparametric spatial temporal models were fitted to the number of monthly confirmed cases as an outcome data, with time and district as independent variables, where district was the spatial unit, while accounting for sociodemographic factors. Results The study found significant effects of location and time, with the two interacting. The spatial distribution of COVID-19 risk showed major cities being at greater risk than rural areas. Over time, the COVID-19 risk was increasing then decreasing in most districts with the rural districts being consistently at lower risk. High proportion of elderly people was positively associated with COVID-19 risk (β = 1.272, 95% CI [0.171, 2.370]) than low proportion of elderly people. There was negative association between poverty incidence and COVID-19 risk (β = −0.100, 95% CI [−0.136, −0.065]). Conclusion Future or present strategies to limit the spread of COVID-19 should target major cities and the focus should be on time periods that had shown high risk. Furthermore, the focus should be on elderly and rich people.

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Section: Introductionmentioning

confidence: 99%

Spatial temporal distribution of COVID-19 risk during the early phase of the pandemic in Malawi

Ngwira¹,

Kumwenda²,

Munthali³

et al. 2021

PeerJ

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“…Kang et al (2020) used spatial epidemic dynamics to investigate the outbreak of COVID-19 disease in china [6]. Martellucci et al (2020) illustrated the changes in the spatial distribution of COVID-19 incidence in Italy using GIS-based maps [7]. 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].…”

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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“…As mentioned earlier, there were two types of outcomes in our results. As can be seen in was a substantial disparity between those who died (9 days, IQR [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]) and those who survived (6 days, IQR [4][5][6][7][8][9][10][11]) in the median LOS. Figure 2.…”

Section: Patient Outcomesmentioning

confidence: 99%

“…There is no doubt that GIS-based map of distribution of SARS-CoV-2 infection though various societies can support health care providers systems as a key component in avoiding and managing the spread of infection (17). Some researchers paid for this theme and provided a GIS-based Map of COVID-19 infection in the area concerned, including Italy (18), the USA (19), Pakistan (17) and India (20).…”

Section: Risk Factorsmentioning

confidence: 99%

Spatial epidemiology of COVID-19 infection through the first outbreak (March-May 2020) in the city of Mashhad, Iran

Mansouritorghabeh¹,

Bagherimoghaddam²,

MohammadEbrahimi³

et al. 2021

Preprint

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Background: Since December 2019, SARS-CoV-2 infection has converted to a severe threat to global health. It is now considered as the fifth worldwide pandemic problem. This study aims to explore spatial-time distribution of COVID-19 in the first outbreak of COVID-19 in the second major city of Iran (Mashhad). The results will pave the way for better tracking of COVID-19.Methods: Data were collected from two tertiary hospitals in Mashhad in June 2020. They included demographic findings and residential address of the patients with confirmed COVID-19 disease by polymerase chain reaction test. The univariate logistic regression model was used to assess the influence of age and sex on mortality. For spatial-time analysis, after calculating empirical Bayesian rate for every neighborhood, the local Moran's I statistic was used to quantify spatial autocorrelation of COVID-19 frequency at the city neighborhood level.Results: Of 1,535 confirmed cases of COVID-19 included in this study, 951 (62%) were male. Odds of death for patients over 60 years of age was more than three times higher (odds ratio [OR]: 3.7, CI [2.8-4.8]) than for those under the 60 years. In addition, the ratio of relative mortality for male patients was significantly higher than the female (OR: 1.58, CI [1.2-2]). The univariate regression model also revealed that odds of death increased along with increase in duration of hospitalization secondary to COVID-19 disease (OR: 1.02, IQR [1.01-1.02]). The downtown area had a significant high-high cluster throughout much of the study period (March-May 2020). Conclusions: Collection of geographic information system (GIS) map data on SARS-CoV-2 provides insight into clusters of infection and high risk places for COVID-19 transmission. GIS-

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Changes in the spatial distribution of COVID-19 incidence in Italy using GIS-based maps

Cited by 37 publications

References 15 publications

Spatial temporal distribution of COVID-19 risk during the early phase of the pandemic in Malawi

Spatial temporal distribution of COVID-19 risk during the early phase of the pandemic in Malawi

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

Spatial epidemiology of COVID-19 infection through the first outbreak (March-May 2020) in the city of Mashhad, Iran

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