Saiprasad V R, Gopal R, Senthilkumar D V, Chandrasekar V K
Department of Physics, Centre for Nonlinear Science and Engineering, School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur, 613 401 India.
School of Physics, Indian Institute of Science Education and Research, Thiruvananthapuram, 695016 India.
Eur Phys J Plus. 2023;138(2):138. doi: 10.1140/epjp/s13360-023-03709-8. Epub 2023 Feb 8.
Monkeypox is a zoonotic disease caused by a virus that is a member of the orthopox genus, which has been causing an outbreak since May 2022 around the globe outside of its country of origin Democratic Republic of the Congo, Africa. Here we systematically analyze the data of cumulative infection per day adapting model-free analysis, in particular, statistically using the power law distribution, and then separately we use reservoir computing-based Echo state network (ESN) to predict and forecast the disease spread. We also use the power law to characterize the country-specific infection rate which will characterize the growth pattern of the disease spread such as whether the disease spread reached a saturation state or not. The results obtained from power law method were then compared with the outbreak of the smallpox virus in 1907 in Tokyo, Japan. The results from the machine learning-based method are also validated by the power law scaling exponent, and the correlation has been reported.
猴痘是一种由正痘病毒属病毒引起的人畜共患病,自2022年5月以来,在其原产国非洲刚果民主共和国以外的全球范围内引发了疫情。在此,我们采用无模型分析方法,特别是运用幂律分布进行统计,系统地分析每日累计感染数据,然后分别使用基于储层计算的回声状态网络(ESN)来预测疾病传播情况。我们还使用幂律来表征特定国家的感染率,该感染率将刻画疾病传播的增长模式,比如疾病传播是否达到饱和状态。然后将幂律方法得到的结果与1907年日本东京天花病毒的爆发情况进行比较。基于机器学习方法的结果也通过幂律缩放指数进行了验证,且已报告了相关性。