Applying GIS Methods to Public Health Research at Harvard University

Blossom, Jeffrey; Finkelstein, Julia L.; Guan, Weihe Wendy; Burns, Bonnie

doi:10.1080/15420353.2011.599770

Cited by 11 publications

(4 citation statements)

References 18 publications

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“…For computing hypothesis (Ho) test and the presence spatial autocorrelation we used global and local Moran’s I test statistic [11]. Significant clustering, variable or dissimilar patterns and random patterns were declared when the mean Moran’s I values are positive, negative and zero respectively.…”

Section: Methodsmentioning

confidence: 99%

Spatial modeling, prediction and seasonal variation of malaria in northwest Ethiopia

2019

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Objectives The aim of this study was to determine the spatial modeling, seasonal variation of malaria and making prediction map of malaria in northwest Ethiopia. Results The overall average cumulative annual malaria incidence during the study period was 30 per 100 populations at risk. The highest proportion (29.2%) was observed from June 2015 to October 2016. In temporal analysis of clusters, the epidemic was observed from 2015/7/1 to 2016/12/31 throughout the study period in all districts. Hotspot areas with high clusters (p < 0.001) were observed in Metema district it accounts 18.6% of the total malaria cases. An area of high median predicted incidence proportion (> 50%) was seen in the southwest part of the region. Most of the northern part of the study area was predicted to have a low median incidence proportion (< 10%). Electronic supplementary material The online version of this article (10.1186/s13104-019-4305-1) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Spatial modeling, prediction and seasonal variation of malaria in northwest Ethiopia

2019

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“…In this study, the spatial interpolation technique was used to predict unsampled from sampled measurements and ordinary Kriging spatial interpolation method was used for predictions and to generate smooth surfaces of the variables of interest ( 27 ).…”

Section: Methodsmentioning

confidence: 99%

Spatio-temporal distributions and determinants of diarrhea among under-five children in Ethiopia

Tiku,

Zeru,

Belay

2024

Front. Public Health

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ObjectiveThe purpose of this study was to evaluate the spatio-temporal pattern of Ethiopia’s childhood diarrheal disease and identify its contributing factors.MethodsWe conducted analyses on secondary data from four Ethiopian Demographic and Health Surveys conducted between 2000 and 2016. Moran’s I was used to determine spatial dependence and spatial models were used to evaluate variables associated with diarrhea in under-five children at the zonal level.ResultsChildhood diarrhea showed spatial clustering in Ethiopia (Moran’s I; p < 0.05). The spatial regression model revealed significant factors at the zonal level: children born at home (eθ = 1.355, 95% CI: 1.052–1.544, p < 0.001), low birth weight (eθ = 1.18, 95% CI: 1.017–1.691, p < 0.05), and unimproved source of water (eθ = 0.8568, 95% CI: 0.671–1.086, p < 0.01).ConclusionThe prevalence of diarrhea among under-five children varied over time by zone, with the Assosa, Hundene, and Dire Diwa zones having the highest rates. Home births and low birth weight contributed to the prevalence of childhood diarrhea. In high-risk zones of Ethiopia, reducing childhood diarrhea requires integrated child health interventions and raising awareness about the potential hazards associated with unimproved water sources.

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“…Health professionals are using conventional mapping, and geographic information systems (GIS) as a tool for tracking diseases and combating contagion [ 11 ]. Geographic information system methods to improve case detection through improving community health for SARS outbreak [ 12 ], its wide application in public health research [ 13 ], and in mapping and visualizing disease distribution towards understanding diversities of diseases and spatial patterns in health science [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Use of geographic information systems web mapping application to support active case search to guide public health and social measures in the context of COVID-19 in Zimbabwe: a preliminary report to guide replication of methods in similar resource settings

Bello¹,

Moyo²,

Munyanyi³

et al. 2021

Pan Afr Med J

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Introduction the new coronavirus (COVID-19) that emerged from Wuhan, Hubei Province of China in December 2019, causing severe acute respiratory syndrome (SARS) has fast spread across the entire globe, with most countries struggling to slow and reduce the spread of the virus through rapid screening, testing, isolation, case management, contact tracing, implementing social distancing and lockdowns. This has been shown to be a major factor in countries that have been successful in containing COVID-19 transmission. Early detection of cases is important, and the use of geospatial technology can support to detect and easily identify potential hotspots that will require timely response. The use of spatial analysis with geographic information systems (GIS) had proved to be effective in providing timely and effective solutions in supporting epidemic response and pandemics over the years. It has developed and evolved rapidly with a complete technological tool for representing data, model construction, visualization and platform construction among others. Methods we conducted a geospatial analysis to develop a web mapping application using ArcMap and ArcGIS online to guide and support active case search of potential COVID-19 cases, within 500m radius of COVID-19 confirmed cases to improve detection and testing of suspected cases. Results the web mapping application tool guides the active case search teams in the field, with clear boundaries on the houses to be visited within 500-meter radius of confirmed positive cases, to conduct active case search of all cases of severe acute respiratory illnesses (SARI), acute respiratory illnesses (ARI), pneumonia etc, to detect and test for COVID-19 towards containing the pandemic. Conclusion the use of GIS and spatial statistical tools have become an important and valuable tool in decision-making and, more importantly, guiding health care professional and other stakeholders in the response being carried out in a more coherent and easy manner. It has proven to be effective in supporting the active case search process to rapidly detect, test and isolate cases during the process, towards containing the COVID-19 pandemic.

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Applying GIS Methods to Public Health Research at Harvard University

Cited by 11 publications

References 18 publications

Spatial modeling, prediction and seasonal variation of malaria in northwest Ethiopia

Spatial modeling, prediction and seasonal variation of malaria in northwest Ethiopia

Spatio-temporal distributions and determinants of diarrhea among under-five children in Ethiopia

Use of geographic information systems web mapping application to support active case search to guide public health and social measures in the context of COVID-19 in Zimbabwe: a preliminary report to guide replication of methods in similar resource settings

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