Visualization of COVID-19 spread based on spread and extinction indexes
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ABSTRACT:
SUBMITTER: Zhang S
PROVIDER: S-EPMC7235974 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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
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| 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.
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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:Human to human transmissible infectious diseases spread in a population using human interactions as its transmission vector. The early stages of such an outbreak can be modeled by a graph whose edges encode these interactions between individuals, the vertices. This article attempts to account for the case when each individual entails in different kinds of interactions which have therefore different probabilities of transmitting the disease. The majority of these results can be also stated in the language of percolation theory. The main contributions of the article are: (1) Extend to this setting some results which were previously known in the case when each individual has only one kind of interactions. (2) Find an explicit formula for the basic reproduction number [Formula: see text] which depends only on the probabilities of transmitting the disease along the different edges and the first two moments of the degree distributions of the associated graphs. (3) Motivated by the recent Covid-19 pandemic, we use the framework developed to compute the [Formula: see text] of a model disease spreading in populations whose trees and degree distributions are adjusted to several different countries. In this setting, we shall also compute the probability that the outbreak will not lead to an epidemic. In all cases we find such probability to be very low if no interventions are put in place.
| S-EPMC7499022 | biostudies-literature
Project description:Visual inspection of world maps shows that coronavirus disease 2019 (COVID-19) is less prevalent in countries closer to the equator, where heat and humidity tend to be higher. Scientists disagree how to interpret this observation because the relationship between COVID-19 and climatic conditions may be confounded by many factors. We regress the logarithm of confirmed COVID-19 cases per million inhabitants in a country against the country's distance from the equator, controlling for key confounding factors: air travel, vehicle concentration, urbanization, COVID-19 testing intensity, cell phone usage, income, old-age dependency ratio, and health expenditure. A one-degree increase in absolute latitude is associated with a 4.3% increase in cases per million inhabitants as of January 9, 2021 (p value < 0.001). Our results imply that a country, which is located 1000 km closer to the equator, could expect 33% fewer cases per million inhabitants. Since the change in Earth's angle towards the sun between equinox and solstice is about 23.5°, one could expect a difference in cases per million inhabitants of 64% between two hypothetical countries whose climates differ to a similar extent as two adjacent seasons. According to our results, countries are expected to see a decline in new COVID-19 cases during summer and a resurgence during winter. However, our results do not imply that the disease will vanish during summer or will not affect countries close to the equator. Rather, the higher temperatures and more intense UV radiation in summer are likely to support public health measures to contain SARS-CoV-2.
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| S-EPMC7193306 | biostudies-literature
Project description:Given that real-world infection-spread scenarios pose many uncertainties, and predictions and simulations may differ from reality, this study explores factors essential for more realistically describing an infection situation. It furnishes three approaches to the argument that human mobility can create an acceleration of the spread of COVID-19 infection and its cyclicality under the simultaneous relationship. First, the study presents a dynamic model comprising the infection-mobility trade-off and mobility demand, where an increase in human mobility can cause infection explosion and where, conversely, an increase in new infections can be made temporary by suppressing mobility. Second, using time-series data for Japan, it presents empirical evidence for a stochastic trend and cycle in new infection cases. Third, it employs macroeconometrics to ascertain the feasibility of our model's predictions. Accordingly, from March 2020 to May 2021, the sources of COVID-19 infection spread in Japan varied significantly over time, and each change in the trend and cycle of new infection cases explained approximately half the respective variation.
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