Early tools

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Tools used by early people 1. They were evolved from apes. Then the hominids evolved to more smarter and stronger humans. The biggest difference between early humans and apes are the early men have the abilties to hold things, so tools are being used after they realized how their hands worked. Tools are the most important thing invented by early men, it helped them to hunt, make clothing, and more other uses. The first tools were stones they picked up on the ground.

The first made tool was found 2 million years ago used by the Homo habilis, but the first hominids knew to used found tools like sharp stone or branches. The tools helped them evolved to more smarter human. The dawn of stone tools dates back some 2. Known as the Oldowan, these include not just fist-sized hunks of rock for pounding, but also the first known manufacture of stone tools — sharp flakes created by knapping, or striking a hard stone against quartz, obsidian, flint or any other rock whose flakes can hold an edge.

At this time are also the oldest known butchered animal bones. This was the extent of the technology for nearly a million years. Such technology is just slightly past the range of what apes generally do, Wynn added. Indeed, chimpanzees in the wild can use stones as simple tools for hammering, and the chimpanzee-like bonobo ape can even be taught how to flake stone to make cutting tools.

The appearance of stone tools falls roughly in the middle of a drying trend in Africa between 2 million and 3 million years ago that would have presented our distant ancestors with a greater variety of habitats than they would have known before, such as woodlands to grasslands, explained paleoanthropologist Thomas Plummer at Queens College in New York.

This is the beginning of what we call the Acheulean. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience. Necessary Necessary. Live daily updated predictions for all world countries and each of the United States of America are publicly available online.

For Italy, the overall sensitivity for EVI was 0. For New York, the corresponding values were 0. Consecutive issuance of early warnings is a strong indicator of main epidemic waves in any country or state. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act swiftly and thereby enhance containment of outbreaks.

Early warning tools are crucial for the timely application of intervention strategies and the mitigation of adverse health, social and economic effects associated with epidemics. Sentinel networks in combination with information technology infrastructures in public health 1 provide data for the detection of spatial and temporal aberrations in the expected number of cases for groups of clinical signs and symptoms 2.

Several modelling frameworks exist for the analysis of such data. For example, the moving epidemic method is used to monitor, among others, the start of flu epidemics 3. Once an epidemic erupts, growth models can be used to predict the course of the outbreak and quantify its consequences. The advantages and limitations of these methods have been extensively discussed 6. Machine learning algorithms have also been utilized with the most recent application being in the current COVID pandemic 7.

For example, monitoring of digital data streams can provide an early indication of a rise in the COVID cases and deaths within the subsequent two to three weeks 8.

All models have limitations arising from the imperfect nature of available data. The need for open, better, detailed data is imperative for the deployment of models with improved accuracy, better predictive ability, and therefore enhanced utility for the timely application of appropriate control measures for the COVID pandemic 9.

Our work introduces the Epidemic Volatility Index EVI , which is inspired by the use of volatility indices in the stock market 10 , EVI is based on the moving standard deviation of the newly reported cases during an epidemic. EVI is based on the calculation of the rolling standard deviation for a time series of epidemic data i. The number of consecutive observations used for this calculation is the rolling window size- m.

At each time step, for a rolling window of size m , the observations within the window are obtained by shifting the window forward, over the time series data, one observation at a time Fig. Rolling window for EVI is not fixed and is selected at each time point to achieve optimal performance. For each rolling window the standard deviation of the newly reported cases is then calculated, allowing EVI to be estimated as the relative change of the standard deviation between two consecutive rolling windows.

For a specified criterion, the accuracy of EVI depends on the window size m and the threshold c , which should be selected in a way that achieves a desired accuracy target. Advanced Receiver Operating Characteristic curve analysis can also be performed 13 and selection of critical values can be based on indices that quantify the relative cost of false positive i.

The graphical representation of the entire process is given in Fig. Solid lines are explanatory; at each time point dashed lines represent the iterative optimization process while the bold solid line denotes the end of the algorithm. It is possible, at each time point t , to calculate the positive and negative predictive values, defined as the probability of observing a rise or drop in the future number of cases, given that an early warning was issued or not, respectively.

For each epidemic, the accuracy of EVI depends on the specified criterion. Ideally, different criterion values should be explored to identify which are suitable for the optimal monitoring of the epidemic. In the following example, sensitivity analysis based on an alternative criterion was performed. Due to unnatural variability in the reported cases between working days and weekends, a 7-day moving average rather than the actual observed cases were analyzed.

The criterion used was an increase in the mean of expected cases, between two consecutive weeks, equal or higher than twenty percent.

For sensitivity analysis, the detection of an increase in the mean of expected cases equal or higher than 50 percent was considered. Data were analyzed separately for each country and for each of the states of the United States of America that had experienced a total number of cases higher than All models were run in R The packages readxl 18 , ggplot2 19 , cowplot 18 , 20 and readr 21 were used.

Results for Italy, one of the most severely affected EU countries 23 , and New York, which was in the epicenter of the pandemic in the United States 24 , are presented in the main manuscript. Red dots correspond to time points when an early warning was issued and indicate that, according to the defined criterion, an increase in the mean of expected cases equal or higher to twenty percent is expected in the coming week.

Grey dots correspond to time points without an early warning indication. Further, positive and negative predictive values at each time point are in Figs.

Data are presented on the original scale 1a and the logarithmic scale 1b , which facilitates the comparison of the steepness of the epidemic curve between the different waves. Higher color intensity corresponds to predictive values closer to the value of 1.

Sensitivity analysis results for Italy are in Fig. Data are presented on the original scale 1a and the logarithmic scale 1b which facilitates the comparison of the steepness of the epidemic curve between the different waves.

A consistent finding in the results from all countries was that consecutive early warnings are linked to the start of a new epidemic wave, while the absence of warnings indicates a stable course or a future drop in the number of new COVID cases Fig.

EVI is a useful and easy to implement early-warning tool for an upcoming rise in the number of new cases. A more important aspect lies in the fact that repetitive issuance of early warnings indicates the beginning of an epidemic wave.

This is a consistent and stable finding across all countries and each of the United States Figs. In a similar manner, the absence of a series of early warnings implies that the number of new cases will remain stable or drop.

The latter was also a consistent finding. Additionally, false early warnings i. There were few occasions with a consecutive absence of early warnings despite a continuing rise in the number of cases i.