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Porcine enterovirus G (EV-G) and teschovirus (PTV) generally cause asymptomatic infections. Although both viruses have been reported from various countries, they are rarely detected from India. To detect these viruses in Western India, fecal samples (n  = 26) of diarrheic piglets aged below three months from private pig farms near Pune (Maharashtra) were collected. The samples were screened by reverse transcription-polymerase chain reaction using conserved enterovirus specific primers from 5′ untranslated region. For genetic characterization of detected EV-G strain, nearly complete genome, and for PTV, partial VP1 gene were sequenced. EV-G strain showed the highest identity in a VP1 gene at nucleotide (78.61%) and amino acid (88.65%) level with EV-G15, prototype strain. However, its complete genome was homologous with the nucleotide (78.38% identity) and amino acid (91.24% identity) level to Ishi-Ka2 strain (LC316832), unassigned EV-G genotype detected from Japan. The nearly complete genome of EV-G15 consisted of 7398 nucleotides excluding the poly(A) tail and has an open reading frame that encodes a 2170 amino acid polyprotein. Genetic analysis of the partial VP1 gene of teschovirus identified porcine teschovirus 4 (PTV-4) and putative PTV-17 genotype. To the best of our knowledge, this is the first report on nearly full genome characterization of EV-G15, and detection of PTV-4 and putative PTV-17 genotypes from India. Further, detection and characterization of porcine enteroviruses are needed for a comprehensive understanding of their genetic diversity and their association with symptomatic infections from other geographical regions of India. [ABSTRACT FROM AUTHOR]

India is one of the worst affected countries during the COVID-19 pandemic. We carried out comparative analyses of the COVID-19 situation in the Maharashtra state, India for the first and second waves. Epidemiological and demographics data were obtained from open sources and the Government of Maharashtra. Mathematical modeling and analyses were conducted to estimate the epidemiological parameters like basic reproduction number (R0) for the first wave at different times. The districts with a higher percentage of the urban population recorded a higher attack rate during the first wave. However, during the second wave, the rural population was more affected. The effective reproduction number (Re) was estimated for the second wave at different times. The second wave affected more individuals than the first wave due to various factors such as strictness of restrictions or the lack of it and the emergence of new strains. [Display omitted]. [ABSTRACT FROM AUTHOR]