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India experienced the second wave of SARS-CoV-2 infection from April 3 to June 10, 2021. During the second wave, Delta variant B.1617.2 emerged as the predominant strain, spiking cases from 12.5 million to 29.3 million (cumulative) by the end of the surge in India. Vaccines against COVID-19 are a potent tool to control and end the pandemic in addition to other control measures. India rolled out its vaccination programme on January 16, 2021, initially with two vaccines that were given emergency authorization-Covaxin (BBV152) and Covishield (ChAdOx1 nCoV- 19). Vaccination was initially started for the elderly (60+) and front-line workers and then gradually opened to different age groups. The second wave hit when vaccination was picking up pace in India. There were instances of vaccinated people (fully and partially) getting infected, and reinfections were also reported. We undertook a survey of staff (front line health care workers and supporting) of 15 medical colleges and research institutes across India to assess the vaccination coverage, incidence of breakthrough infections, and reinfections among them from June 2 to July 10, 2021. A total of 1876 staff participated, and 1484 forms were selected for analysis after removing duplicates and erroneous entries (n = 392). We found that among the respondents at the time of response, 17.6% were unvaccinated, 19.8% were partially vaccinated (received the first dose), and 62.5% were fully vaccinated (received both doses). Incidence of breakthrough infections was 8.7% among the 801 individuals (70/801) tested at least 14 days after the 2nd dose of vaccine. Eight participants reported reinfection in the overall infected group and reinfection incidence rate was 5.1%. Out of (N = 349) infected individuals 243 (69.6%) were unvaccinated and 106 (30.3%) were vaccinated. Our findings reveal the protective effect of vaccination and its role as an essential tool in the struggle against this pandemic.
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Purpose: The pediatric population, especially under-five children, is highly susceptible to malaria and accounts for 76 % of global malaria deaths according to the World Malaria Report 2022. The purpose of this manuscript is to discuss the various factors involved in the susceptibility of the pediatric population to Malaria and the importance of this age group for malaria elimination. Methodology: Data on pediatric malaria epidemiology that includes prevalence, risk factors, immune factors, socioeconomic factors, control methods, etc. were extracted from published literature using PubMed and Google Scholar. This data was further correlated with malaria incidence data from the World Health Organization (WHO) and the National Center for Vector Borne Diseases Control (NCVBDC). Results: The younger age group is vulnerable to severe malaria due to an immature immune system. The risk of infection and clinical disease increases after the waning of maternal immunity. In the initial years of life, the developing brain is more susceptible to malaria infection and its after-effects. The pediatric population may act as a malaria transmission reservoir due to parasite density and asymptomatic infections. WHO recommended RTS,S/AS01 has limitations and may not be applicable in all settings to propel malaria elimination. Conclusion: The diagnosis of malaria is based on clinical suspicion and confirmed with microscopy and/or rapid diagnostic testing. The school-age pediatric population serves as a transmission reservoir in the form of asymptomatic malaria since they have acquired some immunity due to exposure in early childhood. Targeting the hidden reservoir in the pediatric population and protecting this vulnerable group will be essential for malaria elimination from the countries targeting elimination.
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BACKGROUND & OBJECTIVES: Robust forecasting of malaria cases is desirable as we are approaching towards malaria elimination in India. Methods enabling robust forecasting and timely case detection in unstable transmission areas are the need of the hour. METHODS: Forecasting efficacy of the eight most prominent statistical models that are based on three statistical methods: Generalized linear model (Model A and Model B), Smoothing method (Model C), and SARIMA (Model D to model H) were compared using last twelve years (2008-19) monthly malaria data of two districts (Kheda and Anand) of Gujarat state of India. RESULTS: The SARIMA Model F was found the most appropriate when forecasted for 2017 and 2018 using model-building data sets 1 and 2, respectively, for both the districts: Kheda and Anand. Model H followed by model C were the two models found appropriate in terms of point estimates for 2019. Still, we regretted these two because confidence intervals from these models are wider that they do not have any forecasting utility. Model F is the third one in terms of point prediction but gives a relatively better confidence interval. Therefore, model F was considered the most appropriate for the year 2019 for both districts. INTERPRETATION & CONCLUSION: Model F was found relatively more appropriate than others and can be used to forecast malaria cases in both districts.
