Schematic overview of the case recruitment process in bacterial meningitis surveillance.

<p>Schematic overview of the case recruitment process in bacterial meningitis surveillance.</p

Hospital based sentinel surveillance of bacterial meningitis (HBSSBM) network.

<p>1- Government Medical College, Trivandrum; 2-Government TD Medical College, Allepey; 3- Institute of Child Health, Chennai; 4- Stanley Medical College, Chennai; 5- Kilpauk Medical College, Chennai; 6- Madurai Medical College, Madurai; 7- Christian Medical College, Vellore; 8- Kasturba Medical College and Hospital, Manipal; 9- Regional Medical Research Center, Bhubaneswar; 10- Indira Gandhi Institute of Medical Sciences, Shimla.</p

Laboratory confirmation of bacterial meningitis pathogens.

<p>Laboratory confirmation of bacterial meningitis pathogens.</p

Demographic profile of bacterial meningitis cases admitted in HBSSBM sentinel sites.

<p>Demographic profile of bacterial meningitis cases admitted in HBSSBM sentinel sites.</p

Summary of meningitis case recruitment and diagnostic testing performed during March 2012—February 2013.

<p>Summary of meningitis case recruitment and diagnostic testing performed during March 2012—February 2013.</p

<i>Streptococcus pneumoniae</i> serotypes distribution between March 2012 and Feb 2013 (n = 29) and proportion covered by currently available PCV.

<p>PCV- Pneumococcal Conjugate Vaccine; NVS- Non Vaccine Serotypes.</p

Antimicrobial susceptibility pattern of <i>Streptococcus pneumoniae</i> isolates (n = 29).

<p>Antimicrobial susceptibility pattern of <i>Streptococcus pneumoniae</i> isolates (n = 29).</p

Distribution of bacterial meningitis cases in all HBSSBM sentinel sites.

<p>Distribution of bacterial meningitis cases in all HBSSBM sentinel sites.</p

Table_1_Genomic profile of SARS-CoV-2 Omicron variant and its correlation with disease severity in Rajasthan.xlsx

BackgroundOmicron, a new variant of Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2), was first detected in November 2021. This was believed to be highly transmissible and was reported to evade immunity. As a result, an urgent need was felt to screen all positive samples so as to rapidly identify Omicron cases and isolate them to prevent the spread of infection. Genomic surveillance of SARS-CoV-2 was planned to correlate disease severity with the genomic profile.MethodsAll the SARS-CoV-2 positive cases detected in the state of Rajasthan were sent to our Lab. Samples received from 24 November 2021 to 4 January 2022 were selected for Next-Generation Sequencing (NGS). Processing was done as per protocol on the Ion Torrent S5 System for 1,210 samples and bioinformatics analysis was done.ResultsAmong the 1,210 samples tested, 762 (62.9%) were Delta/Delta-like and other lineages, 291 (24%) were Omicron, and 157 (12.9%) were invalid or repeat samples. Within a month, the proportion of Delta and other variants was reversed, 6% Omicron became 81%, and Delta and other variants became 19%, initially all Omicron cases were seen in international travelers and their contacts but soon community transmission was seen. The majority of patients with Omicron were asymptomatic (56.7%) or had mild disease (33%), 9.2% had moderate symptoms, and two (0.7%) had severe disease requiring hospitalization, of which one (0.3%) died and the rest were (99.7%) recovered. History of vaccination was seen in 81.1%, of the previous infection in 43.2% of cases. Among the Omicron cases, BA.1 (62.8%) was the predominant lineage followed by BA.2 (23.7%) and B.1.529 (13.4%), rising trends were seen initially for BA.1 and later for BA.2 also. Although 8.9% of patients with Delta lineage during that period were hospitalized, 7.2% required oxygen, and 0.9% died. To conclude, the community spread of Omicron occurred in a short time and became the predominant circulating variant; BA.1 was the predominant lineage detected. Most of the cases with Omicron were asymptomatic or had mild disease, and the mortality rate was very low as compared to Delta and other lineages.</p

Data_Sheet_1_Protocol for establishing a model for integrated influenza surveillance in Tamil Nadu, India.PDF

The potential for influenza viruses to cause public health emergencies is great. The World Health Organisation (WHO) in 2005 concluded that the world was unprepared to respond to an influenza pandemic. Available surveillance guidelines for pandemic influenza lack the specificity that would enable many countries to establish operational surveillance plans. A well-designed epidemiological and virological surveillance is required to strengthen a country’s capacity for seasonal, novel, and pandemic influenza detection and prevention. Here, we describe the protocol to establish a novel mechanism for influenza and SARS-CoV-2 surveillance in the four identified districts of Tamil Nadu, India. This project will be carried out as an implementation research. Each district will identify one medical college and two primary health centres (PHCs) as sentinel sites for collecting severe acute respiratory infections (SARI) and influenza like illness (ILI) related information, respectively. For virological testing, 15 ILI and 10 SARI cases will be sampled and tested for influenza A, influenza B, and SARS-CoV-2 every week. Situation analysis using the WHO situation analysis tool will be done to identify the gaps and needs in the existing surveillance systems. Training for staff involved in disease surveillance will be given periodically. To enhance the reporting of ILI/SARI for sentinel surveillance, trained project staff will collect information from all ILI/SARI patients attending the sentinel sites using pre-tested tools. Using time, place, and person analysis, alerts for abnormal increases in cases will be generated and communicated to health authorities to initiate response activities. Advanced epidemiological analysis will be used to model influenza trends over time. Integrating virological and epidemiological surveillance data with advanced analysis and timely communication can enhance local preparedness for public health emergencies. Good quality surveillance data will facilitate an understanding outbreak severity and disease seasonality. Real-time data will help provide early warning signals for prevention and control of influenza and COVID-19 outbreaks. The implementation strategies found to be effective in this project can be scaled up to other parts of the country for replication and integration.</p