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Lacy A, Igoe M, Das P, Farthing T, Lloyd AL, Lanzas C, Odoi A, Lenhart S. Modeling impact of vaccination on COVID-19 dynamics in St. Louis. JOURNAL OF BIOLOGICAL DYNAMICS 2023; 17:2287084. [PMID: 38053251 DOI: 10.1080/17513758.2023.2287084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 11/17/2023] [Indexed: 12/07/2023]
Abstract
The region of St. Louis, Missouri, has displayed a high level of heterogeneity in COVID-19 cases, hospitalization, and vaccination coverage. We investigate how human mobility, vaccination, and time-varying transmission rates influenced SARS-CoV-2 transmission in five counties in the St. Louis area. A COVID-19 model with a system of ordinary differential equations was developed to illustrate the dynamics with a fully vaccinated class. Using the weekly number of vaccinations, cases, and hospitalization data from five counties in the greater St. Louis area in 2021, parameter estimation for the model was completed. The transmission coefficients for each county changed four times in that year to fit the model and the changing behaviour. We predicted the changes in disease spread under scenarios with increased vaccination coverage. SafeGraph local movement data were used to connect the forces of infection across various counties.
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Affiliation(s)
- Alexanderia Lacy
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
| | - Morganne Igoe
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
| | - Praachi Das
- Biomathematics Graduate Program and Department of Mathematics, North Carolina State University, Raleigh, NC, USA
| | - Trevor Farthing
- Department of Population Health and Pathobiology and Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA
| | - Alun L Lloyd
- Biomathematics Graduate Program and Department of Mathematics, North Carolina State University, Raleigh, NC, USA
| | - Cristina Lanzas
- Department of Population Health and Pathobiology and Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA
| | - Agricola Odoi
- Department of Biomedical and Diagnostics Sciences, University of Tennessee, Knoxville, TN, USA
| | - Suzanne Lenhart
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
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Sattenspiel L, Orbann C, Bogan A, Ramirez H, Pirrone S, Dahal S, McElroy JA, Wikle CK. Associations between rurality and regional differences in sociodemographic factors and the 1918-20 influenza and 2020-21 COVID-19 pandemics in Missouri counties: An ecological study. PLoS One 2023; 18:e0290294. [PMID: 37647267 PMCID: PMC10468050 DOI: 10.1371/journal.pone.0290294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 08/05/2023] [Indexed: 09/01/2023] Open
Abstract
This study compares pandemic experiences of Missouri's 115 counties based on rurality and sociodemographic characteristics during the 1918-20 influenza and 2020-21 COVID-19 pandemics. The state's counties and overall population distribution have remained relatively stable over the last century, which enables identification of long-lasting pandemic attributes. Sociodemographic data available at the county level for both time periods were taken from U.S. census data and used to create clusters of similar counties. Counties were also grouped by rural status (RSU), including fully (100%) rural, semirural (1-49% living in urban areas), and urban (>50% of the population living in urban areas). Deaths from 1918 through 1920 were collated from the Missouri Digital Heritage database and COVID-19 cases and deaths were downloaded from the Missouri COVID-19 dashboard. Results from sociodemographic analyses indicate that, during both time periods, average farm value, proportion White, and literacy were the most important determinants of sociodemographic clusters. Furthermore, the Urban/Central and Southeastern regions experienced higher mortality during both pandemics than did the North and South. Analyses comparing county groups by rurality indicated that throughout the 1918-20 influenza pandemic, urban counties had the highest and rural had the lowest mortality rates. Early in the 2020-21 COVID-19 pandemic, urban counties saw the most extensive epidemic spread and highest mortality, but as the epidemic progressed, cumulative mortality became highest in semirural counties. Additional results highlight the greater effects both pandemics had on county groups with lower rates of education and a lower proportion of Whites in the population. This was especially true for the far southeastern counties of Missouri ("the Bootheel") during the COVID-19 pandemic. These results indicate that rural-urban and socioeconomic differences in health outcomes are long-standing problems that continue to be of significant importance, even though the overall quality of health care is substantially better in the 21st century.
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Affiliation(s)
- Lisa Sattenspiel
- Department of Anthropology, University of Missouri, Columbia, MO, United States of America
| | - Carolyn Orbann
- Department of Health Sciences, University of Missouri, Columbia, MO, United States of America
| | - Aaron Bogan
- Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic, Scottsdale, AZ, United States of America
| | - Hailey Ramirez
- Bond Life Science Center, University of Missouri, Columbia, MO, United States of America
| | - Sean Pirrone
- School of Medicine, University of Missouri, Columbia, MO, United States of America
| | - Sushma Dahal
- School of Public Health, Georgia State University, Atlanta, GA, United States of America
| | - Jane A. McElroy
- Department of Family and Community Medicine, University of Missouri, Columbia, MO, United States of America
| | - Christopher K. Wikle
- Department of Statistics, University of Missouri, Columbia, MO, United States of America
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Deb Nath N, Khan MM, Schmidt M, Njau G, Odoi A. Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United States. BMC Public Health 2023; 23:720. [PMID: 37081453 PMCID: PMC10116449 DOI: 10.1186/s12889-023-15571-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 03/30/2023] [Indexed: 04/22/2023] Open
Abstract
BACKGROUND COVID-19 is an important public health concern due to its high morbidity, mortality and socioeconomic impact. Its burden varies by geographic location affecting some communities more than others. Identifying these disparities is important for guiding health planning and service provision. Therefore, this study investigated geographical disparities and temporal changes of the percentage of positive COVID-19 tests and COVID-19 incidence risk in North Dakota. METHODS COVID-19 retrospective data on total number of tests and confirmed cases reported in North Dakota from March 2020 to September 2021 were obtained from the North Dakota COVID-19 Dashboard and Department of Health, respectively. Monthly incidence risks of the disease were calculated and reported as number of cases per 100,000 persons. To adjust for geographic autocorrelation and the small number problem, Spatial Empirical Bayesian (SEB) smoothing was performed using queen spatial weights. Identification of high-risk geographic clusters of percentages of positive tests and COVID-19 incidence risks were accomplished using Tango's flexible spatial scan statistic. ArcGIS was used to display and visiualize the geographic distribution of percentages of positive tests, COVID-19 incidence risks, and high-risk clusters. RESULTS County-level percentages of positive tests and SEB incidence risks varied by geographic location ranging from 0.11% to 13.67% and 122 to 16,443 cases per 100,000 persons, respectively. Clusters of high percentages of positive tests were consistently detected in the western part of the state. High incidence risks were identified in the central and south-western parts of the state, where significant high-risk spatial clusters were reported. Additionally, two peaks (August 2020-December 2020 and August 2021-September 2021) and two non-peak periods of COVID-19 incidence risk (March 2020-July 2020 and January 2021-July 2021) were observed. CONCLUSION Geographic disparities in COVID incidence risks exist in North Dakota with high-risk clusters being identified in the rural central and southwest parts of the state. These findings are useful for guiding intervention strategies by identifying high risk communities so that resources for disease control can be better allocated to communities in need based on empirical evidence. Future studies will investigate predictors of the identified disparities so as to guide planning, disease control and health policy.
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Affiliation(s)
- Nirmalendu Deb Nath
- Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN, USA
| | - Md Marufuzzaman Khan
- Department of Public Health, College of Education, Health, and Human Sciences, University of Tennessee, Knoxville, TN, USA
| | - Matthew Schmidt
- North Dakota Department of Health and Human Services, Special Projects and Health Analytics, Bismarck, ND, USA
| | - Grace Njau
- North Dakota Department of Health and Human Services, Special Projects and Health Analytics, Bismarck, ND, USA
| | - Agricola Odoi
- Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN, USA.
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Khan MM, Deb Nath N, Schmidt M, Njau G, Odoi A. Geographic disparities and temporal changes of COVID-19 hospitalization risks in North Dakota. Front Public Health 2023; 11:1062177. [PMID: 37006524 PMCID: PMC10061029 DOI: 10.3389/fpubh.2023.1062177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 02/24/2023] [Indexed: 03/18/2023] Open
Abstract
BackgroundAlthough the burden of the coronavirus disease 2019 (COVID-19) has been different across communities in the US, little is known about the disparities in COVID-19 burden in North Dakota (ND) and yet this information is important for guiding planning and provision of health services. Therefore, the objective of this study was to identify geographic disparities of COVID-19 hospitalization risks in ND.MethodsData on COVID-19 hospitalizations from March 2020 to September 2021 were obtained from the ND Department of Health. Monthly hospitalization risks were computed and temporal changes in hospitalization risks were assessed graphically. County-level age-adjusted and spatial empirical Bayes (SEB) smoothed hospitalization risks were computed. Geographic distributions of both unsmoothed and smoothed hospitalization risks were visualized using choropleth maps. Clusters of counties with high hospitalization risks were identified using Kulldorff's circular and Tango's flexible spatial scan statistics and displayed on maps.ResultsThere was a total of 4,938 COVID-19 hospitalizations during the study period. Overall, hospitalization risks were relatively stable from January to July and spiked in the fall. The highest COVID-19 hospitalization risk was observed in November 2020 (153 hospitalizations per 100,000 persons) while the lowest was in March 2020 (4 hospitalizations per 100,000 persons). Counties in the western and central parts of the state tended to have consistently high age-adjusted hospitalization risks, while low age-adjusted hospitalization risks were observed in the east. Significant high hospitalization risk clusters were identified in the north-west and south-central parts of the state.ConclusionsThe findings confirm that geographic disparities in COVID-19 hospitalization risks exist in ND. Specific attention is required to address counties with high hospitalization risks, especially those located in the north-west and south-central parts of ND. Future studies will investigate determinants of the identified disparities in hospitalization risks.
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Affiliation(s)
- Md Marufuzzaman Khan
- Department of Public Health, College of Education, Health, and Human Sciences, University of Tennessee, Knoxville, TN, United States
| | - Nirmalendu Deb Nath
- Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN, United States
| | - Matthew Schmidt
- North Dakota Department of Health and Human Services, Special Projects and Health Analytics, Bismarck, ND, United States
| | - Grace Njau
- North Dakota Department of Health and Human Services, Special Projects and Health Analytics, Bismarck, ND, United States
| | - Agricola Odoi
- Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN, United States
- *Correspondence: Agricola Odoi
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Spence L, Anderson DE, Aslan IH, Demir M, Okafor CC, Souza M, Lenhart S. The effect of changing COVID-19 restrictions on the transmission rate in a veterinary clinic. Infect Dis Model 2023; 8:294-308. [PMID: 36819739 PMCID: PMC9916190 DOI: 10.1016/j.idm.2023.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/18/2022] [Accepted: 01/27/2023] [Indexed: 02/12/2023] Open
Abstract
With the declaration of the COVID-19 pandemic by the World Health Organization on March 11, 2020, the University of Tennessee College of Veterinary Medicine (UTCVM), like other institutions, restructured their services to reduce the potential spread of the COVID-19 virus while simultaneously providing critical and essential veterinary services. A mathematical model was developed to predict the change in the level of possible COVID-19 infections due to the increased number of potential contacts within the UTCVM hospital. A system of ordinary differential equations with different compartments for UTCVM individuals and the Knox county population was formulated to show the dynamics of transmission and the number of confirmed cases. Key transmission rates in the model were estimated using COVID-19 case data from the surrounding county and UTCVM personnel. Simulations from this model show the increasing number of COVID-19 cases among UTCVM personnel as the number of daily clients and the number of veterinary staff in the clinic are increased. We also investigate how changes within the Knox county community impact the UTCVM hospital. These scenarios show the importance of understanding the effects of re-opening scenarios in veterinary teaching hospitals.
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Affiliation(s)
- Lee Spence
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
- Corresponding author. Lee Spence.
| | - David E. Anderson
- Department of Large Animal Clinical Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN, USA
| | | | - Mahir Demir
- Department of Mathematics, Giresun University, Giresun, Turkey
| | - Chika C. Okafor
- Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN, USA
| | - Marcy Souza
- Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN, USA
| | - Suzanne Lenhart
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
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