@article{lacy_khan_nath_das_igoe_lenhart_lloyd_lanzas_odoi_2024, title={Geographic disparities and predictors of COVID-19 vaccination in Missouri: a retrospective ecological study}, volume={12}, ISSN={["2296-2565"]}, DOI={10.3389/fpubh.2024.1329382}, abstractNote={BackgroundLimited information is available on geographic disparities of COVID-19 vaccination in Missouri and yet this information is essential for guiding efforts to improve vaccination coverage. Therefore, the objectives of this study were to (a) investigate geographic disparities in the proportion of the population vaccinated against COVID-19 in Missouri and (b) identify socioeconomic and demographic predictors of the identified disparities.MethodsThe COVID-19 vaccination data for time period January 1 to December 31, 2021 were obtained from the Missouri Department of Health. County-level data on socioeconomic and demographic factors were downloaded from the 2020 American Community Survey. Proportions of county population vaccinated against COVID-19 were computed and displayed on choropleth maps. Global ordinary least square regression model and local geographically weighted regression model were used to identify predictors of proportions of COVID-19 vaccinated population.ResultsCounties located in eastern Missouri tended to have high proportions of COVID-19 vaccinated population while low proportions were observed in the southernmost part of the state. Counties with low proportions of population vaccinated against COVID-19 tended to have high percentages of Hispanic/Latino population (p = 0.046), individuals living below the poverty level (p = 0.049), and uninsured (p = 0.015) populations. The strength of association between proportion of COVID-19 vaccinated population and percentage of Hispanic/Latino population varied by geographic location.ConclusionThe study findings confirm geographic disparities of proportions of COVID-19 vaccinated population in Missouri. Study findings are useful for guiding programs geared at improving vaccination coverage and uptake by targeting resources to areas with low proportions of vaccinated individuals.}, journal={FRONTIERS IN PUBLIC HEALTH}, author={Lacy, Alexanderia and Khan, Md Marufuzzaman and Nath, Nirmalendu Deb and Das, Praachi and Igoe, Morganne and Lenhart, Suzanne and Lloyd, Alun L. and Lanzas, Cristina and Odoi, Agricola}, year={2024}, month={Mar} }
 @article{das_igoe_lacy_farthing_timsina_lanzas_lenhart_odoi_lloyd_2024, title={Modeling county level COVID-19 transmission in the greater St. Louis area: Challenges of uncertainty and identifiability when fitting mechanistic models to time-varying processes}, volume={371}, ISSN={["1879-3134"]}, DOI={10.1016/j.mbs.2024.109181}, abstractNote={We use a compartmental model with a time-varying transmission parameter to describe county level COVID-19 transmission in the greater St. Louis area of Missouri and investigate the challenges in fitting such a model to time-varying processes. We fit this model to synthetic and real confirmed case and hospital discharge data from May to December 2020 and calculate uncertainties in the resulting parameter estimates. We also explore non-identifiability within the estimated parameter set. We determine that that death rate of infectious non-hospitalized individuals, the testing parameter and the initial number of exposed individuals are not identifiable based on an investigation of correlation coefficients between pairs of parameter estimates. We also explore how this non-identifiability ties back into uncertainties in the estimated parameters and find that it inflates uncertainty in the estimates of our time-varying transmission parameter. However, we do find that R0 is not highly affected by non-identifiability of its constituent components and the uncertainties associated with the quantity are smaller than those of the estimated parameters. Parameter values estimated from data will always be associated with some uncertainty and our work highlights the importance of conducting these analyses when fitting such models to real data. Exploring identifiability and uncertainty is crucial in revealing how much we can trust the parameter estimates.}, journal={MATHEMATICAL BIOSCIENCES}, author={Das, Praachi and Igoe, Morganne and Lacy, Alexanderia and Farthing, Trevor and Timsina, Archana and Lanzas, Cristina and Lenhart, Suzanne and Odoi, Agricola and Lloyd, Alun L.}, year={2024}, month={May} }
 @article{lacy_igoe_das_farthing_lloyd_lanzas_odoi_lenhart_2023, title={Modeling impact of vaccination on COVID-19 dynamics in St. Louis}, volume={17}, ISSN={["1751-3766"]}, DOI={10.1080/17513758.2023.2287084}, abstractNote={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.}, number={1}, journal={JOURNAL OF BIOLOGICAL DYNAMICS}, author={Lacy, Alexanderia and Igoe, Morganne and Das, Praachi and Farthing, Trevor and Lloyd, Alun L. and Lanzas, Cristina and Odoi, Agricola and Lenhart, Suzanne}, year={2023}, month={Dec} }
 @article{gavina_reyes_olufsen_lenhart_ottesen_2023, title={Toward an optimal contraception dosing strategy}, volume={19}, ISSN={["1553-7358"]}, DOI={10.1371/journal.pcbi.1010073}, abstractNote={Anovulation refers to a menstrual cycle characterized by the absence of ovulation. Exogenous hormones such as synthetic progesterone and estrogen have been used to attain this state to achieve contraception. However, large doses are associated with adverse effects such as increased risk for thrombosis and myocardial infarction. This study utilizes optimal control theory on a modified menstrual cycle model to determine the minimum total exogenous estrogen/progesterone dose, and timing of administration to induce anovulation. The mathematical model correctly predicts the mean daily levels of pituitary hormones LH and FSH, and ovarian hormones E2, P4, and Inh throughout a normal menstrual cycle and reflects the reduction in these hormone levels caused by exogenous estrogen and/or progesterone. Results show that it is possible to reduce the total dose by 92% in estrogen monotherapy, 43% in progesterone monotherapy, and that it is most effective to deliver the estrogen contraceptive in the mid follicular phase. Finally, we show that by combining estrogen and progesterone the dose can be lowered even more. These results may give clinicians insights into optimal formulations and schedule of therapy that can suppress ovulation.}, number={4}, journal={PLOS COMPUTATIONAL BIOLOGY}, author={Gavina, Brenda Lyn A. and Reyes, V. Aurelio A. and Olufsen, Mette and Lenhart, Suzanne and Ottesen, Johnny}, year={2023}, month={Apr} }
 @article{davies_lenhart_day_lloyd_lanzas_2022, title={Extensions of mean-field approximations for environmentally-transmitted pathogen networks}, volume={20}, ISSN={["1551-0018"]}, DOI={10.3934/mbe.2023075}, abstractNote={<abstract><p>Many pathogens spread via environmental transmission, without requiring host-to-host direct contact. While models for environmental transmission exist, many are simply constructed intuitively with structures analogous to standard models for direct transmission. As model insights are generally sensitive to the underlying model assumptions, it is important that we are able understand the details and consequences of these assumptions. We construct a simple network model for an environmentally-transmitted pathogen and rigorously derive systems of ordinary differential equations (ODEs) based on different assumptions. We explore two key assumptions, namely homogeneity and independence, and demonstrate that relaxing these assumptions can lead to more accurate ODE approximations. We compare these ODE models to a stochastic implementation of the network model over a variety of parameters and network structures, demonstrating that with fewer restrictive assumptions we are able to achieve higher accuracy in our approximations and highlighting more precisely the errors produced by each assumption. We show that less restrictive assumptions lead to more complicated systems of ODEs and the potential for unstable solutions. Due to the rigour of our derivation, we are able to identify the reason behind these errors and propose potential resolutions.</p></abstract>}, number={2}, journal={MATHEMATICAL BIOSCIENCES AND ENGINEERING}, author={Davies, Kale and Lenhart, Suzanne and Day, Judy and Lloyd, Alun L. and Lanzas, Cristina}, year={2022}, pages={1637–1673} }
 @article{das_igoe_lenhart_luong_lanzas_lloyd_odoi_2022, title={Geographic disparities and determinants of COVID-19 incidence risk in the greater St. Louis Area, Missouri (United States)}, volume={17}, ISSN={["1932-6203"]}, DOI={10.1371/journal.pone.0274899}, abstractNote={BackgroundEvidence seems to suggest that the risk of Coronavirus Disease 2019 (COVID-19) might vary across communities due to differences in population characteristics and movement patterns. However, little is known about these differences in the greater St Louis Area of Missouri and yet this information is useful for targeting control efforts. Therefore, the objectives of this study were to investigate (a) geographic disparities of COVID-19 risk and (b) associations between COVID-19 risk and socioeconomic, demographic, movement and chronic disease factors in the Greater St. Louis Area of Missouri, USA.MethodsData on COVID-19 incidence and chronic disease hospitalizations were obtained from the Department of Health and Missouri Hospital Association, respectively. Socioeconomic and demographic data were obtained from the 2018 American Community Survey while population mobility data were obtained from the SafeGraph website. Choropleth maps were used to identify geographic disparities of COVID-19 risk and several sociodemographic and chronic disease factors at the ZIP Code Tabulation Area (ZCTA) spatial scale. Global negative binomial and local geographically weighted negative binomial models were used to investigate associations between ZCTA-level COVID-19 risk and socioeconomic, demographic and chronic disease factors.ResultsThere were geographic disparities found in COVID-19 risk. Risks tended to be higher in ZCTAs with high percentages of the population with a bachelor’s degree (p<0.0001) and obesity hospitalizations (p<0.0001). Conversely, risks tended to be lower in ZCTAs with high percentages of the population working in agriculture (p<0.0001). However, the association between agricultural occupation and COVID-19 risk was modified by per capita between ZCTA visits. Areas that had both high per capita between ZCTA visits and high percentages of the population employed in agriculture had high COVID-19 risks. The strength of association between agricultural occupation and COVID-19 risk varied by geographic location.ConclusionsGeographic disparities of COVID-19 risk exist in the St. Louis area and are associated with sociodemographic factors, population movements, and obesity hospitalization risks. The latter is particularly concerning due to the growing prevalence of obesity and the known immunological impairments among obese individuals. Therefore, future studies need to focus on improving our understanding of the relationships between COVID-19 vaccination efficacy, obesity and waning of immunity among obese individuals so as to better guide vaccination regimens and reduce disparities.}, number={9}, journal={PLOS ONE}, author={Das, Praachi and Igoe, Morganne and Lenhart, Suzanne and Luong, Lan and Lanzas, Cristina and Lloyd, Alun L. and Odoi, Agricola}, year={2022}, month={Sep} }