@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.}, 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{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.}, 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} }