A web-based analytical framework for the detection and visualization space-time clusters of COVID-19

Figures & data

Table 1. Example of daily case data from JHU.

UID	iso2	iso3	code3	FIPS	Admin2	State	Country	Lat	Long	1/22/2020	10/16/2021	10/17/2021
84001001	US	USA	840	1001	Autauga	Alabama	US	32.53953	−86.6441	0	9893	9901
84001003	US	USA	840	1003	Baldwin	Alabama	US	30.72775	−87.7221	0	37069	37087
84001005	US	USA	840	1005	Barbour	Alabama	US	31.86826	−85.3871	0	3554	3556
84001007	US	USA	840	1007	Bibb	Alabama	US	32.99642	−87.1251	0	4216	4217
84001009	US	USA	840	1009	Blount	Alabama	US	33.98211	−86.5679	0	10094	10102
84001011	US	USA	840	1011	Bullock	Alabama	US	32.10031	−85.7127	0	1517	1517
84001013	US	USA	840	1013	Butler	Alabama	US	31.753	−86.6806	0	3247	3248

Figure 1. The framework of the YuTu system to detect space-time clustering of COVID-19.

Figure 2. The illustration of space-time scan statistics.

Figure 3. The animated bivariate map of space-time cluster using the space-time scan statistics.

Figure 4. The animated bivariate map of space-time cluster using space-time scan statistic (left) and LISA (right) around 11 August 2022.

Figure 5. The animated bivariate map of space-time cluster using the space-time scan statistic (left) and a spiral map reflecting the average relative risk for each conterminous US state (right).

Figure 6. The animated bivariate map of space-time cluster using space-time scan statistic (top) and the TimeChart of different variables (bottom).

Figure 7. The 3D space-time cubes of clusters with displaying the relative risk of the cluster (left) and relative risk of the county (right).

Figure 8. The four waves and their estimated peak dates using the data from WHO coronavirus (COVID-19) dashboard (World Health Organization, 2020).

Figure 9. The animated bivariate maps at peak 1 using a) the prospective space-time scan statistics and b) LISA.

Figure 10. The animated bivariate maps at peak 2 using a) the prospective space-time scan statistics and b) LISA.

Figure 11. The animated bivariate maps on November 26th, 2020, 40 days before the second peak using a) the prospective space-time scan statistics and b) LISA.

Figure 12. The animated bivariate maps a) & b) during the third peak, and c) & d) the maps on July 24th, 2021.

Figure 13. Animated bivariate maps at a) & b) peak 4 and c) & d) the maps on December 16th, 2021, 31 days before peak 4.

Figure 14. a) & b) The animated bivariate maps of selected three counties on the date that all of them reported most cases c) the TimeChart of 7-day average cases, and d) the timechart of relative risk of clusters.

Figure 15. The 3D space-time cubes during the third wave from June 2020 to December 2020 with different threshold of relative risk.

Figure 16. The animated bivariate (left) and spiral map (right) of Wisconsin for 20 January 2022 (the state of Wisconsin is highlighted by a red circle).

Figure 17. The animated bivariate map of Wisconsin at multiple levels on 1 December 2021. It included scales from a) the county levels with all other states, b) the county level with one state, c) the zip code level, and d) the census tract level.

Figure 18. The animated bivariate map of Wisconsin at multiple levels on 5 January 2022. It included scales from a) the county levels with all other states, b) the county level with one state, c) the zip code level, and d) the census tract level.

Figure 19. The animated bivariate map of Wisconsin at multiple levels on 28 February 2021. It included scales from a) the county levels with all other states, b) the county level with one state, c) the zip code level, and d) the census tract level.

World Health Organization. (2020). WHO coronavirus (COVID-19) dashboard. Retrieved October 10, 2022, from https://covid19.who.int/

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Data availability statement

The data and codes that support the findings of this study are available on GitHub under the identifier https://github.com/YuLanGeoHealth/US-Covid-19-YuTu.