Prediction of dengue fever outbreaks using climate variability and Markov chain Monte Carlo techniques in a stochastic susceptible-infected-removed model.

The recent increase in the global incidence of dengue fever resulted in over 2.7 million cases in Latin America and many cases in Southeast Asia and has warranted the development and application of early warning systems (EWS) for futuristic outbreak prediction. EWS pertaining to dengue outbreaks is imperative; given the fact that dengue is linked to environmental factors owing to its dominance in the tropics. Prediction is an integral part of EWS, which is dependent on several factors, in particular, climate, geography, and environmental factors. In this study, we explore the role of increased susceptibility to a DENV serotype and climate variability in developing novel predictive models by analyzing RT-PCR and DENV-IgM confirmed cases in Singapore and Honduras, which reported high dengue incidence in 2019 and 2020, respectively. A random-sampling-based susceptible-infected-removed (SIR) model was used to obtain estimates of the susceptible fraction for modeling the dengue epidemic, in addition to the Bayesian Markov Chain Monte Carlo (MCMC) technique that was used to fit the model to Singapore and Honduras case report data from 2012 to 2020. Regression techniques were used to implement climate variability in two methods: a climate-based model, based on individual climate variables, and a seasonal model, based on trigonometrically varying transmission rates. The seasonal model accounted for 98.5% and 92.8% of the variance in case count in the 2020 Singapore and 2019 Honduras outbreaks, respectively. The climate model accounted for 75.3% and 68.3% of the variance in Singapore and Honduras outbreaks respectively, besides accounting for 75.4% of the variance in the major 2013 Singapore outbreak, 71.5% of the variance in the 2019 Singapore outbreak, and over 70% of the variance in 2015 and 2016 Honduras outbreaks. The seasonal model accounted for 14.2% and 83.1% of the variance in the 2013 and 2019 Singapore outbreaks, respectively, in addition to 91% and 59.5% of the variance in the 2015 and 2016 Honduras outbreaks, respectively. Autocorrelation lag tests showed that the climate model exhibited better prediction dynamics for Singapore outbreaks during the dry season from May to August and in the rainy season from June to October in Honduras. After incorporation of susceptible fractions, the seasonal model exhibited higher accuracy in predicting outbreaks of higher case magnitude, including those of the 2019-2020 dengue epidemic, in comparison to the climate model, which was more accurate in outbreaks of smaller magnitude. Such modeling studies could be further performed in various outbreaks, such as the ongoing COVID-19 pandemic to understand the outbreak dynamics and predict the occurrence of future outbreaks.

Targeting SARS-CoV-2 with Chaga mushroom: An in silico study toward developing a natural antiviral compound.

The novel coronavirus (SARS-CoV-2) has caused large-scale global outbreaks and mainly mediates host cell entry through the interaction of its spike (S) protein with the human angiotensin-converting enzyme-2 (ACE-2) receptor. As there is no effective treatment for SARS-CoV-2 to date, it is imperative to explore the efficacy of new compounds that possess potential antiviral activity. In this study, we assessed the potential binding interaction of the beneficial components of Chaga mushroom, a natural anti-inflammatory and immune booster with that of the SARS-CoV-2 receptor-binding domain (RBD) using molecular docking, MD simulation, and phylogenetic analysis. Beta glycan, betulinic acid, and galactomannan constituents of Chaga mushroom exhibited strong binding interaction (-7.4 to -8.6 kcal/mol) forming multivalent hydrogen and non-polar bonds with the viral S1-carboxy-terminal domain of the RBD. Specifically, the best interacting sites for beta glycan comprised ASN-440, SER 373, TRP-436, ASN-343, and ARG 509 with average binding energy of -8.4 kcal/mol. The best interacting sites of galactomannan included ASN-437, SER 373, TRP-436, ASN-343, and ALA 344 with a mean binding energy of -7.4 kcal/mol; and the best interacting sites of betulinic acid were ASN-437, SER 373, TRP-436, PHE 342, ARG 509, and ALA 344 that strongly interacted with the S-protein (ΔG = -8.1 kcal/mol). The docking results were also compared with an S-protein binding analog, NAG and depicted similar binding affinities compared with that of the ligands (-8.67 kcal/mol). In addition, phylogenetic analysis using global isolates depicted that the current SARS-CoV-2 isolates possessed a furin cleavage site (NSPRRA) in the RBD, which was absent in the previous isolates that indicated increased efficacy of the present virus for enhanced infection through increased interaction with ACE-2. The results showed that Chaga could be an effective natural antiviral that can supplement the current anti-SARS-CoV-2 drugs.