US Nobel laureates: Logistic growth versus Volterra–Lotka

The logistic-growth equation is a special case of the Volterra-Lotka equations. The former describes competition only between members of the same species whereas the latter describes competition also with other species. In the study of US Nobel laureates considering laureates per population improves the quality of the logistic fit but the Volterra-Lotka approach suggests that a logistic description would be a good approximation for data per unit of time rather than cumulative data. Fitting logistic S curves on cumulative data — although proven successful in many business and other applications — constitutes treacherous terrain for inexperienced S-curve enthusiasts. The Volterra-Lotka analysis of Nobel laureates reveals other insights such as that Americans and other nationalities are locked in a win-win struggle with Americans drawing more of a benefit, and also that American Nobel laureates “incubate” new Nobel laureates to a lesser extent than other nationalities.

Mapping First to Second wave transition of covid19 Indian data via Sigmoid function and prediction of Third wave

Understanding first and second wave of covid19 Indian data along with its few selective states, we have realized a transition between two Sigmoid pattern with twice larger growth parameter and maximum values of cumulative data. As a result of those transition, time duration of second wave shrink to half of that first wave with four times larger peak values. It is really interesting that the facts can be easily understood by simple algebraic expressions of Sigmoid function. After understanding the crossing zone between first and second wave curves, a third wave Sigmoid pattern is guessed.

INDEPENDENT CONFIRMATION OF FLUVIAL REWORKING AT A LANCE FORMATION (MAASTRICHTIAN) BONEBED BY TRADITIONAL AND CHEMICAL TAPHONOMIC ANALYSES

ABSTRACT A dinosaur-bearing bonebed (Rose Quarry) from the uppermost Cretaceous (Maastrichtian) Lance Formation has yielded abundant, yet fragmentary, disarticulated, and disassociated bones and teeth of dinosaurs, turtles, crocodilians, and fish contained within a channelized sandstone unit along with large mud clasts. The vertebrate fossils of Rose Quarry possess varying abrasion states, tooth traces, and trampling marks, suggesting a complicated taphonomic history. To independently test hypotheses about the genesis of the assemblage, Rose Quarry bone samples were sent to members of our team who conducted “blind” analyses of their trace element signatures without knowledge of the physical taphonomic attributes of each specimen. The independent analyses of the chemical and physical taphonomic signatures both support a mixed, attritional bone concentration. Based on our cumulative data, we present a depositional model for the Rose Quarry bonebed in which a flooding event mixed bones already present in the channel or from an older bonebed with bones from the floodplain that had been scavenged, trampled, and broken. Our study demonstrates that striking variability is possible among fluvial bonebeds, and that such variability is influenced by pre-burial and post-burial factors, as well as depositional subenvironments and burial mechanisms. Additionally, we demonstrate that physical and chemical taphonomic analyses can independently confirm the taphonomic history of a bonebed.

COVID-19 Data Based on Wavelet Coherence Estimates for Selected Countries in the Eastern Mediterranean

Background and Objective: The development of the COVID-19 pandemic makes it necessary to conduct various studies on this topic. One of the key questions is the study of the dynamics of the development of this disease. It is important to know for each country. At the same time, the study of the dynamics of the development of COVID-19 for countries in a particular region is relevant. The main objective of this study is to analyze the main indicators of the development of the COVID-19 epidemic for individual countries in the eastern Mediterranean.abbreviations if possible. Materials and Methods: We review statistics that characterize the total number of confirmed cases of COVID-19, total number of recovered, total number of deaths. This is cumulative data. These data are considered for each individual country from the selected region. Also summarized data for the selected region are considered. To analyze the data, we use estimates of the wavelet coherence values. Results: We obtained estimates of wavelet coherence values for countries such as: Egypt, Israel, Jordan, Lebanon, Syria, Turkey and Cyprus. These estimates reflect the depth of the relationship between total number of confirmed cases of COVID-19 and total number of recovered, between total number of confirmed cases of COVID-19 and total number of deaths. This makes it possible to assess the degree of influence between the series of data that are being investigated. This allows us to draw conclusions about the development of the COVID-19 pandemic. Conclusions: The results are obtained that explain some aspects of the dynamics of the COVID-19 pandemic in individual countries of the selected region

What variables can better predict the number of infections and deaths worldwide by SARS-CoV-2? Variation through time

ABSTRACTUsing data from 50 very different countries (which represent nearly 70% of world’s population) and by means of a regression analysis, we studied the predictive power of different variables (mobility, air pollution, health & research, economic and social & geographic indicators) over the number of infected and dead by SARS-CoV-2. We also studied if the predictive power of these variables changed during a 4 months period (March, April, May and June). We approached data in two different ways, cumulative data and non-cumulative data.The number of deaths by Covid-19 can always be predicted with great accuracy from the number of infected, regardless of the characteristics of the country. Inbound tourism emerged as the variable that best predicts the number of infected (and, consequently, the number of deaths) happening in the different countries. Electricity consumption and air pollution of a country (CO2 emissions, nitrous oxide and methane) are also capable of predicting, with great precision, the number of infections and deaths from Covid-19. Characteristics such as the area and population of a country can also predict, although to a lesser extent, the number of infected and dead. All predictive variables remained significant through time.In contrast, a series of variables, which in principle would seem to have a greater influence on the evolution of Covid-19 (hospital bed density, Physicians per 1000 people, Researches in R & D, urban population…), turned out to have very little - or none-predictive power.Our results explain why countries that opted for travel restrictions and social withdrawal policies at a very early stage of the pandemic outbreak, obtained better results. Preventive policies proved to be the key, rather than having large hospital and medical resources.

Predicting the number of reported and unreported cases for the COVID-19 epidemics in China, South Korea, Italy, France, Germany and United Kingdom

1SummaryBackgroundThe novel coronavirus (SARS-CoV-2) is currently causing concern in the medical, epidemiological and mathematical communities as the virus is rapidly spreading around the world. Internationally, there are more than 1 200 000 cases detected and confirmed in the world on April 6. The asymptomatic and mild symptomatic cases are just going to be really crucial for us to understand what is driving this epidemic to transmit rapidly. Combining a mathematical model of severe (SARS-CoV-transmission with data from China, South Korea, Italy, France, Germany and United Kingdom, we provide the epidemic predictions of the number of reported and unreported cases for the SARS-CoV-2 epidemics and evaluate the effectiveness of control measures for each country.MethodsWe combined a mathematical model with data on cumulative confirmed cases from China, South Korea, Italy, France, Germany and United Kingdom to provide the epidemic predictions and evaluate the effectiveness of control measures. We divide infectious individuals into asymptomatic and symptomatic infectious individuals. The symptomatic infectious phase is also divided into reported (severe symptoms) and unreported (mild symptoms) cases. In fact, there exists a period for the cumulative number of reported cases to grow (approximately) exponentially in the early phase of virus transmission which is around the implementation of the national prevention and control measures. We firstly combine the date of the implementation of the measures with the daily and cumulative data of the reported confirmed cases to find the most consistent period for the cumulative number of reported cases to grow − approximately exponentially with the formula χ1 exp(χ2t) χ3, thus we can determine the parameters χ1, χ2, χ3 in this formula and then determine the parameters and initial conditions for our model by using this formula and the plausible biological parameters for SARS-CoV-2 based on current evidence.We then provide the epidemic predictions, evaluate the effectiveness of control measures by simulations of our model.FindingsBased on the simulations using multiple groups of parameters (d1, d2, N), here [d1, d2] is the consistent period for the cumulative number of reported cases to grow approximately exponentially with the formula χ1 exp(χ2t) χ3 and N is the date at which public intervention measures became effective, we found that the ranges of the turning point, the final size of reported and unreported cases are respectively Feb.6 − 7, 67 000 − 69 000 and 45 000 − 46 000 for China, Feb.29−Mar.1, 9 000 − 9 400and 2 250 − 2 350 for South Korea, Mar.24 − 26, 156 000 − 177 000, and 234 000 − 265 000 for Italy, Mar.30−Apr.9, 104 000 − 212 000, and 177 000 − 318 000 for France, Mar.30−Apr.20, 141 000 − 912 000, and 197 000 − 1 369 000 for Germany, Apr.1−May12, 140 000 − 473 000, and 210 000 − 709 000 for UnitedKingdom. Our prediction relies on the cumulative data of the reported confirmed cases. As more data become available, the ranges become smaller and smaller, that means the prediction becomes better and better. It is evident that our estimates and simulations have shown good correspondence with the distribution of the cumulative data available of the reported confirmed cases for each country and in particularly, the curves plotted by using different parameter groups (d1, d2, N) for reported and unreported cases tend to be consistent in China and South Korea (see (e) in Figures 2-3). For Italy, France, Germany and United Kingdom, the prediction can be updated to higher accuracy with on-going day by day reported case data (see Figures 4-7).InterpretationWe used the plausible biological parameters f, ν, η for SARS-CoV-2 based on current evidence which might be refined as more comprehensive data become available. Our prediction also relies on the cumulative data of the reported confirmed cases. Using multiple groups of parameters (d1, d2, N), we have attempted to make the best possible prediction using the available data. We found that with more cumulative data available, the curves plotted by using different parameter groups (d1, d2, N) for reported and unreported cases will be closer and closer, and finally tend to be consistent. This shows that when we have no enough cumulative data available, we need to use all possible parameter groups to predict the range of turning point, the final size of reported and unreported cases. When we have enough cumulative data, for example, when we get the data after the turning point, we only need to use any one of these parameter groups to get a prediction with high accuracy.FundingNSFC (Grant No. 11871007), NSFC and CNRS (Grant No. 11811530272) and the Fundamental Research Funds for the Central Universities.

Contextual Safeguarding: theorising the contexts of child protection and peer abuse

This chapter presents the extra-familial dynamics of peer abuse against the familial parameters of child protection. Analysed through the constructivist structuralist concepts offered by Pierre Bourdieu, cumulative data from a multi-study programme into extra-familial abuse provides a roadmap towards identifying the components of a contextual account of, and response to, peer abuse. Through this process, it is possible to bridge the gap between the field of child protection and the social fields of peer groups. This can be done by theorising and testing a new approach to extra-familial child protection — Contextual Safeguarding. In so doing, the chapter explains a framework through which peer abuse can be both perceived, and responded to, as a child protection issue.