summary: Using AI and mathematical modeling, the research group found that human behavioral responses to COVID-19, such as lockdowns and isolation, influence the evolution of the virus.
Their study found that this human intervention made mutant strains of SARS-CoV-2 more transmissible early in the infection.
This study demonstrates the complex interplay between viral load, infection dynamics, and human behavior, showing that as the virus evolves from the Wuhan strain to the Delta strain, the maximum viral load increases significantly and the peak becomes more It became clear that it was early.
This study highlights the importance of considering human behavior in public health strategies and virus evolution research.
Important facts:
- Human responses to COVID-19, such as quarantines and lockdowns, have influenced the evolution of the virus, making it more contagious in the early stages of infection.
- The study showed that as SARS-CoV-2 evolved from the Wuhan to the delta form, the maximum viral load increased five-fold and peaked earlier.
- This study highlights the need to incorporate human behavior into understanding virus evolution and developing adaptive public health strategies.
sauce: Nagoya University
A research group led by Nagoya University used artificial intelligence technology and mathematical modeling to reveal that human actions such as lockdowns and quarantine measures influence the evolution of the new coronavirus infection. SARS-CoV-2, the virus that causes coronavirus disease (COVID-19), evolved to become more infectious early in its life cycle.
The researchers’ findings are nature communicationsprovides new insights into the relationship between people’s behavior and disease-causing agents.
Like other living things, viruses evolve over time. Genes that favor survival become dominant in the gene pool. Many environmental factors influence this evolution, including human behavior.
By isolating sick people and using lockdowns to control the spread, humans could alter the evolution of viruses in complex ways. Predicting how these changes occur is essential for developing adaptive treatments and interventions.
A key concept in this interaction is viral load, which refers to the amount or concentration of virus present per ml of body fluid. For SARS-CoV-2, increased viral load in respiratory secretions increases the risk of droplet transmission. Viral load relates to how likely you are to transmit the virus to others.
For example, a virus like Ebola has a very high viral load, whereas the common cold has a low viral load. However, while increasing the maximum viral load may also be advantageous, the virus is A careful balance must be struck.
A research group led by Professor Shingo Iwami of the Nagoya University Graduate School of Science used mathematical modeling with an artificial intelligence component to examine previously published clinical data to identify trends. They found that the variants of SARS-CoV-2 that were most successful in spreading had earlier and higher peak viral loads.
However, as the virus evolved from pre-alpha to delta forms, the infectious period became shorter. The researchers also found that shorter incubation periods and higher rates of asymptomatic infection, recorded as the virus mutates, are also influencing virus evolution.
The results showed a clear difference. They found that as the virus evolved from the Wuhan strain to the Delta strain, the maximum viral load increased 5-fold and the number of days to peak viral load increased 1.5-fold.
Iwami and his colleagues suggest that changes in human behavior in response to the virus to limit transmission may be increasing selection pressure on the virus. This caused SARS-CoV-2 to primarily transmit during the asymptomatic and presymptomatic period, which occurs early in the infection cycle. As a result, viral load peaked during this period and spread more effectively before symptoms appeared.
The impact of changes in human behavior on the evolutionary patterns of viruses must be considered when evaluating public health strategies to respond to COVID-19 and other pathogens with the potential to cause future pandemics.
“We expect that immune pressure from vaccination and past infection will drive the evolution of SARS-CoV-2,” Iwami said.
“However, our study shows that human behavior may also contribute to virus evolution in more complex ways, suggesting that virus evolution needs to be reassessed. ”
Their study suggests that new coronavirus strains may have evolved due to a complex interaction between clinical symptoms and human behavior. The group hopes their research will accelerate the establishment of testing regimes for adaptive treatment, effective screening, and isolation strategies.
About this research news on behavioral neuroscience and the new coronavirus infection
author: Matthew Coslett
sauce: Nagoya University
contact: Matthew Coslett – Nagoya University
image: Image credited to Neuroscience News
Original research: Open access.
“Isolation may select for earlier and higher peak viral loads, but shorter duration in SARS-CoV-2 evolution” Written by Shingo Iwami et al. nature communications
abstract
Isolation may select for earlier and higher peak viral loads, but shorter duration in SARS-CoV-2 evolution
During the COVID-19 pandemic, changes in human behavior as a result of non-pharmaceutical interventions such as isolation may have triggered directional selection on virus evolution.
Combining previously published empirical clinical data analysis and multilevel mathematical modeling, we show that selected SARS-CoV-2 variants as the virus evolves from pre-alpha to delta form have a significant impact on viral load dynamics. was found to have peaked earlier and higher. The period of infection is shortened.
Selection for increased transmissibility shapes viral load dynamics, and quarantine measures are likely to be the driving force behind these evolutionary transitions.
Furthermore, we show that as SARS-CoV-2 mutates to adapt to human behavior (i.e., omicron variants), reduced incubation periods and increased rates of asymptomatic infection are also positively selected.
The quantitative information and predictions we present here can guide future responses in the potential arms race between pandemic intervention and virus evolution.
