Prospective spatial-temporal clusters of COVID-19 in local communities: case study of Kansas City, Missouri, United States

AlQadi, Hadeel; Yaghoub, Majid Bani; Wu, Siqi; Balakumar, Sindhu; Francisco, Alex

doi:10.1017/s0950268822000462

Cited by 5 publications

(2 citation statements)

References 28 publications

(57 reference statements)

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“…Observe that the statistics of susceptible and recovered data are comparable (see S1 File for the basic descriptive statistics). However, the statistics of infected individuals are at a much [46,47].…”

Section: Datamentioning

confidence: 99%

Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model

AlQadi

Bani-Yaghoub

2022

PLoS ONE

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Mathematical models of infectious diseases exhibit robust dynamics, such as stable endemic, disease-free equilibriums or convergence of the solutions to periodic epidemic waves. The present work shows that the accuracy of such dynamics can be significantly improved by including global effects of host movements in disease models. To demonstrate improved accuracy, we extended a standard Susceptible-Infected-Recovered (SIR) model by incorporating the global dynamics of the COVID-19 pandemic. The extended SIR model assumes three possibilities for susceptible individuals traveling outside of their community: • They can return to the community without any exposure to the infection. • They can be exposed and develop symptoms after returning to the community. • They can be tested positively during the trip and remain quarantined until fully recovered. To examine the predictive accuracy of the extended SIR model, we studied the prevalence of the COVID-19 infection in six randomly selected cities and states in the United States: Kansas City, Saint Louis, San Francisco, Missouri, Illinois, and Arizona. The extended SIR model was parameterized using a two-step model-fitting algorithm. The extended SIR model significantly outperformed the standard SIR model and revealed oscillatory behaviors with an increasing trend of infected individuals. In conclusion, the analytics and predictive accuracy of disease models can be significantly improved by incorporating the global dynamics of the infection.

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Section: Datamentioning

confidence: 99%

Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model

AlQadi

Bani-Yaghoub

2022

PLoS ONE

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“…Several other papers have centered on assessing the degree of geographic disparities with regards to COVID-19 risk and have tried to link these disparities to the geographic distribution of cases and/or deaths. Studies of this type have been carried out on the two major urban areas in the state, St. Louis [ 26 , 27 ] and Kansas City [ 28 , 29 ], but no studies dealing with other parts of the state have been identified.…”

Section: Introductionmentioning

confidence: 99%

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

Sattenspiel,

Orbann,

Bogan

et al. 2023

PLoS ONE

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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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Bayesian Inference of NosocomialMethicillin-resistant Staphylococcus aureusTransmission Rates in an Urban Safety-Net Hospital

Corkran,

Bani-Yaghoub,

Sutkin

et al. 2024

Preprint

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Methicillin-resistantStaphylococcus aureus(MRSA) is a strain ofStaphylococcus aureusthat poses significant challenges in treatment and infection control within healthcare settings. Recent research suggests that the incidence of healthcare-associated MRSA (HA-MRSA) is higher among patients treated in safety-net hospitals compared to those in non-safety-net hospitals.This study aimed to identify HA-MRSA transmission patterns across various nursing units of a safety-net hospital to improve to enhance patient outcomes and facilitate the implementation of targeted infection control measures.A retrospective analysis was conducted using surveillance data from 2019 to 2023. A compartmental disease model was applied to estimate MRSA transmission rates and basic reproduction number (R0) for each nursing unit of an urban, multicenter safety-net hospital before and during the COVID-19 pandemic. Posterior probability distributions for transmission, isolation, and hospital discharge rates were computed using the Delayed Rejection Adaptive Metropolis (DRAM) Bayesian algorithm.Analysis of 187,040 patient records revealed that inpatient nursing units exhibited the highest MRSA transmission rates in three out of the five years studied. Notable transmission rates were observed in certain inpatient and progressive care units (0.55 per individual per month; 0.018 per individual per day) and the surgical ICU (0.44 per individual per month; 0.015 per individual per day). In contrast, the Nursery NICU and Medical ICU had the lowest transmission rates. Although MRSA transmission rates significantly declined across all units in 2021, these rates rebounded to pre-pandemic levels in subsequent years. Notably, outbreaks emerged in units such as ICUs and progressive care units that had not experienced prior MRSA outbreaks since 2019.While MRSA transmission significantly declined during the initial phase of the pandemic, the pathogen reestablished itself in later years. These findings highlight the need for sustained resources and adaptive infection control strategies to reduce the incidence of HA-MRSA in safety-net hospitals.

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Prospective spatial-temporal clusters of COVID-19 in local communities: case study of Kansas City, Missouri, United States

Cited by 5 publications

References 28 publications

Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model

Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model

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

Bayesian Inference of NosocomialMethicillin-resistant Staphylococcus aureusTransmission Rates in an Urban Safety-Net Hospital

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