A wastewater-based epidemic model for SARS-CoV-2 with application to three Canadian cities.

The COVID-19 pandemic has stimulated wastewater-based surveillance, allowing public health to track the epidemic by monitoring the concentration of the genetic fingerprints of SARS-CoV-2 shed in wastewater by infected individuals. Wastewater-based surveillance for COVID-19 is still in its infancy. In particular, the quantitative link between clinical cases observed through traditional surveillance and the signals from viral concentrations in wastewater is still developing and hampers interpretation of the data and actionable public-health decisions. We present a modelling framework that includes both SARS-CoV-2 transmission at the population level and the fate of SARS-CoV-2 RNA particles in the sewage system after faecal shedding by infected persons in the population. Using our mechanistic representation of the combined clinical/wastewater system, we perform exploratory simulations to quantify the effect of surveillance effectiveness, public-health interventions and vaccination on the discordance between clinical and wastewater signals. We also apply our model to surveillance data from three Canadian cities to provide wastewater-informed estimates for the actual prevalence, the effective reproduction number and incidence forecasts. We find that wastewater-based surveillance, paired with this model, can complement clinical surveillance by supporting the estimation of key epidemiological metrics and hence better triangulate the state of an epidemic using this alternative data source.

Effectiveness and cost-effectiveness of RSV infant and maternal immunization programs: A case study of Nunavik, Canada.

BACKGROUND: Despite passive immunization with palivizumab to select high-risk children under two years of age, the health and economic burden of respiratory syncytial virus (RSV) remains substantial. We evaluated the effectiveness and cost-effectiveness of immunization programs with new generations of RSV prophylactics, including long-acting monoclonal antibodies (LAMA) and maternal vaccines, in terms of reducing hospitalizations in Nunavik, a Canadian Arctic region. METHODS: We developed an agent-based model of RSV transmission and parameterized it with the demographics and burden of RSV in Nunavik, Québec. We compared various immunization strategies, taking into account the costs associated with program delivery and calculating the incremental cost-effectiveness ratio (ICER) using quality-adjusted life-years (QALYs) gained as a measure of effectiveness. Scenario analyses included immunization with palivizumab and LAMA for infants under one year of age, and maternal vaccination in mild, moderate, and severe RSV seasons. Data were analysed from November 1, 2019 to May 1, 2021. FINDINGS: We found that a Nunavik pilot program with palivizumab which included healthy full-term infants aged 0-2 months in addition to those considered high-risk for complicated RSV disease is not cost-effective, compared to offering palivizumab only to preterm/chronically ill infants under 1 year of age. Using LAMA as prophylaxis produces ICER values of CAD $39,414/QALY (95% Credible Interval [CrI]: $39,314-$40,017) in a mild season (moderately cost-effective) and CAD $5,255/QALY (95% CrI: $5,222-$5,307) in a moderate season (highly cost-effective). LAMA was a dominant (cost-saving with negative incremental costs and positive incremental effects) strategy in a severe RSV season. Maternal vaccination combined with immunization of preterm/chronically ill infants 3-11 months was also a dominant (cost-saving) strategy in all seasons. INTERPRETATION: The switch from palivizumab in RSV immunization programs to new prophylactics would lead to significant savings, with LAMA being an effective strategy without compromising benefits in terms of reducing hospitalizations.

Modeling the Protective Role of Bacterial Lipopolysaccharides against Membrane-Rupturing Peptides.

Lipopolysaccharide (LPS) is a key surface component of Gram-negative bacteria, populating the outer layer of their outer membrane. A number of experimental studies highlight its protective role against harmful molecules such as antibiotics and antimicrobial peptides (AMPs). In this work, we present a theoretical model for describing the interaction between LPS and cationic antimicrobial peptides, which combines the following two key features. The polysaccharide part is viewed as forming a polymer brush, exerting an osmotic pressure on inclusions such as antimicrobial peptides. The charged groups on LPS (those in lipid A and the two Kdo groups in the inner core) form electrostatic binding sites for cationic AMPs or cations. Using the resulting model, we offer a quantitative picture of how the brush component enhances the protective role of LPS against magainin-like peptides, in the presence of divalent cations such as Mg2+. The LPS brush tends to diminish the interfacial binding of the peptides, at the lipid headgroup region, by about 30%. In the presence of 5 mM of Mg2+, the interfacial binding does not reach a threshold value for wild-type LPS, beyond which the LPS layer is ruptured, even though it does for LPS Re (the simplest form of LPS, lacking the brush part), as long as [AMP] ≤ 20 µM, where [AMP] is the concentration of AMPs. At a low concentration of Mg2+ (≈1 mM), however, a smaller [AMP] value (≳2 µM) is needed to reach the threshold coverage for wild-type LPS. Our results also suggest that the interfacial binding of peptides is insensitive to their possible weak interaction with the surrounding brush chains.

Evaluation of COVID-19 vaccination strategies with a delayed second dose.

Two of the Coronavirus Disease 2019 (COVID-19) vaccines currently approved in the United States require 2 doses, administered 3 to 4 weeks apart. Constraints in vaccine supply and distribution capacity, together with a deadly wave of COVID-19 from November 2020 to January 2021 and the emergence of highly contagious Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants, sparked a policy debate on whether to vaccinate more individuals with the first dose of available vaccines and delay the second dose or to continue with the recommended 2-dose series as tested in clinical trials. We developed an agent-based model of COVID-19 transmission to compare the impact of these 2 vaccination strategies, while varying the temporal waning of vaccine efficacy following the first dose and the level of preexisting immunity in the population. Our results show that for Moderna vaccines, a delay of at least 9 weeks could maximize vaccination program effectiveness and avert at least an additional 17.3 (95% credible interval [CrI]: 7.8-29.7) infections, 0.69 (95% CrI: 0.52-0.97) hospitalizations, and 0.34 (95% CrI: 0.25-0.44) deaths per 10,000 population compared to the recommended 4-week interval between the 2 doses. Pfizer-BioNTech vaccines also averted an additional 0.60 (95% CrI: 0.37-0.89) hospitalizations and 0.32 (95% CrI: 0.23-0.45) deaths per 10,000 population in a 9-week delayed second dose (DSD) strategy compared to the 3-week recommended schedule between doses. However, there was no clear advantage of delaying the second dose with Pfizer-BioNTech vaccines in reducing infections, unless the efficacy of the first dose did not wane over time. Our findings underscore the importance of quantifying the characteristics and durability of vaccine-induced protection after the first dose in order to determine the optimal time interval between the 2 doses.

Multifaceted strategies for the control of COVID-19 outbreaks in long-term care facilities in Ontario, Canada.

The novel coronavirus disease 2019 (COVID-19) has caused severe outbreaks in Canadian long-term care facilities (LTCFs). In Canada, over 80% of COVID-19 deaths during the first pandemic wave occurred in LTCFs. We sought to evaluate the effect of mitigation measures in LTCFs including frequent testing of staff, and vaccination of staff and residents. We developed an agent-based transmission model and parameterized it with disease-specific estimates, temporal sensitivity of nasopharyngeal and saliva testing, results of vaccine efficacy trials, and data from initial COVID-19 outbreaks in LTCFs in Ontario, Canada. Characteristics of staff and residents, including contact patterns, were integrated into the model with age-dependent risk of hospitalization and death. Estimates of infection and outcomes were obtained and 95% credible intervals were generated using a bias-corrected and accelerated bootstrap method. Weekly routine testing of staff with 2-day turnaround time reduced infections among residents by at least 25.9% (95% CrI: 23.3%-28.3%), compared to baseline measures of mask-wearing, symptom screening, and staff cohorting alone. A similar reduction of hospitalizations and deaths was achieved in residents. Vaccination averted 2-4 times more infections in both staff and residents as compared to routine testing, and markedly reduced hospitalizations and deaths among residents by 95.9% (95% CrI: 95.4%-96.3%) and 95.8% (95% CrI: 95.5%-96.1%), respectively, over 200 days from the start of vaccination. Vaccination could have a substantial impact on mitigating disease burden among residents, but may not eliminate the need for other measures before population-level control of COVID-19 is achieved.

Evaluation of COVID-19 vaccination strategies with a delayed second dose.

Two of the COVID-19 vaccines currently approved in the United States require two doses, administered three to four weeks apart. Constraints in vaccine supply and distribution capacity, together with a deadly wave of COVID-19 from November 2020 to January 2021 and the emergence of highly contagious SARS-CoV-2 variants, sparked a policy debate on whether to vaccinate more individuals with the first dose of available vaccines and delay the second dose, or to continue with the recommended two-dose series as tested in clinical trials. We developed an agent-based model of COVID-19 transmission to compare the impact of these two vaccination strategies, while varying the temporal waning of vaccine efficacy following the first dose and the level of pre-existing immunity in the population. Our results show that for Moderna vaccines, a delay of at least 9 weeks could maximize vaccination program effectiveness and avert at least an additional 17.3 (95% CrI: 7.8 - 29.7) infections, 0.71 (95% CrI: 0.52 - 0.97) hospitalizations, and 0.34 (95% CrI: 0.25 - 0.44) deaths per 10,000 population compared to the recommended 4-week interval between the two doses. Pfizer-BioNTech vaccines also averted an additional 0.61 (95% CrI: 0.37 - 0.89) hospitalizations and 0.31 (95% CrI: 0.23 - 0.45) deaths per 10,000 population in a 9-week delayed second dose strategy compared to the 3-week recommended schedule between doses. However, there was no clear advantage of delaying the second dose with Pfizer-BioNTech vaccines in reducing infections, unless the efficacy of the first dose did not wane over time. Our findings underscore the importance of quantifying the characteristics and durability of vaccine-induced protection after the first dose in order to determine the optimal time interval between the two doses.

Modeling Cell Selectivity of Antimicrobial Peptides: How Is the Selectivity Influenced by Intracellular Peptide Uptake and Cell Density.

Antimicrobial peptides (AMPs) are known to attack bacteria selectively over their host cells. Many attempts have been made to use them as a template for designing peptide antibiotics for fighting drug-resistant bacteria. A central concept in this endeavor is "peptide selectivity," which measures the "quality" of peptides. However, the relevance of selectivity measurements has often been obscured by the cell-density dependence of the selectivity. For instance, the selectivity can be overestimated if the cell density is larger for the host cell. Furthermore, recent experimental studies suggest that peptide trapping in target bacteria magnifies the cell-density dependence of peptide activity. Here, we propose a biophysical model for peptide activity and selectivity, which assists with the correct interpretation of selectivity measurements. The resulting model shows how cell density and peptide trapping in cells influence peptide activity and selectivity: while these effects can alter the selectivity by more than an order of magnitude, peptide trapping works in favor of host cells at high host-cell densities. It can be used to correct selectivity overestimates.

Multifaceted strategies for the control of COVID-19 outbreaks in long-term care facilities in Ontario, Canada.

The novel coronavirus disease 2019 (COVID-19) has caused severe outbreaks in Canadian long-term care facilities (LTCFs). In Canada, over 80% of COVID-19 deaths during the first pandemic wave occurred in LTCFs. We sought to evaluate the effect of mitigation measures in LTCFs including frequent testing of staff, and vaccination of staff and residents. We developed an agent-based transmission model and parameterized it with disease-specific estimates, temporal sensitivity of nasopharyngeal and saliva testing, results of vaccine efficacy trials, and data from initial COVID-19 outbreaks in LTCFs in Ontario, Canada. Characteristics of staff and residents, including contact patterns, were integrated into the model with age-dependent risk of hospitalization and death. Estimates of infection and outcomes were obtained and 95% credible intervals were generated using a bias-corrected and accelerated bootstrap method. Weekly routine testing of staff with 2-day turnaround time reduced infections among residents by at least 25.9% (95% CrI: 23.3% - 28.3%), compared to baseline measures of mask-wearing, symptom screening, and staff cohorting alone. A similar reduction of hospitalizations and deaths was achieved in residents. Vaccination averted 2-4 times more infections in both staff and residents as compared to routine testing, and markedly reduced hospitalizations and deaths among residents by 95.9% (95% CrI: 95.4% - 96.3%) and 95.8% (95% CrI: 95.5% - 96.1%), respectively, over 200 days from the start of vaccination. Vaccination could have a substantial impact on mitigating disease burden among residents, but may not eliminate the need for other measures before population-level control of COVID-19 is achieved.

Toward building a physical model for membrane selectivity of antimicrobial peptides: making a quantitative sense of the selectivity.

Antimicrobial peptides (AMPs) are naturally-occurring peptide antibiotics. AMPs are typically cationic and utilize their electrostatic interactions with the bacterial membrane to selectively attack bacteria. The way they work has inspired a vigorous search for optimized peptides for fighting resistant bacteria. Here, we present a physical model of membrane selectivity of AMPs. The challenge for theoretical modeling of membrane-peptide systems arises from the simultaneous presence of several competing effects, including lipid demixing and peptide-peptide interactions on the membrane surface. We first examine critically a number of models of peptide-membrane interactions and map out one, which incorporates adequately these competing effects as well as the geometry of various regions in membranes, occupied by bound peptides, anionic lipids within the interaction range of each peptide, and those outside this range. This effort leads to a systematically-improved model for peptide selectivity. Using the model, we relate peptide's intrinsic (Ccell-independent) selectivity to an apparent, Ccell-dependent one, and clarify the relative roles of peptide parameters and cell densities in determining their selectivity. This relationship suggests that the selectivity is more sensitive to peptide parameters at low cell densities; as a result, the optimal peptide charge, at which the selectivity is maximized, increases with the cell density in such a manner that this notion becomes less meaningful at high cell densities.