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1.
Chemical Engineering Journal Advances ; : 100374, 2022.
Article in English | ScienceDirect | ID: covidwho-1966422

ABSTRACT

Modeling complex chemical reaction networks has inspired a considerable body of research, and a variety of approaches to modeling nonlinear pathways are being developed. Here, a general methodology is formulated to convert an arbitrary reaction network into its equivalent electrical analog. The topological equivalence of the electrical analog is mathematically established for unimolecular reactions using Kirchoff's laws. The modular approach is generalized to bimolecular and nonlinear autocatalytic reactions. It is then applied to simulate the dynamics of nonlinear autocatalytic networks without making simplifying assumptions, such as use of the quasi-steady state/Bodenstein approximation and the assumption of an absence of nonlinear steps in the intermediates. This is among the few papers that quantify the dynamics of a nonlinear chemical reaction network by generating and simulating an electrical network analog. As a realistic biological application, the early phase of the spread of COVID-19 is modeled as an autocatalytic process, and the predicted dynamics are in good agreement with experimental data. The rate-limiting step of viral transmission is identified, leading to novel mechanistic insights.

2.
Entropy (Basel) ; 24(5)2022 May 20.
Article in English | MEDLINE | ID: covidwho-1953150

ABSTRACT

This work investigates the temporal statistical structure of time series of electric field (EF) intensity recorded with the aim of exploring the dynamical patterns associated with periods with different human activity in urban areas. The analyzed time series were obtained from a sensor of the EMF RATEL monitoring system installed in the campus area of the University of Novi Sad, Serbia. The sensor performs wideband cumulative EF intensity monitoring of all active commercial EF sources, thus including those linked to human utilization of wireless communication systems. Monitoring was performed continuously during the years 2019 and 2020, allowing us to investigate the effects on the patterns of EF intensity of varying conditions of human mobility, including regular teaching and exam activity within the campus, as well as limitations to mobility related to the COVID-19 pandemic. Time series analysis was performed using both simple statistics (mean and variance) and combining the information-theoretic measure of information storage (IS) with the method of surrogate data to quantify the regularity of EF dynamic patterns and detect the presence of nonlinear dynamics. Moreover, to assess the possible coexistence of dynamic behaviors across multiple temporal scales, IS analysis was performed over consecutive observation windows lasting one day, week, month, and year, respectively coarse grained at time scales of 6 min, 30 min, 2 h, and 1 day. Our results document that the EF intensity patterns of variability are modulated by the movement of people at daily, weekly, and monthly scales, and are blunted during periods of restricted mobility related to the COVID-19 pandemic. Mobility restrictions also affected significantly the regularity of the EF intensity time series, resulting in lower values of IS observed simultaneously with a loss of nonlinear dynamics. Thus, our analysis can be useful to investigate changes in the global patterns of human mobility both during pandemics or other types of events, and from this perspective may serve to implement strategies for safety assessment and for optimizing the design of networks of EF sensors.

3.
Nonlinear Dyn ; 109(1): 225-238, 2022.
Article in English | MEDLINE | ID: covidwho-1919894

ABSTRACT

The COVID-19 pandemic shows to have a huge impact on people's health and countries' infrastructures around the globe. Iran was one of the first countries that experienced the vast prevalence of the coronavirus outbreak. The Iranian authorities applied various non-pharmaceutical interventions to eradicate the epidemic in different periods. This study aims to investigate the effectiveness of non-pharmaceutical interventions in managing the current Coronavirus pandemic and to predict the next wave of infection in Iran. To achieve the research objective, the number of cases and deaths before and after the interventions was studied and the effective reproduction number of the infection was analyzed under various scenarios. The SEIR generic model was applied to capture the dynamic of the pandemic in Iran. To capture the effects of different interventions, the corresponding reproduction number was considered. Depending on how people are responsive to interventions, the effectiveness of each intervention has been investigated. Results show that the maximum number of the total of infected individuals will occur around the end of May and the start of June 2021. It is concluded that the outbreak could be smoothed if full lockdown and strict quarantine continue. The proposed modeling could be used as an assessment tool to evaluate the effects of different interventions in new outbreaks.

4.
Algorithms ; 15(5):175, 2022.
Article in English | ProQuest Central | ID: covidwho-1870967

ABSTRACT

The human immunodeficiency virus (HIV) mainly attacks CD4+ T cells in the host. Chronic HIV infection gradually depletes the CD4+ T cell pool, compromising the host’s immunological reaction to invasive infections and ultimately leading to acquired immunodeficiency syndrome (AIDS). The goal of this study is not to provide a qualitative description of the rich dynamic characteristics of the HIV infection model of CD4+ T cells, but to produce accurate analytical solutions to the model using the modified iterative approach. In this research, a new efficient method using the new iterative method (NIM), the coupling of the standard NIM and Laplace transform, called the modified new iterative method (MNIM), has been introduced to resolve the HIV infection model as a class of system of ordinary differential equations (ODEs). A nonlinear HIV infection dynamics model is adopted as an instance to elucidate the identification process and the solution process of MNIM, only two iterations lead to ideal results. In addition, the model has also been solved using NIM and the fourth order Runge–Kutta (RK4) method. The results indicate that the solutions by MNIM match with those of RK4 method to a minimum of eight decimal places, whereas NIM solutions are not accurate enough. Numerical comparisons between the MNIM, NIM, the classical RK4 and other methods reveal that the modified technique has potential as a tool for the nonlinear systems of ODEs.

5.
Techne ; 23:285-286, 2022.
Article in English | ProQuest Central | ID: covidwho-1863823

ABSTRACT

According to the International Risk Governance Council, interconnectivity within and between complex adaptive systems is one of the characteristics that define and determine today's world. The systemic nature of the Coronavirus spread is widely considered a paradigmatic example of this vulnerability: the pervasiveness and modalities of manifestation of the health crisis, and the consequent cascade developments, have produced multiple impacts which, going beyond the strictly epidemiological aspect, they poured on anthropic and ecological systems, according to non-linear dynamics, growing exponentially to involve every aspect of our interdependent world (UN ODRR, 2021). Factors such as climate change, loss of biodiversity, intensively exploitation of agrifood systems, together with the resources depletion and the reduction of natural habitats, which favor zoonoses responsible for pandemic infections, are associated with widespread social, health and environmental inequalities, creating contexts of marginalization, in which the effects of crises are less controllable and therefore more devastating (Cogliati Denza, 2020). The discussion is articulated around three crucial dates: 1989 which marks the start of the single market with the definitive collapse of Communism;2020 which, with the Pandemic, brought out the limits and contradictions of the globalized society;2050, which is considered the deadline for the survival of the Planet, the horizon within which, according to the scientific community, greenhouse gas emissions must be eliminated to prevent climate change from causing the collapse of the Earth.

6.
Complexity ; 2022, 2022.
Article in English | ProQuest Central | ID: covidwho-1832689

ABSTRACT

During the COVID-19 epidemic, draconian countermeasures forbidding nonessential human activities have been adopted in several countries worldwide, providing an unprecedented setup for testing and quantifying the current impact of humankind on climate and for driving potential sustainability policies in the postpandemic era from a perspective of complex systems. In this study, we consider heterogeneous sources of environmental and human activity observables, considered as components of a complex socioenvironmental system, and apply information theory, network science, and Bayesian inference to analyze their structural relations and nonlinear dynamics between January 2019 and August 2020 in northern Italy, i.e., before, during, and after the national lockdown. The topological structure of a complex system strongly impacts its collective behavior;therefore, mapping this structure is essential to fully understand the functions of the system as a whole and its fragility to unexpected disruptions or shocks. To this aim, we unravel the causal relationships between the 16 environmental conditions and human activity variables, mapping the backbone of the complex interplay between intervening physical observables—such as NO2 emissions, energy consumption, intervening climate variables, and different flavors of human mobility flows—to a causal network model. To identify a tipping point during the period of observation, denoting the presence of a regime shift between distinct network states (i.e., before and during the shock), we introduce a novel information-theoretic method based on statistical divergence widely used in statistical physics. We find that despite a measurable decrease in NO2 concentration, due to an overall decrease in human activities, locking down a region as a climate change mitigation is an insufficient remedy to reduce emissions. Our results provide a functional characterization of socioenvironmental interdependent systems, and our analytical framework can be used, more generally, to characterize environmental changes and their interdependencies using statistical physics.

7.
European Journal of Physics ; 43(3):18, 2022.
Article in English | Web of Science | ID: covidwho-1764480

ABSTRACT

Advanced fitting of ordinary differential equations models to experimental results is presented within the context of different academic levels of students and diverse research fields. In many areas, the analysis of experimental results cannot be restricted to cases where particular solutions of the models' differential equations, valid only for specific limit conditions, apply. In those cases, analytical mathematical equations are not available and a complete description of the systems extends beyond the numerical minimization of statistical estimators, like the chi-square, because it requires solving numerically the models' differential equations. Dedicated fitting procedures that involve the interdependent processes of solving the ordinary differential equations and fitting the numerical solutions to the experimental results are required to obtain the best fitting sets of parameters with consistent physical meaning. A simple, but powerful, web-based ordinary differential equations solver and fitter is presented, and used to analyse both the complete motion of a rigid pendulum and the dynamics of a viral infection.

8.
2021 Modeling, Estimation and Control Conference, MECC 2021 ; 54:251-257, 2021.
Article in English | Scopus | ID: covidwho-1703265

ABSTRACT

This paper focuses on the dynamics of the COVID-19 pandemic and estimation of associated real-time variables characterizing disease spread. A nonlinear dynamic model is developed which enhances the traditional SEIR epidemic model to include additional variables of hospitalizations, ICU admissions, and deaths. A 6-month data set containing Minnesota data on infections, hospital-ICU admissions and deaths is used to find least-squares solutions to the parameters of the model. The model is found to fit the measured data accurately. Subsequently, a cascaded observer is developed to find real-time values of the infected population, the infection rate, and the basic reproduction number. The observer is found to yield good real-time estimates that match the least-squares parameters obtained from the complete data set. The importance of the work is that it enables real-time estimation of the basic reproduction number which is a key variable for controlling disease spread. Copyright © 2021 The Authors. This is an open access article under the CC BY-NC-ND license

9.
Journal of Statistical Mechanics-Theory and Experiment ; 2022(1):18, 2022.
Article in English | Web of Science | ID: covidwho-1665848

ABSTRACT

Epidemic spreading can be suppressed by the introduction of containment measures such as social distancing and lockdowns. Yet, when such measures are relaxed, new epidemic waves and infection cycles may occur. Here we explore this issue in compartmentalized epidemic models on graphs in presence of a feedback between the infection state of the population and the structure of its social network for the case of discontinuous control. We show that in random graphs the effect of containment measures is simply captured by a renormalization of the effective infection rate that accounts for the change in the branching ratio of the network. In our simple setting, a piece-wise mean-field approximation can be used to derive analytical formulae for the number of epidemic waves and their length. A variant of the model with imperfect information is used to model data of the recent COVID-19 epidemics in the Basque Country and Lombardy, where we estimate the extent of social network disruption during lockdowns and characterize the dynamical trajectories in the phase space.

10.
Physica D ; 432: 133158, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1633572

ABSTRACT

This paper compares and contrasts the spread and impact of COVID-19 in the three countries most heavily impacted by the pandemic: the United States (US), India and Brazil. All three of these countries have a federal structure, in which the individual states have largely determined the response to the pandemic. Thus, we perform an extensive analysis of the individual states of these three countries to determine patterns of similarity within each. First, we analyse structural similarity and anomalies in the trajectories of cases and deaths as multivariate time series. Next, we study the lengths of the different waves of the virus outbreaks across the three countries and their states. Finally, we investigate suitable time offsets between cases and deaths as a function of the distinct outbreak waves. In all these analyses, we consistently reveal more characteristically distinct behaviour between US and Indian states, while Brazilian states exhibit less structure in their wave behaviour and changing progression between cases and deaths.

11.
10th International Conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2021 ; 2090, 2021.
Article in English | Scopus | ID: covidwho-1593613

ABSTRACT

We analyzed herein the new covid-19 daily positive cases recorded in Albania. We observed that the distribution of the daily new cases is non-stationary and usually has a power law behavior in the low incidence zone, and a bell curve for the remaining part of the incidence interval. We qualified this finding as the indicator intensive dynamics and as proof that up now, the heard immunity has not been reached. By parallelizing the preferential attachment mechanisms responsible for a power law distribution in the social graphs elsewhere, we explain the low daily incidence distribution as result of the imprudent gatherings of peoples. Additionally, the bell-shaped distribution observed for the high daily new cases is agued as outcome of the competition between illness advances and restriction measures. The distribution is acceptably smooth, meaning that the management has been accommodated appropriately. This behavior is observed also for two neighbor countries Greece and Italy respectively, but was not observed for Turkey, Serbia, and North Macedonia. Next, we used the multifractal analysis to conclude about the features related with heterogeneity of the data. We have identified the local presence self-organization behavior in some separate time intervals. Formally and empirically we have identified that the full set of the data contain two regimes finalized already, followed by a third one which started in July 2021. © 2021 Institute of Physics Publishing. All rights reserved.

12.
R Soc Open Sci ; 8(11): 210682, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1522462

ABSTRACT

Air travel has been one of the hardest hit industries of COVID-19, with many flight cancellations and airport closures as a consequence. By analysing structural characteristics of the Official Aviation Guide flight data, we show that this resulted in an increased average distance between airports, and in an increased number of long-range routes. Based on our study of network robustness, we uncover that this disruption is consistent with the impact of a mixture of targeted and random global attack on the worldwide air transportation network. By considering the individual functional evolution of airports, we identify anomalous airports with high centrality but low degree, which further enables us to reveal the underlying transitions among airport-specific representations in terms of both geographical and geopolitical factors. During the evolution of the air transportation network, we also observe how the network attempted to cope by shifting centralities between different airports around the world. Since these shifts are not aligned with optimal strategies for minimizing delays and disconnects, we conclude that they are consistent with politics trumping science from the viewpoint of epidemic containment and transport.

13.
Heliyon ; 7(10): e08207, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1471987

ABSTRACT

Computational modeling and simulation of viral dynamics would explain the pathogenesis for any virus. Such computational attempts have been successfully made to predict and control HIV-1 or hepatitis B virus. However, the dynamics for SARS-CoV-2 has not been adequately investigated. The purpose of this research is to propose different SARS-CoV-2 dynamics models based on differential equations and numerical analysis towards distilling the models to explain the mechanism of SARS-CoV-2 pathogenesis. The proposed four models formalize the dynamical system of SARS-CoV-2 infection, which consists of host cells and viral particles. These models undergo numerical analysis, including sensitivity analysis and stability analysis. Based on the sensitivity indices of the four models' parameters, the four models are simplified into two models. In advance of the following calibration experiments, the eigenvalues of the Jacobian matrices of these two models are calculated, thereby guaranteeing that any solutions are stable. Then, the calibration experiments fit the simulated data sequences of the two models to two observed data sequences, SARS-CoV-2 viral load in mild cases and that in severe cases. Comparing the estimated parameters in mild cases and severe cases indicates that cell-to-cell transmission would significantly correlate to the COVID-19 severity. These experiments for modeling and simulation provide plausible computational models for the SARS-CoV-2 dynamics, leading to further investigation for identifying the essential factors in severe cases.

14.
Med J Aust ; 215(9): 427-432, 2021 11 01.
Article in English | MEDLINE | ID: covidwho-1389702

ABSTRACT

OBJECTIVES: To analyse the outcomes of COVID-19 vaccination by vaccine type, age group eligibility, vaccination strategy, and population coverage. DESIGN: Epidemiologic modelling to assess the final size of a COVID-19 epidemic in Australia, with vaccination program (Pfizer, AstraZeneca, mixed), vaccination strategy (vulnerable first, transmitters first, untargeted), age group eligibility threshold (5 or 15 years), population coverage, and pre-vaccination effective reproduction number ( R eff v ¯ ) for the SARS-CoV-2 Delta variant as factors. MAIN OUTCOME MEASURES: Numbers of SARS-CoV-2 infections; cumulative hospitalisations, deaths, and years of life lost. RESULTS: Assuming R eff v ¯ = 5, the current mixed vaccination program (vaccinating people aged 60 or more with the AstraZeneca vaccine and people under 60 with the Pfizer vaccine) will not achieve herd protection unless population vaccination coverage reaches 85% by lowering the vaccination eligibility age to 5 years. At R eff v ¯ = 3, the mixed program could achieve herd protection at 60-70% population coverage and without vaccinating 5-15-year-old children. At R eff v ¯ = 7, herd protection is unlikely to be achieved with currently available vaccines, but they would still reduce the number of COVID-19-related deaths by 85%. CONCLUSION: Vaccinating vulnerable people first is the optimal policy when population vaccination coverage is low, but vaccinating more socially active people becomes more important as the R eff v ¯ declines and vaccination coverage increases. Assuming the most plausible R eff v ¯ of 5, vaccinating more than 85% of the population, including children, would be needed to achieve herd protection. Even without herd protection, vaccines are highly effective in reducing the number of deaths.


Subject(s)
COVID-19 Vaccines/immunology , COVID-19/prevention & control , Immunity, Herd , Mass Vaccination/organization & administration , SARS-CoV-2/pathogenicity , Adolescent , Adult , Age Factors , Australia/epidemiology , COVID-19/epidemiology , COVID-19/immunology , COVID-19/virology , COVID-19 Vaccines/administration & dosage , Child , Child, Preschool , Computer Simulation , Humans , Immunogenicity, Vaccine , Mass Vaccination/statistics & numerical data , Middle Aged , Models, Immunological , SARS-CoV-2/genetics , SARS-CoV-2/immunology , Vaccination Coverage/organization & administration , Vaccination Coverage/statistics & numerical data , Young Adult
15.
Transbound Emerg Dis ; 2021 Jul 28.
Article in English | MEDLINE | ID: covidwho-1329028

ABSTRACT

Existing models about the dynamics of COVID-19 transmission often assume the mechanism of virus transmission and the form of the differential equations. These assumptions are hard to verify. Due to the biases of country-level data, it is inaccurate to construct the global dynamic of COVID-19. This research aims to provide a robust data-driven global model of the transmission dynamics. We apply sparse identification of nonlinear dynamics (SINDy) to model the dynamics of COVID-19 global transmission. One advantage is that we can discover the nonlinear dynamics from data without assumptions in the form of the governing equations. To overcome the problem of biased country-level data on the number of reported cases, we propose a robust global model of the dynamics by using maximin aggregation. Real data analysis shows the efficiency of our model.

16.
Rev. latinoam. enferm. (Online) ; 29: e3453, 2021.
Article in English | WHO COVID, LILACS (Americas) | ID: covidwho-1306578

ABSTRACT

Objective: to carry out a theoretical reflection on the Nursing Now Campaign and the experience of the unexpected irruptions facing the pandemic period. Method: a theoretical-reflective study, supported by the theoretical framework of complexity thinking. It aims at understanding the dialogic between the notions of order, disorder and organization, which translate the transition from simplification to complexity of the pandemic phenomenon and its relation to the theme of Nursing Now and Nursing in the future. Results: the universe of phenomena is simultaneously composed of order, disorder and organization. Reasserting the central role of Nursing in the health team, facing the irruptions and uncertainties caused by the current pandemic, implies the ability to dialog with disorder and raise a new and more complex global (re)organization of the being and doing Nursing. Conclusion: in addition to answers, theoretical reflection raises new questions and irruptions. The inseparability between the notions of order and disorder in the evolutionary dynamics of the Nursing system is conceived and the promotion of even more complex levels of organization, management and Nursing assistance to achieve universal access to health is advocated.


Objetivo: realizar reflexão teórica acerca da Campanha Nursing Now e a experiência das irrupções do inesperado face ao período pandêmico. Método: estudo teórico-reflexivo, apoiado no referencial teórico do pensamento da complexidade. Visa-se à compreensão da dialógica entre as noções de ordem, de desordem e de organização, as quais traduzem a passagem da simplificação à complexidade do fenômeno da pandemia e sua relação com a temática Nursing Now and Nursing in the future. Resultados: o universo dos fenômenos é tecido, simultaneamente, de ordem, de desordem e de organização. Reafirmar o papel central da Enfermagem na equipe de saúde, face às irrupções e incertezas provocadas pela pandemia em curso, implica capacidade de dialogar com a desordem e suscitar uma nova e mais complexa (re)organização global do ser e do fazer Enfermagem. Conclusão: a reflexão teórica suscita, além de respostas, novos questionamentos e novas irrupções. Concebe-se a inseparabilidade entre as noções de ordem e de desordem na dinâmica evolutiva do sistema de Enfermagem e defende-se a promoção de níveis de organização, gestão e assistência de Enfermagem ainda mais complexos para o alcance do acesso universal à saúde.


Objetivo: realizar una reflexión teórica sobre la Campaña Nursing Now y la experiencia de las irrupciones de lo inesperado ante el período pandémico. Método: estudio teórico-reflexivo, basado en el marco teórico del pensamiento complejo. Tiene como objetivo comprender la dialógica entre las nociones de orden, desorden y organización, que traducen la transición de la simplificación a la complejidad del fenómeno pandémico y su relación con el tema Nursing Now and Nursing in the future. Resultados: el universo de los fenómenos se entreteje, simultáneamente, de orden, desorden y organización. Reafirmar el papel central de la Enfermería en el equipo de salud, ante las irrupciones e incertidumbres provocadas por la pandemia actual, implica la capacidad de dialogar con el desorden y propiciar una nueva y más compleja (re)organización global del ser y hacer Enfermería. Conclusión: la reflexión teórica plantea, además de respuestas, nuevas interrogantes y nuevas irrupciones. Se concibe la inseparabilidad entre las nociones de orden y desorden en la dinámica evolutiva del sistema de Enfermería y se aboga por la promoción de niveles aún más complejos de organización, gestión y asistencia de Enfermería para lograr el acceso universal a la salud.


Subject(s)
Nursing , Nonlinear Dynamics , Coronavirus , Pandemics , Nursing Care
17.
Physica D ; 425: 132968, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1260834

ABSTRACT

This paper introduces new methods to study the changing dynamics of COVID-19 cases and deaths among the 50 worst-affected countries throughout 2020. First, we analyse the trajectories and turning points of rolling mortality rates to understand at which times the disease was most lethal. We demonstrate five characteristic classes of mortality rate trajectories and determine structural similarity in mortality trends over time. Next, we introduce a class of virulence matrices to study the evolution of COVID-19 cases and deaths on a global scale. Finally, we introduce three-way inconsistency analysis to determine anomalous countries with respect to three attributes: countries' COVID-19 cases, deaths and human development indices. We demonstrate the most anomalous countries across these three measures are Pakistan, the United States and the United Arab Emirates.

18.
Chaos Solitons Fractals ; 150: 111063, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1240210

ABSTRACT

In this paper, we use the Z-control approach to get further insight on the role of awareness in the management of epidemics that, just like Covid-19, display a high rate of overexposure because of the large number of asymptomatic people. We focus on a SEIR model including a overexposure mechanism and consider awareness as a time-dependent variable whose dynamics is not assigned a priori. Exploiting the potential of awareness to produce social distancing and self-isolation among susceptibles, we use it as an indirect control on the class of infective individuals and apply the Z-control approach to detect what trend must awareness display over time in order to eradicate the disease. To this aim, we generalize the Z-control procedure to appropriately treat an uncontrolled model with more than two governing equations. Analytical and numerical investigations on the resulting Z-controlled system show its capability in controlling some representative dynamics within both the backward and the forward scenarios. The awareness variable is qualitatively compared to Google Trends data on Covid-19 that are discussed in the perspective of the Z-control approach, inferring qualitative indications in view of the disease control. The cases of Italy and New Zealand in the first phase of the pandemic are analyzed in detail. The theoretical framework of the Z-control approach can hence offer the chance to reflect on the use of Google Trends as a possible indicator of good management of the epidemic.

19.
J Theor Biol ; 520: 110632, 2021 07 07.
Article in English | MEDLINE | ID: covidwho-1100735

ABSTRACT

We study the dynamics of epidemics in a networked metapopulation model. In each subpopulation, representing a locality, the disease propagates according to a modified susceptible-exposed-infected-recovered (SEIR) dynamics. In the modified SEIR dynamics, individuals reduce their number of contacts as a function of the weighted sum of cumulative number of cases within the locality and in neighboring localities. We consider a scenario with two localities where disease originates in one locality and is exported to the neighboring locality via travel of exposed (latently infected) individuals. We establish a lower bound on the outbreak size at the origin as a function of the speed of spread. Using the lower bound on the outbreak size at the origin, we establish an upper bound on the outbreak size at the importing locality as a function of the speed of spread and the level of preparedness for the low mobility regime. We evaluate the critical levels of preparedness that stop the disease from spreading at the importing locality. Finally, we show how the benefit of preparedness diminishes under high mobility rates. Our results highlight the importance of preparedness at localities where cases are beginning to rise such that localities can help stop local outbreaks when they respond to the severity of outbreaks in neighboring localities.


Subject(s)
Disease Outbreaks , Epidemics , Disease Susceptibility , Humans , Travel
20.
Nonlinear Dyn ; 106(2): 1325-1346, 2021.
Article in English | MEDLINE | ID: covidwho-1144379

ABSTRACT

COVID-19 dynamics is one of the most relevant subjects nowadays, and, in this regard, mathematical modeling and numerical simulations are of special interest. This paper describes COVID-19 dynamics based on a novel version of the susceptible-exposed-infectious-removed model. Removed population is split into recovered and death populations allowing a better comprehension of real situations. Besides, the total population is reduced based on the number of deaths. Hospital infrastructure is also included into the mathematical description allowing the consideration of collapse scenarios. Initially, a model verification is carried out calibrating system parameters with data from China outbreak that is considered a benchmark due the availability of data for the entire cycle. Afterward, Brazil outbreak is of concern, calibrating the model and developing numerical simulations. Results show several scenarios highlighting the importance of social isolation and hospital infrastructure. System dynamics has a strong sensitivity to transmission rate showing the importance of numerical simulations to guide public health decision strategies. Results also show that complex dynamical responses can emerge due to the oscillations of the transmission rate, being associated with distinct infection subsequent waves.

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