Multiple models for outbreak decision support in the face of uncertainty.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Biological effects of inhaled crude oil vapor V. Altered biogenic amine neurotransmitters and neural protein expression.

Workers in the oil and gas industry are at risk for exposure to a number of physical and chemical hazards at the workplace. Chemical hazard risks include inhalation of crude oil or its volatile components. While several studies have investigated the neurotoxic effects of volatile hydrocarbons, in general, there is a paucity of studies assessing the neurotoxicity of crude oil vapor (COV). Consequent to the 2010 Deepwater Horizon (DWH) oil spill, there is growing concern about the short- and long-term health effects of exposure to COV. NIOSH surveys suggested that the DWH oil spill cleanup workers experienced neurological symptoms, including depression and mood disorders, but the health effects apart from oil dispersants were difficult to discern. To investigate the potential neurological risks of COV, male Sprague-Dawley rats were exposed by whole-body inhalation to COV (300 ppm; Macondo surrogate crude oil) following an acute (6 h/d × 1 d) or sub-chronic (6 h/d × 4 d/wk. × 4 wks) exposure regimen. At 1, 28 or 90 d post-exposure, norepinephrine (NE), epinephrine (EPI), dopamine (DA) and serotonin (5-HT) were evaluated as neurotransmitter imbalances are associated with psychosocial-, motor- and cognitive- disorders. Sub-chronic COV exposure caused significant reductions in NE, EPI and DA in the dopaminergic brain regions, striatum (STR) and midbrain (MB), and a large increase in 5-HT in the STR. Further, sub-chronic exposure to COV caused upregulation of synaptic and Parkinson's disease-related proteins in the STR and MB. Whether such effects will lead to neurodegenerative outcomes remain to be investigated.

Modeling hospital energy and economic costs for COVID-19 infection control interventions.

The study objective assessed the energy demand and economic cost of two hospital-based COVID-19 infection control interventions: negative pressure (NP) treatment rooms and xenon pulsed ultraviolet (XP-UV) equipment. After projecting COVID-19 hospitalizations, a Hospital Energy Model and Infection De-escalation Models quantified increases in energy demand and reductions in infections. The NP intervention was applied to 11, 22, and 44 rooms for small, medium, and large hospitals, while the XP-UV equipment was used eight, nine, and ten hours a day. For small, medium, and large hospitals, the annum kWh for NP rooms were 116,700 kWh, 332,530 kWh, 795,675 kWh, which correspond to annum energy costs of $11,845 ($1,077/room), $33,752 ($1,534/room), and $80,761 ($1,836/room). For XP-UV, the annum-kilowatt-hours (and costs) were 438 ($45), 493 ($50), and 548 ($56) for small, medium, and large hospitals. While energy efficiencies may be expected for the large hospital, the hospital contained more energy-intensive use rooms (ICUs) which resulted in higher operational and energy costs. XP-UV had a greater reduction in secondary COVID-19 infections in large and medium hospitals. NP rooms had a greater reduction in secondary SARS-CoV-2 transmission in small hospitals. Early implementation of interventions can result in realized cost savings through reduced hospital-acquired infections.

Biological effects of inhaled hydraulic fracturing sand dust VII. Neuroinflammation and altered synaptic protein expression.

Hydraulic fracturing (fracking) is a process used to recover oil and gas from shale rock formation during unconventional drilling. Pressurized liquids containing water and sand (proppant) are used to fracture the oil- and natural gas-laden rock. The transportation and handling of proppant at well sites generate dust aerosols; thus, there is concern of worker exposure to such fracking sand dusts (FSD) by inhalation. FSD are generally composed of respirable crystalline silica and other minerals native to the geological source of the proppant material. Field investigations by NIOSH suggest that the levels of respirable crystalline silica at well sites can exceed the permissible exposure limits. Thus, from an occupational safety perspective, it is important to evaluate the potential toxicological effects of FSD, including any neurological risks. Here, we report that acute inhalation exposure of rats to one FSD, i.e., FSD 8, elicited neuroinflammation, altered the expression of blood brain barrier-related markers, and caused glial changes in the olfactory bulb, hippocampus and cerebellum. An intriguing observation was the persistent reduction of synaptophysin 1 and synaptotagmin 1 proteins in the cerebellum, indicative of synaptic disruption and/or injury. While our initial hazard identification studies suggest a likely neural risk, more research is necessary to determine if such molecular aberrations will progressively culminate in neuropathology/neurodegeneration leading to behavioral and/or functional deficits.

Molecular beacon anchored onto a graphene oxide substrate.

In this article, we report a graphene oxide-based nanosensor incorporating semiconductor quantum dots linked to DNA-aptamers that functions as a 'turn-off' fluorescent nanosensor for detection of low concentrations of analytes. A specific demonstration of this turn-off aptasensor is presented for the case of the detection of mercury (II) ions. In this system, ensembles of aptamer-based quantum-dot sensors are anchored onto graphene oxide (GO) flakes which provide a platform for analyte detection in the vicinity of GO. Herein, the operation of this ensemble-based nanosensor is demonstrated for mercury ions, which upon addition of mercury, quenching of the emission intensity from the quantum dots is observed due to resonance energy transfer between quantum dots and the gold nanoparticle connected via a mercury target aptamer. A key result is that the usually dominant effect of quenching of the quantum dot due to close proximity to the GO can be reduced to negligible levels by using a linker molecule in conjunctions with the aptamer-based nanosensor. The effect of ionic concentration of the background matrix on the emission intensity was also investigated. The sensor system is found to be highly selective towards mercury and exhibits a linear behavior (r 2 > 0.99) in the nanomolar concentration range. The detection limit of the sensor towards mercury with no GO present was found to be 16.5 nM. With GO attached to molecular beacon via 14 base, 35 base, and 51 base long linker DNA, the detection limit was found to be 38.4 nM, 9.45 nM, and 11.38 nM; respectively.

Neurotoxicity following acute inhalation of aerosols generated during resistance spot weld-bonding of carbon steel.

Welding generates complex metal aerosols, inhalation of which is linked to adverse health effects among welders. An important health concern of welding fume (WF) exposure is neurological dysfunction akin to Parkinson's disease (PD). Some applications in manufacturing industry employ a variant welding technology known as "weld-bonding" that utilizes resistance spot welding, in combination with adhesives, for metal-to-metal welding. The presence of adhesives raises additional concerns about worker exposure to potentially toxic components like Methyl Methacrylate, Bisphenol A and volatile organic compounds (VOCs). Here, we investigated the potential neurotoxicological effects of exposure to welding aerosols generated during weld-bonding. Male Sprague-Dawley rats were exposed (25 mg/m³ targeted concentration; 4 h/day × 13 days) by whole-body inhalation to filtered air or aerosols generated by either weld-bonding with sparking (high metal, low VOCs; HM) or without sparking (low metal; high VOCs; LM). Fumes generated under these conditions exhibited complex aerosols that contained both metal oxide particulates and VOCs. LM aerosols contained a greater fraction of VOCs than HM, which comprised largely metal particulates of ultrafine morphology. Short-term exposure to LM aerosols caused distinct changes in the levels of the neurotransmitters, dopamine (DA) and serotonin (5-HT), in various brain areas examined. LM aerosols also specifically decreased the mRNA expression of the olfactory marker protein (Omp) and tyrosine hydroxylase (Th) in the olfactory bulb. Consistent with the decrease in Th, LM also reduced the expression of dopamine transporter (Slc6a3; Dat), as well as, dopamine D2 receptor (Drd2) in the olfactory bulb. In contrast, HM aerosols induced the expression of Th and dopamine D5 receptor (Drd5) mRNAs, elicited neuroinflammation and blood-brain barrier-related changes in the olfactory bulb, but did not alter the expression of Omp. Our findings divulge the differential effects of LM and HM aerosols in the brain and suggest that exposure to weld-bonding aerosols can potentially elicit neurotoxicity following a short-term exposure. However, further investigations are warranted to determine if the aerosols generated by weld-bonding can contribute to persistent long-term neurological deficits and/or neurodegeneration.

Quantifying the impact of prevalence-dependent adaptive behavior on COVID-19 transmission: A modeling case study in Maryland.

The COVID-19 pandemic highlighted the need for robust epidemic forecasts, projecting health burden over short- and medium-term time horizons. Many COVID-19 forecasting models incorporate information on infection transmission, disease progression, and the effects of interventions, but few combine information on how individuals change their behavior based on altruism, fear, risk perception, or personal economic circumstances. Moreover, early models of COVID-19 produced under- and over-estimates, failing to consider the complexity of human responses to disease threat and prevention measures. In this study, we modeled adaptive behavior during the first year of the COVID-19 pandemic in Maryland, USA. The adapted compartmental model incorporates time-varying transmissibility informed on data of environmental factors (e.g., absolute humidity) and behavioral factors (aggregate mobility and perceived risk). We show that humidity and mobility alone did little to explain transmissibility after the first 100 days. Including adaptive behavior in the form of perceived risk as a function of hospitalizations more effectively explained inferred transmissibility and improved out-of-sample fit, demonstrating the model's potential in real-time forecasting. These results demonstrate the importance of incorporating endogenous behavior in models, particularly during a pandemic, to produce more accurate projections, which could lead to more impactful and efficient decision making and resource allocation.

Incorporating endogenous human behavior in models of COVID-19 transmission: A systematic scoping review.

Background: During the COVID-19 pandemic there was a plethora of dynamical forecasting models created, but their ability to effectively describe future trajectories of disease was mixed. A major challenge in evaluating future case trends was forecasting the behavior of individuals. When behavior was incorporated into models, it was primarily incorporated exogenously (e.g., fitting to cellphone mobility data). Fewer models incorporated behavior endogenously (e.g., dynamically changing a model parameter throughout the simulation). Methods: This review aimed to qualitatively characterize models that included an adaptive (endogenous) behavioral element in the context of COVID-19 transmission. We categorized studies into three approaches: 1) feedback loops, 2) game theory/utility theory, and 3) information/opinion spread. Findings: Of the 92 included studies, 72% employed a feedback loop, 27% used game/utility theory, and 9% used a model if information/opinion spread. Among all studies, 89% used a compartmental model alone or in combination with other model types. Similarly, 15% used a network model, 11% used an agent-based model, 7% used a system dynamics model, and 1% used a Markov chain model. Descriptors of behavior change included mask-wearing, social distancing, vaccination, and others. Sixty-eight percent of studies calibrated their model to observed data and 25% compared simulated forecasts to observed data. Forty-one percent of studies compared versions of their model with and without endogenous behavior. Models with endogenous behavior tended to show a smaller and delayed initial peak with subsequent periodic waves. Interpretation: While many COVID-19 models incorporated behavior exogenously, these approaches may fail to capture future adaptations in human behavior, resulting in under- or overestimates of disease burden. By incorporating behavior endogenously, the next generation of infectious disease models could more effectively predict outcomes so that decision makers can better prepare for and respond to epidemics. Funding: This study was funded in-part by Centers for Disease Control and Prevention (CDC) MInD-Healthcare Program (1U01CK000536), the National Science Foundation (NSF) Modeling Dynamic Disease-Behavior Feedbacks for Improved Epidemic Prediction and Response grant (2229996), and the NSF PIPP Phase I: Evaluating the Effectiveness of Messaging and Modeling during Pandemics grant (2200256).

Susceptibility and infectiousness of SARS-CoV-2 in children versus adults, by variant (wild-type, alpha, delta): A systematic review and meta-analysis of household contact studies.

IMPORTANCE: Understanding the susceptibility and infectiousness of children and adolescents in comparison to adults is important to appreciate their role in the COVID-19 pandemic. OBJECTIVE: To determine SARS-CoV-2 susceptibility and infectiousness of children and adolescents with adults as comparator for three variants (wild-type, alpha, delta) in the household setting. We aimed to identify the effects independent of vaccination or prior infection. DATA SOURCES: We searched EMBASE, PubMed and medRxiv up to January 2022. STUDY SELECTION: Two reviewers independently identified studies providing secondary household attack rates (SAR) for SARS-CoV-2 infection in children (0-9 years), adolescents (10-19 years) or both compared with adults (20 years and older). DATA EXTRACTION AND SYNTHESIS: Two reviewers independently extracted data, assessed risk of bias and performed a random-effects meta-analysis model. MAIN OUTCOMES AND MEASURES: Odds ratio (OR) for SARS-CoV-2 infection comparing children and adolescents with adults stratified by wild-type (ancestral type), alpha, and delta variant, respectively. Susceptibility was defined as the secondary attack rate (SAR) among susceptible household contacts irrespective of the age of the index case. Infectiousness was defined as the SAR irrespective of the age of household contacts when children/adolescents/adults were the index case. RESULTS: Susceptibility analysis: We included 27 studies (308,681 contacts), for delta only one (large) study was available. Compared to adults, children and adolescents were less susceptible to the wild-type and delta, but equally susceptible to alpha. Infectiousness analysis: We included 21 studies (201,199 index cases). Compared to adults, children and adolescents were less infectious when infected with the wild-type and delta. Alpha -related infectiousness remained unclear, 0-9 year old children were at least as infectious as adults. Overall SAR among household contacts varied between the variants. CONCLUSIONS AND RELEVANCE: When considering the potential role of children and adolescents, variant-specific susceptibility, infectiousness, age group and overall transmissibility need to be assessed.

Deconstructing the effects of stochasticity on transmission of hospital-acquired infections in ICUs.

The inherent stochasticity in transmission of hospital-acquired infections (HAIs) has complicated our understanding of transmission pathways. It is particularly difficult to detect the impact of changes in the environment on acquisition rate due to stochasticity. In this study, we investigated the impact of uncertainty (epistemic and aleatory) on nosocomial transmission of HAIs by evaluating the effects of stochasticity on the detectability of seasonality of admission prevalence. For doing so, we developed an agent-based model of an ICU and simulated the acquisition of HAIs considering the uncertainties in the behaviour of the healthcare workers (HCWs) and transmission of pathogens between patients, HCWs, and the environment. Our results show that stochasticity in HAI transmission weakens our ability to detect the effects of a change, such as seasonality patterns, on acquisition rate, particularly when transmission is a low-probability event. In addition, our findings demonstrate that data compilation can address this issue, while the amount of required data depends on the size of the said change and the degree of uncertainty. Our methodology can be used as a framework to assess the impact of interventions and provide decision-makers with insight about the minimum required size and target of interventions in a healthcare facility.

Modeling of malaria vaccine effectiveness on disease burden and drug resistance in 42 African countries.

BACKGROUND: The emergence of antimalarial drug resistance poses a major threat to effective malaria treatment and control. This study aims to inform policymakers and vaccine developers on the potential of an effective malaria vaccine in reducing drug-resistant infections. METHODS: A compartmental model estimating cases, drug-resistant cases, and deaths averted from 2021 to 2030 with a vaccine against Plasmodium falciparum infection administered yearly to 1-year-olds in 42 African countries. Three vaccine efficacy (VE) scenarios and one scenario of rapidly increasing drug resistance are modeled. RESULTS: When VE is constant at 40% for 4 years and then drops to 0%, 235.7 (Uncertainty Interval [UI] 187.8-305.9) cases per 1000 children, 0.6 (UI 0.4-1.0) resistant cases per 1000, and 0.6 (UI 0.5-0.9) deaths per 1000 are averted. When VE begins at 80% and drops 20 percentage points each year, 313.9 (UI 249.8-406.6) cases per 1000, 0.9 (UI 0.6-1.3) resistant cases per 1000, and 0.9 (UI 0.6-1.2) deaths per 1000 are averted. When VE remains 40% for 10 years, 384.7 (UI 311.7-496.5) cases per 1000, 1.0 (0.7-1.6) resistant cases per 1000, and 1.1 (UI 0.8-1.5) deaths per 1000 are averted. Assuming an effective vaccine and an increase in current levels of drug resistance to 80% by 2030, 10.4 (UI 7.3-15.8) resistant cases per 1000 children are averted. CONCLUSIONS: Widespread deployment of a malaria vaccine could substantially reduce health burden in Africa. Maintaining VE longer may be more impactful than a higher initial VE that falls rapidly.

Malaria can become resistant to the drugs used to treat it, posing a major threat to malaria treatment and control. An effective vaccine has the potential to reduce both resistant infections and antimalarial drug use. However, how successfully a vaccine can protect against infection (vaccine efficacy) and the impact of increasing drug resistance remain unclear. Using a mathematical model, we estimate the impact of malaria vaccination in 42 African countries over a 10-year period in multiple scenarios with differing vaccine efficacy and drug resistance. Our model suggests that a moderately effective vaccine with sustained protection over a long period could avert more resistant infections and deaths than a vaccine that is highly protective initially but lowers in efficacy over time. Nevertheless, implementation of an effective malaria vaccine should be accelerated to mitigate the health and economic burden of drug resistance.

Work of Breathing During Proportional Assist Ventilation as a Predictor of Extubation Failure.

BACKGROUND: Despite decades of research on predictors of extubation success, use of ventilatory support after extubation is common and 10-20% of patients require re-intubation. Proportional assist ventilation (PAV) mode automatically calculates estimated total work of breathing (total WOB). Here, we assessed the performance of total WOB to predict extubation failure in invasively ventilated subjects. METHODS: This prospective observational study was conducted in 6 adult ICUs at an academic medical center. We enrolled intubated subjects who successfully completed a spontaneous breathing trial, had a rapid shallow breathing index < 105 breaths/min/L, and were deemed ready for extubation by the primary team. Total WOB values were recorded at the end of a 30-min PAV trial. Extubation failure was defined as any respiratory support and/or re-intubation within 72 h of extubation. We compared total WOB scores between groups and performance of total WOB for predicting extubation failure with receiver operating characteristic curves. RESULTS: Of 61 subjects enrolled, 9.8% (n = 6) required re-intubation, and 50.8% (n = 31) required any respiratory support within 72 h of extubation. Median total WOB at 30 min on PAV was 0.9 J/L (interquartile range 0.7-1.3 J/L). Total WOB was significantly different between subjects who failed or were successfully extubated (median 1.1 J/L vs 0.7 J/L, P = .004). The area under the curve was 0.71 [95% CI 0.58-0.85] for predicting any requirement of respiratory support and 0.85 [95% CI 0.69-1.00] for predicting re-intubation alone within 72 h of extubation. Total WOB cutoff values maximizing sensitivity and specificity equally were 1.0 J/L for any respiratory support (positive predictive value [PPV] 70.0%, negative predictive value [NPV] 67.7%) and 1.3 J/L for re-intubation (PPV 26.3%, NPV 97.6%). CONCLUSIONS: The discriminative performance of a PAV-derived total WOB value to predict extubation failure was good, indicating total WOB may represent an adjunctive tool for assessing extubation readiness. However, these results should be interpreted as preliminary, with specific thresholds of PAV-derived total WOB requiring further investigation in a large multi-center study.

Investigating the effects of absolute humidity and movement on COVID-19 seasonality in the United States.

Mounting evidence suggests the primary mode of SARS-CoV-2 transmission is aerosolized transmission from close contact with infected individuals. While transmission is a direct result of human encounters, falling humidity may enhance aerosolized transmission risks similar to other respiratory viruses (e.g., influenza). Using Google COVID-19 Community Mobility Reports, we assessed the relative effects of absolute humidity and changes in individual movement patterns on daily cases while accounting for regional differences in climatological regimes. Our results indicate that increasing humidity was associated with declining cases in the spring and summer of 2020, while decreasing humidity and increase in residential mobility during winter months likely caused increases in COVID-19 cases. The effects of humidity were generally greater in regions with lower humidity levels. Given the possibility that COVID-19 will be endemic, understanding the behavioral and environmental drivers of COVID-19 seasonality in the United States will be paramount as policymakers, healthcare systems, and researchers forecast and plan accordingly.

Cost-effectiveness of carbapenem-resistant Enterobacteriaceae (CRE) surveillance in Maryland.

OBJECTIVE: We analyzed the efficacy, cost, and cost-effectiveness of predictive decision-support systems based on surveillance interventions to reduce the spread of carbapenem-resistant Enterobacteriaceae (CRE). DESIGN: We developed a computational model that included patient movement between acute-care hospitals (ACHs), long-term care facilities (LTCFs), and communities to simulate the transmission and epidemiology of CRE. A comparative cost-effectiveness analysis was conducted on several surveillance strategies to detect asymptomatic CRE colonization, which included screening in ICUs at select or all hospitals, a statewide registry, or a combination of hospital screening and a statewide registry. SETTING: We investigated 51 ACHs, 222 LTCFs, and skilled nursing facilities, and 464 ZIP codes in the state of Maryland. PATIENTS OR PARTICIPANTS: The model was informed using 2013-2016 patient-mix data from the Maryland Health Services Cost Review Commission. This model included all patients that were admitted to an ACH. RESULTS: On average, the implementation of a statewide CRE registry reduced annual CRE infections by 6.3% (18.8 cases). Policies of screening in select or all ICUs without a statewide registry had no significant impact on the incidence of CRE infections. Predictive algorithms, which identified any high-risk patient, reduced colonization incidence by an average of 1.2% (3.7 cases) without a registry and 7.0% (20.9 cases) with a registry. Implementation of the registry was estimated to save $572,000 statewide in averted infections per year. CONCLUSIONS: Although hospital-level surveillance provided minimal reductions in CRE infections, regional coordination with a statewide registry of CRE patients reduced infections and was cost-effective.

Mitochondrial dysfunction and loss of Parkinson's disease-linked proteins contribute to neurotoxicity of manganese-containing welding fumes.

Welding generates complex metal aerosols, inhalation of which is linked to adverse health effects among welders. An important health concern of welding fume (WF) exposure is neurological dysfunction akin to Parkinson's disease (PD), thought to be mediated by manganese (Mn) in the fumes. Also, there is a proposition that welding might accelerate the onset of PD. Our recent findings link the presence of Mn in the WF with dopaminergic neurotoxicity seen in rats exposed to manual metal arc-hard surfacing (MMA-HS) or gas metal arc-mild steel (GMA-MS) fumes. To elucidate the molecular mechanisms further, we investigated the association of PD-linked (Park) genes and mitochondrial function in causing dopaminergic abnormality. Repeated instillations of the two fumes at doses that mimic ∼1 to 5 yr of worker exposure resulted in selective brain accumulation of Mn. This accumulation caused impairment of mitochondrial function and loss of tyrosine hydroxylase (TH) protein, indicative of dopaminergic injury. A fascinating finding was the altered expression of Parkin (Park2), Uchl1 (Park5), and Dj1 (Park7) proteins in dopaminergic brain areas. A similar regimen of manganese chloride (MnCl(2)) also caused extensive loss of striatal TH, mitochondrial electron transport components, and Park proteins. As mutations in PARK genes have been linked to early-onset PD in humans, and because welding is implicated as a risk factor for parkinsonism, PARK genes might play a critical role in WF-mediated dopaminergic dysfunction. Whether these molecular alterations culminate in neurobehavioral and neuropathological deficits reminiscent of PD remains to be ascertained.

Neurotoxicity following acute inhalation exposure to the oil dispersant COREXIT EC9500A.

Consequent to the 2010 Deepwater Horizon oil spill in the Gulf of Mexico, there is an emergent concern about the short- and long-term adverse health effects of exposure to crude oil, weathered-oil products, and oil dispersants among the workforce employed to contain and clean up the spill. Oil dispersants typically comprise of a mixture of solvents and surfactants that break down floating oil to micrometer-sized droplets within the water column, thus preventing it from reaching the shorelines. As dispersants are generally sprayed from the air, workers are at risk for exposure primarily via inhalation. Such inhaled fractions might potentially permeate or translocate to the brain via olfactory or systemic circulation, producing central nervous system (CNS) abnormalities. To determine whether oil dispersants pose a neurological risk, male Sprague-Dawley rats were exposed by whole-body inhalation exposure to a model oil dispersant, COREXIT EC9500A (CE; approximately 27 mg/m(3) × 5 h/d × 1 d), and various molecular indices of neural dysfunction were evaluated in discrete brain areas, at 1 or 7 d postexposure. Exposure to CE produced partial loss of olfactory marker protein in the olfactory bulb. CE also reduced tyrosine hydroxylase protein content in the striatum. Further, CE altered the levels of various synaptic and neuronal intermediate filament proteins in specific brain areas. Reactive astrogliosis, as evidenced by increased expression of glial fibrillary acidic protein, was observed in the hippocampus and frontal cortex following exposure to CE. Collectively, these findings are suggestive of disruptions in olfactory signal transduction, axonal function, and synaptic vesicle fusion, events that potentially result in an imbalance in neurotransmitter signaling. Whether such acute molecular aberrations might persist and produce chronic neurological deficits remains to be ascertained.

Analysis of the potential impact of durability, timing, and transmission blocking of COVID-19 vaccine on morbidity and mortality.

BACKGROUND: COVID-19 vaccines have been approved and made available. While questions of vaccine allocation strategies have received significant attention, important questions remain regarding the potential impact of the vaccine given uncertainties regarding efficacy against transmission, availability, timing, and durability. METHODS: We adapted a susceptible-exposed-infectious-recovered (SEIR) model to examine the potential impact on hospitalization and mortality assuming increasing rates of vaccine efficacy, coverage, and administration. We also evaluated the uncertainty of the vaccine to prevent infectiousness as well as the impact on outcomes based on the timing of distribution and the potential effects of waning immunity. FINDINGS: Increased vaccine efficacy against disease reduces hospitalizations and deaths from COVID-19; however, the relative benefit of transmission blocking varied depending on the timing of vaccine distribution. Early in an outbreak, a vaccine that reduces transmission will be relatively more effective than one introduced later in the outbreak. In addition, earlier and accelerated implementation of a less effective vaccine is more impactful than later implementation of a more effective vaccine. These findings are magnified when considering the durability of the vaccine. Vaccination in the spring will be less impactful when immunity is less durable. INTERPRETATION: Policy choices regarding non-pharmaceutical interventions, such as social distancing and face mask use, will need to remain in place longer if the vaccine is less effective at reducing transmission or distributed slower. In addition, the stage of the local outbreak greatly impacts the overall effectiveness of the vaccine in a region and should be considered when allocating vaccines. FUNDING: Centers for Disease Control and Prevention (CDC) MInD-Healthcare Program (U01CK000589, 1U01CK000536), James S. McDonnell Foundation 21st Century Science Initiative Collaborative Award in Understanding Dynamic and Multiscale Systems, National Science Foundation (CNS-2027908), National Science Foundation Expeditions (CCF1917819), C3.ai Digital Transformation Institute (AWD1006615), and Google, LLC.

Optimal Design of Paired Built Environment Interventions for Control of MDROs in Acute Care and Community Hospitals.

OBJECTIVES: Our goal was to optimize infection control of paired environmental control interventions within hospitals to reduce methicillin-resistant Staphylococcus aureus (MRSA), carbapenem-resistant Enterobacteriaceae (CRE), and vancomycin-resistant Enterococci (VRE). BACKGROUND: The most widely used infection control interventions are deployment of handwashing (HW) stations, control of relative humidity (RH), and negative pressure (NP) treatment rooms. Direct costs of multidrug-resistant organism (MDRO) infections are typically not included in the design of such interventions. METHODS: We examined the effectiveness of pairing HW with RH and HW with NP. We used the following three data sets: A meta-analysis of progression rates from uncolonized to colonized to infected, 6 years of MDRO treatment costs from 400 hospitals, and 8 years of MDRO incidence rates at nine army hospitals. We used these data as inputs into an Infection De-Escalation Model with varying budgets to obtain optimal intervention designs. We then computed the infection and prevention rates and cost savings resulting from these designs. RESULTS: The average direct cost of an MDRO infection was $3,289, $1,535, and $1,067 for MRSA, CRE, and VRE. The mean annual incidence rates per facility were 0.39%, 0.034%, and 0.011% for MRSA, CRE, and VRE. After applying the cost-minimizing intervention pair to each scenario, the percentage reductions in infections (and annual direct cost savings) in large, community, and small acute care hospitals were 69% ($1.5 million), 73% ($631K), 60% ($118K) for MRSA, 52% ($460.5K), 58% ($203K), 50% ($37K) for CRE, and 0%, 0%, and 50% ($12.8K) for VRE. CONCLUSION: The application of this Infection De-Escalation Model can guide cost-effective decision making in hospital built environment design to improve control of MDRO infections.

Examining association between cohesion and diversity in collaboration networks of pharmaceutical clinical trials with drug approvals.

OBJECTIVE: Clinical trials ensure that pharmaceutical treatments are safe, efficacious, and effective for public consumption, but are extremely complex, taking up to 10 years and $2.6 billion to complete. One main source of complexity arises from the collaboration between actors, and network science methodologies can be leveraged to explore that complexity. We aim to characterize collaborations between actors in the clinical trials context and investigate trends of successful actors. MATERIALS AND METHODS: We constructed a temporal network of clinical trial collaborations between large and small-size pharmaceutical companies, academic institutions, nonprofit organizations, hospital systems, and government agencies from public and proprietary data and introduced metrics to quantify actors' collaboration network structure, organizational behavior, and partnership characteristics. A multivariable regression analysis was conducted to determine the metrics' relationship with success. RESULTS: We found a positive correlation between the number of successful approved trials and interdisciplinary collaborations measured by a collaboration diversity metric (P < .01). Our results also showed a negative effect of the local clustering coefficient (P < .01) on the success of clinical trials. Large pharmaceutical companies have the lowest local clustering coefficient and more diversity in partnerships across biomedical specializations. CONCLUSIONS: Large pharmaceutical companies are more likely to collaborate with a wider range of actors from other specialties, especially smaller industry actors who are newcomers in clinical research, resulting in exclusive access to smaller actors. Future investigations are needed to show how concentrations of influence and resources might result in diminished gains in treatment development.

Machine learning and artificial intelligence: applications in healthcare epidemiology.

Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without being directly programmed. ML builds from traditional statistical methods and has drawn significant interest in healthcare epidemiology due to its potential for improving disease prediction and patient care. This review provides an overview of ML in healthcare epidemiology and practical examples of ML tools used to support healthcare decision making at 4 stages of hospital-based care: triage, diagnosis, treatment, and discharge. Examples include model-building efforts to assist emergency department triage, predicting time before septic shock onset, detecting community-acquired pneumonia, and classifying COVID-19 disposition risk level. Increasing availability and quality of electronic health record (EHR) data as well as computing power provides opportunities for ML to increase patient safety, improve the efficiency of clinical management, and reduce healthcare costs.