Review of Geospatial Technology for Infectious Disease Surveillance: Use Case on COVID-19

Saran, Sameer; Singh, Priyanka; Kumar, Vishal; Chauhan, Prakash

doi:10.1007/s12524-020-01140-5

Cited by 63 publications

(40 citation statements)

References 201 publications

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“…Geospatial analyses are becoming increasingly important and relevant in the area of public health [22]. With visualization and analytical capabilities, geospatial analyses in public health are now ubiquitous [23][24][25][26][27][28][29][30][31].…”

Section: Relevant Methodsmentioning

confidence: 99%

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Models for Heart Failure Admissions and Admission Rates, 2016 through 2018

et al. 2020

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Background: Approximately 6.5 to 6.9 million individuals in the United States have heart failure, and the disease costs approximately $43.6 billion in 2020. This research provides geographical incidence and cost models of this disease in the U.S. and explanatory models to account for hospitals’ number of heart failure DRGs using technical, workload, financial, geographical, and time-related variables. Methods: The number of diagnoses is forecast using regression (constrained and unconstrained) and ensemble (random forests, extra trees regressor, gradient boosting, and bagging) techniques at the hospital unit of analysis. Descriptive maps of heart failure diagnostic-related groups (DRGs) depict areas of high incidence. State- and county-level spatial and non-spatial regression models of heart failure admission rates are performed. Expenditure forecasts are estimated. Results: The incidence of heart failure has increased over time with the highest intensities in the East and center of the country; however, several Northern states have seen large increases since 2016. The best predictive model for the number of diagnoses (hospital unit of analysis) was an extremely randomized tree ensemble (predictive R2 = 0.86). The important variables in this model included workload metrics and hospital type. State-level spatial lag models using first-order Queen criteria were best at estimating heart failure admission rates (R2 = 0.816). At the county level, OLS was preferred over any GIS model based on Moran’s I and resultant R2; however, none of the traditional models performed well (R2 = 0.169 for the OLS). Gradient-boosted tree models predicted 36% of the total sum of squares; the most important factors were facility workload, mean cash on hand of the hospitals in the county, and mean equity of those hospitals. Online interactive maps at the state and county levels are provided. Conclusions. Heart failure and associated expenditures are increasing. Costs of DRGs in the study increased $61 billion from 2016 through 2018. The increase in the more expensive DRG 291 outpaced others with an associated increase of $92 billion. With the increase in demand and steady-state supply of cardiologists, the costs are likely to balloon over the next decade. Models such as the ones presented here are needed to inform healthcare leaders.

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Section: Relevant Methodsmentioning

confidence: 99%

“…With visualization and analytical capabilities, geospatial analyses in public health are now ubiquitous [23][24][25][26][27][28][29][30][31]. A review of geospatial methods used for public health as well as the historical development is available from Saran, Singh, Kumar, and Chauhan [22].…”

Section: Relevant Methodsmentioning

confidence: 99%

Models for Heart Failure Admissions and Admission Rates, 2016 through 2018

et al. 2020

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“…The above finding underscores the direction of transmission, outbreak tracking and risk mapping as some of the key importance of geospatial technology application in infectious disease surveillance such as COVID 19 (Pigott et. al., 2015;Saran et. al.,2020).…”

Section: Evaluation Of the Database Outputmentioning

confidence: 99%

Design and deployment of relational geodatabase on mobile GIS platform for real-time COVID-19 contact tracing in Ghana

et al. 2021

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This study reviewed the design and deployment of relational geodatabase on mobile GIS application, using collector for ArcGIS and survey 123 for ArcGIS platforms for COVID-19 contact tracing in Ghana during the lockdown. The study assessed whether cases spread by physical neighborhood contacts, defined by a 2km buffer of initial known 60 cases location. The application was deployed on the android tablet, which was used by field workers. Application Post-deployment review shows that from 30th March to 4th April 2020, 828 samples were collected with 34 confirmed cases, of which 61% occurred outside the 2km buffer. From 1-30 April 2020, 8,748 individuals with 16,087 contacts were tested within the physical neighbourhoods, 2.4% turned positive. Similarly, 7,501 individuals with 17,071 contacts were tested outside the physical neighbourhoods with 4.3% positives. Results suggest that more infections occurred outside the case’s physical neighbourhoods possibly due to; (1) existence of unknown cases prior to lockdown; (2) cases were moving outside their physical neighborhood and infecting others; (3) panic movements of cases within the 3 days window between announcement and enforcement of lockdown; (4) movement of cases into the country through unapproved routes. New cases were identified outside the lockdown areas, which could not be explained. This study raises questions about (1) the understanding of the mode of spread of the virus (2) the implementation of the lockdown, including the geographic coverage and timing. It is recommended that future decisions on contact tracing and lockdown should be guided by an understanding of the disease geography.

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“…In this regard, spatial aspects have been demonstrated to have a crucial role in predicting disease outbreaks (Melin et al, 2020). Geographic information system (GIS) is useful tool in the public health domain, particularly for the infectious disease surveillance and modelling strategies (Saran et al, 2020). GIS offers spatial modeling options that accounts for the in uence of various factors (Abousaeidi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

The Spatial Predicting of COVID-19 Incidence and Its Mortality Based On OLS and GWR Models in Iran

Ghanbari

Baradaran

Ahmadi

et al. 2021

Preprint

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Background: Within six months of the COVID-19 outbreak, 350279 people were infected, and 20125 people died of COVID-19 in Iran. There is an urgent need to find the most accurate effective indicators on this disease's outbreak in order to control and predict. Methods: We examined the effect of 36 demographic, economic, environmental, health infrastructure, social, and topographic independent variables on the COVID-19 infection and mortality rates using the ordinary least squares (OLS) model in ArcGIS 10.5. Regarding adjusted R-squared>0/7, we selected 20 variables for COVID-19 infection rate and 16 variables for the mortality rate. The collinearity problem between the selected variables resolved after using the variance inflation factor (VIF). Then, we performed the OLS and geographically weighted regression (GWR) models in ArcGIS 10.5.Results: Having a large number of men, having a large population, lack of specialist doctors, lack of hospital, having a large urban population, having a large number of people aged 65 and over or older individuals, and high natural mortality rate had the most prominent impact on the COVID-19 infection increasing rate. Also, lack of ICU beds, low number of insured people, lack of subspecialist physicians, and lack of hospital beds had the most prominent impact on increasing of COVID-19 mortality. Then the variables with VIF above 7.5 were removed and finally, high incoming immigrants rate and lack of nurses were identified as two independent variables to predict COVID-19 infection rate. In addition, high incoming immigrants rate and high number of doctor consultation were recognized as two variables to predict mortality rate due to COVID-19. The results of the Akaike information criterion (AIC) and adj.R2 showed that both models were appropriate for these analyses.Conclusions: Based on our results, there would be a considerable increase in COVID-19 infection in Kerman, Esfahan, and Kermanshah provinces. In addition, there would be a remarkable decrease in COVID-19 infection in Khuzestan, Lorestan, Azarbayjan Shargi, and Tehran provinces. Regarding COVID-19 mortality, there would be a substantial rise in Fars and Khorasan Razavi provinces. Moreover, our analyses predicted a considerable diminish in COVID-19 mortality in Tehran, Ardebil, Zanjan, Gilan, Golestan, Lorestan, Khuzestan, Bushehr, and Hormozgan provinces.

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Review of Geospatial Technology for Infectious Disease Surveillance: Use Case on COVID-19

Cited by 63 publications

References 201 publications

Models for Heart Failure Admissions and Admission Rates, 2016 through 2018

Models for Heart Failure Admissions and Admission Rates, 2016 through 2018

Design and deployment of relational geodatabase on mobile GIS platform for real-time COVID-19 contact tracing in Ghana

The Spatial Predicting of COVID-19 Incidence and Its Mortality Based On OLS and GWR Models in Iran

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