Visualization of COVID-19 spread based on spread and extinction indexes
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ABSTRACT:
SUBMITTER: Zhang S
PROVIDER: S-EPMC7235974 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe COVID-19 has been spreading globally since 2019 and causes serious damage to the whole society. A macro perspective study to explore the changes of some social-related indexes of different countries is meaningful.MethodsWe collected nine social-related indexes and the score of COVID-safety-assessment. Data analysis is carried out using three time series models. In particular, a prediction-correction procedure was employed to explore the impact of the pandemic on the indexes of developed and developing countries.ResultsIt shows that COVID-19 epidemic has an impact on the life of residents in various aspects, specifically in quality of life, purchasing power, and safety. Cluster analysis and bivariate statistical analysis further indicate that indexes affected by the pandemic in developed and developing countries are different.ConclusionThis pandemic has altered the lives of residents in many ways. Our further research shows that the impacts of social-related indexes in developed and developing countries are different, which is bounded up with their epidemic severity and control measures. On the other hand, the climate is crucial for the control of COVID-19. Consequently, exploring the changes of social-related indexes is significative, and it is conducive to provide targeted governance strategies for various countries. Our article will contribute to countries with different levels of development pay more attention to social changes and take timely and effective measures to adjust social changes while trying to control this pandemic.
| S-EPMC9096155 | biostudies-literature
Project description:We conducted a retrospective analysis of the clinical characteristics and dynamic variations of immune indexes in nine COVID-19 patients in Zigong, China. We used flow cytometry and enzyme-linked immunosorbent assays to measure the absolute levels of CD4 and CD8 lymphocytes and SARS-CoV-2 antibodies, respectively. We found that CRP, LDH, HBDH, CD4/CD8 and IgE levels were increased in 6/9 patients, while PA and the absolute numbers of CD4 and CD8 lymphocytes decreased in 7/9 patients. From disease onset through 63 days of follow-up, SARS-CoV-2 IgG levels were consistently higher than those of SARS-CoV-2 IgM, reaching peaks on days 28 and 13, respectively. IgM levels decreased to normal 35 days after disease onset, while IgG levels remained elevated through day 63. IgE levels varied similarly to SARS-CoV-2 IgM. Our results suggest that SARS-CoV-2 may elicit allergic immune responses in patients and that the levels of CRP, PA, LDH, and HBDH, as well as the absolute numbers of CD4 and CD8 lymphocytes could be used as early diagnostic markers of SARS-CoV-2 infection. Lastly, the dynamic variation of SARS-CoV-2 antibodies could guide the timing of blood collection for plasma exchange.
| S-EPMC7803549 | biostudies-literature
Project description:In the three years since SARS-CoV-2 was first detected in China, hundreds of millions of people have been infected and millions have died. Along with the immediate need for treatment solutions, the COVID-19 epidemic has reinforced the need for mathematical models that can predict the spread of the pandemic in an ever-changing environment. The susceptible-infectious-removed (SIR) model has been widely used to model COVID-19 transmission, however, with limited success. Here, we present a novel, dynamic Monte-Carlo Agent-based Model (MAM), which is based on the basic principles of statistical physics. Using public aggregative data from Israel on three major outbreaks, we compare predictions made by SIR and MAM, and show that MAM outperforms SIR in all aspects. Furthermore, MAM is a flexible model and allows to accurately examine the effects of vaccinations in different subgroups, and the effects of the introduction of new variants.
| S-EPMC10098313 | biostudies-literature
Project description: Not available
| S-EPMC8477979 | biostudies-literature
Project description:BackgroundMany nations swiftly designed and executed government policies to contain the rapid rise in COVID-19 cases. Government actions can be broadly segmented as movement and mass gathering restrictions (such as travel restrictions and lockdown), public awareness (such as face covering and hand washing), emergency health care investment, and social welfare provisions (such as poor welfare schemes to distribute food and shelter). The Blavatnik School of Government, University of Oxford, tracked various policy initiatives by governments across the globe and released them as composite indices. We assessed the overall government response using the Oxford Comprehensive Health Index (CHI) and Stringency Index (SI) to combat the COVID-19 pandemic.ObjectiveThis study aims to demonstrate the utility of CHI and SI to gauge and evaluate the government responses for containing the spread of COVID-19. We expect a significant inverse relationship between policy indices (CHI and SI) and COVID-19 severity indices (morbidity and mortality).MethodsIn this ecological study, we analyzed data from 2 publicly available data sources released between March 2020 and October 2021: the Oxford Covid-19 Government Response Tracker and the World Health Organization. We used autoregressive integrated moving average (ARIMA) and seasonal ARIMA to model the data. The performance of different models was assessed using a combination of evaluation criteria: adjusted R2, root mean square error, and Bayesian information criteria.Resultsimplementation of policies by the government to contain the COVID-19 crises resulted in higher CHI and SI in the beginning. Although the value of CHI and SI gradually fell, they were consistently higher at values of >80% points. During the initial investigation, we found that cases per million (CPM) and deaths per million (DPM) followed the same trend. However, the final CPM and DPM models were seasonal ARIMA (3,2,1)(1,0,1) and ARIMA (1,1,1), respectively. This study does not support the hypothesis that COVID-19 severity (CPM and DPM) is associated with stringent policy measures (CHI and SI).ConclusionsOur study concludes that the policy measures (CHI and SI) do not explain the change in epidemiological indicators (CPM and DPM). The study reiterates our understanding that strict policies do not necessarily lead to better compliance but may overwhelm the overstretched physical health systems. Twenty-first-century problems thus demand 21st-century solutions. The digital ecosystem was instrumental in the timely collection, curation, cloud storage, and data communication. Thus, digital epidemiology can and should be successfully integrated into existing surveillance systems for better disease monitoring, management, and evaluation.
| S-EPMC9924057 | biostudies-literature
Project description:ObjectiveThis study explored the consistency and differences in the immune cells and cytokines between patients with COVID-19 or cancer. We further analyzed the correlations between the acute inflammation and cancer-related immune disorder.MethodsThis retrospective study involved 167 COVID-19 patients and 218 cancer patients. COVID-19 and cancer were each further divided into two subgroups. Quantitative and qualitative variables were measured by one-way ANOVA and chi-square test, respectively. Herein, we carried out a correlation analysis between immune cells and cytokines and used receiver operating characteristic (ROC) curves to discover the optimal diagnostic index.ResultsCOVID-19 and cancers were associated with lymphopenia and high levels of monocytes, neutrophils, IL-6, and IL-10. IL-2 was the optimal indicator to differentiate the two diseases. Compared with respiratory cancer patients, COVID-19 patients had lower levels of IL-2 and higher levels of CD3+CD4+ T cells and CD19+ B cells. In the subgroup analysis, IL-6 was the optimal differential diagnostic parameter that had the ability to identify if COVID-19 patients would be severely affected, and severe COVID-19 patients had lower levels of lymphocyte subsets (CD3+ T cells, CD3+CD4+ T cells, CD3+CD8+T cells, and CD19+ B cells) and CD16+CD56+ NK cells and higher level of neutrophils. There were significant differences in the levels of CD3+CD4+ T cells and CD19+ B cells between T1-2 and T3-4 stages as well as IL-2 and CD19+ B cells between N0-1 and N2-3 stages while no significant differences between the metastatic and nonmetastatic cancer patients. Additionally, there were higher correlations between IL-2 and IL-4, TNF-α and IL-2, TNF-α and IL-4, TNF-α and IFN-γ, and CD16+CD56+NK cells and various subsets of T cells in COVID-19 patients. There was a higher correlation between CD3+CD4+ T cells and CD19+ B cells in cancer patients.ConclusionInflammation associated with COVID-19 or cancer had effects on patients' outcomes. Accompanied by changes in immune cells and cytokines, there were consistencies, differences, and satisfactory correlations between patients with COVID-19 and those with cancers.
| S-EPMC8546403 | biostudies-literature
Project description: Not available
| S-BSST563 | biostudies-other
Project description:BackgroundThe novel coronavirus disease (COVID-19) culminated in a pandemic with many countries affected in varying stages. We aimed to develop a simulation environment for COVID-19 spread, taking environmental and social factors into account.MethodsThe program was written in R language. A stochastic point process simulation model for simulating epidemics, a maximum-likelihood estimation model, an exponential growth rate model for calculating the basic reproduction number (R0), and functions for generating graphical representations of the simulations were utilized.Geographical area definition, population size, the number of initial infected individuals, period of simulation, parameters accounting for the radius of spread like masks usage, mobility level, intrinsic viral virulence, average infectious period, fraction of population vaccinated, time of vaccination, the efficacy of the vaccine, presence or absence of quarantine centers, time of establishment of quarantine centers, the efficacy of case detection and average time to quarantine from the detection of the infection were considered.ResultsWhen the defined parameters were input, the model performed successfully producing the epidemic curve, R0 and an animation of infection spread. It was found that when parameters of known epidemics such as COVID-19 in California, Texas and, Florida were input, the epidemic curve generated was comparable to the epidemic curve in reality.ConclusionThis model can be utilized by many countries to visualize the effects of various mitigation strategies applied in their stage of disease and for policy makers to make informed decisions. It is applicable to many infectious diseases and hence can be used for research and educational purposes.
| S-EPMC8897234 | biostudies-literature
Project description:Highlights • Epidemic curves can be approached from a fractal interpolation point of view.• Looking at the Covid-19 epidemic curves from a fractal point of view might benefit the medical community to understand better the dynamics and evolution of the pandemic.• Fractal interpolation of data regarding Covid-19 can help retrieve missing pieces of information due to limited testing.• Analyzing maps of the spreading of Covid-19 in different countries from a fractal dimension point of view can be a useful tool in assessing the complexity of the pandemic on the territory of a country. The present paper proposes a reconstruction of the epidemic curves from the fractal interpolation point of view. Looking at the epidemic curves as fractal structures might be an efficient way to retrieve missing pieces of information due to insufficient testing and predict the evolution of the disease. A fractal approach of the epidemic curve can contribute to the assessment and modeling of other epidemics. On the other hand, we have considered the spread of the epidemic in countries like Romania, Italy, Spain, and Germany and analyzed the spread of the disease in those countries based on their fractal dimension.
| S-EPMC7332948 | biostudies-literature
Project description:The COVID-19 pandemic in 2020 caused unprecedented shocks to agricultural food systems, including increased risk to worker health, labor-related input costs, and production uncertainty. Despite employer precautions, there were numerous worksite outbreaks of COVID-19. This paper examines the relationship between month-to-month variation in historical agricultural employment and changes in the incidence of confirmed COVID-19 cases and deaths within U.S. counties from April to August 2020. The results show that employment of 100 additional workers in fruit, vegetable, and horticultural production was associated with 4.5% more COVID-19 cases within counties or an additional 18.65 COVID-19 cases and 0.34 additional COVID-19 deaths per 100,000 individuals in the county workforce.
| S-EPMC8646853 | biostudies-literature