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1.
EBioMedicine ; 72: 103610, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1514150

ABSTRACT

BACKGROUND: Recent studies have provided evidence of T cell reactivity to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in significant numbers of non-infected individuals, which has been attributed to cross-reactive CD4 memory T cells from previous exposure to seasonal coronaviruses. Less evidence of cross-reactive memory CD8 T cells has been documented to date. METHODS: We used the NetCTLPan neural network of the Epitope Database and Analysis Resource to select a series of 27 HLA-A*02:01 epitopes derived from the proteome of SARS-CoV-2. Their binding capacity was assessed by a HLA-A*02:01 stabilization assay and by quantifying their binding to HLA-A*02:01 monomers for the generation of tetramers. Their ability to stimulate and induce expansion of SARS-CoV-2 reactive CD8 T cells was measured by flow cytometry. The TCR repertoire of COVID convalescent and healthy unexposed donors was analysed using the MIRA database. FINDINGS: The HLA-A*02:01 epitopes tested were able to stabilise HLA molecules and induce activation of CD8 T cells of healthy unexposed donors. Our results, based on specific tetramer binding, provide evidence supporting the presence of frequent cross-reactive CD8 T cells to SARS-CoV-2 antigens in non-exposed individuals. Interestingly, the reactive cells were distributed into naïve, memory and effector subsets. INTERPRETATION: Our data are consistent with a significant proportion of the reactive CD8 T clones belonging to the public shared repertoire, readily available in absence of previous contact with closely related coronaviruses. Furthermore, we demonstrate the immunogenic capacity of long peptides carrying T cell epitopes, which can serve to isolate virus-specific T cell receptors among the ample repertoire of healthy unexposed subjects and could have application in COVID-19 immunotherapy. Limitations of our study are that it concentrated on one MHC I allele (HLA-A*02:01) and the low numbers of samples and epitopes tested. FUNDING: See the Acknowledgements section.


Subject(s)
CD8-Positive T-Lymphocytes/immunology , COVID-19/immunology , Epitopes, T-Lymphocyte/immunology , SARS-CoV-2/immunology , Computer Simulation , Cross Reactions , Humans , Immunotherapy , Receptors, Antigen, T-Cell
2.
Int J Environ Res Public Health ; 18(21)2021 11 05.
Article in English | MEDLINE | ID: covidwho-1512304

ABSTRACT

Natural disasters have obvious cross-regional and compound characteristics. Cross-regional emergency cooperation for natural disasters deepens the diversification of coordination relations and the complexity of interaction modes among emergency response organizations, including horizontal and vertical organizational interactions. In order to clarify the cooperation mechanism of emergency organizations during cross-regional emergency cooperation for natural disasters and to explore the key factors that affect the cooperative relationships of emergency organizations, in this study, a game model is constructed based on evolutionary game theory, which is composed of local, neighboring, and central governments. Then, the stability of the emergency game strategy is analyzed. On this basis, a numerical simulation is used to simulate the dynamic evolution trajectory of the game system. The results show that there is an embedded mutual promotion mechanism that evolves towards a positive emergency strategy combination among the game subjects. The selection strategies of the game subjects show the characteristics of consistency and the following: enhanced cooperation efficiency between local and neighboring governments, emergency capital stock, and shared resources, therefore, guiding social emergency forces to actively participate in emergency operations. Strengthening the emergency dispatching strength of the central government and the effectiveness of central-local emergency dispatching, can support the performance of cross-regional emergency cooperation for natural disasters. Furthermore, the efficiency of cooperation between local and neighboring governments will be enhanced.


Subject(s)
Game Theory , Natural Disasters , Biological Evolution , Computer Simulation , Cooperative Behavior , Emergency Service, Hospital , Humans
3.
PLoS One ; 16(10): e0259108, 2021.
Article in English | MEDLINE | ID: covidwho-1496529

ABSTRACT

Governments around the globe use non-pharmaceutical interventions (NPIs) to curb the spread of coronavirus disease 2019 (COVID-19) cases. Making decisions under uncertainty, they all face the same temporal paradox: estimating the impact of NPIs before they have been implemented. Due to the limited variance of empirical cases, researchers could so far not disentangle effects of individual NPIs or their impact on different demographic groups. In this paper, we utilize large-scale agent-based simulations in combination with Susceptible-Exposed-Infectious-Recovered (SEIR) models to investigate the spread of COVID-19 for some of the most affected federal states in Germany. In contrast to other studies, we sample agents from a representative survey. Including more realistic demographic attributes that influence agents' behavior yields accurate predictions of COVID-19 transmissions and allows us to investigate counterfactual what-if scenarios. Results show that quarantining infected people and exploiting industry-specific home office capacities are the most effective NPIs. Disentangling education-related NPIs reveals that each considered institution (kindergarten, school, university) has rather small effects on its own, yet, that combined openings would result in large increases in COVID-19 cases. Representative survey-characteristics of agents also allow us to estimate NPIs' effects on different age groups. For instance, re-opening schools would cause comparatively few infections among the risk-group of people older than 60 years.


Subject(s)
COVID-19/transmission , Early Medical Intervention/methods , Quarantine/methods , Computer Simulation , Early Medical Intervention/trends , Germany , Hand Disinfection , Humans , Masks , Models, Theoretical , Pandemics/prevention & control , Physical Distancing , SARS-CoV-2/metabolism , SARS-CoV-2/pathogenicity , Schools
4.
PLoS One ; 16(10): e0259037, 2021.
Article in English | MEDLINE | ID: covidwho-1496524

ABSTRACT

Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19. This paper presents an approach that combines a well-established approach from transportation modelling that uses person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. The model includes the consequences of different room sizes, air exchange rates, disease import, changed activity participation rates over time (coming from mobility data), masks, indoors vs. outdoors leisure activities, and of contact tracing. It is validated against the infection dynamics in Berlin (Germany). The model can be used to understand the contributions of different activity types to the infection dynamics over time. It predicts the effects of contact reductions, school closures/vacations, masks, or the effect of moving leisure activities from outdoors to indoors in fall, and is thus able to quantitatively predict the consequences of interventions. It is shown that these effects are best given as additive changes of the reproduction number R. The model also explains why contact reductions have decreasing marginal returns, i.e. the first 50% of contact reductions have considerably more effect than the second 50%. Our work shows that is is possible to build detailed epidemiological simulations from microscopic mobility models relatively quickly. They can be used to investigate mechanical aspects of the dynamics, such as the transmission from political decisions via human behavior to infections, consequences of different lockdown measures, or consequences of wearing masks in certain situations. The results can be used to inform political decisions.


Subject(s)
COVID-19/prevention & control , Communicable Disease Control/methods , Contact Tracing/methods , Berlin , COVID-19/metabolism , Cell Phone/trends , Computer Simulation , Germany , Hand Disinfection/trends , Humans , Masks/trends , Models, Theoretical , Physical Distancing , Population Dynamics/trends , SARS-CoV-2/pathogenicity , Systems Analysis
5.
PLoS Comput Biol ; 17(10): e1009518, 2021 10.
Article in English | MEDLINE | ID: covidwho-1496328

ABSTRACT

Stay-at-home orders and shutdowns of non-essential businesses are powerful, but socially costly, tools to control the pandemic spread of SARS-CoV-2. Mass testing strategies, which rely on widely administered frequent and rapid diagnostics to identify and isolate infected individuals, could be a potentially less disruptive management strategy, particularly where vaccine access is limited. In this paper, we assess the extent to which mass testing and isolation strategies can reduce reliance on socially costly non-pharmaceutical interventions, such as distancing and shutdowns. We develop a multi-compartmental model of SARS-CoV-2 transmission incorporating both preventative non-pharmaceutical interventions (NPIs) and testing and isolation to evaluate their combined effect on public health outcomes. Our model is designed to be a policy-guiding tool that captures important realities of the testing system, including constraints on test administration and non-random testing allocation. We show how strategic changes in the characteristics of the testing system, including test administration, test delays, and test sensitivity, can reduce reliance on preventative NPIs without compromising public health outcomes in the future. The lowest NPI levels are possible only when many tests are administered and test delays are short, given limited immunity in the population. Reducing reliance on NPIs is highly dependent on the ability of a testing program to identify and isolate unreported, asymptomatic infections. Changes in NPIs, including the intensity of lockdowns and stay at home orders, should be coordinated with increases in testing to ensure epidemic control; otherwise small additional lifting of these NPIs can lead to dramatic increases in infections, hospitalizations and deaths. Importantly, our results can be used to guide ramp-up of testing capacity in outbreak settings, allow for the flexible design of combined interventions based on social context, and inform future cost-benefit analyses to identify efficient pandemic management strategies.


Subject(s)
COVID-19/prevention & control , Pandemics/prevention & control , SARS-CoV-2 , COVID-19/epidemiology , COVID-19 Testing/methods , Communicable Disease Control/methods , Computational Biology , Computer Simulation , Cost-Benefit Analysis , Humans , Models, Biological , Physical Distancing
6.
PLoS Comput Biol ; 17(10): e1009360, 2021 10.
Article in English | MEDLINE | ID: covidwho-1496326

ABSTRACT

The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world's most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. In this paper we present an agent-based modeling approach to simulating the spread of disease in refugee and IDP settlements under various non-pharmaceutical intervention strategies. The model, based on the June open-source framework, is informed by data on geography, demographics, comorbidities, physical infrastructure and other parameters obtained from real-world observations and previous literature. The development and testing of this approach focuses on the Cox's Bazar refugee settlement in Bangladesh, although our model is designed to be generalizable to other informal settings. Our findings suggest the encouraging self-isolation at home of mild to severe symptomatic patients, as opposed to the isolation of all positive cases in purpose-built isolation and treatment centers, does not increase the risk of secondary infection meaning the centers can be used to provide hospital support to the most intense cases of COVID-19. Secondly we find that mask wearing in all indoor communal areas can be effective at dampening viral spread, even with low mask efficacy and compliance rates. Finally, we model the effects of reopening learning centers in the settlement under various mitigation strategies. For example, a combination of mask wearing in the classroom, halving attendance regularity to enable physical distancing, and better ventilation can almost completely mitigate the increased risk of infection which keeping the learning centers open may cause. These modeling efforts are being incorporated into decision making processes to inform future planning, and further exercises should be carried out in similar geographies to help protect those most vulnerable.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Epidemics , Refugees , SARS-CoV-2 , Bangladesh/epidemiology , COVID-19/prevention & control , Comorbidity , Computational Biology , Computer Simulation , Data Visualization , Disease Progression , Humans , Masks , Physical Distancing , Refugees/statistics & numerical data , Schools , Systems Analysis
7.
Br J Clin Pharmacol ; 87(9): 3425-3438, 2021 09.
Article in English | MEDLINE | ID: covidwho-1494607

ABSTRACT

AIMS: We propose the use of in silico mathematical models to provide insights that optimize therapeutic interventions designed to effectively treat respiratory infection during a pandemic. A modelling and simulation framework is provided using SARS-CoV-2 as an example, considering applications for both treatment and prophylaxis. METHODS: A target cell-limited model was used to quantify the viral infection dynamics of SARS-CoV-2 in a pooled population of 105 infected patients. Parameter estimates from the resulting model were used to simulate and compare the impact of various interventions against meaningful viral load endpoints. RESULTS: Robust parameter estimates were obtained for the basic reproduction number, viral release rate and infected-cell mortality from the infection model. These estimates were informed by the largest dataset currently available for SARS-CoV-2 viral time course. The utility of this model was demonstrated using simulations, which hypothetically introduced inhibitory or stimulatory drug mechanisms at various target sites within the viral life-cycle. We show that early intervention is crucial to achieving therapeutic benefit when monotherapy is administered. In contrast, combination regimens of two or three drugs may provide improved outcomes if treatment is initiated late. The latter is relevant to SARS-CoV-2, where the period between infection and symptom onset is relatively long. CONCLUSIONS: The use of in silico models can provide viral load predictions that can rationalize therapeutic strategies against an emerging viral pathogen.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/drug therapy , Computer Simulation , Humans , Pandemics , SARS-CoV-2/drug effects , Viral Load
8.
Commun Biol ; 4(1): 1240, 2021 10 29.
Article in English | MEDLINE | ID: covidwho-1493232

ABSTRACT

Circular tandem repeat proteins ('cTRPs') are de novo designed protein scaffolds (in this and prior studies, based on antiparallel two-helix bundles) that contain repeated protein sequences and structural motifs and form closed circular structures. They can display significant stability and solubility, a wide range of sizes, and are useful as protein display particles for biotechnology applications. However, cTRPs also demonstrate inefficient self-assembly from smaller subunits. In this study, we describe a new generation of cTRPs, with longer repeats and increased interaction surfaces, which enhanced the self-assembly of two significantly different sizes of homotrimeric constructs. Finally, we demonstrated functionalization of these constructs with (1) a hexameric array of peptide-binding SH2 domains, and (2) a trimeric array of anti-SARS CoV-2 VHH domains. The latter proved capable of sub-nanomolar binding affinities towards the viral receptor binding domain and potent viral neutralization function.


Subject(s)
Angiotensin-Converting Enzyme 2/metabolism , COVID-19/metabolism , Protein Engineering/methods , Proteins/chemistry , Proteins/metabolism , SARS-CoV-2/metabolism , Tandem Repeat Sequences , Amino Acid Sequence , COVID-19/virology , Computer Simulation , Crystallization , HEK293 Cells , Humans , Models, Molecular , Neutralization Tests , Protein Binding , Protein Domains , Protein Folding , Protein Structure, Secondary , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/metabolism
9.
Sci Rep ; 11(1): 21248, 2021 10 28.
Article in English | MEDLINE | ID: covidwho-1493206

ABSTRACT

The COVID-19 pandemic was an inevitable outcome of a globalized world in which a highly infective disease is able to reach every country in a matter of weeks. While lockdowns and strong mobility restrictions have proven to be efficient to contain the exponential transmission of the virus, its pervasiveness has made it impossible for economies to maintain this kind of measures in time. Understanding precisely how the spread of the virus occurs from a territorial perspective is crucial not only to prevent further infections but also to help with policy design regarding human mobility. From the large spatial differences in the behavior of the virus spread we can unveil which areas have been more vulnerable to it and why, and with this information try to assess the risk that each community has to suffer a future outbreak of infection. In this work we have analyzed the geographical distribution of the cumulative incidence during the first wave of the pandemic in the region of Galicia (north western part of Spain), and developed a mathematical approach that assigns a risk factor for each of the different municipalities that compose the region. This risk factor is independent of the actual evolution of the pandemic and incorporates geographic and demographic information. The comparison with empirical information from the first pandemic wave demonstrates the validity of the method. Our results can potentially be used to design appropriate preventive policies that help to contain the virus.


Subject(s)
COVID-19/epidemiology , Pandemics , SARS-CoV-2 , COVID-19/transmission , Computer Simulation , Demography , Humans , Incidence , Linear Models , Models, Statistical , Pandemics/statistics & numerical data , Risk Factors , Spain/epidemiology
10.
Math Biosci ; 338: 108645, 2021 08.
Article in English | MEDLINE | ID: covidwho-1492387

ABSTRACT

With more than 1.7 million COVID-19 deaths, identifying effective measures to prevent COVID-19 is a top priority. We developed a mathematical model to simulate the COVID-19 pandemic with digital contact tracing and testing strategies. The model uses a real-world social network generated from a high-resolution contact data set of 180 students. This model incorporates infectivity variations, test sensitivities, incubation period, and asymptomatic cases. We present a method to extend the weighted temporal social network and present simulations on a network of 5000 students. The purpose of this work is to investigate optimal quarantine rules and testing strategies with digital contact tracing. The results show that the traditional strategy of quarantining direct contacts reduces infections by less than 20% without sufficient testing. Periodic testing every 2 weeks without contact tracing reduces infections by less than 3%. A variety of strategies are discussed including testing second and third degree contacts and the pre-exposure notification system, which acts as a social radar warning users how far they are from COVID-19. The most effective strategy discussed in this work was combining the pre-exposure notification system with testing second and third degree contacts. This strategy reduces infections by 18.3% when 30% of the population uses the app, 45.2% when 50% of the population uses the app, 72.1% when 70% of the population uses the app, and 86.8% when 95% of the population uses the app. When simulating the model on an extended network of 5000 students, the results are similar with the contact tracing app reducing infections by up to 79%.


Subject(s)
COVID-19/prevention & control , Contact Tracing/statistics & numerical data , Disease Notification/standards , Models, Theoretical , Social Network Analysis , Adult , Computer Simulation , Humans , Medical Informatics Applications , Mobile Applications , Quarantine/statistics & numerical data , Students , Young Adult
11.
Molecules ; 26(21)2021 Oct 30.
Article in English | MEDLINE | ID: covidwho-1488678

ABSTRACT

Papain-like protease is an essential enzyme in the proteolytic processing required for the replication of SARS-CoV-2. Accordingly, such an enzyme is an important target for the development of anti-SARS-CoV-2 agents which may reduce the mortality associated with outbreaks of SARS-CoV-2. A set of 69 semi-synthesized molecules that exhibited the structural features of SARS-CoV-2 papain-like protease inhibitors (PLPI) were docked against the coronavirus papain-like protease (PLpro) enzyme (PDB ID: (4OW0). Docking studies showed that derivatives 34 and 58 were better than the co-crystallized ligand while derivatives 17, 28, 31, 40, 41, 43, 47, 54, and 65 exhibited good binding modes and binding free energies. The pharmacokinetic profiling study was conducted according to the four principles of the Lipinski rules and excluded derivative 31. Furthermore, ADMET and toxicity studies showed that derivatives 28, 34, and 47 have the potential to be drugs and have been demonstrated as safe when assessed via seven toxicity models. Finally, comparing the molecular orbital energies and the molecular electrostatic potential maps of 28, 34, and 47 against the co-crystallized ligand in a DFT study indicated that 28 is the most promising candidate to interact with the target receptor (PLpro).


Subject(s)
Coronavirus Papain-Like Proteases/metabolism , SARS-CoV-2/drug effects , Virus Replication/drug effects , Antiviral Agents/pharmacology , COVID-19/drug therapy , COVID-19/metabolism , Computer Simulation , Coronavirus Papain-Like Proteases/drug effects , Drug Evaluation, Preclinical/methods , Humans , Ligands , Molecular Docking Simulation , Molecular Dynamics Simulation , Papain/metabolism , Peptide Hydrolases/metabolism , Protease Inhibitors/chemistry , Protease Inhibitors/metabolism , Protease Inhibitors/pharmacology , SARS-CoV-2/metabolism , SARS-CoV-2/pathogenicity
12.
Sci Rep ; 11(1): 20864, 2021 10 21.
Article in English | MEDLINE | ID: covidwho-1479817

ABSTRACT

Following SARS-CoV-2 infection, some COVID-19 patients experience severe host driven adverse events. To treat these complications, their underlying etiology and drug treatments must be identified. Thus, a novel AI methodology MOATAI-VIR, which predicts disease-protein-pathway relationships and repurposed FDA-approved drugs to treat COVID-19's clinical manifestations was developed. SARS-CoV-2 interacting human proteins and GWAS identified respiratory failure genes provide the input from which the mode-of-action (MOA) proteins/pathways of the resulting disease comorbidities are predicted. These comorbidities are then mapped to their clinical manifestations. To assess each manifestation's molecular basis, their prioritized shared proteins were subject to global pathway analysis. Next, the molecular features associated with hallmark COVID-19 phenotypes, e.g. unusual neurological symptoms, cytokine storms, and blood clots were explored. In practice, 24/26 of the major clinical manifestations are successfully predicted. Three major uncharacterized manifestation categories including neoplasms are also found. The prevalence of neoplasms suggests that SARS-CoV-2 might be an oncovirus due to shared molecular mechanisms between oncogenesis and viral replication. Then, repurposed FDA-approved drugs that might treat COVID-19's clinical manifestations are predicted by virtual ligand screening of the most frequent comorbid protein targets. These drugs might help treat both COVID-19's severe adverse events and lesser ones such as loss of taste/smell.


Subject(s)
COVID-19/complications , COVID-19/diagnosis , COVID-19/drug therapy , Computational Biology/methods , Neoplasms/complications , Nervous System Diseases/complications , Thrombosis/complications , Virus Replication , Benchmarking , Comorbidity , Computer Simulation , Cytokine Release Syndrome , Drug Discovery , Humans , Machine Learning , Molecular Medicine , Phenotype , SARS-CoV-2 , Treatment Outcome
13.
Sci Rep ; 11(1): 20715, 2021 10 21.
Article in English | MEDLINE | ID: covidwho-1479810

ABSTRACT

The current COVID-19 pandemic has created unmeasurable damages to society at a global level, from the irreplaceable loss of life, to the massive economic losses. In addition, the disease threatens further biodiversity loss. Due to their shared physiology with humans, primates, and particularly great apes, are susceptible to the disease. However, it is still uncertain how their populations would respond in case of infection. Here, we combine stochastic population and epidemiological models to simulate the range of potential effects of COVID-19 on the probability of extinction of mountain gorillas. We find that extinction is sharply driven by increases in the basic reproductive number and that the probability of extinction is greatly exacerbated if the immunity lasts less than 6 months. These results stress the need to limit exposure of the mountain gorilla population, the park personnel and visitors, as well as the potential of vaccination campaigns to extend the immunity duration.


Subject(s)
Ape Diseases/epidemiology , Ape Diseases/physiopathology , COVID-19/epidemiology , COVID-19/physiopathology , Animals , Animals, Newborn , COVID-19/veterinary , Computer Simulation , Endangered Species , Female , Gorilla gorilla , Immune System , Male , Models, Statistical , Pandemics , Probability , SARS-CoV-2 , Stochastic Processes
14.
Elife ; 102021 10 12.
Article in English | MEDLINE | ID: covidwho-1478420

ABSTRACT

Polygenic risk scores (PRSs) have been offered since 2019 to screen in vitro fertilization embryos for genetic liability to adult diseases, despite a lack of comprehensive modeling of expected outcomes. Here we predict, based on the liability threshold model, the expected reduction in complex disease risk following polygenic embryo screening for a single disease. A strong determinant of the potential utility of such screening is the selection strategy, a factor that has not been previously studied. When only embryos with a very high PRS are excluded, the achieved risk reduction is minimal. In contrast, selecting the embryo with the lowest PRS can lead to substantial relative risk reductions, given a sufficient number of viable embryos. We systematically examine the impact of several factors on the utility of screening, including: variance explained by the PRS, number of embryos, disease prevalence, parental PRSs, and parental disease status. We consider both relative and absolute risk reductions, as well as population-averaged and per-couple risk reductions, and also examine the risk of pleiotropic effects. Finally, we confirm our theoretical predictions by simulating 'virtual' couples and offspring based on real genomes from schizophrenia and Crohn's disease case-control studies. We discuss the assumptions and limitations of our model, as well as the potential emerging ethical concerns.


Subject(s)
Crohn Disease/genetics , Fertilization in Vitro , Genetic Testing , Models, Genetic , Multifactorial Inheritance , Preimplantation Diagnosis , Schizophrenia/genetics , Computer Simulation , Female , Genetic Predisposition to Disease , Humans , Male , Predictive Value of Tests , Pregnancy , Risk Assessment , Risk Factors
15.
PLoS Comput Biol ; 17(10): e1009474, 2021 10.
Article in English | MEDLINE | ID: covidwho-1477508

ABSTRACT

The role of heating, ventilation, and air-conditioning (HVAC) systems in the transmission of SARS-CoV-2 is unclear. To address this gap, we simulated the release of SARS-CoV-2 in a multistory office building and three social gathering settings (bar/restaurant, nightclub, wedding venue) using a well-mixed, multi-zone building model similar to those used by Wells, Riley, and others. We varied key factors of HVAC systems, such as the Air Changes Per Hour rate (ACH), Fraction of Outside Air (FOA), and Minimum Efficiency Reporting Values (MERV) to examine their effect on viral transmission, and additionally simulated the protective effects of in-unit ultraviolet light decontamination (UVC) and separate in-room air filtration. In all building types, increasing the ACH reduced simulated infections, and the effects were seen even with low aerosol emission rates. However, the benefits of increasing the fraction of outside air and filter efficiency rating were greatest when the aerosol emission rate was high. UVC filtration improved the performance of typical HVAC systems. In-room filtration in an office setting similarly reduced overall infections but worked better when placed in every room. Overall, we found little evidence that HVAC systems facilitate SARS-CoV-2 transmission; most infections in the simulated office occurred near the emission source, with some infections in individuals temporarily visiting the release zone. HVAC systems only increased infections in one scenario involving a marginal increase in airflow in a poorly ventilated space, which slightly increased the likelihood of transmission outside the release zone. We found that improving air circulation rates, increasing filter MERV rating, increasing the fraction of outside air, and applying UVC radiation and in-room filtration may reduce SARS-CoV-2 transmission indoors. However, these mitigation measures are unlikely to provide a protective benefit unless SARS-CoV-2 aerosol emission rates are high (>1,000 Plaque-forming units (PFU) / min).


Subject(s)
Air Conditioning , COVID-19/transmission , Heating , SARS-CoV-2 , Ventilation , Aerosols , Air Microbiology , Air Movements , COVID-19/prevention & control , COVID-19/virology , Computational Biology , Computer Simulation , Humans , Models, Biological , Pandemics , SARS-CoV-2/radiation effects , Social Interaction , Ultraviolet Rays , Workplace
16.
Comput Math Methods Med ; 2021: 6636396, 2021.
Article in English | MEDLINE | ID: covidwho-1476878

ABSTRACT

Group testing (or pool testing), for example, Dorfman's method or grid method, has been validated for COVID-19 RT-PCR tests and implemented widely by most laboratories in many countries. These methods take advantages since they reduce resources, time, and overall costs required for a large number of samples. However, these methods could have more false negative cases and lower sensitivity. In order to maintain both accuracy and efficiency for different prevalence, we provide a novel pooling strategy based on the grid method with an extra pool set and an optimized rule inspired by the idea of error-correcting codes. The mathematical analysis shows that (i) the proposed method has the best sensitivity among all the methods we compared, if the false negative rate (FNR) of an individual test is in the range [1%, 20%] and the FNR of a pool test is closed to that of an individual test, and (ii) the proposed method is efficient when the prevalence is below 10%. Numerical simulations are also performed to confirm the theoretical derivations. In summary, the proposed method is shown to be felicitous under the above conditions in the epidemic.


Subject(s)
COVID-19 Testing/methods , COVID-19 Testing/standards , COVID-19/diagnosis , Algorithms , Computer Simulation , False Negative Reactions , Humans , Laboratories/standards , Models, Theoretical , Prevalence , Probability , Reproducibility of Results
17.
Comput Math Methods Med ; 2021: 5384481, 2021.
Article in English | MEDLINE | ID: covidwho-1476872

ABSTRACT

In this study we propose a Coronavirus Disease 2019 (COVID-19) mathematical model that stratifies infectious subpopulations into: infectious asymptomatic individuals, symptomatic infectious individuals who manifest mild symptoms and symptomatic individuals with severe symptoms. In light of the recent revelation that reinfection by COVID-19 is possible, the proposed model attempt to investigate how reinfection with COVID-19 will alter the future dynamics of the recent unfolding pandemic. Fitting the mathematical model on the Kenya COVID-19 dataset, model parameter values were obtained and used to conduct numerical simulations. Numerical results suggest that reinfection of recovered individuals who have lost their protective immunity will create a large pool of asymptomatic infectious individuals which will ultimately increase symptomatic individuals with mild symptoms and symptomatic individuals with severe symptoms (critically ill) needing urgent medical attention. The model suggests that reinfection with COVID-19 will lead to an increase in cumulative reported deaths. Comparison of the impact of non pharmaceutical interventions on curbing COVID19 proliferation suggests that wearing face masks profoundly reduce COVID-19 prevalence than maintaining social/physical distance. Further, numerical findings reveal that increasing detection rate of asymptomatic cases via contact tracing, testing and isolating them can drastically reduce COVID-19 surge, in particular individuals who are critically ill and require admission into intensive care.


Subject(s)
COVID-19/transmission , Models, Biological , Pandemics , SARS-CoV-2 , Asymptomatic Infections/epidemiology , COVID-19/epidemiology , COVID-19/prevention & control , Computational Biology , Computer Simulation , Contact Tracing , Databases, Factual , Disease Susceptibility , Humans , Kenya/epidemiology , Masks , Pandemics/prevention & control , Pandemics/statistics & numerical data , Physical Distancing , Reinfection/epidemiology , Reinfection/transmission , SARS-CoV-2/immunology
18.
Sci Rep ; 11(1): 20739, 2021 10 20.
Article in English | MEDLINE | ID: covidwho-1475485

ABSTRACT

Since the first coronavirus disease 2019 (COVID-19) outbreak appeared in Wuhan, mainland China on December 31, 2019, the geographical spread of the epidemic was swift. Malaysia is one of the countries that were hit substantially by the outbreak, particularly in the second wave. This study aims to simulate the infectious trend and trajectory of COVID-19 to understand the severity of the disease and determine the approximate number of days required for the trend to decline. The number of confirmed positive infectious cases [as reported by Ministry of Health, Malaysia (MOH)] were used from January 25, 2020 to March 31, 2020. This study simulated the infectious count for the same duration to assess the predictive capability of the Susceptible-Infectious-Recovered (SIR) model. The same model was used to project the simulation trajectory of confirmed positive infectious cases for 80 days from the beginning of the outbreak and extended the trajectory for another 30 days to obtain an overall picture of the severity of the disease in Malaysia. The transmission rate, ß also been utilized to predict the cumulative number of infectious individuals. Using the SIR model, the simulated infectious cases count obtained was not far from the actual count. The simulated trend was able to mimic the actual count and capture the actual spikes approximately. The infectious trajectory simulation for 80 days and the extended trajectory for 110 days depicts that the inclining trend has peaked and ended and will decline towards late April 2020. Furthermore, the predicted cumulative number of infectious individuals tallies with the preparations undertaken by the MOH. The simulation indicates the severity of COVID-19 disease in Malaysia, suggesting a peak of infectiousness in mid-March 2020 and a probable decline in late April 2020. Overall, the study findings indicate that outbreak control measures such as the Movement Control Order (MCO), social distancing and increased hygienic awareness is needed to control the transmission of the outbreak in Malaysia.


Subject(s)
COVID-19/epidemiology , COVID-19/physiopathology , Public Health Informatics/methods , Computer Simulation , Disease Outbreaks , Disease Susceptibility/epidemiology , Epidemics , Humans , Malaysia , Models, Theoretical , Public Health , Quarantine , SARS-CoV-2
19.
Comput Methods Programs Biomed ; 212: 106469, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1471519

ABSTRACT

BACKGROUND AND OBJECTIVE: In this work, we analyze the spatial-temporal dynamics of a susceptible-infected-recovered (SIR) epidemic model with time delays. To better describe the dynamical behavior of the model, we take into account the cumulative effects of diffusion in the population dynamics, and the time delays in both the Holling type II treatment and the disease transmission process, respectively. METHODS: We perform linear stability analyses on the disease-free and endemic equilibria. We provide the expression of the basic reproduction number and set conditions on the backward bifurcation using Castillo's theorem. The values of the critical time transmission, the treatment delays and the relationship between them are established. RESULTS: We show that the treatment rate decreases the basic reproduction number while the transmission rate significantly affects the bifurcation process in the system. The transmission and treatment time-delays are found to be inversely proportional to the susceptible and infected diffusion rates. The analytical results are numerically tested. The results show that the treatment rate significantly reduces the density of infected population and ensures the transition from the unstable to the stable domain. Moreover, the system is more sensible to the treatment in the stable domain. CONCLUSIONS: The density of infected population increases with respect to the infected and susceptible diffusion rates. Both effects of treatment and transmission delays significantly affect the behavior of the system. The transmission time-delay at the critical point ensures the transition from the stable (low density) to the unstable (high density) domain.


Subject(s)
Epidemics , Models, Biological , Basic Reproduction Number , Computer Simulation
20.
PLoS Comput Biol ; 17(10): e1009326, 2021 10.
Article in English | MEDLINE | ID: covidwho-1468147

ABSTRACT

Assessing the impact of mobility on epidemic spreading is of crucial importance for understanding the effect of policies like mass quarantines and selective re-openings. While many factors affect disease incidence at a local level, making it more or less homogeneous with respect to other areas, the importance of multi-seeding has often been overlooked. Multi-seeding occurs when several independent (non-clustered) infected individuals arrive at a susceptible population. This can lead to independent outbreaks that spark from distinct areas of the local contact (social) network. Such mechanism has the potential to boost incidence, making control efforts and contact tracing less effective. Here, through a modeling approach we show that the effect produced by the number of initial infections is non-linear on the incidence peak and peak time. When case importations are carried by mobility from an already infected area, this effect is further enhanced by the local demography and underlying mixing patterns: the impact of every seed is larger in smaller populations. Finally, both in the model simulations and the analysis, we show that a multi-seeding effect combined with mobility restrictions can explain the observed spatial heterogeneities in the first wave of COVID-19 incidence and mortality in five European countries. Our results allow us for identifying what we have called epidemic epicenter: an area that shapes incidence and mortality peaks in the entire country. The present work further clarifies the nonlinear effects that mobility can have on the evolution of an epidemic and highlight their relevance for epidemic control.


Subject(s)
COVID-19/epidemiology , Communicable Disease Control , Computer Simulation , COVID-19/prevention & control , COVID-19/transmission , Disease Outbreaks , Europe/epidemiology , Humans , Incidence , Travel
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