scholarly journals Enhancing the estimation of compartmental model parameters for COVID-19 data with a high level of uncertainty

2020 ◽  
Author(s):  
Gustavo B. Libotte ◽  
Lucas dos Anjos ◽  
Regina C. Almeida ◽  
Sandra M. C. Malta ◽  
Renato S. Silva
Keyword(s):  
Large Scale ◽  
Gaussian Process Regression ◽  
Predictive Ability ◽  
Compartmental Models ◽  
Model Parameters ◽  
The Novel ◽  
Novel Coronavirus ◽  
Reported Data ◽  
High Level ◽  
Political Economic

AbstractResearch on predictions related to the spread of the novel coronavirus are crucial in decision-making to mitigate the disease. Computational simulations are often used as a basis for forecasting the dynamics of epidemics and, for this purpose, compartmental models have been widely used to assess the situation resulting from the spread of the disease in the population. Reliable data is essential to obtain adequate simulations. However, several political, economic, and social factors have caused inconsistencies in the reported data, which are reflected in the capacity for realistic simulations and predictions. Such uncertainties are mainly motivated by a large-scale underreporting of cases due to the reduced testing capacity in some locations. In order to mitigate the effects of noise in the data used to estimate parameters of compartmental models, we propose strategies capable of improving the ability to predict the spread of the disease. We show that the regularization of data by means of Gaussian Process Regression can reduce the variability of successive forecasts, thus improving predictive ability. We also present the advantages of adopting parameters of compartmental models that vary over time, in detriment to the usual approach with constant values.

scholarly journals Framework for enhancing the estimation of model parameters for data with a high level of uncertainty

2021 ◽  
Author(s):  
Gustavo B Libotte ◽  
Lucas Anjos ◽  
Regina Almeida ◽  
Sandra Malta ◽  
Renato Silva
Keyword(s):  
Large Scale ◽  
Compartmental Model ◽  
Gaussian Process Regression ◽  
Predictive Ability ◽  
Model Parameters ◽  
Study Case ◽  
Estimation Of Model Parameters ◽  
Reported Data ◽  
High Level ◽  
Political Economic

Abstract Reliable data is essential to obtain adequate simulations for forecasting the dynamics of epidemics. In this context, several political, economic, and social factors may cause inconsistencies in the reported data, which reflect the capacity for realistic simulations and predictions. In the case of COVID-19, for example, such uncertainties are mainly motivated by large-scale underreporting of cases due to reduced testing capacity in some locations. In order to mitigate the effects of noise in the data used to estimate parameters of models, we propose strategies capable of improving the ability to predict the spread of the diseases. Using a compartmental model in a COVID-19 study case, we show that the regularization of data by means of Gaussian Process Regression can reduce the variability of successive forecasts, improving predictive ability. We also present the advantages of adopting parameters of compartmental models that vary over time, in detriment to the usual approach with constant values.

Social inequality in the evolution of human societies

2019 ◽  
pp. 98-120
Author(s):  
Georgi Derluguian
Keyword(s):  
Social Inequality ◽  
Large Scale ◽  
Human Beings ◽  
Mass Violence ◽  
Public And Private ◽  
Tangible Assets ◽  
Transition To Agriculture ◽  
New Institutions ◽  
High Level ◽  
Political Economic

The author develops ideas about the origin of social inequality during the evolution of human societies and reflects on the possibilities of its overcoming. What makes human beings different from other primates is a high level of egalitarianism and altruism, which contributed to more successful adaptability of human collectives at early stages of the development of society. The transition to agriculture, coupled with substantially increasing population density, was marked by the emergence and institutionalisation of social inequality based on the inequality of tangible assets and symbolic wealth. Then, new institutions of warfare came into existence, and they were aimed at conquering and enslaving the neighbours engaged in productive labour. While exercising control over nature, people also established and strengthened their power over other people. Chiefdom as a new type of polity came into being. Elementary forms of power (political, economic and ideological) served as a basis for the formation of early states. The societies in those states were characterised by social inequality and cruelties, including slavery, mass violence and numerous victims. Nowadays, the old elementary forms of power that are inherent in personalistic chiefdom are still functioning along with modern institutions of public and private bureaucracy. This constitutes the key contradiction of our time, which is the juxtaposition of individual despotic power and public infrastructural one. However, society is evolving towards an ever more efficient combination of social initiatives with the sustainability and viability of large-scale organisations.

scholarly journals Multi-Objective Optimization of Integrated Civilian-Military Scheduling of Medical Supplies for Epidemic Prevention and Control

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 126
Author(s):  
Hai-Feng Ling ◽  
Zheng-Lian Su ◽  
Xun-Lin Jiang ◽  
Yu-Jun Zheng
Keyword(s):  
Prevention And Control ◽  
Large Scale ◽  
Medical Services ◽  
The Novel ◽  
Multi Objective ◽  
Medical Supplies ◽  
Problem Instances ◽  
Novel Coronavirus ◽  
And Control ◽  
Minimum Ratio

In a large-scale epidemic, such as the novel coronavirus pneumonia (COVID-19), there is huge demand for a variety of medical supplies, such as medical masks, ventilators, and sickbeds. Resources from civilian medical services are often not sufficient for fully satisfying all of these demands. Resources from military medical services, which are normally reserved for military use, can be an effective supplement to these demands. In this paper, we formulate a problem of integrated civilian-military scheduling of medical supplies for epidemic prevention and control, the aim of which is to simultaneously maximize the overall satisfaction rate of the medical supplies and minimize the total scheduling cost, while keeping a minimum ratio of medical supplies reservation for military use. We propose a multi-objective water wave optimization (WWO) algorithm in order to efficiently solve this problem. Computational results on a set of problem instances constructed based on real COVID-19 data demonstrate the effectiveness of the proposed method.

scholarly journals Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 109 ◽  
Author(s):  
Iman Rahimi ◽  
Amir H. Gandomi ◽  
Panagiotis G. Asteris ◽  
Fang Chen
Keyword(s):  
Machine Learning ◽  
Logistic Function ◽  
Prediction Performance ◽  
Machine Learning Algorithms ◽  
Model Parameters ◽  
The Novel ◽  
Chinese City ◽  
Limited Memory ◽  
Increasing Trend ◽  
Novel Coronavirus

The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and UK is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and UK, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS (L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases.

scholarly journals Pulmonary Cavitation – An Under-recognized Late Complication of Severe COVID-19 Lung Disease

2020 ◽  
Author(s):  
Zaid Zoumot ◽  
Maria-Fernanda Bonilla ◽  
Ali S. Wahla ◽  
Irfan Shafiq ◽  
Mateen Uzbeck ◽  
...  
Keyword(s):  
Lung Disease ◽  
Medical Records ◽  
Late Complication ◽  
Ground Glass ◽  
The Novel ◽  
Radiological Findings ◽  
Imaging Characteristics ◽  
Microbiological Parameters ◽  
Novel Coronavirus ◽  
High Level

Abstract Background: Pulmonary radiological findings of the novel coronavirus disease 2019 (COVID-19) have been well documented and range from scattered ground-glass infiltrates in milder cases to confluent ground-glass change, dense consolidation, and crazy paving in the critically ill. However, lung cavitation has not been commonly described in these patients. The objective of this study was to assess the incidence of pulmonary cavitation in patients with COVID-19 and describe its characteristics and evolution.Methods: We conducted a retrospective review of all patients admitted to our institution with COVID-19 and reviewed electronic medical records and imaging to identify patients who developed pulmonary cavitation.Results: Twelve out of 689 (1.7%) patients admitted to our institution with COVID-19 developed pulmonary cavitation, comprising 3.3% (n=12/359) of patients who developed COVID-19 pneumonia, and 11% (n=12/110) of those admitted to the intensive care unit. We describe the imaging characteristics of the cavitation and present the clinical, pharmacological, laboratory, and microbiological parameters for these patients. In this cohort six patients have died, two are recovering in hospital and four have been discharged home. Conclusion: Cavitary lung disease in patients with severe COVID-19 disease is not uncommon, and is associated with a high level of morbidity and mortality.

Bribery and Corruption

2015 ◽  
pp. 196-210
Keyword(s):  
Large Scale ◽  
White Collar Crime ◽  
Public Officials ◽  
Government Officials ◽  
Decision Making Processes ◽  
Oil Contracts ◽  
The Individual ◽  
High Level ◽  
Personal Welfare ◽  
Political Economic

Bribes are mainly directed at government officials, although they could be directed at the employees and managers of business firms. However, bribery appears to be a self-defined crime. Bribery of small public sector employees is a white-collar crime. However, bribery also exists in high-level decision-making processes, whether political, economic, or corporate situations. These are large-scale bribes, consisting of millions and/or billions of dollars, paid out to executives and public officials in return for construction contracts, oil contracts, telecommunication contracts, etc. Although punishments exist and are implemented, it is up to the individual alone to make the final decision and choose between personal moral value system and personal welfare in opposition to serving the public welfare. This chapter explores bribery.

scholarly journals Targeting the sAC-Dependent cAMP Pool to Prevent SARS-Cov-2 Infection

Cells ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1962
Author(s):  
Muhammad Aslam ◽  
Yury Ladilov
Keyword(s):  
Endocytic Pathway ◽  
Host Cells ◽  
The Novel ◽  
Cellular Mechanisms ◽  
Late Endosomes ◽  
Proven Efficacy ◽  
Potential Impact ◽  
Potential Strategy ◽  
Novel Coronavirus ◽  
High Level

An outbreak of the novel coronavirus (CoV) SARS-CoV-2, the causative agent of COVID-19 respiratory disease, infected millions of people since the end of 2019, led to high-level morbidity and mortality and caused worldwide social and economic disruption. There are currently no antiviral drugs available with proven efficacy or vaccines for its prevention. An understanding of the underlying cellular mechanisms involved in virus replication is essential for repurposing the existing drugs and/or the discovery of new ones. Endocytosis is the important mechanism of entry of CoVs into host cells. Endosomal maturation followed by the fusion with lysosomes are crucial events in endocytosis. Late endosomes and lysosomes are characterized by their acidic pH, which is generated by a proton transporter V-ATPase and required for virus entry via endocytic pathway. The cytoplasmic cAMP pool produced by soluble adenylyl cyclase (sAC) promotes V-ATPase recruitment to endosomes/lysosomes and thus their acidification. In this review, we discuss targeting the sAC-specific cAMP pool as a potential strategy to impair the endocytic entry of the SARS-CoV-2 into the host cell. Furthermore, we consider the potential impact of sAC inhibition on CoV-induced disease via modulation of autophagy and apoptosis.

scholarly journals Modeling the Novel Coronavirus (SARS-CoV-2) Outbreak in Sicily, Italy

2020 ◽  
Vol 17 (14) ◽  
pp. 4964 ◽  
Author(s):  
Andrea Maugeri ◽  
Martina Barchitta ◽  
Sebastiano Battiato ◽  
Antonella Agodi
Keyword(s):  
Transmission Rate ◽  
Control Measures ◽  
Transmission Dynamics ◽  
The Novel ◽  
Epidemic Spread ◽  
Transmission Rates ◽  
Contact Precautions ◽  
Travel Restrictions ◽  
Novel Coronavirus ◽  
Reported Data

Italy was the first country in Europe which imposed control measures of travel restrictions, quarantine and contact precautions to tackle the epidemic spread of the novel coronavirus (SARS-CoV-2) in all its regions. While such efforts are still ongoing, uncertainties regarding SARS-CoV-2 transmissibility and ascertainment of cases make it difficult to evaluate the effectiveness of restrictions. Here, we employed a Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model to assess SARS-CoV-2 transmission dynamics, working on the number of reported patients in intensive care unit (ICU) and deaths in Sicily (Italy), from 24 February to 13 April. Overall, we obtained a good fit between estimated and reported data, with a fraction of unreported SARS-CoV-2 cases (18.4%; 95%CI = 0–34.0%) before 10 March lockdown. Interestingly, we estimated that transmission rate in the community was reduced by 32% (95%CI = 23–42%) after the first set of restrictions, and by 80% (95%CI = 70–89%) after those adopted on 23 March. Thus, our estimates delineated the characteristics of SARS-CoV2 epidemic before restrictions taking into account unreported data. Moreover, our findings suggested that transmission rates were reduced after the adoption of control measures. However, we cannot evaluate whether part of this reduction might be attributable to other unmeasured factors, and hence further research and more accurate data are needed to understand the extent to which restrictions contributed to the epidemic control.

scholarly journals The early scientific literature response to the novel Coronavirus outbreak: who published what?

2020 ◽  
Author(s):  
Davide Gori ◽  
Erik Boetto ◽  
Maria Pia Fantini
Keyword(s):  
Public Health ◽  
Scientific Literature ◽  
Health Concern ◽  
Primary Data ◽  
The Novel ◽  
Epidemic Outbreak ◽  
Emerging Pathogens ◽  
Pubmed Database ◽  
Novel Coronavirus ◽  
Reported Data

AbstractIntroductionRecent events highlight how emerging and re-emerging pathogens are becoming global challenges for public health. In December 2019, a novel coronavirus has emerged. This has suddenly turned out into global health concern.ObjectivesAim of this research is to focus on the bibliometric aspects in order to measure what is published in the first 30-days of a global epidemic outbreakMethodsWe searched PubMed database in order to find all relevant studies in the first 30-days from the first publication.ResultsFrom the initial 442 identified articles, 234 were read in-extenso. The majority of papers come from China, UK and USA. 63.7% of the papers were commentaries, editorials and reported data and only 17.5% of the sources used data directly collected on the field. Topics mainly addressed were “epidemiology”, “preparedness” and “generic discussion”. NNR showed a reduction for both the objectives assessed from January to February.Conclusions“Diagnosis” and effective preventive and therapeutic measures were the fields in which more research is still needed. The vast majority of scientific literature in the first 30-days of an epidemic outbreak is based on reported data rather than primary data. Nevertheless, the scientific statements and public health decisions rely on these data.Strengths of our studyThis is the first bibliometric research in Pubmed Database on the first 30 days of publications regarding the novel Coronavirus (SARS-nCoV-2) outbreak of 2019.The vast majority of publication in the first 30-days of an epidemic outbreak are reported data or comments, and only a small fraction of the papers have directly collected data.Limitations of our studyOur research is only PubMed based. It ill be auspicable to consult more than one relevant database in future papers.In addition, we excluded non-English publications leading to a potential bias due to the fact that the outbreak started in China.

scholarly journals Pooling of samples to optimize SARS-CoV-2 diagnosis by RT-qPCR: comparative analysis of two protocols.

2020 ◽  
Author(s):  
Fabiana Volpato ◽  
Daiana Lima-Morales ◽  
Priscila Lamb Wink ◽  
Julia Willig ◽  
Fernanda de-Paris ◽  
...  
Keyword(s):  
Large Scale ◽  
Diagnostic Yield ◽  
Rna Extraction ◽  
Clinical Samples ◽  
The Novel ◽  
Positive Individual ◽  
Sample Pooling ◽  
Novel Coronavirus ◽  
The Individual ◽  
Pooled Samples

RT-qPCR for SARS-CoV-2 is the main diagnostic test used to identify the novel coronavirus. Several countries have used large scale SARS-CoV-2 RT-qPCR testing as one of the important strategies for combating the pandemic. In order to process the massive needs for coronavirus testing, the usual throughput of routine clinical laboratories has reached and often surpassed its limits and new approaches to cope with this challenge must be developed. This study has aimed to evaluate the use pool of samples as a strategy to optimize the diagnostic of SARS-CoV-2 by RT-qPCR in a general population. A total of 220 naso/orofaryngeal swab samples were collected and tested using two different protocols of sample pooling. In the first protocol (Protocol A); 10 clinical samples were pooled before RNA extraction. The second protocol (Protocol B) consisted of pooling the already extracted RNAs from 10 individual samples. Results from Protocol A were identical (100% agreement) with the individual results. However, for results from Protocol B, reduced agreement (91%) was observed in relation to results obtained by individual testing. Inconsistencies observed were related to RT-qPCR results with higher Cycle Thresholds (Ct > 32.73). Furthermore, in pools containing more than one positive individual, the Ct of the pool was equivalent to the lowest Ct among the individual results. These results provide additional evidence in favor of the clinical use of pooled samples for SARS-CoV-2 diagnosis by RT-qPCR and suggest that pooling of samples before RNA extraction is preferrable in terms of diagnostic yield.

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