Disease-induced Mortality Research Articles

MATHEMATICAL ANALYSIS OF TRANSMISSION DYNAMICS OF HIV/ AIDS IN RISK STRUCTURED POPULATION

A model for assessing the impact of key population at risk in spreading HIV/ AIDS is designed. The model is shown to have a globally asymptotically stable (GAS) disease-free equilibrium whenever the associated reproduction number is less than unity. It has a unique GAS endemic equilibrium whenever reproduction number exceeds unity, if there is no back and forth movement in susceptible classes. It is shown that if infected individuals with high risk abandon their risky behavioral practices, then the disease burden will reduce. Furthermore, if infected individuals with low risk do not adopt risky behaviors then the transmission of the disease will decrease. Partial Rank Correlation Coefficient (PRCC) indices are assessed using all the infection-related classes and basic reproduction number of the model. Uncertainty and sensitivity analysis results show that controlling the effective contact rate, disease-induced death rate in AIDS stage with low and high risk classes will help controlling the transmission of the disease.

Euler-Maruyama and Kloeden-Platen-Schurz computing paradigm for stochastic vector-borne plant epidemic model

The dynamics of viral infection within-plant hosts are of critical importance for characterizing the prevalence and impact of plant diseases. However, few mathematical modeling efforts have been made to characterize viral dynamics within plants. In this study, the dynamics of the vector-borne plant epidemic (VBPE) model are modeled with two nonlinear mathematical models, the deterministic vector-borne plant epidemic (DVBPE) model and the stochastic vector-borne plant epidemic (SVBPE) model to portray and forecast the virus dynamical behavior. The VBPE model segregated the population into three classes: susceptible plants (S), infectious plants (I), and infectious vectors (Y). For classes S, I, and Y, the approximate solution is established by creating a sufficient number of scenarios by varying the ratio of infection, infected vector biting rate, infection rates of plant model, host’s capacity of infection, disease-induced death rate of infectious hosts, insect vectors’ natural mortality rate, the stochastic term for susceptible plants, the stochastic term for infected plants and stochastic term for infected vectors The Adams method is utilized to determine the approximate solution for the DVBPE model, while the Euler-Maruyama and Kloeden-Platen-Schurz methods are used to evaluate the SVBPE model. Finally, a comparison between the DVBPE and SVBPE models is presented.

Evaluating the impact of multiple factors on the control of COVID-19 epidemic: A modelling analysis using India as a case study.

The currently ongoing COVID-19 outbreak remains a global health concern. Understanding the transmission modes of COVID-19 can help develop more effective prevention and control strategies. In this study, we devise a two-strain nonlinear dynamical model with the purpose to shed light on the effect of multiple factors on the outbreak of the epidemic. Our targeted model incorporates the simultaneous transmission of the mutant strain and wild strain, environmental transmission and the implementation of vaccination, in the context of shortage of essential medical resources. By using the nonlinear least-square method, the model is validated based on the daily case data of the second COVID-19 wave in India, which has triggered a heavy load of confirmed cases. We present the formula for the effective reproduction number and give an estimate of it over the time. By conducting Latin Hyperbolic Sampling (LHS), evaluating the partial rank correlation coefficients (PRCCs) and other sensitivity analysis, we have found that increasing the transmission probability in contact with the mutant strain, the proportion of infecteds with mutant strain, the ratio of probability of the vaccinated individuals being infected, or the indirect transmission rate, all could aggravate the outbreak by raising the total number of deaths. We also found that increasing the recovery rate of those infecteds with mutant strain while decreasing their disease-induced death rate, or raising the vaccination rate, both could alleviate the outbreak by reducing the deaths. Our results demonstrate that reducing the prevalence of the mutant strain, improving the clearance of the virus in the environment, and strengthening the ability to treat infected individuals are critical to mitigate and control the spread of COVID-19, especially in the resource-constrained regions.

Dynamical analysis of an anthrax disease model in animals with nonlinear transmission rate

<abstract><p>Anthrax is a bacterial infection caused by <italic>Bacillus anthracis</italic>, primarily affecting animals and occasionally affecting humans. This paper presents two compartmental deterministic models of anthrax transmission having vaccination compartments. In both models, a nonlinear ratio-dependent disease transmission function is employed, and the latter model distinguishes itself by incorporating fractional order derivatives, which adds a novel aspect to the study. The basic reproduction number $ \mathcal{R}_0 $ of the epidemic is determined, below which the disease is eradicated. It is observed that among the various parameters, the contact rate, disease-induced mortality rate, and rate of animal recovery have the potential to influence this basic reproduction number. The endemic equilibrium becomes disease-free via transcritical bifurcations for different threshold parameters of animal recovery rate, disease-induced mortality rate and disease transmission rate, which is validated by utilizing Sotomayor's theorem. Numerical simulations have revealed that a higher vaccination rate contributes to eradicating the disease within the ecosystem. This can be achieved by effectively controlling the disease-induced death rate and promoting animal recovery. The extended fractional model is analyzed numerically using the Adams-Bashforth-Moulton type predictor-corrector scheme. Finally, it is observed that an increase in the fractional order parameter has the potential to reduce the time duration required to eradicate the disease from the ecosystem.</p></abstract>

Optimal control of a two-group malaria transmission model with vaccination.

Malaria is a vector-borne disease that poses major health challenges globally, with the highest burden in children less than 5 years old. Prevention and treatment have been the main interventions measures until the recent groundbreaking highly recommended malaria vaccine by WHO for children below five. A two-group malaria model structured by age with vaccination of individuals aged below 5 years old is formulated and theoretically analyzed. The disease-free equilibrium is globally asymptotically stable when the disease-induced death rate in both human groups is zero. Descarte's rule of signs is used to discuss the possible existence of multiple endemic equilibria. By construction, mathematical models inherit the loss of information that could make prediction of model outcomes imprecise. Thus, a global sensitivity analysis of the basic reproduction number and the vaccination class as response functions using Latin-Hypercube Sampling in combination with partial rank correlation coefficient are graphically depicted. As expected, the most sensitive parameters are related to children under 5 years old. Through the application of optimal control theory, the best combination of interventions measures to mitigate the spread of malaria is investigated. Simulations results show that concurrently applying the three intervention measures, namely: personal protection, treatment, and vaccination ofchildreen under-five is the best strategy for fighting against malaria epidemic in a community, relative to using either single or any dual combination of intervention(s) at a time.

A mathematical model reveals the influence of NPIs and vaccination on SARS-CoV-2 Omicron Variant.

An SVEIR SARS-CoV-2 Omicron variant model is proposed to provide some insights to coordinate non-pharmaceutical interventions (NPIs) and vaccination. Mathematically, we define the basic reproduction number and the effective reproduction number to measure the infection potential of Omicron variant and formulate an optimal disease control strategy. Our inversion results imply that the sick period of Omicron variant in the United States is longer than that of Delta variant in India. The decrease in the infectious period of the infection with infectiousness implies that the risk of hospitalization is reduced; but the increasing period of the infection with non-infectiousness signifies that Omicron variant lengthens the period of nucleic acid test being negative. Optimistically, Omicron's death rate is only a quarter of Delta's. Moreover, we forecast that the cumulative cases will exceed 100 million in the United States on February 28, 2022, and the daily confirmed cases will reach a peak on February 2, 2022. The results of parameters sensitivity analysis imply that NPIs are helpful to reduce the number of confirmed cases. In particular, NPIs are indispensable even if all the people were vaccinated when the efficiency of vaccine is relatively low. By simulating the relationships of the effective reproduction number , the vaccination rate and the efficacy of vaccine, we find that it is impossible to achieve the herd immunity without NPIs while the efficiency of vaccine is lower than . Therefore, the herd immunity area is defined by the evolution of relationships between the vaccination rate and the efficacy of vaccine. Finally, we present that the disease-induced mortality rate demonstrates the periodic oscillation and an almost periodic function is deduced to match the curve. A discussion completes the paper.

Unraveling the dynamics of the Omicron and Delta variants of the 2019 coronavirus in the presence of vaccination, mask usage, and antiviral treatment

The effectiveness of control interventions against COVID-19 is threatened by the emergence of SARS-CoV-2 variants of concern. We present a mathematical model for studying the transmission dynamics of two of these variants (Delta and Omicron) in the United States, in the presence of vaccination, treatment of individuals with clinical symptoms of the disease and the use of face masks. The model is parameterized and cross-validated using observed daily case data for COVID-19 in the United States for the period from November 2021 (when Omicron first emerged) to March 2022. Rigorous qualitative analysis of the model shows that the disease-free equilibrium of the model is locally-asymptotically stable when the control reproduction number of the model (denoted by Rc) is less than one. This equilibrium is shown to be globally-asymptotically stable for a special case of the model, where disease-induced mortality is negligible and both vaccine-derived immunity in fully-vaccinated individuals and natural immunity do not wane, when the associated reproduction number is less than one. The epidemiological implication of the latter result is that the combined vaccination-boosting strategy can lead to the elimination of the pandemic if its implementation can bring (and maintain) the associated reproduction number to a value less than one. An analytical expression for the vaccine-derived herd immunity threshold is derived. Using this expression, together with the baseline values of the parameters of the parameterized model, we showed that the vaccine-derived herd immunity can be achieved in the United States (so that the pandemic will be eliminated) if at least 68% of the population is fully-vaccinated with two of the three vaccines approved for use in the United States (Pfizer or Moderna vaccine). Furthermore, this study showed (as of the time of writing in March 2022) that the control reproduction number of the Omicron variant was approximately 3.5 times that of the Delta variant (the reproduction of the latter is computed to be ≈0.2782), indicating that Delta had practically died out and that Omicron has competitively-excluded Delta (to become the predominant variant in the United States). Based on our analysis and parameterization at the time of writing of this paper (March 2022), our study suggests that SARS-CoV-2 elimination is feasible by June 2022 if the current baseline level of the coverage of fully-vaccinated individuals is increased by about 20%. The prospect of pandemic elimination is significantly improved if vaccination is combined with a face mask strategy that prioritizes moderately effective and high-quality masks. Having a high percentage of the populace wearing the moderately-effective surgical mask is more beneficial to the community than having low percentage of the populace wearing the highly-effective N95 masks. We showed that waning natural and vaccine-derived immunity (if considered individually) offer marginal impact on disease burden, except for the case when they wane at a much faster rate (e.g., within three months), in comparison to the baseline (estimated to be within 9 months to a year). Treatment of symptomatic individuals has marginal effect in reducing daily cases of SARS-CoV-2, in comparison to the baseline, but it has significant impact in reducing daily hospitalizations. Furthermore, while treatment significantly reduces daily hospitalizations (and, consequently, deaths), the prospects of COVID-19 elimination in the United States are significantly enhanced if investments in control resources are focused on mask usage and vaccination rather than on treatment.

Modelling and optimising healthcare interventions in a model with explicit within- and between-host dynamics

Given an endemic infectious disease and a budget, how do we optimally allocate interventions to control the disease? This paper shows that the optimal strategy varies depending on the budget, the type of intervention, the trajectory of pathogen load, and the objective.Using a model with explicit within- and between-host dynamics, we model isolation, supportive treatment, and specific treatment. Isolation and supportive treatment affect the transmission coefficient and the disease-induced mortality rate, respectively, in the between-host dynamics. Specific treatment affects the clearance rate of pathogens in the within-host dynamics.We study the optimisation of the three interventions for various budget levels via evaluating isolation and supportive treatment at the population level and specific treatment at both the population and individual levels. At the population level, we consider the risk of transmission, the burden of illness, and the survival probability, and to that end, we choose the population-level infection rate, the population density of infected individuals, and the total disease-induced mortality rate as objective functions. At the individual level, we consider the length of infection and the pathogen load, and to that end, we choose the maximum infection-age and the maximum pathogen load as objective functions. The objective is to minimise these functions through varying two variables that refer to when the intervention starts and when it stops for an infected individual and also indicate what kind of individuals can get the intervention from the population perspective.We find that the optimal strategy of isolation is to isolate individuals with a higher pathogen load, given a lower budget. The optimal strategy of supportive treatment can be the same as isolation or simply no treatment. The optimal strategy of specific treatment is complicated, and it can be to treat individuals with pathogen loads above a particular level until they recover or until the pathogens can decrease when treatment stops, or it can be another scenario.

Parasite-induced shifts in host movement may explain the transient coexistence of high- and low-pathogenic disease strains.

Many parasites induce decreased host movement, known as lethargy, which can impact disease spread and the evolution of virulence. Mathematical models have investigated virulence evolution when parasites cause host death, but disease-induced decreased host movement has received relatively less attention. Here, we consider a model where, due to the within-host parasite replication rate, an infected host can become lethargic and shift from a moving to a resting state, where it can die. We find that when the lethargy and disease-induced mortality costs to the parasites are not high, then evolutionary bistability can arise, and either moderate or high virulence can evolve depending on the initial virulence and the magnitude of mutation. These results suggest, firstly, the coexistence of strains with different virulence, which may explain the transient coexistence of low- and high-pathogenic strains of avian influenza viruses, and secondly, that medical interventions to treat the symptoms of lethargy or prevent disease-induced host deaths can result in a large jump in virulence and the rapid evolution of high virulence. In complement to existing results that show bistability when hosts are heterogeneous at the population level, we show that evolutionary bistability may arise due to transmission heterogeneity at the individual host level.

Paradoxes and synergies: optimizing management of a deadly virus in an endangered carnivore.

Pathogen management strategies in wildlife are typically accompanied by an array of uncertainties such as the efficacy of vaccines or potential unintended consequences of interventions. In the context of such uncertainties, models of disease transmission can provide critical insight for optimizing pathogen management, especially for species of conservation concern. The endangered Florida panther experienced an outbreak of feline leukemia virus (FeLV) in 2002-04, and continues to be affected by this deadly virus. Ongoing management efforts aim to mitigate the effects of FeLV on panthers, but with limited information about which strategies may be most effective and efficient.We used a simulation-based approach to determine optimal FeLV management strategies in panthers. We simulated use of proactive FeLV management strategies (i.e., proactive vaccination) and several reactive strategies, including reactive vaccination and test-and-removal. Vaccination strategies accounted for imperfect vaccine-induced immunity, specifically partial immunity in which all vaccinates achieve partial pathogen protection. We compared the effectiveness of these different strategies in mitigating the number of FeLV mortalities and the duration of outbreaks.Results showed that inadequate proactive vaccination can paradoxically increase the number of disease-induced mortalities in FeLV outbreaks. These effects were most likely due to imperfect vaccine immunity causing vaccinates to serve as a semi-susceptible population, thereby allowing outbreaks to persist in circumstances otherwise conducive to fadeout. Combinations of proactive vaccination with reactive test-and-removal or vaccination, however, had a synergistic effect in reducing impacts of FeLV outbreaks, highlighting the importance of using mixed strategies in pathogen management.Synthesis and applications: Management-informed disease simulations are an important tool for identifying unexpected negative consequences and synergies among pathogen management strategies. In particular, we find that imperfect vaccine-induced immunity necessitates further consideration to avoid unintentionally worsening epidemics in some conditions. However, mixing proactive and reactive interventions can improve pathogen control while mitigating uncertainties associated with imperfect interventions.

Using a mechanistic framework to model the density of an aquatic parasite Ceratonova shasta.

Ceratonova shasta is a myxozoan parasite endemic to the Pacific Northwest of North America that is linked to low survival rates of juvenile salmonids in some watersheds such as the Klamath River basin. The density of C. shasta actinospores in the water column is typically highest in the spring (March–June), and directly influences infection rates for outmigrating juvenile salmonids. Current management approaches require quantities of C. shasta density to assess disease risk and estimate survival of juvenile salmonids. Therefore, we developed a model to simulate the density of waterborne C. shasta actinospores using a mechanistic framework based on abiotic drivers and informed by empirical data. The model quantified factors that describe the key features of parasite abundance during the period of juvenile salmon outmigration, including the week of initial detection (onset), seasonal pattern of spore density, and peak density of C. shasta. Spore onset was simulated by a bio-physical degree-day model using the timing of adult salmon spawning and accumulation of thermal units for parasite development. Normalized spore density was simulated by a quadratic regression model based on a parabolic thermal response with river water temperature. Peak spore density was simulated based on retained explanatory variables in a generalized linear model that included the prevalence of infection in hatchery-origin Chinook juveniles the previous year and the occurrence of flushing flows (≥171 m3/s). The final model performed well, closely matched the initial detections (onset) of spores, and explained inter-annual variations for most water years. Our C. shasta model has direct applications as a management tool to assess the impact of proposed flow regimes on the parasite, and it can be used for projecting the effects of alternative water management scenarios on disease-induced mortality of juvenile salmonids such as with an altered water temperature regime or with dam removal.

Fuzzy SEIR Epidemic Model of Amoebiasis Infection in Human

Amoebiasis is a disease caused by the protozoan Entamoeba histolytica. It is often asymptomatic but may cause dysentery and invasive extraintestinal infection in humans. To gain an understanding of the disease, a fuzzy SEIR model of amoebiasis infection is presented with the fuzziness introduced in the disease transmission rate, the disease-induced death rate, and the rate of recovery from infection. The basic reproduction number ℛ 0 v is computed and used to make a comparison with the fuzzy basic reproduction number ℛ 0 f at different amounts of cysts as well as to analyse the stability of equilibrium points. Stability results indicate that the disease-free equilibrium and the endemic equilibrium are locally and globally asymptotically stable. Further, numerical simulations are carried out to illustrate the analytical results of the fuzzy model system. The result shows that whenever there is an outbreak, the disease is likely to persist in the first month and thereafter it will start to slow down to disease-free equilibrium.

Identifying conifer mortality induced by Armillaria root disease using airborne lidar and orthoimagery in south central Oregon

Armillaria root disease causes tree stress and mortality worldwide, including in conifer forests of western North America. Armillaria-induced tree mortality is slower than other disturbances, such as fire, but persistent over time, and therefore difficult to detect across large areas. Hence methods to detect Armillaria-affected trees and identify infested stands are of great value to forest managers. We used high-density light detection and ranging (lidar), high-resolution aerial orthoimagery, and associated field observations to map individual tree health across an Armillaria-affected forest dominated by Abies in south central Oregon. The lidar point cloud was segmented into individual tree objects (polygons representing tree crown extents as viewed from nadir), for which lidar metrics and spectral metrics derived from orthoimagery were computed. Lidar-detected tree objects were paired with 150 field-observed trees with corresponding health measurements, and a random forest classifier was developed that separated trees into: 1) asymptomatic; 2) live, Armillaria-infected; 3) recently killed (≥50% of red needles remaining); and 4) dead (<50% of dead needles remaining) classes with 83% accuracy using lidar and spectral metrics. The classifier was applied to map individual tree health status for 290,964 tree objects across the 1257-ha study area. Approximately 20% of trees were classified as unhealthy including live, infected and 4% of trees were classified as recently killed or dead. We created hotspot maps using the Getis-Ord Gi* statistic and analyzed clustering of tree health spatial patterns using Ripley’s L statistic. Hotspot maps effectively identified clusters of live unhealthy trees and tree mortality; unhealthy and dead trees were found to be significantly spatially clustered at distances of 100–2500 m. We created a dead tree density grid, which we coupled with a lidar-derived canopy gap grid to identify sites and stands affected by root disease. Canopy openings were mapped using a canopy height model with a minimum opening area of ≥ 202 m2. Clusters of mapped dead trees (excluding recently killed trees) intersecting canopy gaps were used to detect sites with root disease-induced mortality. Twenty-seven stands containing long-term plots from previous studies were evaluated for the presence of root disease-induced mortality. All 27 stands were correctly identified with conifer mortality induced by root disease. This approach for detecting dead trees intersecting canopy openings induced by root disease can aid in: i) prioritizing subsequent field data collection; ii) planning silvicultural prescriptions; and iii) assessing management expectations for snags and wildlife habitat where root disease-induced mortality is altering stand structure.

Endemic Lineages of Batrachochytrium dendrobatidis Are Associated With Reduced Chytridiomycosis-Induced Mortality in Amphibians: Evidence From a Meta-Analysis of Experimental Infection Studies.

Emerging infectious wildlife diseases have caused devastating declines, particularly when pathogens have been introduced in naïve host populations. The outcome of disease emergence in any host population will be dictated by a series of factors including pathogen virulence, host susceptibility, and prior opportunity for coevolution between hosts and pathogens. Historical coevolution can lead to increased resistance in hosts and/or reduced virulence in endemic pathogens that allows stable persistence of host and pathogen populations. Adaptive coevolution may also occur on relatively short time scales following introduction of a novel pathogen. Here, we performed a meta-analysis of multi-strain Batrachochytrium dendrobatidis (Bd) infection experiments to test whether: (1) amphibian hosts exhibit lower mortality rates when infected with strains belonging to endemic Bd lineages relative to the Global Panzootic Lineage (Bd-GPL), hypothetically owing to long co-evolutionary histories between endemic Bd lineages and their amphibian hosts; and (2) amphibians exhibit lower mortality rates when infected with local Bd-GPL strains compared with non-local Bd-GPL strains, hypothetically owing to recent selection for tolerance or resistance to local Bd-GPL strains. We found that in a majority of cases, amphibians in endemic Bd treatments experienced reduced mortality relative to those in Bd-GPL treatments. Hosts presumed to have historically coexisted with endemic Bd did not show reduced mortality to Bd-GPL compared with hosts that have not historically coexisted with endemic Bd. Finally, we detected no overall difference in amphibian mortality between local and non-local Bd-GPL treatments. Taken together, our results suggest that long-term historical coexistence is associated with less disease-induced mortality potentially due to hypovirulence in endemic Bd lineages, and that more recent coexistence between amphibians and Bd-GPL has not yet resulted in reduced host susceptibility or pathogen virulence. This corroborates previous findings that Bd-GPL introduced via the global amphibian trade has a high capacity for causing disease-induced mortality.

Pasteurellosis transmission dynamics in free range chicken and wild birds: A deterministic and stochastic modeling approach

Pasteurellosis remains a major problem for poultry worldwide. The disease causes high mortality in chicken, affecting the livelihood of rural poultry farmers. In this paper, both deterministic and continuous time Markov chain (CTMC) models are formulated and analyzed to study the dynamics of pasteurellosis in chicken and wild birds. The next generation matrix method is adopted to determine the basic reproduction number for the deterministic model whereas the multitype branching process is used to compute stochastic threshold for the CTMC model. The normalized forward sensitivity index method is implemented to derive sensitivity indices of model parameters. The results show that the per capita recruitment rate of susceptible chicken and its infection rate due to direct contact are the most positive sensitive parameters, whereas the consumption rate of susceptible chicken by humans and the disease induced death rate are the most negative sensitive parameter. This suggests that infectious chicken drive the disease. Thus efforts on vaccinating and treating susceptible and infectious chicken respectively, will reduce the number of pasteurellosis cases in both chicken and wild bird populations. Numerical simulations for the CTMC stochastic model indicate that the solutions of CTMC stochastic model are relatively close to the solutions of the deterministic model. The probability of pasteurellosis extinction is high when it emerges from infectious birds unlike if it emerges from infectious chicken or P. multocida bacteria. Thus, any intervention that focuses on reducing the number of infectious chicken at the beginning of pasteurellosis outbreak is essential for reducing the transmission of pasteurellosis in chicken and wild birds.

Increased growth metabolism promotes viral infection in a susceptible oyster population

The magnitude of an epidemic depends on host susceptibility to the disease, a trait influenced by the genetic constitution of the host and its environment. While the genetic basis of disease susceptibility is often associated with immune capacities, environmental effects generally reflect complex physiological trade-offs. We suggest here that in the case of obligate pathogens whose proliferation depends on the cellular machinery of the host (e.g. viruses), disease susceptibility is directly influenced by host growth. To test our hypothesis, we focussed on a viral disease affecting an ecologically relevant model exploited worldwide, the Pacific oyster Crassostrea gigas. Oysters originating from 3 lines with contrasting resistance to the disease were divided into 3 groups displaying different growth rates and acclimated to 3 food levels and 2 temperatures to generate different growth rates. These oysters were then exposed to the virus, and survival and viral shedding were measured. Finally, we developed a risk model to rank the relative importance of temperature, food, genetic selection and growth on disease-induced mortality. We found that increasing growth through temperature, food level or selection of fast-growing animals all increased mortality, especially in host populations where susceptible phenotypes dominated. Food provisioning was the most influential factor associated with higher viral shedding, followed by temperature, resistance phenotype and growth rate. We suggest that growth-forcing factors may promote the development of obligate intracellular pathogens and epidemic risk, thus opening up avenues for disease management based on the manipulation of host metabolism.

The Impact of Coinfection Dynamics on Host Competition and Coexistence.

AbstractParasites can mediate competition among host species in an ecological community by differentially affecting key parameters that normally give one species a competitive edge. In nature, however, coinfecting parasites that antagonize or facilitate each other-for example, by altering cross-protective host immune responses-can modulate host infection outcomes and parasite transmission relative to a single infection. Under what conditions is coinfection likely to interfere with parasite-mediated apparent competition among hosts? To address this question, we created a model of two coinfected host species. Parasites could interact indirectly by affecting host reproduction or directly by modulating recovery and disease-induced mortality of each host species to a focal infection. We grounded our model with parameters from a classic apparent competition system but allowed for multiple parasite transmission modes and interaction scenarios. Our results suggest that infection-induced mortality has an outsized effect on competition outcomes relative to recovery but that coinfection-mediated modulation of mortality can produce a range of coexistence or competitive exclusion outcomes. Moreover, while infection prevalence is sensitive to variation in parasite transmission mode, host competitive outcomes are not. Our generalizable model highlights the influence of immunological variation and parasite ecology on community ecology.

Graft-Versus-Host Disease Prevention by In Vitro-Generated Myeloid-Derived Suppressor Cells Is Exclusively Mediated by the CD11b+CD11c+ MDSC Subpopulation.

Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of myeloid progenitor cells that dampen overwhelming adaptive immune responses through multiple mechanisms and are recognized as an attractive novel immune intervention therapy for counteracting the destructive effects of graft- versus -host disease (GVHD) developing after allogeneic bone marrow transplantation (BMT). MDSCs can be produced in great numbers for cellular therapy, but they present a mixture of subsets whose functions in GVHD prevention are undefined. Here, we generated MDSCs in vitro from murine BM cells in the presence of GM-CSF and defined the integrin CD11c as a marker to subdivide MDSCs into two functional subgroups: CD11b+CD11c+ and CD11b+CD11c− MDSCs. Isolated CD11b+CD11c+ and CD11b+CD11c− MDSCs both inhibited alloantigen-stimulated T-cell proliferation in vitro, although CD11b+CD11c+ MDSCs were more efficient and expressed higher levels of different immunosuppressive molecules. Likewise, expression of surface markers such as MHC class II, CD80, CD86, or PD-L1 further delineated both subsets. Most importantly, only the adoptive transfer of CD11b+CD11c+ MDSCs into a single MHC class I-disparate allogeneic BMT model prevented GVHD development and strongly decreased disease-induced mortality, while CD11b+CD11c− MDSCs were totally ineffective. Surprisingly, allogeneic T-cell homing and expansion in lymphatic and GVHD target organs were not affected by cotransplanted CD11b+CD11c+ MDSCs indicating a clear contradiction between in vitro and in vivo functions of MDSCs. However, CD11b+CD11c+ MDSCs shifted immune responses towards type 2 immunity reflected by increased Th2-specific cytokine expression of allogeneic T cells. Induction of type 2 immunity was mandatory for GVHD prevention, since CD11b+CD11c+ MDSCs were ineffective if recipients were reconstituted with STAT6-deficient T cells unable to differentiate into Th2 cells. Most importantly, the beneficial graft- versus -tumor (GVT) effect was maintained in the presence of CD11b+CD11c+ MDSCs since syngeneic tumor cells were efficiently eradicated. Strong differences in the transcriptomic landscape of both subpopulations underlined their functional differences. Defining CD11b+CD11c+ MDSCs as the subset of in vitro-generated MDSCs able to inhibit GVHD development might help to increase efficiency of MDSC therapy and to further delineate relevant target molecules and signaling pathways responsible for GVHD prevention.

Adequate Antithrombin III Level Predicts Survival in Severe COVID-19 Pneumonia.

Critically ill patients with COVID-19 are at an increased thrombotic risk, hence thromboprophylaxis with heparin is considered mandatory. Antithrombin III (ATIII) is the most potent endogenous anticoagulant and is required for the clinical efficacy of heparin. Profound hypercoagulable and inflammatory state associated with COVID-19 can result in decreased ATIII levels and ineffective heparin treatment resulting in increased mortality. The present study evaluated ATIII levels in critically ill patients of COVID-19 and correlated them with other coagulation parameters and disease outcomes. A retrospective review of those critically ill COVID-19 patients was performed who were on a therapeutic dose of low molecular weight heparin (LMWH) and had serial measurements of ATIII, anti-factor Xa (antiFXa) assay and other routine coagulation parameters. A total of 27 critically ill COVID-19 patients were identified, out of these, 12 survived and 15 had disease-induced mortality. ATIII levels were found to be significantly lower in non-survivors on the third day of serial measurement along with worsening of other coagulation parameters. AntiFXa levels were found to be higher in non-survivors as compared to survivors. Further studies are required to establish ATIII as a prognostic marker and to determine the utility of monitoring antiFXa levels in COVID-19 patients on LMWH therapy.

Fc-engineered antibody therapeutics with improved anti-SARS-CoV-2 efficacy.

Monoclonal antibodies with neutralizing activity against SARS-CoV-2 have demonstrated clinical benefits in cases of mild-to-moderate SARS-CoV-2 infection, substantially reducing the risk for hospitalization and severe disease1-4. Treatment generally requires the administration of high doses of these monoclonal antibodies and has limited efficacy in preventing disease complications or mortality among hospitalized patients with COVID-195. Here we report the development and evaluation of anti-SARS-CoV-2 monoclonal antibodies with optimized Fc domains that show superior potency for prevention or treatment of COVID-19. Using several animal disease models of COVID-196,7, we demonstrate that selective engagement of activating Fcγ receptors results in improved efficacy in both preventing and treating disease-induced weight loss and mortality, significantly reducing the dose required to confer full protection against SARS-CoV-2 challenge and for treatment of pre-infected animals. Our results highlight the importance of Fcγ receptor pathways in driving antibody-mediated antiviral immunity and exclude the possibility of pathogenic or disease-enhancing effects of Fcγ receptor engagement of anti-SARS-CoV-2 antibodies upon infection. These findings have important implications for the development of Fc-engineered monoclonal antibodies with optimal Fc-effector function and improved clinical efficacy against COVID-19 disease.