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.

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 the potential health impacts of delayed strain selection on influenza hospitalization and mortality with mRNA vaccines.

Background: Influenza viruses are constantly evolving through antigenic drift, which makes vaccines potentially ill-matched to circulating strains due to the time between strain selection and distribution. mRNA technology could improve vaccine effectiveness (VE) by reducing this time. Significant private and public investments would be required to accommodate accelerated vaccine development and approval. Hence, it is important to understand the potential impact of mRNA technology on influenza hospitalizations and mortality. Methods: We developed an age-stratified dynamic model of influenza transmission to evaluate the potential impact of increased VE (increased protection against either infection or only hospitalization) on hospitalizations and mortality in the United States. We assume that mRNA technology allows for delaying the time to strain choice, which might increase efficacy, but it does not reduce the time needed for distribution and administration, which might reduce availability. To assess this tradeoff, we evaluated two scenarios where strain choice was delayed until late summer resulting in a more effective vaccine available to (1) all age groups by October, or (2) adults 65 years and older starting in August. Results: If not available until October, the vaccine would need a minimum of 95% effectiveness against infection to see a decrease in hospitalizations and deaths in all age groups. When delayed until November, even a 100% effective vaccine had no significant impact. For the elderly, the minimum required VE (against infection) was 50% to reduce hospitalizations and deaths. Moreover, a vaccine with 80% VE against infection available in August for the 65 + age group was better than a 95% effective vaccine available in October for all ages. Conclusions: As the majority of influenza-associated hospitalizations and deaths are in adults 65 years and older, a combination policy targeting higher VE and coverage for this age group in the short term would be the most efficacious.

Risk-factor analysis for extended-spectrum beta-lactamase-producing Enterobacterales colonization or infection: Evaluation of a novel approach to assess local prevalence as a risk factor.

OBJECTIVE: To explore an approach to identify the risk of local prevalence of extended-spectrum ß-lactamase-producing Enterobacterales (ESBL-E) on ESBL-E colonization or infection and to reassess known risk factors. DESIGN: Case-control study. SETTING: Johns Hopkins Health System emergency departments (EDs) in the Baltimore-Washington, DC, region. PATIENTS: Patients aged ≥18 years with a culture growing Enterobacterales between April 2019 and December 2021. Cases had a culture growing an ESBL-E. METHODS: Addresses were linked to Census Block Groups and placed into communities using a clustering algorithm. Prevalence in each community was estimated using the proportion of ESBL-E among Enterobacterales isolates. Logistic regression was used to determine risk factors for ESBL-E colonization or infection. RESULTS: ESBL-E were detected in 1,167 of 11,224 patients (10.4%). Risk factors included a history of ESBL-E in the prior 6 months (aOR, 20.67; 95% CI, 13.71-31.18), exposure to a skilled nursing or long-term care facility (aOR, 1.64; 95% CI, 1.37-1.96), exposure to a third-generation cephalosporin (aOR, 1.79; 95% CI, 1.46-2.19), exposure to a carbapenem (aOR, 2.31; 95% CI, 1.68-3.18), or exposure to a trimethoprim-sulfamethoxazole (aOR, 1.54; 95% CI, 1.06-2.25) within the prior 6 months. Patients were at lower risk if their community had a prevalence <25th percentile in the prior 3 months (aOR, 0.83; 95% CI, 0.71-0.98), 6 months (aOR, 0.83; 95% CI, 0.71-0.98), or 12 months (aOR, 0.81; 95% CI, 0.68-0.95). There was no association between being in a community in the >75th percentile and the outcome. CONCLUSIONS: This method of defining the local prevalence of ESBL-E may partially capture differences in the likelihood of a patient having an ESBL-E.

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.

Multi-hazard hospital evacuation planning during disease outbreaks using agent-based modeling.

As different types of hazards, including natural and man-made, can occur simultaneously, to implement an integrated and holistic risk management, a multi-hazard perspective on disaster risk management, including preparedness and planning, must be taken for a safer and more resilient society. Considering the emerging challenges that the COVID-19 pandemic has been introducing to regular hospital operations, there is a need to adapt emergency plans with the changing conditions, as well. Evacuation of patients with different mobility disabilities is a complicated process that needs planning, training, and efficient decision-making. These protocols need to be revisited for multi-hazard scenarios such as an ongoing disease outbreak during which additional infection control protocols might be in place to prevent transmission. Computational models can provide insights on optimal emergency evacuation strategies, such as the location of isolation units or alternative evacuation prioritization strategies. This study introduces a non-ICU patient classification framework developed based on available patient mobility data. An agent-based model was developed to simulate the evacuation of the emergency department at the Johns Hopkins Hospital during the COVID-19 pandemic due to a fire emergency. The results show a larger nursing team can reduce the median and upper bound of the 95% confidence interval of the evacuation time by 36% and 33%, respectively. A dedicated exit door for COVID-19 patients is relatively less effective in reducing the median time, while it can reduce the upper bound by more than 50%.

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.

Numerical study of temperature effects on the poro-viscoelastic behavior of articular cartilage.

This paper presents a new approach to study the effects of temperature on the poro- elastic and viscoelastic behavior of articular cartilage. Biphasic solid-fluid mixture theory is applied to study the poro-mechanical behavior of articular cartilage in a fully saturated state. The balance of linear momentum, mass, and energy are considered to describe deformation of the solid skeleton, pore fluid pressure, and temperature distribution in the mixture. The mechanical model assumes both linear elastic and viscoelastic isotropic materials, infinitesimal strain theory, and a time-dependent response. The influence of temperature on the mixture behavior is modeled through temperature dependent mass density and volumetric thermal strain. The fluid flow through the porous medium is described by the Darcy's law. The stress-strain relation for time-dependent viscoelastic deformation in the solid skeleton is described using the generalized Maxwell model. A verification example is presented to illustrate accuracy and efficiency of the developed finite element model. The influence of temperature is studied through examining the behavior of articular cartilage for confined and unconfined boundary conditions. Furthermore, articular cartilage under partial loading condition is modeled to investigate the deformation, pore fluid pressure, and temperature dissipation processes. The results suggest significant impacts of temperature on both poro- elastic and viscoelastic behavior of articular cartilage.