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1.
Environ Monit Assess ; 192(11): 678, 2020 Oct 06.
Artigo em Inglês | MEDLINE | ID: mdl-33025274

RESUMO

Detecting the probable impact of climate change responses on hydrological components is most important for understanding such changes on water resources. The impact of climate change on virtual parameters of water was assessed through hydrological modeling of the Wunna, Mahanadi (Middle), and Bharathpuzha watersheds. In this article, future hydrological component responses under two Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios were considered for investigating the runoff, sediment, and water storage components. RegCM4 CSIRO-Mk3.6.0 CORDEX South Asia of RCM model was used which is specially downscaled for the Asian region by IITM-India. Delta change method was adopted to remove bias correction in RCM data. Hydrological simulation for current and future periods was performed by GIS interfaced Soil Water and Assessment Tool (SWAT) model. The surface runoff of Wunna and Bharathpuzha watersheds and the yield of sediment are expected to increase further under RCP8.5 than RCP4.5 and in contrast to Mahanadi watershed. Both blue water storage (BW) and green water storage (GWS) of Wunna watershed are expected to decline under RCP4.5, and rise under RCP8.5 scenario. Both BW and GWS of Bharathpuzha are expected to increase in the future except in western region under RCP4.5 scenario. BW of Mahanadi is expected to increase in the future. However, GWS will decrease in some of the sub-basins. The model-generated results will be helpful for future water resources planning and development.


Assuntos
Mudança Climática , Hidrologia , Ásia , Monitoramento Ambiental , Índia , Modelos Teóricos
2.
F1000Res ; 9: 283, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32983416

RESUMO

Coronavirus disease 2019 (COVID-19) is a worldwide pandemic that has been affecting Portugal since 2 March 2020. The Portuguese government has been making efforts to contradict the exponential growth through social isolation measures. We have developed a mathematical model to predict the impact of such measures in the number of infected cases and peak of infection. We estimate the peak to be around 2 million infected cases by the beginning of May if no additional measures are taken. The model shows that current measures effectively isolated 25-30% of the population, contributing to some reduction on the infection peak. Importantly, our simulations show that the infection burden can be further reduced with higher isolation degree, providing information for a second intervention.


Assuntos
Infecções por Coronavirus/epidemiologia , Pneumonia Viral/epidemiologia , Betacoronavirus , Controle de Doenças Transmissíveis , Infecções por Coronavirus/prevenção & controle , Previsões , Humanos , Modelos Teóricos , Pandemias/prevenção & controle , Cooperação do Paciente , Pneumonia Viral/prevenção & controle , Portugal/epidemiologia
3.
J Environ Qual ; 49(1): 128-139, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33016363

RESUMO

The Variable Volume Water Model (VVWM), the receiving water body model for the USEPA regulatory assessment of aquatic pesticide exposures, is composed of a set of static and quasistatic receiving water body conceptual models, but research comparing performance of these models to observations is limited. The water body models included are the constant volume (CVol), constant volume with overflow (CVO), and varying volume with overflow (VVO) models. This work quantified the performance of these three VVWM conceptual models compared with atrazine observations in 50 community water systems (CWSs), and the effect of alternative conceptual models on estimated environmental concentrations of pesticides in regulatory screening assessments. The 50 selected CWSs most relevant to the static and quasistatic VVWM concepts were small in size, with estimated time to peak flow of <1.5 d for consistency with the daily runoff assumption in USEPA landscape Pesticide Root Zone Model (PRZM). The CVO and VVO conceptual models resulted in similar distributions of bias across CWSs with the median result being close to no bias, but the CVol model resulted in overestimation in the majority of CWSs with median model bias near three times the observed values. At present, the CVol conceptual model parameterized with conservative input assumptions has been the regulatory standard invoked in VVWM, yet our results showed that a more physically correct conceptual model (CVO or VVO) could be invoked in regulatory exposure modeling for ecological risk assessment, reducing structural model bias while still allowing users to introduce conservative model inputs for screening purposes.


Assuntos
Atrazina , Praguicidas/análise , Praguicidas/toxicidade , Poluentes Químicos da Água/análise , Poluentes Químicos da Água/toxicidade , Modelos Teóricos , Água
4.
PLoS One ; 15(10): e0239960, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33017421

RESUMO

The outbreak of Corona Virus Disease 2019 (COVID-19) in Wuhan has significantly impacted the economy and society globally. Countries are in a strict state of prevention and control of this pandemic. In this study, the development trend analysis of the cumulative confirmed cases, cumulative deaths, and cumulative cured cases was conducted based on data from Wuhan, Hubei Province, China from January 23, 2020 to April 6, 2020 using an Elman neural network, long short-term memory (LSTM), and support vector machine (SVM). A SVM with fuzzy granulation was used to predict the growth range of confirmed new cases, new deaths, and new cured cases. The experimental results showed that the Elman neural network and SVM used in this study can predict the development trend of cumulative confirmed cases, deaths, and cured cases, whereas LSTM is more suitable for the prediction of the cumulative confirmed cases. The SVM with fuzzy granulation can successfully predict the growth range of confirmed new cases and new cured cases, although the average predicted values are slightly large. Currently, the United States is the epicenter of the COVID-19 pandemic. We also used data modeling from the United States to further verify the validity of the proposed models.


Assuntos
Infecções por Coronavirus/epidemiologia , Modelos Teóricos , Pneumonia Viral/epidemiologia , Probabilidade , Máquina de Vetores de Suporte , China/epidemiologia , Previsões , Lógica Fuzzy , Humanos , Redes Neurais de Computação , Pandemias , Estados Unidos/epidemiologia
5.
BMC Bioinformatics ; 21(Suppl 14): 408, 2020 Sep 30.
Artigo em Inglês | MEDLINE | ID: mdl-32998723

RESUMO

BACKGROUND: Second messengers, c-di-GMP and (p)ppGpp, are vital regulatory molecules in bacteria, influencing cellular processes such as biofilm formation, transcription, virulence, quorum sensing, and proliferation. While c-di-GMP and (p)ppGpp are both synthesized from GTP molecules, they play antagonistic roles in regulating the cell cycle. In C. crescentus, c-di-GMP works as a major regulator of pole morphogenesis and cell development. It inhibits cell motility and promotes S-phase entry by inhibiting the activity of the master regulator, CtrA. Intracellular (p)ppGpp accumulates under starvation, which helps bacteria to survive under stressful conditions through regulating nucleotide levels and halting proliferation. (p)ppGpp responds to nitrogen levels through RelA-SpoT homolog enzymes, detecting glutamine concentration using a nitrogen phosphotransferase system (PTS Ntr). This work relates the guanine nucleotide-based second messenger regulatory network with the bacterial PTS Ntr system and investigates how bacteria respond to nutrient availability. RESULTS: We propose a mathematical model for the dynamics of c-di-GMP and (p)ppGpp in C. crescentus and analyze how the guanine nucleotide-based second messenger system responds to certain environmental changes communicated through the PTS Ntr system. Our mathematical model consists of seven ODEs describing the dynamics of nucleotides and PTS Ntr enzymes. Our simulations are consistent with experimental observations and suggest, among other predictions, that SpoT can effectively decrease c-di-GMP levels in response to nitrogen starvation just as well as it increases (p)ppGpp levels. Thus, the activity of SpoT (or its homologues in other bacterial species) can likely influence the cell cycle by influencing both c-di-GMP and (p)ppGpp. CONCLUSIONS: In this work, we integrate current knowledge and experimental observations from the literature to formulate a novel mathematical model. We analyze the model and demonstrate how the PTS Ntr system influences (p)ppGpp, c-di-GMP, GMP and GTP concentrations. While this model does not consider all aspects of PTS Ntr signaling, such as cross-talk with the carbon PTS system, here we present our first effort to develop a model of nutrient signaling in C. crescentus.


Assuntos
Caulobacter crescentus/fisiologia , Modelos Teóricos , Sistemas do Segundo Mensageiro , Pontos de Checagem do Ciclo Celular , GMP Cíclico/análogos & derivados , GMP Cíclico/metabolismo , Nitrogênio/metabolismo , Fosfotransferases/metabolismo , Sistemas do Segundo Mensageiro/fisiologia
6.
Chaos ; 30(9): 091102, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-33003920

RESUMO

This paper introduces a mathematical framework for determining second surge behavior of COVID-19 cases in the United States. Within this framework, a flexible algorithmic approach selects a set of turning points for each state, computes distances between them, and determines whether each state is in (or over) a first or second surge. Then, appropriate distances between normalized time series are used to further analyze the relationships between case trajectories on a month-by-month basis. Our algorithm shows that 31 states are experiencing second surges, while four of the 10 largest states are still in their first surge, with case counts that have never decreased. This analysis can aid in highlighting the most and least successful state responses to COVID-19.


Assuntos
Infecções por Coronavirus/epidemiologia , Modelos Teóricos , Pneumonia Viral/epidemiologia , Algoritmos , Betacoronavirus , Humanos , Pandemias , Capacidade de Resposta ante Emergências , Estados Unidos/epidemiologia
7.
Chaos ; 30(9): 093123, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-33003939

RESUMO

COVID-19 is an emerging respiratory infectious disease caused by the coronavirus SARS-CoV-2. It was first reported on in early December 2019 in Wuhan, China and within three months spread as a pandemic around the whole globe. Here, we study macro-epidemiological patterns along the time course of the COVID-19 pandemic. We compute the distribution of confirmed COVID-19 cases and deaths for countries worldwide and for counties in the US and show that both distributions follow a truncated power-law over five orders of magnitude. We are able to explain the origin of this scaling behavior as a dual-scale process: the large-scale spread of the virus between countries and the small-scale accumulation of case numbers within each country. Assuming exponential growth on both scales, the critical exponent of the power-law is determined by the ratio of large-scale to small-scale growth rates. We confirm this theory in numerical simulations in a simple meta-population model, describing the epidemic spread in a network of interconnected countries. Our theory gives a mechanistic explanation why most COVID-19 cases occurred within a few epicenters, at least in the initial phase of the outbreak. By combining real world data, modeling, and numerical simulations, we make the case that the distribution of epidemic prevalence might follow universal rules.


Assuntos
Infecções por Coronavirus/epidemiologia , Modelos Teóricos , Pneumonia Viral/epidemiologia , Betacoronavirus , Humanos , Pandemias , Dinâmica Populacional
8.
Glob Health Action ; 13(1): 1816044, 2020 12 31.
Artigo em Inglês | MEDLINE | ID: mdl-33012269

RESUMO

COVID-19 has wreaked havoc globally with particular concerns for sub-Saharan Africa (SSA), where models suggest that the majority of the population will become infected. Conventional wisdom suggests that the continent will bear a higher burden of COVID-19 for the same reasons it suffers from other infectious diseases: ecology, socio-economic conditions, lack of water and sanitation infrastructure, and weak health systems. However, so far SSA has reported lower incidence and fatalities compared to the predictions of standard models and the experience of other regions of the world. There are three leading explanations, each with different implications for the final epidemic burden: (1) low case detection, (2) differences in epidemiology (e.g. low R 0 ), and (3) policy interventions. The low number of cases have led some SSA governments to relaxing these policy interventions. Will this result in a resurgence of cases? To understand how to interpret the lower-than-expected COVID-19 case data in Madagascar, we use a simple age-structured model to explore each of these explanations and predict the epidemic impact associated with them. We show that the incidence of COVID-19 cases as of July 2020 can be explained by any combination of the late introduction of first imported cases, early implementation of non-pharmaceutical interventions (NPIs), and low case detection rates. We then re-evaluate these findings in the context of the COVID-19 epidemic in Madagascar through August 2020. This analysis reinforces that Madagascar, along with other countries in SSA, remains at risk of a growing health crisis. If NPIs remain enforced, up to 50,000 lives may be saved. Even with NPIs, without vaccines and new therapies, COVID-19 could infect up to 30% of the population, making it the largest public health threat in Madagascar for the coming year, hence the importance of clinical trials and continually improving access to healthcare.


Assuntos
Infecções por Coronavirus/epidemiologia , Modelos Teóricos , Pneumonia Viral/epidemiologia , África ao Sul do Saara/epidemiologia , Humanos , Incidência , Madagáscar/epidemiologia , Pandemias
9.
Colomb Med (Cali) ; 51(2): e4277, 2020 Jun 30.
Artigo em Inglês | MEDLINE | ID: mdl-33012889

RESUMO

Currently, there are several mathematical models that have been developed to understand the dynamics of COVID-19 infection. However, the difference in the sociocultural contexts between countries requires the specific adjustment of these estimates to each scenario. This article analyses the main elements used for the construction of models from epidemiological patterns, to describe the interaction, explain the dynamics of infection and recovery, and to predict possible scenarios that may arise with the introduction of public health measures such as social distancing and quarantines, specifically in the case of the pandemic unleashed by the new SARS-CoV-2/COVID-19 virus. Comment: Mathematical models are highly relevant for making objective and effective decisions to control and eradicate the disease. These models used for COVID-19 have supported and will continue to provide information for the selection and implementation of programs and public policies that prevent associated complications, reduce the speed of the virus spread and minimize the occurrence of severe cases of the disease that may collapse health systems.


Assuntos
Infecções por Coronavirus/epidemiologia , Política de Saúde , Modelos Teóricos , Pneumonia Viral/epidemiologia , Infecções por Coronavirus/prevenção & controle , Assistência à Saúde/organização & administração , Humanos , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Saúde Pública , Quarentena , Isolamento Social
10.
Comput Math Methods Med ; 2020: 9017157, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33029196

RESUMO

This paper deals with the mathematical modeling and numerical simulations related to the coronavirus dynamics. A description is developed based on the framework of the susceptible-exposed-infectious-removed model. Initially, a model verification is carried out calibrating system parameters with data from China, Italy, Iran, and Brazil. Results show the model capability to predict infectious evolution. Afterward, numerical simulations are performed in order to analyze different scenarios of COVID-19 in Brazil. Results show the importance of the governmental and individual actions to control the number and the period of the critical situations related to the pandemic.


Assuntos
Simulação por Computador , Infecções por Coronavirus/epidemiologia , Pneumonia Viral/epidemiologia , Algoritmos , Betacoronavirus , Brasil/epidemiologia , China/epidemiologia , Doenças Transmissíveis/epidemiologia , Humanos , Irã (Geográfico)/epidemiologia , Itália/epidemiologia , Modelos Teóricos , Pandemias , Informática em Saúde Pública , Reprodutibilidade dos Testes
11.
F1000Res ; 9: 232, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32864101

RESUMO

Since the first identified case of COVID-19 in Wuhan, China, the disease has developed into a pandemic, imposing a major challenge for health authorities and hospitals worldwide. Mathematical transmission models can help hospitals to anticipate and prepare for an upcoming wave of patients by forecasting the time and severity of infections. Taking the city of Heidelberg as an example, we predict the ongoing spread of the disease for the next months including hospital and ventilator capacity and consider the possible impact of currently imposed countermeasures.


Assuntos
Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/transmissão , Modelos Teóricos , Pneumonia Viral/epidemiologia , Pneumonia Viral/transmissão , Betacoronavirus , Cidades/epidemiologia , Alemanha/epidemiologia , Humanos , Pandemias
12.
F1000Res ; 9: 352, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32864104

RESUMO

Background: School closures have been a recommended non-pharmaceutical intervention in pandemic response owing to the potential to reduce transmission of infection between children, school staff and those that they contact. However, given the many roles that schools play in society, closure for any extended period is likely to have additional impacts. Literature reviews of research exploring school closure to date have focused upon epidemiological effects; there is an unmet need for research that considers the multiplicity of potential impacts of school closures. Methods: We used systematic searching, coding and synthesis techniques to develop a systems-based logic model. We included literature related to school closure planned in response to epidemics large and small, spanning the 1918-19 'flu pandemic through to the emerging literature on the 2019 novel coronavirus. We used over 170 research studies and a number of policy documents to inform our model. Results: The model organises the concepts used by authors into seven higher level domains: children's health and wellbeing, children's education, impacts on teachers and other school staff, the school organisation, considerations for parents and families, public health considerations, and broader economic impacts. The model also collates ideas about potential moderating factors and ethical considerations. While dependent upon the nature of epidemics experienced to date, we aim for the model to provide a starting point for theorising about school closures in general, and as part of a wider system that is influenced by contextual and population factors. Conclusions: The model highlights that the impacts of school closures are much broader than those related solely to health, and demonstrates that there is a need for further concerted work in this area. The publication of this logic model should help to frame future research in this area and aid decision-makers when considering future school closure policy and possible mitigation strategies.


Assuntos
Controle de Doenças Transmissíveis/métodos , Infecções por Coronavirus/prevenção & controle , Influenza Humana/prevenção & controle , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Instituições Acadêmicas , Betacoronavirus , Surtos de Doenças/prevenção & controle , Humanos , Modelos Teóricos
13.
BMC Infect Dis ; 20(1): 643, 2020 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-32873241

RESUMO

BACKGROUND: The transmission features and the feasibility of containing shigellosis remain unclear among a population-based study in China. METHODS: A population-based Susceptible - Exposed - Infectious / Asymptomatic - Recovered (SEIAR) model was built including decreasing the infectious period (DIP) or isolation of shigellosis cases. We analyzed the distribution of the reported shigellosis cases in Hubei Province, China from January 2005 to December 2017, and divided the time series into several stages according to the heterogeneity of reported incidence during the period. In each stage, an epidemic season was selected for the modelling and assessing the effectiveness of DIP and case isolation. RESULTS: A total of 130,770 shigellosis cases were reported in Hubei Province. The median of Reff was 1.13 (range: 0.86-1.21), 1.10 (range: 0.91-1.13), 1.09 (range: 0.92-1.92), and 1.03 (range: 0.94-1.22) in 2005-2006 season, 2010-2011 season, 2013-2014 season, and 2016-2017 season, respectively. The reported incidence decreased significantly (trend χ2 = 8260.41, P <  0.001) among four stages. The incidence of shigellosis decreased sharply when DIP implemented in three scenarios (γ = 0.1, 0.1429, 0.3333) and when proportion of case isolation increased. CONCLUSIONS: Year heterogeneity of reported shigellosis incidence exists in Hubei Province. It is feasible to contain the transmission by implementing DIP and case isolation.


Assuntos
Disenteria Bacilar/epidemiologia , Epidemias , Modelos Teóricos , Infecções Assintomáticas , China/epidemiologia , Simulação por Computador , Coleta de Dados , Disenteria Bacilar/prevenção & controle , Disenteria Bacilar/transmissão , Estudos de Viabilidade , Humanos , Incidência , Estações do Ano
14.
Rev Peru Med Exp Salud Publica ; 37(2): 195-202, 2020.
Artigo em Espanhol, Inglês | MEDLINE | ID: mdl-32876206

RESUMO

OBJECTIVES: To determine the probability of controlling the outbreak of COVID-19 in Peru, in a pre- and post-quarantine scenario using mathematical simulation models. MATERIALS AND METHODS: Outbreak si mulations for the COVID-19 pandemic are performed, using stochastic equations under the following assumptions: a pre-quarantine population R0 of 2.7 or 3.5, a post-quarantine R0 of 1.5, 2 or 2.7, 18% or 40%, of asymptomatic positives and a maximum response capacity of 50 or 150 patients in the intensive care units. The success of isolation and contact tracing is evaluated, no other mitigation measures are included. RESULTS: In the pre-quarantine stage, success in controlling more than 80% of the simulations occurred only if the isolation of positive cases was implemented from the first case, after which there was less than 40% probability of success. In post-quarantine, with 60 positive cases it is necessary to isolate them early, track all of their contacts and decrease the R0 to 1.5 for outbreak control to be successful in more than 80% of cases. Other scenarios have a low probability of success. CONCLUSIONS: The control of the outbreak in Peru during pre-quarantine stage demanded requirements that were difficult to comply with, therefore quarantine was necessary; to successfully suspend it would require a significant reduction in the spread of the disease, early isolation of positives and follow-up of all contacts of positive patients.


Assuntos
Simulação por Computador , Infecções por Coronavirus/epidemiologia , Surtos de Doenças/prevenção & controle , Pneumonia Viral/epidemiologia , Busca de Comunicante/métodos , Infecções por Coronavirus/prevenção & controle , Humanos , Unidades de Terapia Intensiva/estatística & dados numéricos , Modelos Teóricos , Pandemias/prevenção & controle , Peru/epidemiologia , Pneumonia Viral/prevenção & controle , Probabilidade , Quarentena
15.
J Environ Radioact ; 222: 106356, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32892908

RESUMO

Predictions of the atmospheric dispersion of radionuclides accidentally released from a nuclear power plant are influenced by two large sources of uncertainty: one associated with the meteorological data employed, and one with the source term, i.e. the temporal evolution of the amount and physical and chemical properties of the release. A methodology is presented for quantitative estimation of the variability of the prediction of atmospheric dispersion resulting from both sources of uncertainty. The methodology, which allows for efficient calculation, and thus is well suited for real-time assessment, is applied to a hypothetical accidental release of radionuclides.


Assuntos
Poluentes Radioativos do Ar , Monitoramento de Radiação , Liberação Nociva de Radioativos , Modelos Teóricos , Centrais Nucleares , Incerteza
16.
F1000Res ; 9: 570, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32884676

RESUMO

The 2019-2020 global pandemic has been caused by a disease called coronavirus disease 2019 (COVID-19). This disease has been caused by the Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2). By April 30 2020, the World Health Organization reported 3,096,626 cases and 217,896 deaths, which implies an exponential growth for infection and deaths worldwide. Currently, there are various computer-based approaches that present COVID-19 data through different types of charts, which is very useful to recognise its behavior and trends. Nevertheless, such approaches do not allow for observation of any projection regarding confirmed cases and deaths, which would be useful to understand the trends of COVID-19. In this work, we have designed and developed an online dashboard that presents actual information about COVID-19. Furthermore, based on this information, we have designed a mathematical model in order to make projections about the evolution of cases and deaths worldwide and by country.


Assuntos
Infecções por Coronavirus/mortalidade , Análise de Dados , Pneumonia Viral/mortalidade , Software , Betacoronavirus , Humanos , Internet , Modelos Teóricos , Pandemias
17.
J Transl Med ; 18(1): 345, 2020 09 05.
Artigo em Inglês | MEDLINE | ID: mdl-32891155

RESUMO

BACKGROUND: Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spreads rapidly and has attracted worldwide attention. METHODS: To improve the forecast accuracy and investigate the spread of SARS-CoV-2, we constructed four mathematical models to numerically estimate the spread of SARS-CoV-2 and the efficacy of eradication strategies. RESULTS: Using the Susceptible-Exposed-Infected-Removed (SEIR) model, and including measures such as city closures and extended leave policies implemented by the Chinese government that effectively reduced the ß value, we estimated that the ß value and basic transmission number, R0, of SARS-CoV-2 was 0.476/6.66 in Wuhan, 0.359/5.03 in Korea, and 0.400/5.60 in Italy. Considering medicine and vaccines, an advanced model demonstrated that the emergence of vaccines would greatly slow the spread of the virus. Our model predicted that 100,000 people would become infected assuming that the isolation rate α in Wuhan was 0.30. If quarantine measures were taken from March 10, 2020, and the quarantine rate of α was also 0.3, then the final number of infected people was predicted to be 11,426 in South Korea and 147,142 in Italy. CONCLUSIONS: Our mathematical models indicate that SARS-CoV-2 eradication depends on systematic planning, effective hospital isolation, and SARS-CoV-2 vaccination, and some measures including city closures and leave policies should be implemented to ensure SARS-CoV-2 eradication.


Assuntos
Betacoronavirus/fisiologia , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/virologia , Erradicação de Doenças , Modelos Teóricos , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Pneumonia Viral/virologia , China/epidemiologia , Infecções por Coronavirus/epidemiologia , Epidemias/prevenção & controle , Governo , Humanos , Itália/epidemiologia , Pneumonia Viral/epidemiologia , Quarentena , República da Coreia/epidemiologia , Vacinação
18.
PLoS One ; 15(9): e0237627, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32877420

RESUMO

The ongoing COVID-19 epidemics poses a particular challenge to low and middle income countries, making some of them consider the strategy of "vertical confinement". In this strategy, contact is reduced only to specific groups (e.g. age groups) that are at increased risk of severe disease following SARS-CoV-2 infection. We aim to assess the feasibility of this scenario as an exit strategy for the current lockdown in terms of its ability to keep the number of cases under the health care system capacity. We developed a modified SEIR model, including confinement, asymptomatic transmission, quarantine and hospitalization. The population is subdivided into 9 age groups, resulting in a system of 72 coupled nonlinear differential equations. The rate of transmission is dynamic and derived from the observed delayed fatality rate; the parameters of the epidemics are derived with a Markov chain Monte Carlo algorithm. We used Brazil as an example of middle income country, but the results are easily generalizable to other countries considering a similar strategy. We find that starting from 60% horizontal confinement, an exit strategy on May 1st of confinement of individuals older than 60 years old and full release of the younger population results in 400 000 hospitalizations, 50 000 ICU cases, and 120 000 deaths in the 50-60 years old age group alone. Sensitivity analysis shows the 95% confidence interval brackets a order of magnitude in cases or three weeks in time. The health care system avoids collapse if the 50-60 years old are also confined, but our model assumes an idealized lockdown where the confined are perfectly insulated from contamination, so our numbers are a conservative lower bound. Our results discourage confinement by age as an exit strategy.


Assuntos
Infecções por Coronavirus/patologia , Modelos Teóricos , Pneumonia Viral/patologia , Fatores Etários , Betacoronavirus/isolamento & purificação , Brasil/epidemiologia , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/transmissão , Infecções por Coronavirus/virologia , Humanos , Cadeias de Markov , Método de Monte Carlo , Pandemias , Pneumonia Viral/epidemiologia , Pneumonia Viral/transmissão , Pneumonia Viral/virologia , Quarentena
19.
Phys Rev Lett ; 125(9): 094101, 2020 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-32915595

RESUMO

Synchronization is a widespread phenomenon observed in physical, biological, and social networks, which persists even under the influence of strong noise. Previous research on oscillators subject to common noise has shown that noise can actually facilitate synchronization, as correlations in the dynamics can be inherited from the noise itself. However, in many spatially distributed networks, such as the mammalian circadian system, the noise that different oscillators experience can be effectively uncorrelated. Here, we show that uncorrelated noise can in fact enhance synchronization when the oscillators are coupled. Strikingly, our analysis also shows that uncorrelated noise can be more effective than common noise in enhancing synchronization. We first establish these results theoretically for phase and phase-amplitude oscillators subject to either or both additive and multiplicative noise. We then confirm the predictions through experiments on coupled electrochemical oscillators. Our findings suggest that uncorrelated noise can promote rather than inhibit coherence in natural systems and that the same effect can be harnessed in engineered systems.


Assuntos
Relógios Biológicos , Modelos Teóricos , Humanos , Oscilometria/métodos , Processos Estocásticos
20.
PLoS One ; 15(9): e0238559, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32886696

RESUMO

The novel coronavirus (SARS-CoV-2), identified in China at the end of December 2019 and causing the disease COVID-19, has meanwhile led to outbreaks all over the globe with about 2.2 million confirmed cases and more than 150,000 deaths as of April 17, 2020. In this work, mathematical models are used to reproduce data of the early evolution of the COVID-19 outbreak in Germany, taking into account the effect of actual and hypothetical non-pharmaceutical interventions. Systems of differential equations of SEIR type are extended to account for undetected infections, stages of infection, and age groups. The models are calibrated on data until April 5. Data from April 6 to 14 are used for model validation. We simulate different possible strategies for the mitigation of the current outbreak, slowing down the spread of the virus and thus reducing the peak in daily diagnosed cases, the demand for hospitalization or intensive care units admissions, and eventually the number of fatalities. Our results suggest that a partial (and gradual) lifting of introduced control measures could soon be possible if accompanied by further increased testing activity, strict isolation of detected cases, and reduced contact to risk groups.


Assuntos
Infecções por Coronavirus/epidemiologia , Modelos Teóricos , Pneumonia Viral/epidemiologia , Adolescente , Adulto , Fatores Etários , Idoso , Idoso de 80 Anos ou mais , Criança , Pré-Escolar , Controle de Doenças Transmissíveis/métodos , Controle de Doenças Transmissíveis/estatística & dados numéricos , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Transmissão de Doença Infecciosa/estatística & dados numéricos , Alemanha/epidemiologia , Hospitalização/estatística & dados numéricos , Humanos , Lactente , Pessoa de Meia-Idade , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão
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