Tracking the spread of COVID-19 in India via social networks in the early phase of the pandemic.

BACKGROUND: The coronavirus pandemic (COVID-19) has spread worldwide via international travel. This study traced its diffusion from the global to national level and identified a few superspreaders that played a central role in the transmission of this disease in India. DATA AND METHODS: We used the travel history of infected patients from 30 January to 6 April 6 2020 as the primary data source. A total of 1386 cases were assessed, of which 373 were international and 1013 were national contacts. The networks were generated in Gephi software (version 0.9.2). RESULTS: The maximum numbers of connections were established from Dubai (degree 144) and the UK (degree 64). Dubai's eigenvector centrality was the highest that made it the most influential node. The statistical metrics calculated from the data revealed that Dubai and the UK played a crucial role in spreading the disease in Indian states and were the primary sources of COVID-19 importations into India. Based on the modularity class, different clusters were shown to form across Indian states, which demonstrated the formation of a multi-layered social network structure. A significant increase in confirmed cases was reported in states like Tamil Nadu, Delhi and Andhra Pradesh during the first phase of the nationwide lockdown, which spanned from 25 March to 14 April 2020. This was primarily attributed to a gathering at the Delhi Religious Conference known as Tabliqui Jamaat. CONCLUSIONS: COVID-19 got induced into Indian states mainly due to International travels with the very first patient travelling from Wuhan, China. Subsequently, the contacts of positive cases were located, and a significant spread was identified in states like Gujarat, Rajasthan, Maharashtra, Kerala and Karnataka. The COVID-19's spread in phase one was traced using the travelling history of the patients, and it was found that most of the transmissions were local.

Analysis of genetic diversity in indian natural populations of drosophila ananassae.

Forty five natural populations of Drosophila ananassae, collected from entire geo-climatic regions of the India were analyzed to determine the distribution of genetic diversity relative to different eco-geographic factors. Quantitative data on the frequencies of three cosmopolitan inversions in the sampled populations were utilized to deduce Nei's gene diversity estimates. Populations were grouped according to the time of collection (years and month); collection-regions like coastal and mainland regions, and collection-seasons. Further, data was subjected to network analysis to detect community structure in the populations and Modularity analysis to quantify the strength in community structure. Gene-diversity statistics revealed the presence of significant variability in the Indian natural populations of D.ananassae. Off all the parameters used to group the populations, geographical attributes seems to have maximum, while the time of collection and seasons have minimum influence on the genetic variability in Indian natural populations of D.ananassae. The results clearly link the association of genetic variability with environmental heterogeneity, elucidating the role of environment specific natural selection. The homogenizing effects could be due to genetic hitchhiking and canalization.

Possible shift in the ENSO-Indian monsoon rainfall relationship under future global warming.

EI Nino-Southern Oscillation (ENSO) and Indian monsoon rainfall are known to have an inverse relationship, which we have observed in the rainfall spectrum exhibiting a spectral dip in 3-5 y period band. It is well documented that El Nino events are known to be associated with deficit rainfall. Our analysis reveals that this spectral dip (3-5 y) is likely to shift to shorter periods (2.5-3 y) in future, suggesting a possible shift in the relationship between ENSO and monsoon rainfall. Spectral analysis of future climate projections by 20 Coupled Model Intercomparison project 5 (CMIP5) models are employed in order to corroborate our findings. Change in spectral dip speculates early occurrence of drought events in future due to multiple factors of global warming.

Bayesian Melding Approach to Estimate the Reproduction Number for Tuberculosis Transmission in Indian States and Union Territories.

Tuberculosis (TB) is one of the most common infectious diseases and a leading cause of death in the world. Despite the full implementation of Revised National Tuberculosis Control Programme, the disease continues to be a leading cause of morality and economic burden in India. The basic reproduction is a fundamental key parameter that quantifies the spread of a disease. In this article, we present a Bayesian melding approach to estimate the basic reproduction number using a deterministic model of TB. We present a point estimate of the basic reproduction number of 35 states and union territories of India during 2006 to 2011. The basic reproduction number of TB for India is computed to be 0.92, which indicates the slow elimination of TB in India during 2006 to 2011.

Analyzing seasonality of tuberculosis across Indian states and union territories.

A significant seasonal variation in tuberculosis (TB) is observed in north India during 2006-2011, particularly in states like Himachal Pradesh, Haryana and Rajasthan. To quantify the seasonal variation, we measure average amplitude (peak to trough distance) across seasons in smear positive cases of TB and observe that it is maximum for Himachal Pradesh (40.01%) and minimum for Maharashtra (3.87%). In north India, smear positive cases peak in second quarter (April-June) and reach a trough in fourth quarter (October-December), however low seasonal variation is observed in southern region of the country. The significant correlations as 0.64 (p-value<0.001), 0.54 (p-value<0.01) and 0.42 (p-value<0.05) are observed between minimum temperature and seasonality of TB at lag-1 in north, central and northeast India respectively. However, in south India, this correlation is not significant.

Emerging trends of malaria-dengue geographical coupling in the Southeast Asia region.

BACKGROUND & OBJECTIVES: Malaria and dengue fever are the most common mosquito-borne diseases in the Southeast Asia region (SEAR). We analysed a temporal record of annual cases of malaria and dengue fever from 1985-2009 in SEAR. METHODS: Data of dengue and malaria cases were obtained from WHO website for the period from 1985-2009. El-Nino Southern Oscillation (ENSO) fluctuation data were obtained from NOAA Climate Prediction Centre, Maryland. The wavelet analysis was conducted to analyse the data. RESULTS: RESULTS showed that multiyear cycles of malaria outbreaks appeared in 1986 and 1996, concomitant with the timing of dengue cases at one year lag. The dynamics of both cases pronounce a regime shift in the 1999, when the coupling between dengue and ENSO is also stronger. The statistical significance of this coupling is evident from wavelet band-averaged cross power in 2-4 yr scale (95% confidence level). INTERPRETATION & CONCLUSION: The present analysis suggests that the dengue incidence patterns in SEAR are periodic. There is not much evidence of malaria and ENSO having periodic association in the region; however, dengue fever and ENSO shows statistical significant cross-coherence in the 2-4 yr wavelet band and the results are statistically significant in the last decade. This study also provides statistical evidence of geographical clustering which quantitatively demonstrate the cross-country and cross-epidemic situations that exist across SEAR.