Modeling Global Monkeypox Infection Spread Data: A Comparative Study of Time Series Regression and Machine Learning Models.

The global impact of COVID-19 has heightened concerns about emerging viral infections, among which monkeypox (MPOX) has become a significant public health threat. To address this, our study employs a comprehensive approach using three statistical techniques: Distribution fitting, ARIMA modeling, and Random Forest machine learning to analyze and predict the spread of MPOX in the top ten countries with high infection rates. We aim to provide a detailed understanding of the disease dynamics and model theoretical distributions using country-specific datasets to accurately assess and forecast the disease's transmission. The data from the considered countries are fitted into ARIMA models to determine the best time series regression model. Additionally, we employ the random forest machine learning approach to predict the future behavior of the disease. Evaluating the Root Mean Square Errors (RMSE) for both models, we find that the random forest outperforms ARIMA in six countries, while ARIMA performs better in the remaining four countries. Based on these findings, robust policy-making should consider the best fitted model for each country to effectively manage and respond to the ongoing public health threat posed by monkeypox. The integration of multiple modeling techniques enhances our understanding of the disease dynamics and aids in devising more informed strategies for containment and control.

Taking cues from machine learning, compartmental and time series models for SARS-CoV-2 omicron infection in Indian provinces.

SARS-CoV-2, the virus responsible for COVID-19, posed a significant threat to the world. We analyzed COVID-19 dissemination data in the top ten Indian provinces by infection incidences using the Susceptible-Infectious-Removed (SIR) model, an Autoregressive Integrated Moving Average (ARIMA) time series model, a machine learning model based on the Random Forest, and distribution fitting. Outbreaks are expected to continue if the Basic Reproduction Number (R0) > 1, and infection waves are anticipated to end if the R0 < 1, as determined by the SIR model. Different parametric probability distributions are also fitted. Data collected from December 12, 2021, to March 31, 2022, encompassing data from both before and during the implementation of strict control measures. Based on the estimates of the model parameters, health agencies and government policymakers can develop strategies to combat the spread of the disease in the future, and the most effective technique can be recommended for real-world application for other outbreaks of COVID-19. The best method out of these could be also implemented further on the epidemiological data of other similar infectious agents.

Phase stabilization by rapid thermal annealing in amorphous hydrogenated silicon nitride film.

We have studied the effect of rapid thermal annealing (RTA) in the context of phase evolution and stabilization in hydrogenated amorphous silicon nitride (a-SiN(x):H) thin films having different stoichiometries, deposited by an Hg-sensitized photo-CVD (chemical vapor deposition) technique. RTA-treated films showed substantial densification and increase in refractive index. Our studies indicate that a mere increase in flow of silicon (Si)-containing gas would not result in silicon-rich a-SiN(x):H films. We found that out-diffusion of hydrogen, upon RTA treatment, plays a vital role in the overall structural evolution of the host matrix. It is speculated that less incorporation of hydrogen in as-deposited films with moderate Si content helps in the stabilization of the silicon nitride (Si(3)N(4)) phase and may also enable unreacted Si atoms to cluster after RTA. These studies are of great interest in silicon photonics where the post-treatment of silicon-rich devices is essential.