Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada [version 1; peer review: 1 approved, 2 approved with reservations]
Introduction: This study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 posit...
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| Format: | Article |
| Language: | English |
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F1000 Research Ltd
2021-12-01
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| Series: | F1000Research |
| Subjects: | |
| Online Access: | https://f1000research.com/articles/10-1312/v1 |
| _version_ | 1811344750558576640 |
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| author | Ben C. Shirley Peter K. Rogan Eliseos J. Mucaki |
| author_facet | Ben C. Shirley Peter K. Rogan Eliseos J. Mucaki |
| author_sort | Ben C. Shirley |
| collection | DOAJ |
| description | Introduction: This study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. Methods: COVID-19 cases and locations were curated for geostatistical analyses from March 2020 through June 2021, corresponding to the first, second, and third waves of infections. Daily cases were aggregated according to designated forward sortation area (FSA), and postal codes (PC) in municipal regions Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, and Windsor/Essex county. Hotspots were identified with area-to-area tests including Getis-Ord Gi*, Global Moran’s I spatial autocorrelation, and Local Moran’s I asymmetric clustering and outlier analyses. Case counts were also interpolated across geographic regions by Empirical Bayesian Kriging, which localizes high concentrations of COVID-19 positive tests, independent of FSA or PC boundaries. The Geostatistical Disease Epidemiology Toolbox, which is freely-available software, automates the identification of these regions and produces digital maps for public health professionals to assist in pandemic management of contact tracing and distribution of other resources. Results: This study provided indicators in real-time of likely, community-level disease transmission through innovative geospatial analyses of COVID-19 incidence data. Municipal and provincial results were validated by comparisons with known outbreaks at long-term care and other high density residences and on farms. PC-level analyses revealed hotspots at higher geospatial resolution than public reports of FSAs, and often sooner. Results of different tests and kriging were compared to determine consistency among hotspot assignments. Concurrent or consecutive hotspots in close proximity suggested potential community transmission of COVID-19 from cluster and outlier analysis of neighboring PCs and by kriging. Results were also stratified by population based-categories (sex, age, and presence/absence of comorbidities). Conclusions: Earlier recognition of hotspots could reduce public health burdens of COVID-19 and expedite contact tracing. |
| first_indexed | 2024-04-13T19:52:40Z |
| format | Article |
| id | doaj.art-5e9a16819f3947a08cff4f73b10a565f |
| institution | Directory Open Access Journal |
| issn | 2046-1402 |
| language | English |
| last_indexed | 2024-04-13T19:52:40Z |
| publishDate | 2021-12-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj.art-5e9a16819f3947a08cff4f73b10a565f2022-12-22T02:32:28ZengF1000 Research LtdF1000Research2046-14022021-12-011079823Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada [version 1; peer review: 1 approved, 2 approved with reservations]Ben C. Shirley0Peter K. Rogan1https://orcid.org/0000-0003-2070-5254Eliseos J. Mucaki2CytoGnomix Inc, London, Ontario, N5X 3X5, CanadaDepartment of Biochemistry, University of Western Ontario, London, Ontario, N6A 5C1, CanadaDepartment of Biochemistry, University of Western Ontario, London, Ontario, N6A 5C1, CanadaIntroduction: This study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. Methods: COVID-19 cases and locations were curated for geostatistical analyses from March 2020 through June 2021, corresponding to the first, second, and third waves of infections. Daily cases were aggregated according to designated forward sortation area (FSA), and postal codes (PC) in municipal regions Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, and Windsor/Essex county. Hotspots were identified with area-to-area tests including Getis-Ord Gi*, Global Moran’s I spatial autocorrelation, and Local Moran’s I asymmetric clustering and outlier analyses. Case counts were also interpolated across geographic regions by Empirical Bayesian Kriging, which localizes high concentrations of COVID-19 positive tests, independent of FSA or PC boundaries. The Geostatistical Disease Epidemiology Toolbox, which is freely-available software, automates the identification of these regions and produces digital maps for public health professionals to assist in pandemic management of contact tracing and distribution of other resources. Results: This study provided indicators in real-time of likely, community-level disease transmission through innovative geospatial analyses of COVID-19 incidence data. Municipal and provincial results were validated by comparisons with known outbreaks at long-term care and other high density residences and on farms. PC-level analyses revealed hotspots at higher geospatial resolution than public reports of FSAs, and often sooner. Results of different tests and kriging were compared to determine consistency among hotspot assignments. Concurrent or consecutive hotspots in close proximity suggested potential community transmission of COVID-19 from cluster and outlier analysis of neighboring PCs and by kriging. Results were also stratified by population based-categories (sex, age, and presence/absence of comorbidities). Conclusions: Earlier recognition of hotspots could reduce public health burdens of COVID-19 and expedite contact tracing.https://f1000research.com/articles/10-1312/v1COVID-19 epidemiology geostatistics space-time analysis kriging infectious diseaseseng |
| spellingShingle | Ben C. Shirley Peter K. Rogan Eliseos J. Mucaki Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada [version 1; peer review: 1 approved, 2 approved with reservations] F1000Research COVID-19 epidemiology geostatistics space-time analysis kriging infectious diseases eng |
| title | Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada [version 1; peer review: 1 approved, 2 approved with reservations] |
| title_full | Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada [version 1; peer review: 1 approved, 2 approved with reservations] |
| title_fullStr | Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada [version 1; peer review: 1 approved, 2 approved with reservations] |
| title_full_unstemmed | Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada [version 1; peer review: 1 approved, 2 approved with reservations] |
| title_short | Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada [version 1; peer review: 1 approved, 2 approved with reservations] |
| title_sort | likely community transmission of covid 19 infections between neighboring persistent hotspots in ontario canada version 1 peer review 1 approved 2 approved with reservations |
| topic | COVID-19 epidemiology geostatistics space-time analysis kriging infectious diseases eng |
| url | https://f1000research.com/articles/10-1312/v1 |
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