Understanding and predicting the spread of infectious diseases is a major challenge in global public health. A new method called Epi-Clock has been developed to help solve this problem. It uses genetic analysis, studies of DNA and RNA to understand mutations (permanent changes in the genetic code that can alter viral behavior and spread) and changes to detect early warning signs of potential outbreaks. This study was conducted by Dr. Cong Ji and Shanghai ZJ Bio-Tech Co., Ltd. The findings were conducted by Dr. Junbin Shao of the Liferiver Science Institute for Technology, and the findings were published in the journal Heliyon.
Epi-Clock relies on Zhu calculation method, a specialized computer algorithm used to detect patterns in genetic changes. It is a tool designed to identify patterns in genetic changes that occur before the outbreak. The focus of this study is Co-19, analyzing different genetic datasets, derived from studying virus DNA and RNA to track changes over time, starting with outbreaks to find related to virus transmission Change. “Our findings suggest that genetic differences between species in the coronavirus family may indicate a transitional phase in the evolution of the virus, helping us understand adaptation and spread between hosts,” explains Dr. JI. “By examining genetic insertions and Deletion, in the changes in viral genetic material, new genetic sequences are added or removed between different hosts, and the researchers identified key mutations that affect the way the virus spreads and develops.
One of the most notable findings from the research of Dr. JI and Dr. Shao is that how certain types of DNA or DNA change in Covid-19 around the world play an important role in changes around the world. This study highlights how genetic changes drive the evolution of different viral strains, especially in variants, different versions of the virus caused by genetic mutations, and may have unique characteristics such as B.1.640.2 and B.1.617.2 (delta). The study also found that specific genetic changes tend to accumulate before an outbreak, often replacing earlier versions of the virus. By analyzing these changes, researchers were able to predict the early stages of the outbreak about a week.
The practical benefits of Epi-Clock go beyond analyzing past outbreaks. By identifying key genetic changes in the signal’s imminent outbreak, the system can help health officials take precautions. “With Epi-Clock, we were able to identify a large number of key genetic changes in multiple countries, setting new standards for predicting new standards for outbreaks,” said Dr. Shao. The study used multiple independent genetic data to confirm these findings , showing that this method is very accurate in predicting viral surges.
Although Epi-Clock is the main improvement in monitoring disease outbreaks, researchers acknowledge that other factors, such as environmental conditions and the interaction between the virus and the host, also play a role. They stressed that including these elements would make predictions more accurate. Nevertheless, the model is a step in real-time epidemic tracking, involving monitoring the spread and development of disease outbreaks in the population and providing global health agencies with useful tools to better prepare and respond to future outbreaks.
By continuing to refine this approach, Dr. Ji and Dr. Shao hope to improve early training systems that will help reduce the impact of global infectious diseases. The combination of genetic tracking and advanced computer modeling provides widespread dissemination for predicting and controlling new possibilities for outbreaks.
Journal Reference
Junbin Shao. “Epi-Clock: A sensitive platform that helps understand pathogenic disease outbreaks and promotes response to future focus outbreaks.” Heliyon, 2024. Doi:
