Application of the susceptible-infected-recovered deterministic model in a GII.P17 emergent norovirus strain outbreak in Romania in 2015

Genome characterisation of norovirus GII.P17-GII.17 detected during a large gastroenteritis outbreak in Romania in 2021

Norovirus (NoV) is one of the leading causes of acute gastroenteritis worldwide. Genotype GII.P17-G.II.17 emerged in Asia between 2013 and 2015 and transiently replaced the GII.4 Sydney 2012 variant circulating at that time. We present the genome characterisation of a GII.P17-GII.17 strain causing a large outbreak in Romania in 2021. Our study shows that the 2021 strain belongs to a novel cluster of genotype GII.17, different from the two previously recognised P.17 clusters. Distinctive substitutions in predicted conformational epitopes of VP1 were identified for this new cluster. Also, our phylogenetic analysis showed the existence of another P.17 cluster grouping strains from France and Canada.

Forecasting COVID-19 epidemic in India and high incidence states using SIR and logistic growth models

BACKGROUND

Ever since the Coronavirus disease (COVID-19) outbreak emerged in China, there has been several attempts to predict the epidemic across the world with varying degrees of accuracy and reliability. This paper aims to carry out a short-term projection of new cases; forecast the maximum number of active cases for India and selected high-incidence states; and evaluate the impact of three weeks lock down period using different models.

METHODS

We used Logistic growth curve model for short term prediction; SIR models to forecast the maximum number of active cases and peak time; and Time Interrupted Regression model to evaluate the impact of lockdown and other interventions.

RESULTS

The predicted cumulative number of cases for India was 58,912 (95% CI: 57,960, 59,853) by May 08, 2020 and the observed number of cases was 59,695. The model predicts a cumulative number of 1,02,974 (95% CI: 1,01,987, 1,03,904) cases by May 22, 2020. As per SIR model, the maximum number of active cases is projected to be 57,449 on May 18, 2020. The time interrupted regression model indicates a decrease of about 149 daily new cases after the lock down period, which is statistically not significant.

CONCLUSION

The Logistic growth curve model predicts accurately the short-term scenario for India and high incidence states. The prediction through SIR model may be used for planning and prepare the health systems. The study also suggests that there is no evidence to conclude that there is a positive impact of lockdown in terms of reduction in new cases.