ABSTRACT Since the COVID-19 pandemic, forecasting hubs and non-traditional respiratory disease surveillance streams have become increasingly common. However, many forecasting approaches assume that relationships between surveillance predictors and disease outcomes remain stable over time and that incorporating additional historical data will improve forecast performance. To evaluate these assumptions in a real-world setting, we developed and evaluated forecasts of SARS-CoV-2 and influenza hospitalizations in Utah using syndromic surveillance, test positivity, and wastewater data. Rather than identifying a single, best-performing model, we examined whether relationships between surveillance predictors and hospitalization outcomes remained stable across seasons and whether longer historical training periods consistently improved forecast accuracy. Relationships between surveillance predictors and hospitalizations varied substantially by pathogen and season. Analyses using pooled data across multiple years suggested strong positive correlations between predictors and outcomes, but these aggregated patterns often obscured weak or negative correlations observed during SARS-CoV-2 variant waves and influenza seasons. Forecast performance similarly varied over time. Models that performed well during some seasons, transmission phases, or under certain training strategies frequently performed worse than benchmark models in others. Training on additional historical data generally reduced forecast accuracy, though this varied by disease and transmission phase. Forecasting groups should prioritize continual evaluation of surveillance predictors, adaptive strategies, and diverse ensembles, rather than relying on a single model, data stream, or historical training framework each year. AUTHOR SUMMARY Respiratory disease forecasting hubs and novel data streams have become integral parts of infectious disease surveillance and public health decision-making since the COVID-19 pandemic. Many forecasting groups assume that adding more historical data will improve model performance and that relationships between surveillance predictors, such as emergency department visits or wastewater, and hospitalizations will remain stable over time. We evaluated these assumptions using forecasts of SARS-CoV-2 and influenza hospitalizations in Utah. We found that relationships between surveillance predictors and hospitalizations varied across SARS-CoV-2 variants, influenza seasons, and periods of increasing and decreasing transmission. Forecast performance also varied considerably, with models that performed well in some seasons often performing poorly in others. Public health groups should continually evaluate the utility of surveillance predictors in real-time and prioritize adaptable, diverse modeling approaches.

Background An increase in pandemics of zoonotic origin has led to a growing interest in using statistical prediction to identify hotspots of zoonotic emergence. However, the rare nature of pathogen emergence requires modellers to impose simplifying assumptions, which limit the model's validity. We present a novel approach to hotspot mapping that aims to improve validity by combining model-based insights with expert knowledge. Methods We conducted a systematic literature review to identify predictors for zoonotic emergence events in three priority virus families (Filoviridae, Coronaviridae, and Paramyxoviridae). We searched PubMed, Web of Science, Agricola, medRxiv, bioRxiv, Embase, CAB Global Health, and Google Scholar on Oct 14-28, 2021, with no restrictions on language or the date of publication. Articles suggested by subject matter experts and those identified by a review of reference lists were also included. We used regularised regression to fit a model to the data extracted from the literature and produced maps of ranked risk. In a series of workshops in five countries (Kenya, Peru, Senegal, Thailand, and Viet Nam), experts in zoonotic diseases produced qualitative hotspot maps based on their expertise, which were compared with the model-derived maps. Findings 425 articles were analysed, from which 19 predictors and 1068 outcome events were identified. The in-sample misclassification error was 0365, and 89% of participant-selected zones were ranked as moderate or high risk by the model. Participant-selected zones were too large to be actionable without further refinement. Discordance was probably due to missing predictors for which no valid data exist, and homogeneity imposed by our global model. Interpretation Concordance between the two sets of maps supports the validity of each. Because model-based and participatory strategies have non-overlapping limitations, the results can be harmonised to minimise bias, and model-based results could be used to refine participant-selected zones. This approach shows potential for refining deployment of countermeasures to prevent future pandemics.

Institutions of higher education faced a number of challenges during the COVID-19 pandemic. Chief among them was whether or not to reopen during the second wave of COVID-19 in the fall of 2020, which was controversial because incidence in young adults was on the rise. The migration of students back to campuses worried many that transmission within student populations would spread into surrounding communities. In light of this, many colleges and universities implemented mitigation strategies, with varied degrees of success. Washington State University, located in the city of Pullman in Whitman County, WA, is an example of this type of university-community co-location, where the role of students returning to the area for the fall 2020 semester was contentious. Using COVID-19 incidence in Pullman, WA, reported to the Whitman County Health Department, we retrospectively study the transmission dynamics that occurred between the student and community subpopulations in fall 2020. We develop a two-population ordinary differential equations mechanistic model to infer transmission rates within and across the university student and community subpopulations. We use results from Bayesian parameter estimation to determine if exponential transmission of COVID-19 occurred in Pullman, WA, and the magnitude of cross-transmission from students to community members. We find these results are consistent with the estimation of the time-varying reproductive number that outbreak potential was minimal and resolved quickly, and conclude that the students returning to Washington State University-Pullman did not place the surrounding community at disproportionate risk of COVID-19 during fall 2020 when mitigation efforts were in place.

Financial precarity and food insecurity are strongly associated with poor health outcomes. Recently, the COVID-19 pandemic caused major disruptions to people's livelihoods as well as interrupting supply chains, furthering economic hardship and exacerbating existing inequities. Native Hawaiian and Pacific Islander individuals, particularly those living on the continent, were especially impacted by the pandemic. This analysis used data from the Moana: Alternative Surveillance of COVID-19 in a Unique Population study to assess the economic and health correlates of food insecurity. We used the baseline survey data from 295 adult respondents and applied logistic regression to calculate adjusted Odds Ratios (aORs) and 95% Confidence Intervals (CIs). We used three definitions of food insecurity, including two definitions from the US Department of Agriculture and a third question that queried about food insecurity since the onset of the pandemic. Over half of participants expressed concerns around food security (56.0% worried about food, 52.5% reported food didn't last). Experiencing a reduction in hours due to COVID-19 was positively associated with all measures of food insecurity, while being an essential worker was protective. We also found that people with diagnosed diabetes had higher odds of reporting that food did not last (aOR: 2.12; 95% CI: 1.05-4.41) or experiencing food insecurity since the beginning of the COVID-19 pandemic (aOR: 2.36; 95% CI: 1.19-4.72). Our findings support the growing body of literature indicating a relationship between food insecurity and cardiometabolic diseases. It also suggests that NH/PI on the continent may benefit from additional, culturally tailored interventions.

The built environment provides an excellent setting for interdisciplinary research on the dynamics of microbial communities. The system is simplified compared to many natural settings, and to some extent the entire environment can be manipulated, from architectural design, to materials use, air flow, human traffic, and capacity to disrupt microbial communities through cleaning. Here we provide an overview of the ecology of the microbiome in the built environment. We address niche space and refugia, population and community (metagenomic) dynamics, spatial ecology within a building, including the major microbial transmission mechanisms, as well as evolution. We also address the landscape ecology connecting microbiomes between physically separated buildings. At each stage we pay particular attention to the actual and potential interface between disciplines, such as ecology, epidemiology, materials science, and human social behavior. We end by identifying some opportunities for future interdisciplinary research on the microbiome of the built environment.

Objectives:EpiEstim is a popular statistical framework designed to produce real-time estimates of the time-varying reproductive number, ℛ t . However, the methods in EpiEstim have not been tested in small, non-randomly mixing populations to determine if the resulting ℛ ˆ t estimates are temporally biased. Thus, we evaluate the temporal performance of EpiEstim ℛ ˆ t estimates when population structure is present, and then demonstrate how to recover temporal accuracy using an approximation with EpiEstim. Methods:Following a real-world example of a COVID-19 outbreak in a small university town, we generate simulated case report data from a two-population mechanistic model with an explicit generation interval distribution and expression to compute true ℛ t . To quantify the temporal bias, we compare the time points when true ℛ t and estimated ℛ ˆ t from EpiEstim fall below the critical threshold of 1. Results:When population structure is present but not accounted for ℛ ˆ t estimates from EpiEstim prematurely fall below 1. When incidence data is aggregated over weeks the estimates from EpiEstim fall below the critical threshold at a later time point than estimates from daily data, however, population structure does not further affect timing differences between aggregated and daily data. Last, we show it is possible to recover the correct timing when by using the lagging subpopulation outbreak to estimate ℛ ˆ t for the total population with EpiEstim. Conclusions:ℛ t is a key parameter used for epidemic response. Since population structure can bias ℛ t near the critical threshold of 1, EpiEstim should be prudently applied to incidence data from structured populations.

Treponeme-associated hoof disease (TAHD) is an emerging infectious disease in free-ranging elk (Cervus canadensis) characterized by ulcerative and necrosuppurative pododermatitis with spirochete bacteria that leads to lameness and apparent increased mortality. While TAHD is hypothesized to have a polybacterial etiology, the causative agents remain poorly characterized, particularly across its geographic range. In this study, we developed a histologic categorization system for hoof lesions and employed 16S rRNA gene amplicon sequencing to characterize bacterial communities in samples from 129 free-ranging elk across regions with endemic or sporadic TAHD and where TAHD remains undetected. Differential abundance analysis revealed strong associations between TAHD-positive lesions and the bacterial genera Treponema, unidentified Spirochaetaceae, Mycoplasma, and Fusobacterium, along with their respective families and amplicon sequence variants. Many of these TAHD-associated operational taxonomic units (OTUs) were also more frequently detected at increased abundance in more severe, histologic lesions of pododermatitis. Correlation analysis demonstrated a strong positive association between Treponema and Mycoplasma in TAHD lesions, suggesting a more significant role of Mycoplasma in TAHD's etiology than previously recognized. Additionally, we identified novel TAHD-associated OTUs, including Corynebacterium freneyi-xerosis, that warrant further investigation. Comparative analysis of TAHD-positive lesions from endemic and sporadic areas revealed minimal differences in the microbial community. These findings advance our understanding of the bacterial contributors to TAHD, highlighting putative pathogens for further investigation and as potential targets for diagnostic development.IMPORTANCEWhile detection of Treponema is a hallmark of treponeme-associated hoof disease (TAHD), a comprehensive understanding of other bacterial contributors is necessary to improve diagnostic testing and inform control measures. Our study confirmed strong associations between Spirochaetaceae and TAHD lesions and revealed a previously underappreciated role of Mycoplasma in TAHD's etiology. Treponema and Mycoplasma were significantly enriched in TAHD-positive lesions, absent from TAHD-negative tissues, and strongly and positively correlated with each other, suggesting a potential synergistic relationship. By developing and applying a histologic categorization system, we characterized shifts in bacterial communities as lesion severity progressed. Comparisons of TAHD-positive lesions from endemic and sporadic regions revealed minimal differences in the microbial composition, indicating strong geographic consistency. These findings enhance our understanding of TAHD's etiology and provide a foundation for future research, including the development of improved diagnostic tests and targeted disease management strategies.

Objectives: This study aimed to explore the use of medical dramas to train observers when in-person observations or patient contact in clinical settings is impossible. The study also assessed the media's portrayal of the medical profession and compared time use patterns in medical dramas to previous hospital observational studies. Design: Activity-pattern observational study using Work Observation by Activity Timing software. Setting: The hospital, ICU, and community settings of medical television shows. Participants: The first and last season of the main cast of three medical dramas; Grey's Anatomy, Scrubs, and ER. Main outcome measures: Inter-rater reliability scores were used to assess how well medical dramas can be used as a training tool for observers. Proportions of time spent on daily medical tasks were compared to other in-person hospital studies. Results: Grey's Anatomy and Scrubs had excellent Intraclass Correlation Coefficients (ICC) scores for a general medical setting, with Grey's Anatomy ICC scores for Season 1 0.99 (0.97 to 1.0), and Season 16 0.98 (0.90 to 1.0) and Scrubs ICC scores of 0.91 (0.65 to 0.98) for Season 1 and 0.91 (0.55, 0.99) for Season 8. In contrast, ER had an acceptable ICC score of 0.89 (0.59 to 0.98) for Season 1 and 0.81 (0.35 to 0.96) for Season 15 and might be more suitable for studies exploring activity patterns in the Emergency Department. All had p-values of <0.001. Conclusions: Medical dramas can serve as training tools when clinical observation is limited or impossible, and our methods reflect these shows' ease of use and flexibility. Additionally, medical dramas can be selected for their similarity to in-person studies. Still, one should be mindful that inaccuracies in the representation of clinical activity patterns are present. However, using medical dramas to train research staff in direct observation is a feasible and reliable method.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementKCJ and SSJ were supported by a HIRe Fellowship from the U.S. Centers for Disease Control and Prevention (U01CK000673). ETL was supported by a grant from the National Institutes of Health (R35GM147013).### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This research was determined not to constitute human subjects research by the Washington State University Institutional Review Board.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAll data produced in the present study are available upon reasonable request to the authors.

Importance:This study addresses the pressing clinical question of how variations in physician and nursing staffing levels influence methicillin-resistant Staphylococcus aureus (MRSA) rates, providing essential insights for optimizing staff allocation and improving patient outcomes in critical care settings.Objective:The main objective is to assess whether variations in staffing ratios and workload conceptualization significantly alter the rates of MRSA acquisitions in the ICU setting.Design:This simulation-based study utilizes stochastic compartmental mathematical modeling to explore the impact of staffing ratios and workload conceptualization on MRSA acquisitions in ICUs. Derived from a previously published model, the analysis involves running year-long stochastic simulations for each scenario 1000 times, varying nurse-to-patient ratios and intensivist staffing levels under infinite and finite workload conceptualizations. Our baseline model was a 3:1 nurse ratio with one intensivist.Main Outcome:MRSA acquisitions in ICUs, measured as median acquisitions per 1000 person-years.Results:Under baseline conditions, our model had a median of 8.2 MRSA acquisitions per 1000 person-years. Varying patient-to-nurse ratios and intensivist numbers showed substantial impacts. For infinite models, a 2:1 nurse ratio resulted in a 21% decrease, while a 1:1 nurse ratio led to a 65% reduction. Finite models demonstrated even larger effects, with a 48% decrease when having a 2:1 ratio, and an 83% reduction with a 1:1 nurse ratio. Reducing patient-to-nurse ratios in finite models increased acquisitions exponentially with a 348% increase for a 6:1 ratio. Intensivist variations had modest impacts.Conclusions and Relevance:Our study highlights the crucial role of optimizing staffing levels in ICUs for effective MRSA infection control. While intensivist variations have modest effects, bolstering nursing ratios significantly reduces MRSA acquisitions, underscoring the need for tailored staffing strategies, and recognizing the nuanced impact of workload conceptualization. Our findings offer practical insights for refining staffing protocols, emphasizing the dynamic nature of healthcare-associated infection outcomes.

Background Antibiotics are a strong risk factor for Clostridioides difficile infection (CDI), and CDI incidence is often measured as an important outcome metric for antimicrobial stewardship interventions aiming to reduce antibiotic use. However, risk of CDI from antibiotics varies by agent and dependent on the intensity (ie, spectrum and duration) of antibiotic therapy. Thus, the impact of stewardship interventions on CDI incidence is variable, and understanding this risk requires a more granular measure of intensity of therapy than traditionally used measures like days of therapy (DOT).Methods We performed a retrospective cohort study to measure the independent association between intensity of antibiotic therapy, as measured by the antibiotic spectrum index (ASI), and hospital-associated CDI (HA-CDI) at a large academic medical center between January 2018 and March 2020. We constructed a marginal Poisson regression model to generate adjusted relative risks for a unit increase in ASI per antibiotic day.Results We included 35 457 inpatient encounters in our cohort. Sixty-eight percent of patients received at least 1 antibiotic. We identified 128 HA-CDI cases, which corresponds to an incidence rate of 4.1 cases per 10 000 patient-days. After adjusting for known confounders, each additional unit increase in ASI per antibiotic day was associated with 1.09 times the risk of HA-CDI (relative risk = 1.09; 95% CI: 1.06-1.13).Conclusions The ASI was strongly associated with HA-CDI and could be a useful tool in evaluating the impact of antibiotic stewardship on HA-CDI rates, providing more granular information than the more commonly used DOT. Antibiotic use is a primary risk factor for Clostridioides difficile infection (CDI), although risks are heterogeneous. We used the antibiotic spectrum index to accurately describe risk of hospital-associated CDI, which provides information beyond the more commonly used days of therapy.

When we think of model ensembling or ensemble modeling, there are many possibilities that come to mind in different disciplines. For example, one might think of a set of descriptions of a phenomenon in the world, perhaps a time series or a snapshot of multivariate space, and perhaps that set is comprised of data-independent descriptions, or perhaps it is quite intentionally fit *to* data, or even a suite of data sets with a common theme or intention. The very meaning of 'ensemble' - a collection together - conjures different ideas across and even within disciplines approaching phenomena. In this paper, we present a typology of the scope of these potential perspectives. It is not our goal to present a review of terms and concepts, nor is it to convince all disciplines to adopt a common suite of terms, which we view as futile. Rather, our goal is to disambiguate terms, concepts, and processes associated with 'ensembles' and 'ensembling' in order to facilitate communication, awareness, and possible adoption of tools across disciplines.

Objective: Create a longitudinal, multi-modal and multi-level surveillance cohort that targets early detection of symptomatic and asymptomatic COVID-19 cases among Native Hawaiian and Pacific Islander adults in the Continental US and identify effective modalities for participatory disease surveillance and sustainably integrate them into ongoing COVID-19 and other public health surveillance efforts. Materials and methods: We recruited cohorts from three sites: Federal Way, WA; Springdale, AR; and remotely. Participants received a survey that included demographic characteristics and questions regarding COVID-19. Participants completed symptom checks via text message every month and recorded their temperature daily using a Kinsa smart thermometer. Results: Recruitment and data collection is ongoing. Presently, 441 adults have consented to participate. One-third of participants were classified as essential workers during the pandemic. Discussion: Over the past 18 months, we have improved our strategies to elicit better data from participants and have learned from some of the weaknesses in our initial deployment of this type of surveillance system. Other limitations stem from historic inequities and barriers which limited Native Hawaiian and Pacific Island representation in academic and clinical environments. One manifestation of this was the limited ability to provide study materials and support in multiple languages. We hope that continued partnership with the community will allow further opportunities to help restore trust in academic and medical institutions, thus generating knowledge to advance health equity. Conclusion: This participatory disease surveillance mechanism complements traditional surveillance systems by engaging underserved communities. We may also gain insights generalizable to other pathogens of concern.

Objective: To evaluate the impact of changes in the size and characteristics of the hospitalized patient population during the COVID-19 pandemic on the incidence of hospital-associated Clostridioides difficile infection (HA-CDI). Design: Interrupted time-series analysis. Setting: A 576-bed academic medical center in Portland, Oregon. Methods: We established March 23, 2020 as our pandemic onset and included 24 pre-pandemic and 24 pandemic-era 30-day intervals. We built an autoregressive segmented regression model to evaluate immediate and gradual changes in HA-CDI rate during the pandemic while controlling for changes in known CDI risk factors. Results: We observed 4.5 HA-CDI cases per 10,000 patient-days in the two years prior to the pandemic and 4.7 cases per 10,000 patient-days in the first two years of the pandemic. According to our adjusted segmented regression model, there were neither significant changes in HA-CDI rate at the onset of the pandemic (level-change coefficient = 0.70, P-value = 0.57) nor overtime during the pandemic (slope-change coefficient = 0.003, P-value = 0.97). We observed significant increases in frequency and intensity of antibiotic use, time at risk, comorbidities, and patient age before and after the pandemic onset. Frequency of C. difficile testing did not significantly change during the pandemic (P= 0.72). Conclusions: Despite large increases in several CDI risk factors, we did not observe the expected corresponding changes in HA-CDI rate during the first two years of the COVID-19 pandemic. We hypothesize that infection prevention measures responding to COVID-19 played a role in CDI prevention.

Studies intended to estimate the effect of a treatment, like randomized trials, may not be sampled from the desired target population. To correct for this discrepancy, estimates can be transported to the target population. Methods for transporting between populations are often premised on a positivity assumption, such that all relevant covariate patterns in one population are also present in the other. However, eligibility criteria, particularly in the case of trials, can result in violations of positivity when transporting to external populations. To address nonpositivity, a synthesis of statistical and mathematical models can be considered. This approach integrates multiple data sources (e.g. trials, observational, pharmacokinetic studies) to estimate treatment effects, leveraging mathematical models to handle positivity violations. This approach was previously demonstrated for positivity violations by a single binary covariate. Here, we extend the synthesis approach for positivity violations with a continuous covariate. For estimation, two novel augmented inverse probability weighting estimators are proposed. Both estimators are contrasted with other common approaches for addressing nonpositivity. Empirical performance is compared via Monte Carlo simulation. Finally, the competing approaches are illustrated with an example in the context of two-drug vs. one-drug antiretroviral therapy on CD4 T cell counts among women with HIV.

AbstractImportanceThis study addresses the pressing clinical question of how variations in physician and nursing staffing levels influence methicillin-resistantStaphylococcus aureus(MRSA) rates, providing essential insights for optimizing staff allocation and improving patient outcomes in critical care settings.ObjectiveThe main objective is to assess whether variations in staffing ratios and workload conceptualization significantly alter the rates of MRSA acquisitions in the ICU setting.DesignThis simulation-based study utilizes stochastic compartmental mathematical modeling to explore the impact of staffing ratios and workload conceptualization on MRSA acquisitions in ICUs. Derived from a previously published model, the analysis involves running year-long stochastic simulations for each scenario 1000 times, varying nurse-to-patient ratios and intensivist staffing levels under infinite and finite workload conceptualizations. Our baseline model was a 3:1 nurse ratio with one intensivist.Main OutcomeMRSA acquisitions in ICUs, measured as median acquisitions per 1000 person-years.ResultsUnder baseline conditions, our model had a median of 8.2 MRSA acquisitions per 1000 person-years. Varying patient-to-nurse ratios and intensivist numbers showed substantial impacts. For infinite models, a 2:1 nurse ratio resulted in a 21% decrease, while a 1:1 nurse ratio led to a 65% reduction. Finite models demonstrated even larger effects, with a 48% decrease when having a 2:1 ratio, and an 83% reduction with a 1:1 nurse ratio. Reducing patient-to-nurse ratios in finite models increased acquisitions exponentially with a 348% increase for a 6:1 ratio. Intensivist variations had modest impacts.Conclusions and RelevanceOur study highlights the crucial role of optimizing staffing levels in ICUs for effective MRSA infection control. While intensivist variations have modest effects, bolstering nursing ratios significantly reduces MRSA acquisitions, underscoring the need for tailored staffing strategies, and recognizing the nuanced impact of workload conceptualization. Our findings offer practical insights for refining staffing protocols, emphasizing the dynamic nature of healthcare-associated infection outcomes.Key PointsQuestionHow does the conceptualization of ICU healthcare worker tasks in models—whether infinite or finite— impact the results of changes in staffing ratios affecting methicillin-resistantStaphylococcus aureus(MRSA) acquisition?FindingsIn this compartmental mathematical model approach that included 15 different models, the trends of the impact of staffing ratios were consistent between the Infinite and Finite tasks models. However, both the absolute and relative values were markedly different, with the infinite task models having a much more linear effect on MRSA acquisitions while the number of MRSA cases in the finite model continued to rise exponentially as the number of nurses decreased.MeaningIt is essential when considering model generalizability, to state the assumptions made about how workload and contact patterns within a hospital work, and to ensure these are appropriately tailored for the specific setting being modeled.

Objectives:To ascertain if faculty and staff were the link between the two COVID-19 outbreaks in a rural university county, and if the local university's COVID-19 policies affected contact rates of their employees across all its campuses.Methods:We conducted two anonymous, voluntary online surveys for faculty and staff of a PAC-12 university on their contact patterns both within and outside the university during the COVID-19 pandemic. One was asked when classes were virtual, and another when classes were in-person but masking. Participants were asked about the individuals they encountered, the type and location of the interactions, what COVID-19 precautions were taken - if any, as well as general questions about their location and COVID-19.Results:We received 271 responses from the first survey and 124 responses from the second. The first survey had a median of 3 contacts/respondent, with the second having 7 contacts/respondent (p<0.001). During the first survey, most contacts were family contacts (Spouse, Children), with the second survey period having Strangers and Students having the most contact (p<0.001). Over 50% of the first survey contacts happened at their home, while the second survey had 40% at work and 35% at home. Both respondents and contacts masked 42% and 46% of the time for the two surveys respectively (p<0.01).Conclusion:For future pandemics, it would be wise to take employees into account when trying to plan for the safety of university students, employees, and surrounding communities. The main places to be aware of and potentially push infectious disease precautions would be on campus, especially confined spaces like offices or small classrooms, and the home, as these tend to be the largest areas of non-masked close contact.

Background: The phenomena of emerging infectious diseases accelerating once they reach healthcare facilities have been well documented. Outbreaks of MERS-CoV, SARS-CoV, and COVID-19 have led to in-hospital transmission where the initial patient infects healthcare workers, patients, visitors, etc., with infection control policies unable to curtail the spread early on. We refer to this phenomenon as nosocomial amplification. Nosocomial amplification causes an undue burden on a hospital that’s already strained from the pandemic. We aimed to understand which hospital-level parameters impact the community most and vice versa. Methods: We adapted an SEIR compartmental model to have two interconnected units, a community unit and a hospital special care unit, to determine the number of COVID-19 acquisitions in each of them over a hypothetical year. The model was stochastically simulated using Gillespie’s Direct Method for 1000 iterations. A parameter sensitivity analysis assessed the effects each parameter had on the model. The original values of all parameters were allowed to vary +/- 50%. The number of simulation acquisitions was normalized as a percent change from the original model’s mean acquisition. Results: Our analysis found that parameters impacting the community had a disproportionate impact on COVID-19 acquisitions in the hospital as compared to the special care unit, as did the parameters governing the level of asymptomatic transmission. Transmission between healthcare workers facilitated within-hospital transmission even when strict patient-based cohorting and testing were in place. Extensive community-level transmission was also found to readily overwhelm hospital-level infection control at realistic levels of effectiveness and compliance. Conclusion: These findings illustrate that hospitals and the community are tightly linked systems. Hospitals may reintroduce infection into the community that might have contained or mitigated ongoing outbreaks or introduce the disease into a disease-free population; community transmission puts tremendous pressure on infection control. In the future, we can model policies to curb an existing COVID-19 outbreak or subsequent outbreaks to avoid or minimize nosocomial amplification, thus improving the disproportionate burdens on the healthcare system.

Studies intended to estimate the effect of a treatment, like randomized trials, may not be sampled from the desired target population. To correct for this discrepancy, estimates can be transported to the target population. Methods for transporting between populations are often premised on a positivity assumption, such that all relevant covariate patterns in one population are also present in the other. However, eligibility criteria, particularly in the case of trials, can result in violations of positivity when transporting to external populations. To address nonpositivity, a synthesis of statistical and mathematical models can be considered. This approach integrates multiple data sources (e.g. trials, observational, pharmacokinetic studies) to estimate treatment effects, leveraging mathematical models to handle positivity violations. This approach was previously demonstrated for positivity violations by a single binary covariate. Here, we extend the synthesis approach for positivity violations with a continuous covariate. For estimation, two novel augmented inverse probability weighting estimators are proposed. Both estimators are contrasted with other common approaches for addressing nonpositivity. Empirical performance is compared via Monte Carlo simulation. Finally, the competing approaches are illustrated with an example in the context of two-drug vs. one-drug antiretroviral therapy on CD4 T cell counts among women with HIV.

Predicting a patient's length of stay (LOS) or the units they are likely to visit during the course of the stay can be a vital source of information for healthcare administrators towards effective resource planning. However, predicting these parameters can be challenging due to the lack of sufficient information at admission time, and its potential dependence on inherent practices within the hospital. Prior efforts have focused predominantly on predicting LOS, statically at admission and in isolation. In this paper, we propose an adaptive multi-task learning approach to predict a patient's next unit and the expected length (in days) of the remaining stay. Our approach is capable of capturing any latent relationship that may exist between these two variables. Experimental results on a large real-world in-patient database show that our multi-task model outperforms its single-task counterpart and other classical machine learning models. Our study also demonstrates that: a) it is possible to achieve high prediction scores (e.g., mean absolute error of 2.0 days for remaining LOS, and over 80% accuracy for next unit); and b) such high prediction accuracy can be realized early on---in most cases within the first two days of a patient's stay.

Background Recent epidemiology of Rift Valley fever (RVF) disease in Africa suggests growing frequency and expanding geographic range of small disease clusters in regions that previously had not reported the disease. We investigated factors associated with the phenomenon by characterising recent RVF disease events in East Africa.Methods Data on 100 disease events (2008–2022) from Kenya, Uganda and Tanzania were obtained from public databases and institutions, and modelled against possible geoecological risk factors of occurrence including altitude, soil type, rainfall/precipitation, temperature, normalised difference vegetation index (NDVI), livestock production system, land-use change and long-term climatic variations. Decadal climatic variations between 1980 and 2022 were evaluated for association with the changing disease pattern.Results Of 100 events, 91% were small RVF clusters with a median of one human (IQR, 1–3) and three livestock cases (IQR, 2–7). These clusters exhibited minimal human mortality (IQR, 0–1), and occurred primarily in highlands (67%), with 35% reported in areas that had never reported RVF disease. Multivariate regression analysis of geoecological variables showed a positive correlation between occurrence and increasing temperature and rainfall. A 1°C increase in temperature and a 1-unit increase in NDVI, one months prior were associated with increased RVF incidence rate ratios of 1.20 (95% CI 1.1, 1.2) and 1.93 (95% CI 1.01, 3.71), respectively. Long-term climatic trends showed a significant decadal increase in annual mean temperature (0.12–0.3°C/decade, p<0.05), associated with decreasing rainfall in arid and semi-arid lowlands but increasing rainfall trends in highlands (p<0.05). These hotter and wetter highlands showed increasing frequency of RVF clusters, accounting for 76% and 43% in Uganda and Kenya, respectively.Conclusion These findings demonstrate the changing epidemiology of RVF disease. The widening geographic range of disease is associated with climatic variations, with the likely impact of wider dispersal of virus to new areas of endemicity and future epidemics.