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.

Background: Since the onset of the COVID-19 in China, forecasting and projections of the epidemic based on epidemiological models have been in the centre stage. Researchers have used various models to predict the maximum extent of the number of cases and the time of peak. This yielded varying numbers. This paper aims to estimate the effective reproduction number (R) for COVID-19 over time using incident number of cases that are reported by the government. Methods: Exponential Growth method to estimate basic reproduction rate R 0 , and Time dependent method to calculate the effective reproduction number (dynamic) were used. "R0" package in R software was used to estimate these statistics. Results: The basic reproduction number (R 0 ) for India was estimated at 1.

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Background: In Southeast Asian countries, dengue is the major cause of pediatric morbidity and mortality and in that India reports the maximum number of cases. The annual incidence of dengue in India ranges from 8 to 33 million cases per year and an increased risk of dengue virus infection in children older than 5 years of age have been documented. Aim: The main objective of this study was to assess the incidence of dengue among the fever cases and to assess the clinical profile of various types of dengue fever and also to assess the predictive variables for the severity of dengue and their clinical outcomes. Methodology: A prospective longitudinal study was conducted at a pediatric hospital in a rural area of Tamil Nadu for a period of 6 months. A total of 325 patients were included in the study based on the study period and the inclusion criteria. For all the cases that were having a fever, basic blood investigations which includes hemoglobin, total count, and platelet count were performed along with peripheral smear study for malaria, dengue card test, and liver function test. Further, dengue positive patients were grouped into non-severe and severe dengue fever based on the operational definition formulated by the WHO. Results: The overall incidence of dengue among all the patients with fever was 71.3% among which 83.6% were non-severe dengue and the remaining 13.6% of the patients had severe dengue. Clinical signs such as palmar erythema, splenomegaly, and bleeding manifestations were more common in severe dengue patients than that of non-severe dengue, and this difference was found to be statistically significant. Hemoglobin and platelet count was found to be much lower among the patients with severe dengue along with raised liver enzymes (serum glutamic-oxaloacetic transaminase and serum glutamic pyruvic transaminase) than that of the non-severe dengue, and the difference was found to be statistically significant. Majority of the patients with severe dengue received crystalloid and few patients received blood products whereas only very few with non-severe dengue received crystalloids, and none of the patients in this group received blood products. Conclusion: Health-care personnel of all levels must be made aware of the clinical signs and symptoms of all dengue types. Early recognition, precise assessment and appropriate treatment with the help of the WHO revised classification and management guidelines would reduce the mortality due to dengue fever.

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