Understanding and predicting the spread of infectious diseases is a major challenge for global public health. A new method called Epi-Clock has been developed to help tackle this issue. It uses genetic analysis, the study of DNA and RNA to understand mutations—permanent changes in the genetic code that can alter a virus’s behavior and spread—and variations, to detect early warning signs of potential outbreaks. The research was carried out by Dr. Cong Ji and Dr. Junbin Shao from the Liferiver Science and Technology Institute, Shanghai ZJ Bio-Tech Co., Ltd., and the findings are published in the journal Heliyon.
Epi-Clock relies on the ZHU computational method, a specialized computer algorithm used to detect patterns in genetic changes. It is a tool designed to identify patterns in genetic changes that appear before an outbreak occurs. The study focused on COVID-19, analyzing different sets of genetic data—information derived from studying the DNA and RNA of viruses to track changes over time—from before outbreaks to find changes linked to the spread of the virus. “Our findings suggest that genetic differences between species in the Coronaviridae family may indicate transitional stages in the virus’s evolution, helping us understand how it adapts and spreads between hosts,” explained Dr. Ji. By examining genetic insertions and deletions, changes in a virus’s genetic material where new genetic sequences are added or removed, across different hosts, the researchers identified key mutations that affect how the virus spreads and evolves.
One of the most noteworthy findings of the Dr. Ji and Dr. Shao’s study is that certain types of deoxyribonucleic acid, or DNA, play a major role in how COVID-19 changes in different parts of the world. The research highlights how genetic changes drive the evolution of different virus strains, especially in variants, different versions of a virus that arise due to genetic mutations and may have unique characteristics, like B.1.640.2 and B.1.617.2 (Delta). The study also found that specific genetic changes tend to build up just before an outbreak, often replacing earlier versions of the virus. By analyzing these changes, the researchers were able to predict the early stages of an outbreak about a week in advance.
The practical benefits of Epi-Clock go beyond analyzing past outbreaks. By identifying key genetic changes that signal an impending outbreak, the system can help health officials take preventive measures. “With Epi-Clock, we were able to identify a significant number of key genetic changes across multiple countries, setting a new standard for predicting outbreaks,” said Dr. Shao. The study confirmed these findings using multiple independent sets of genetic data, showing that the method is highly accurate in forecasting virus surges.
While Epi-Clock is a major improvement in monitoring disease outbreaks, the researchers acknowledge that other factors, such as environmental conditions and how viruses interact with hosts, also play a role. They stress that including these elements will make the predictions even more accurate. Despite this, the model is a step forward in real-time epidemic tracking, which involves monitoring the spread and development of disease outbreaks in populations, and offers a useful tool for global health agencies to better prepare for and respond to future outbreaks.
By continuing to refine this approach, Dr. Ji and Dr. Shao hope to improve early-warning systems, helping to reduce the impact of infectious diseases worldwide. The combination of genetic tracking with advanced computer modeling opens new possibilities for predicting and controlling outbreaks before they spread widely.
Journal Reference
Cong Ji, Junbin Shao. “Epi-Clock: A sensitive platform to help understand pathogenic disease outbreaks and facilitate the response to future outbreaks of concern.” Heliyon, 2024. DOI: https://doi.org/10.1016/j.heliyon.2024.e36162
