Detection of SARS-CoV-2 in Wastewater Northeast of Mexico City: Strategy for Monitoring and Prevalence of COVID-19

A month-long wastewater sampling project was conducted along the northeast periphery of Mexico City, specifically in the state of Hidalgo, to assess the presence of SARS-CoV-2. To determine the prevalence of infection and obtain a range of COVID-19 cases in the main metropolitan zones. Viral RNA residues (0–197,655 copies/L) were measured in wastewater from the five central municipalities in the state. By recording the number of RNA viral copies per liter, micro-basins delimitation, demographic and physiological data, an interval of infected people and virus prevalence was estimated using a Monte Carlo model (with 90% confidence) in the micro-basin of five municipalities with metropolitan influence or industrial activity. Our procedure determined that the percentage of the infected population ranges from 1.4% to 41.7%, while the official data reports 0.1–0.3%. This model is proposed as a helpful method of regional epidemiological monitoring through the analysis of viral prevalence.

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Implementation of a Markov Chain Monte Carlo Method to inorganic aerosol modeling of observations from the MCMA-2003 Campaign. Part II: Model application to the CENICA, Pedregal and Santa Ana sites

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-6-5999-2006 ◽

2006 ◽

Vol 6 (4) ◽

pp. 5999-6040

Author(s):

F. M. San Martini ◽

E. J. Dunlea ◽

R. Volkamer ◽

T. B. Onasch ◽

J. T. Jayne ◽

...

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Nitric Acid ◽

Gas Phase ◽

Mexico City ◽

Monte Carlo Model ◽

Rural Site ◽

Inorganic Species ◽

Inorganic Aerosol

Abstract. A Markov Chain Monte Carlo model for integrating the observations of inorganic species with a thermodynamic equilibrium model was presented in Part I of this series. Using observations taken at three ground sites, i.e. a residential, industrial and rural site, during the MCMA-2003 campaign in Mexico City, the model is used to analyze the inorganic aerosol and ammonia data and predict gas phase concentrations of nitric and hydrochloric acid. In general the model is able to accurately predict the observed inorganic aerosol concentrations at all three sites. The agreement between the predicted and observed gas phase ammonia concentration is excellent. The NOz concentration calculated from the NOy, NO and NO2 observations is of limited use in constraining the gas phase nitric acid concentration given the large uncertainties in this measure of nitric acid and additional reactive nitrogen species. Focusing on the acidic period of 9–11 April identified by Salcedo et al. (2006), the model accurately predicts the aerosol phase observations during this period with the exception of the nitrate predictions after 10:00 a.m. (CDT) on 9 April, where the model underpredicts the observations by, on average, 20%. For periods when the aerosol chloride observations are consistently above the detection limit, the model is able to both accurately predict the aerosol chloride predictions and provide well-constrained HCl (g) concentrations. When the aerosols are aqueous, the most likely concentrations of HCl (g) are in the sub-ppbv range. The most likely predicted concentration of HCl (g) was found to reach concentrations of order 10 ppbv if the aerosols are dry. Finally, the atmospheric relevance of HCl (g) is discussed in terms of its indicator properties for the possible influence of chlorine-mediated photochemistry in Mexico City.

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Implementation of a Markov Chain Monte Carlo method to inorganic aerosol modeling of observations from the MCMA-2003 campaign – Part II: Model application to the CENICA, Pedregal and Santa Ana sites

Atmospheric Chemistry and Physics ◽

10.5194/acp-6-4889-2006 ◽

2006 ◽

Vol 6 (12) ◽

pp. 4889-4904 ◽

Cited By ~ 24

Author(s):

F. M. San Martini ◽

E. J. Dunlea ◽

R. Volkamer ◽

T. B. Onasch ◽

J. T. Jayne ◽

...

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Nitric Acid ◽

Gas Phase ◽

Mexico City ◽

Monte Carlo Model ◽

Rural Site ◽

Inorganic Particle ◽

Inorganic Species

Abstract. A Markov Chain Monte Carlo model for integrating the observations of inorganic species with a thermodynamic equilibrium model was presented in Part I of this series. Using observations taken at three ground sites, i.e. a residential, industrial and rural site, during the MCMA-2003 campaign in Mexico City, the model is used to analyze the inorganic particle and ammonia data and to predict gas phase concentrations of nitric and hydrochloric acid. In general, the model is able to accurately predict the observed inorganic particle concentrations at all three sites. The agreement between the predicted and observed gas phase ammonia concentration is excellent. The NOz concentration calculated from the NOy, NO and NO2 observations is of limited use in constraining the gas phase nitric acid concentration given the large uncertainties in this measure of nitric acid and additional reactive nitrogen species. Focusing on the acidic period of 9–11 April identified by Salcedo et al. (2006), the model accurately predicts the particle phase observations during this period with the exception of the nitrate predictions after 10:00 a.m. (Central Daylight Time, CDT) on 9 April, where the model underpredicts the observations by, on average, 20%. This period had a low planetary boundary layer, very high particle concentrations, and higher than expected nitrogen dioxide concentrations. For periods when the particle chloride observations are consistently above the detection limit, the model is able to both accurately predict the particle chloride mass concentrations and provide well-constrained HCl (g) concentrations. The availability of gas-phase ammonia observations helps constrain the predicted HCl (g) concentrations. When the particles are aqueous, the most likely concentrations of HCl (g) are in the sub-ppbv range. The most likely predicted concentration of HCl (g) was found to reach concentrations of order 10 ppbv if the particles are dry. Finally, the atmospheric relevance of HCl (g) is discussed in terms of its indicator properties for the possible influence of chlorine-mediated photochemistry in Mexico City.

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A COMBINED P1 AND MONTE CARLO MODEL FOR MULTIDIMENSIONAL RADIATIVE TRANSFER PROBLEMS IN SCATTERING MEDIA

Computational Thermal Sciences An International Journal ◽

10.1615/computthermalscien.v2.i6.60 ◽

2010 ◽

Vol 2 (6) ◽

pp. 549-560 ◽

Cited By ~ 10

Author(s):

Wojciech Lipinski ◽

Leonid A. Dombrovsky

Keyword(s):

Monte Carlo ◽

Radiative Transfer ◽

Monte Carlo Model ◽

Scattering Media ◽

Transfer Problems

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ESPORTS TOURNAMENTS AS A FORM OF STRATEGIC PROMOTION OF PRODUCTS AND SERVICES

EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA ◽

10.36871/ek.up.p.r.2020.07.02.003 ◽

2020 ◽

Vol 2 (7) ◽

pp. 19-23

Author(s):

V. V. VELIKOROSSOV ◽

◽

Yu. M. BRYUKHANOV ◽

A. O. TITOVA ◽

◽

...

Keyword(s):

World Economy ◽

The State ◽

Generation Z ◽

Current Trends ◽

Analytical Studies ◽

International Consulting ◽

The World ◽

Private And Public ◽

Effective Integration ◽

Official Data

The article is dedicated to eSports as a new and promising sector of the world economy that provides businesses with effective integration scenarios. This contributes to the development of cooperation of private and public investors with eSports holdings, as well as to the involvement of the generation Z audience in promising consumption of interested companies’ products. The article examines the current trends in the development of the eSports market using analytical studies of international consulting companies. The official data characterizing the state of the eSports market in Russia are also represented. The article provides information about the model of monetization of eSports and its perspective directions. In conclusion, the article makes the necessary inferences to assess the prospects of such areas of the economy as eSports, both for the industry of interactive entertainment and for representatives of other market sectors.

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An Efficient Markov-Chain Monte Carlo Method for the State Space Models with Stochastic Volatility

SSRN Electronic Journal ◽

10.2139/ssrn.2340389 ◽

2013 ◽

Author(s):

Yu-Fan Huang

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Monte Carlo Method ◽

State Space ◽

Stochastic Volatility ◽

The State ◽

State Space Models

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Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and Brazil

Sensors ◽

10.3390/s21020540 ◽

2021 ◽

Vol 21 (2) ◽

pp. 540

Author(s):

Fabio Amaral ◽

Wallace Casaca ◽

Cassio M. Oishi ◽

José A. Cuminato

Keyword(s):

Learning Strategies ◽

Time Series Data ◽

Sao Paulo ◽

The State ◽

Data Driven ◽

Series Data ◽

São Paulo ◽

Time Data ◽

Unified Framework ◽

Infected People

São Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country’s fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model’s coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given.

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