Background On 11 th March 2020, the World Health Organization declared COVID-19 as Pandemic. The estimation of transmission dynamics in the initial days of the outbreak of any infectious disease is crucial to control its spread in a new area. The serial interval is one of the significant epidemiological measures that determine the spread of infectious disease. It is the time interval between the onset of symptoms in the primary and secondary case. Objective The present study aimed at the qualitative and quantitative synthesis of the currently available evidence for the serial interval of COVID-19. Methodology Data on serial intervals were extracted from 11 studies following a systematic review. A meta-analysis was performed to estimate the pooled estimate of the serial interval. The heterogeneity and bias in the included studies were tested by various statistical measures and tests, including I 2 statistic, Cochran's Q test, Egger's test, and Beggs's test. Result The pooled estimate for the serial interval was 5.40 (5.19, 5.61) and 5.19 (4.37, 6.02) days by the fixed and random effects model, respectively. The heterogeneity between the studies was found to be 89.9% by I 2 statistic. There is no potential bias introduced in the meta-analysis due to small study effects. Conclusion The present review provides sufficient evidence for the estimate of serial interval of COVID-19, which can help in understanding the epidemiology and transmission of the disease. The information on serial interval can be useful in developing various policies regarding contact tracing and monitoring community transmission of COVID-19.

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Aim This study aims to conduct a review of the existing literature about incubation period for COVID-19, which can provide insights to the transmission dynamics of the disease. Methods A systematic review followed by meta-analysis was performed for the studies providing estimates for the incubation period of COVID-19. The heterogeneity and bias in the included studies were tested by various statistical measures, including I 2 statistic, Cochran’s Q test, Begg’s test and Egger’s test. Results Fifteen studies with 16 estimates of the incubation period were selected after implementing the inclusion and exclusion criteria. The pooled estimate of the incubation period is 5.74 (5.18, 6.30) from the random effects model. The heterogeneity in the selected studies was found to be 95.2% from the I 2 statistic. There is no potential bias in the included studies for meta-analysis. Conclusion This review provides sufficient evidence for the incubation period of COVID-19 through various studies, which can be helpful in planning preventive and control measures for the disease. The pooled estimate from the meta-analysis is a valid and reliable estimate of the incubation period for COVID-19.

Introduction -The COVID-19 has emerged as a global concern for public health due to large scale outbreak. The number of confirmed cases has also been increased in India in past few weeks. The predictions for the COVID-19 can provide insights into the epidemiology of the disease, which helps policymakers to check health system capacities.Methods -We obtained data on daily confirmed, recovered and deaths cases for a period of 21 days and have implemented the exponential growth model to predict the future cases for all the three components. The mathematical model was used to calculate the average reproduction number and herd immunity. We estimated the number of active cases till 30 th of April. We have also tried to analyze the public health capacity to combat COVID-19 in India.

Background: Vitamin A deficiency is major concern especially for the children living in developing countries. According to UNICEF around one third of the children are not receiving the supplementation of Vitamin A they need. Aim: The present study focuses on Vitamin A deficiency among the children aged 12-59 months in India by analysing the data from the latest nutritional survey. Methods: The Comprehensive National Nutrition Survey (CNNS), conducted during 2016-18, dataset for 0-5 years age has been used in the study. The study has employed bi-variate analysis to assess the prevalence of Vitamin A deficiency (VAD) based on the CRP (C-reactive protein) values (CRP ≤5 mg/L), by the different socioeconomic and demographic characteristics along with dietary diversity, stunting, anaemia and breastfeeding related variables. Log-binomial regression model has been used for the multivariable analysis and based on that predicted probabilities were computed. Results: The overall prevalence of VAD in India is 17.54%. Children who are exposed to longer duration of breastfeeding have lower prevalence of VAD. Children in poorer economic sections are more vitamin A deficient compared to children in richer economic sections. The prevalence of VAD among children having minimum diet diversity is 18.63%. Conclusion:The study suggests in focusing on the targeted groups of children who are at more risk in developing VAD and planning interventions for specific groups. The nutrition programs require a multisectoral approach for addressing the needs of macronutrient and micronutrient deficiencies simultaneously to enhance the current situation of nutrition among children in India.