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1.
F1000Res ; 9: 283, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32983416

RESUMEN

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.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Neumonía Viral/epidemiología , Betacoronavirus , Control de Enfermedades Transmisibles , Infecciones por Coronavirus/prevención & control , Predicción , Humanos , Modelos Teóricos , Pandemias/prevención & control , Cooperación del Paciente , Neumonía Viral/prevención & control , Portugal/epidemiología
2.
J Environ Qual ; 49(1): 128-139, 2020 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-33016363

RESUMEN

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.


Asunto(s)
Atrazina , Plaguicidas/análisis , Plaguicidas/toxicidad , Contaminantes Químicos del Agua/análisis , Contaminantes Químicos del Agua/toxicidad , Modelos Teóricos , Agua
3.
Environ Monit Assess ; 192(11): 678, 2020 Oct 06.
Artículo en Inglés | MEDLINE | ID: mdl-33025274

RESUMEN

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.


Asunto(s)
Cambio Climático , Hidrología , Asia , Monitoreo del Ambiente , India , Modelos Teóricos
4.
BMC Bioinformatics ; 21(Suppl 14): 408, 2020 Sep 30.
Artículo en Inglés | MEDLINE | ID: mdl-32998723

RESUMEN

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.


Asunto(s)
Caulobacter crescentus/fisiología , Modelos Teóricos , Sistemas de Mensajero Secundario , Puntos de Control del Ciclo Celular , GMP Cíclico/análogos & derivados , GMP Cíclico/metabolismo , Nitrógeno/metabolismo , Fosfotransferasas/metabolismo , Sistemas de Mensajero Secundario/fisiología
5.
Chaos ; 30(9): 091102, 2020 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-33003920

RESUMEN

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.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Modelos Teóricos , Neumonía Viral/epidemiología , Algoritmos , Betacoronavirus , Humanos , Pandemias , Capacidad de Reacción , Estados Unidos/epidemiología
6.
Chaos ; 30(9): 093123, 2020 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-33003939

RESUMEN

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.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Modelos Teóricos , Neumonía Viral/epidemiología , Betacoronavirus , Humanos , Pandemias , Dinámica Poblacional
7.
Glob Health Action ; 13(1): 1816044, 2020 12 31.
Artículo en Inglés | MEDLINE | ID: mdl-33012269

RESUMEN

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.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Modelos Teóricos , Neumonía Viral/epidemiología , África del Sur del Sahara/epidemiología , Humanos , Incidencia , Madagascar/epidemiología , Pandemias
8.
Colomb Med (Cali) ; 51(2): e4277, 2020 Jun 30.
Artículo en Inglés | MEDLINE | ID: mdl-33012889

RESUMEN

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.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Política de Salud , Modelos Teóricos , Neumonía Viral/epidemiología , Infecciones por Coronavirus/prevención & control , Prestación de Atención de Salud/organización & administración , Humanos , Pandemias/prevención & control , Neumonía Viral/prevención & control , Salud Pública , Cuarentena , Aislamiento Social
9.
Comput Math Methods Med ; 2020: 9017157, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-33029196

RESUMEN

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.


Asunto(s)
Simulación por Computador , Infecciones por Coronavirus/epidemiología , Neumonía Viral/epidemiología , Algoritmos , Betacoronavirus , Brasil/epidemiología , China/epidemiología , Enfermedades Transmisibles/epidemiología , Humanos , Irán/epidemiología , Italia/epidemiología , Modelos Teóricos , Pandemias , Informática en Salud Pública , Reproducibilidad de los Resultados
10.
BMC Med Res Methodol ; 20(1): 248, 2020 10 06.
Artículo en Inglés | MEDLINE | ID: mdl-33023505

RESUMEN

BACKGROUND: Classic epidemic curves - counts of daily events or cumulative events over time -emphasise temporal changes in the growth or size of epidemic outbreaks. Like any graph, these curves have limitations: they are impractical for comparisons of large and small outbreaks or of asynchronous outbreaks, and they do not display the relative growth rate of the epidemic. Our aim was to propose two additional graphical displays for the monitoring of epidemic outbreaks that overcome these limitations. METHODS: The first graph shows the growth of the epidemic as a function of its size; specifically, the logarithm of new cases on a given day, N(t), is plotted against the logarithm of cumulative cases C(t). Logarithm transformations facilitate comparisons of outbreaks of different sizes, and the lack of a time scale overcomes the need to establish a starting time for each outbreak. Notably, on this graph, exponential growth corresponds to a straight line with a slope equal to one. The second graph represents the logarithm of the relative rate of growth of the epidemic over time; specifically, log10(N(t)/C(t-1)) is plotted against time (t) since the 25th event. We applied these methods to daily death counts attributed to COVID-19 in selected countries, reported up to June 5, 2020. RESULTS: In most countries, the log(N) over log(C) plots showed initially a near-linear increase in COVID-19 deaths, followed by a sharp downturn. They enabled comparisons of small and large outbreaks (e.g., Switzerland vs UK), and identified outbreaks that were still growing at near-exponential rates (e.g., Brazil or India). The plots of log10(N(t)/C(t-1)) over time showed a near-linear decrease (on a log scale) of the relative growth rate of most COVID-19 epidemics, and identified countries in which this decrease failed to set in in the early weeks (e.g., USA) or abated late in the outbreak (e.g., Portugal or Russia). CONCLUSIONS: The plot of log(N) over log(C) displays simultaneously the growth and size of an epidemic, and allows easy identification of exponential growth. The plot of the logarithm of the relative growth rate over time highlights an essential parameter of epidemic outbreaks.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Modelos Teóricos , Neumonía Viral/epidemiología , Betacoronavirus , Interpretación Estadística de Datos , Métodos Epidemiológicos , Humanos , Pandemias
11.
Ugeskr Laeger ; 182(41)2020 10 05.
Artículo en Danés | MEDLINE | ID: mdl-33046185

RESUMEN

Transparency in modelling of the COVID-19 spread is necessary for enhanced understanding of the underlying logic and assumptions. In Denmark, the government relies on the expert advice given by Statens Serum Institut, whose model logic requires programming capabilities. This review demonstrates the importance in model transparency by setting up a system dynamics simulation model of the COVID-19 spread. The developed model can be applied to test intended interventions' effects and is promoted as a suitable approach for other equally complex problem solving in healthcare.


Asunto(s)
Infecciones por Coronavirus , Pandemias , Neumonía Viral , Solución de Problemas , Betacoronavirus , Infecciones por Coronavirus/transmisión , Prestación de Atención de Salud , Humanos , Modelos Teóricos , Neumonía Viral/transmisión
12.
PLoS One ; 15(10): e0239960, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-33017421

RESUMEN

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.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Modelos Teóricos , Neumonía Viral/epidemiología , Probabilidad , Máquina de Vectores de Soporte , China/epidemiología , Predicción , Lógica Difusa , Humanos , Redes Neurales de la Computación , Pandemias , Estados Unidos/epidemiología
13.
PLoS One ; 15(9): e0238559, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32886696

RESUMEN

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.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Modelos Teóricos , Neumonía Viral/epidemiología , Adolescente , Adulto , Factores de Edad , Anciano , Anciano de 80 o más Años , Niño , Preescolar , Control de Enfermedades Transmisibles/métodos , Control de Enfermedades Transmisibles/estadística & datos numéricos , Infecciones por Coronavirus/prevención & control , Infecciones por Coronavirus/transmisión , Transmisión de Enfermedad Infecciosa/estadística & datos numéricos , Alemania/epidemiología , Hospitalización/estadística & datos numéricos , Humanos , Lactante , Persona de Mediana Edad , Pandemias/prevención & control , Neumonía Viral/prevención & control , Neumonía Viral/transmisión
14.
J Korean Med Sci ; 35(35): e321, 2020 Sep 07.
Artículo en Inglés | MEDLINE | ID: mdl-32893522

RESUMEN

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has posed significant global public health challenges and created a substantial economic burden. Korea has experienced an extensive outbreak, which was linked to a religion-related super-spreading event. However, the implementation of various non-pharmaceutical interventions (NPIs), including social distancing, spring semester postponing, and extensive testing and contact tracing controlled the epidemic. Herein, we estimated the effectiveness of each NPI using a simulation model. METHODS: A compartment model with a susceptible-exposed-infectious-quarantined-hospitalized structure was employed. Using the Monte-Carlo-Markov-Chain algorithm with Gibbs' sampling method, we estimated the time-varying effective contact rate to calibrate the model with the reported daily new confirmed cases from February 12th to March 31st (7 weeks). Moreover, we conducted scenario analyses by adjusting the parameters to estimate the effectiveness of NPI. RESULTS: Relaxed social distancing among adults would have increased the number of cases 27.4-fold until the end of March. Spring semester non-postponement would have increased the number of cases 1.7-fold among individuals aged 0-19, while lower quarantine and detection rates would have increased the number of cases 1.4-fold. CONCLUSION: Among the three NPI measures, social distancing in adults showed the highest effectiveness. The substantial effect of social distancing should be considered when preparing for the 2nd wave of COVID-19.


Asunto(s)
Control de Enfermedades Transmisibles/métodos , Trazado de Contacto/métodos , Infecciones por Coronavirus/transmisión , Tamizaje Masivo/métodos , Neumonía Viral/transmisión , Distancia Social , Betacoronavirus , Simulación por Computador , Exposición a Riesgos Ambientales/prevención & control , Humanos , Cadenas de Markov , Modelos Teóricos , Método de Montecarlo , Pandemias , Práctica de Salud Pública/legislación & jurisprudencia , República de Corea
15.
Urologiia ; (4): 27-35, 2020 Sep.
Artículo en Ruso | MEDLINE | ID: mdl-32897011

RESUMEN

INTRODUCTION: Reconstructive plastic surgery is the gold standard in the treatment of primary urethral strictures, but the effectiveness of these methods does not reach 100%. In cases of recurrent urethral strictures, the effectiveness of standard operations is lower than with primary strictures, which requires a search for methods to improve the results of surgical treatment. PURPOSE OF THE STUDY: To evaluate the structure of the intercellular matrix, the cellular composition and regenerative potential of a plasma enriched in platelets after performing urethroplasty on a biological model. MATERIALS AND METHODS: Experiment was carried out on male rabbits ("Burgundy" breed) weighting 3.0-4.5 kg (18 individuals). 18 animals were divided into two groups: an experimental one (contained 9 individuals) and a control one (contained 9 individuals). All animals in each group were subjected to end-to-end plastic surgery of the urethral bulbous region using standard procedures. In the control group 4 ml of 0.9% NaCl isotonic solution was injected along the perimeter of the suture into anastomotic zone of the spongy body. Autologous plasma was injected to the animals from the experimental group. Histopathological examination was made by using routine pathological assessment with hematoxylin-eosin staining. The study also assessed the distribution and orientation of collagen fibers with Van Gieson stain. In order to objectively detail inflammatory and regenerative changes an additional immunohistochemical analysis was performed for the following antibodie groups: CD79a, CD43, CD31 (PECAM1), MMP1, MMP9. Quantitative analysis of structural changes was carried out by counting B- and T-lymphocytes having a positive membrane reaction with CD79a and CD43, respectively, in 10 representative sites in view (HPF) with a lens aperture of 0.65 in the highest concentration areas ("hot spots"). The expression level of MMP1, MMP9 was estimated by counting positive cells in 10 representative sites in view with a lens aperture of 0.65 in the submucosal and muscle layers. The level of angiogenesis in micropreparations was evaluated by counting the number of vessels in 10 sites in view (objective aperture of 0.65), reliable endothelial visualization was performed using CD31 expression (PECAM1). RESULTS: In the analysis of histological material use of platelet-rich plasma in the suture zone helps to reduce the area of necrotic changes and the inflammation severity, accelerated migration of macrophage-histiocytic cells to the alteration site and increased blood supply due to enhanced angiogenesis. In experimental samples a higher expression of metalloproteinases (collagenases) types 1 and 9, decreased collagen production and the correct orientation of collagen fibers during repair processes were noticed. CONCLUSIONS: The use of platelet-rich plasma helps to accelerate the reparative processes in the spongy body after urethroplasty. Another important positive effect of platelet-rich plasma is an increase in the expression of metalloproteinases, which leads to a decrease in collagen production and the correct orientation of collagen fibers. This allows to reduce the amount of pathological fibro-scar tissue in the operation area.


Asunto(s)
Plasma Rico en Plaquetas , Estrechez Uretral/cirugía , Animales , Humanos , Masculino , Modelos Teóricos , Conejos , Uretra , Procedimientos Quirúrgicos Urológicos Masculinos
16.
An Acad Bras Cienc ; 92(4): e20201139, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32965306

RESUMEN

The spread of SARS-CoV-2 and the distribution of cases worldwide followed no clear biogeographic, climatic, or cultural trend. Conversely, the internationally busiest cities in all countries tended to be the hardest hit, suggesting a basic, mathematically neutral pattern of the new coronavirus early dissemination. We tested whether the number of flight passengers per time and the number of international frontiers could explain the number of cases of COVID-19 worldwide by a stepwise regression. Analysis were taken by 22 May 2020, a period when one would claim that early patterns of the pandemic establishment were still detectable, despite of community transmission in various places. The number of passengers arriving in a country and the number of international borders explained significantly 49% of the variance in the distribution of the number of cases of COVID-19, and number of passengers explained significantly 14.2% of data variance for cases per million inhabitants. Ecological neutral theory may explain a considerable part of the early distribution of SARS-CoV-2 and should be taken into consideration to define preventive international actions before a next pandemic.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/transmisión , Neumonía Viral/epidemiología , Neumonía Viral/transmisión , Viaje , Aeronaves , Betacoronavirus , Ciudades , Humanos , Modelos Teóricos , Pandemias
17.
Environ Monit Assess ; 192(10): 663, 2020 Sep 28.
Artículo en Inglés | MEDLINE | ID: mdl-32989603

RESUMEN

Reflecting on the change in the global biodiversity pattern, the Tibetan Plateau, considered to be a "natural laboratory" for analyzing environmental change in China and around the world, has suffered profound changes in the vegetation ecosystem. This study introduces the gravity center model and geographical detectors to examine and discuss the spatial-temporal change pattern and the driving mechanism behind vegetation net primary production (NPP) in the Qinghai-Tibet Plateau from the year 2000 to 2015 while also quantitatively classifying the relative roles incorporated in the NPP change process. The study found that (1) from 2000 to 2015, the annual average NPP of the Tibetan Plateau demonstrated a declining trend from southeast to northwest. (2) The gravity center of vegetation NPP on the Qinghai-Tibet Plateau seems to have shifted eastward in the past 16 years, indicating that the level of vegetation NPP in the east depicts a greater increment and growth rate than the west. (3) In the arid regions, temperature and rainfall appear as the dominant factors for vegetation NPP, while slope and aspect parameters have constantly assumed dominancy for the same in the tropical rainforest-monsoon ecological zone in southeastern Tibet. (4) The structure of vegetation NPP exhibits an interaction between human and natural factors, which enhances the influence of single factors. (5) Considering the global ecological change and related human activities, certain differences are observed in the dominant and interaction factors for different study periods and ecological subregions in the Qinghai-Tibet Plateau. The research results could prove conclusive for vegetation ecological protection in the Qinghai-Tibet plateau.


Asunto(s)
Cambio Climático , Ecosistema , China , Monitoreo del Ambiente , Actividades Humanas , Humanos , Modelos Teóricos , Tibet
18.
J Chem Phys ; 153(11): 114119, 2020 Sep 21.
Artículo en Inglés | MEDLINE | ID: mdl-32962383

RESUMEN

The complexity associated with an epidemic defies any quantitatively reliable predictive theoretical scheme. Here, we pursue a generalized mathematical model and cellular automata simulations to study the dynamics of infectious diseases and apply it in the context of the COVID-19 spread. Our model is inspired by the theory of coupled chemical reactions to treat multiple parallel reaction pathways. We essentially ask the question: how hard could the time evolution toward the desired herd immunity (HI) be on the lives of people? We demonstrate that the answer to this question requires the study of two implicit functions, which are determined by several rate constants, which are time-dependent themselves. Implementation of different strategies to counter the spread of the disease requires a certain degree of a quantitative understanding of the time-dependence of the outcome. Here, we compartmentalize the susceptible population into two categories, (i) vulnerables and (ii) resilients (including asymptomatic carriers), and study the dynamical evolution of the disease progression. We obtain the relative fatality of these two sub-categories as a function of the percentages of the vulnerable and resilient population and the complex dependence on the rate of attainment of herd immunity. We attempt to study and quantify possible adverse effects of the progression rate of the epidemic on the recovery rates of vulnerables, in the course of attaining HI. We find the important result that slower attainment of the HI is relatively less fatal. However, slower progress toward HI could be complicated by many intervening factors.


Asunto(s)
Enfermedades Transmisibles/inmunología , Enfermedades Transmisibles/patología , Inmunidad Colectiva , Modelos Teóricos , Control de Enfermedades Transmisibles , Humanos , Modelos Biológicos , Probabilidad , Procesos Estocásticos
19.
F1000Res ; 9: 570, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32884676

RESUMEN

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.


Asunto(s)
Infecciones por Coronavirus/mortalidad , Análisis de Datos , Neumonía Viral/mortalidad , Programas Informáticos , Betacoronavirus , Humanos , Internet , Modelos Teóricos , Pandemias
20.
F1000Res ; 9: 232, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32864101

RESUMEN

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.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/transmisión , Modelos Teóricos , Neumonía Viral/epidemiología , Neumonía Viral/transmisión , Betacoronavirus , Ciudades/epidemiología , Alemania/epidemiología , Humanos , Pandemias
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