Why public health needs GIS: a methodological overview

Wang, Fahui

doi:10.1080/19475683.2019.1702099

Cited by 143 publications

(104 citation statements)

References 45 publications

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“…Furthermore, this study only split Arizona into three regions (ie, central, northern, and southern) rather than examining prediction accuracy at both the state and county level. In fact, there may be geospatial differences in population mobility due to the plausible differential implementation fidelity of NPIs and reactions to reopening policies by county [ 20 , 21 ]. Additionally, there may be geospatial differences in the estimation of population mobility on social media as the number of users and their demographic characteristics may differ by county.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis

Zeng¹,

Zhang²,

Li³

et al. 2021

J Med Internet Res

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Background Population mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases. Objective The aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina. Methods This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting. Results Population mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3%-74.5%. Conclusions Using Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.

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Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis

Zeng¹,

Zhang²,

Li³

et al. 2021

J Med Internet Res

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“…Additionally, the increased abundance of Culex pipiens and C. restuans in NENA forests has led to increased transmission of West Nile Virus to wildlife such as the ruffed grouse (Bonasa umbellus) [7]. The increase in mosquitoes and risk of disease transmission [8] requires a thorough understanding of the environmental variables regulating these disease vectors.…”

Section: Introductionmentioning

confidence: 99%

Dissolved organic matter protects mosquito larvae from damaging solar UV radiation

et al. 2020

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Mosquitoes have increased in their abundance and geographic distribution in northeastern North America, coinciding with an increase in extreme precipitation events and up to a doubling of dissolved organic matter (DOM) concentrations in some inland waters. Increases in DOM can reduce exposure of mosquito larvae to solar ultraviolet (UV) radiation. Although mosquito larvae are most common in shaded habitats, almost nothing is known about their susceptibility to damage by solar UV radiation, or the ability of DOM to create a refuge from damaging UV in their shallow-water habitats. We hypothesize that 1) exposure to solar UV radiation is lethal to mosquito larvae, 2) larvae lack photo-enzymatic repair to fix UV-damaged DNA, and 3) DOM shades larvae from lethal solar UV radiation. We tested these hypotheses with experiments that manipulated UV radiation, the photo-repair radiation necessary for photo-enzymatic DNA repair, and DOM. Exposure to solar UV radiation significantly decreased larval survivorship, while DOM significantly increased it. There was no evidence of photo-enzymatic DNA repair. Our findings confirm that solar UV radiation decreases habitat suitability for mosquito larvae, but DOM provides a refuge from UV. This highlights the need for vector control managers to prioritize high DOM and shaded habitats in their efforts to reduce mosquito populations.

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“…As a one-size-fits-all policy rarely serves as an effective strategy to be adopted in large countries like China [ 57 ], it is highly recommended that geospatial heterogeneity be considered throughout policy-making procedures at regional levels in order to facilitate the formulation and implementation of region-specific strategies for optimization purposes. In order to achieve such region-specific optimization, it is highly recommended that experts from interdisciplinary backgrounds are extensively engaged in policy-making procedures for various healthcare settings as part of the healthcare reform, including public health professionals, health and medical geographers, geographic information science (GIS) practitioners, environmentalists, and spatial statisticians [ 63 , 64 ]. More importantly, the adoption of an innovatively developed Bayesian-based local spatiotemporal regression STVC model in our study demonstrated great value in the spatiotemporal analysis of various influencing factors in the field of public health, which could be further extended for adoption in broader areas such as environment and earth sciences.…”

Section: Discussionmentioning

confidence: 99%

Spatial and Temporal Impacts of Socioeconomic and Environmental Factors on Healthcare Resources: A County-Level Bayesian Local Spatiotemporal Regression Modeling Study of Hospital Beds in Southwest China

Song

Wang

Yang³

et al. 2020

IJERPH

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Comprehensive investigation on understanding geographical inequalities of healthcare resources and their influencing factors in China remains scarce. This study aimed to explore both spatial and temporal heterogeneous impacts of various socioeconomic and environmental factors on healthcare resource inequalities at a fine-scale administrative county level. We collected data on county-level hospital beds per ten thousand people to represent healthcare resources, as well as data on 32 candidate socioeconomic and environmental covariates in southwest China from 2002 to 2011. We innovatively employed a cutting-edge local spatiotemporal regression, namely, a Bayesian spatiotemporally varying coefficients (STVC) model, to simultaneously detect spatial and temporal autocorrelated nonstationarity in healthcare-covariate relationships via estimating posterior space-coefficients (SC) within each county, as well as time-coefficients (TC) over ten years. Our findings reported that in addition to socioeconomic factors, environmental factors also had significant impacts on healthcare resources inequalities at both global and local space–time scales. Globally, the personal economy was identified as the most significant explanatory factor. However, the temporal impacts of personal economy demonstrated a gradual decline, while the impacts of the regional economy and government investment showed a constant growth from 2002 to 2011. Spatially, geographical clustered regions for both hospital bed distributions and various hospital bed-covariates relationships were detected. Finally, the first spatiotemporal series of complete county-level hospital bed inequality maps in southwest China was produced. This work is expected to provide evidence-based implications for future policy making procedures to improve healthcare equalities from a spatiotemporal perspective. The employed Bayesian STVC model provides frontier insights into investigating spatiotemporal heterogeneous variables relationships embedded in broader areas such as public health, environment, and earth sciences.

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Why public health needs GIS: a methodological overview

Cited by 143 publications

References 45 publications

Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis

Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis

Dissolved organic matter protects mosquito larvae from damaging solar UV radiation

Spatial and Temporal Impacts of Socioeconomic and Environmental Factors on Healthcare Resources: A County-Level Bayesian Local Spatiotemporal Regression Modeling Study of Hospital Beds in Southwest China

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