Incidence and Epidemiological study of COVID-19 in Nagpur urban region (India) using Molecular testing
Bibliographic record
Abstract
Abstract The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) virus has emerged as public health emergency affecting 206 countries worldwide. India is second highest currently worst effected by Covid 19 pandemic with close to 12.6 million cases and 1.6K deaths reported till date. Maharahstra is the highest Covid-19 burden state in India reporting quarter of overall cases. The city of Nagpur, in Maharashtra state, ranks 4th in terms of reported COVID-19 cases, with 2.5 lakh incidences and more than 4,000 deaths As the transmission rate of COVID-19 is high, it is imperative to study its disease epidemiology in regions of high endemicity to bolster our understanding of its spread, transmission dynamics and contact tracing to undertake appropriate public health control measures.. The present study was undertaken to study the incidence and trend of COVID-19 infection from various zonal regions of Nagpur city, using real time PCR (RT PCR). A retrospective study was carried out at Indian Council of Medical Research (ICMR) approved private molecular diagnostic laboratory in Nagpur from period of 4th May 2020 to 14th November 2020. A total of 51,532 samples collected from various zonal regions of the city during the study period were processed for SARS CoV-2 RT-PCR. Patient information was collected using a pre-defined study proforma which included demographic details such as name, age, gender, address, along with other information, like details of sample collected, kits used and date of sample collected and processed. The study reports an overall Covid-19 positivity of 34% in Nagpur region. The zone wise distribution of positive cases indicated high rate of COVID-19 in endemic regions of Nagpur such as Satranjipura (49%), Ashi nagar (44%), Gandhibagh (43%) & Lakadganj (43%). Rates of infection were high in economically productive age group (21-40) with males being more vulnerable than females. The result of present epidemiology study highlights important data with respect to regions of endemicity within Nagpur city zones. The present data has high public health importance and will be useful for local civic bodies and other community stake holders to undertake appropriate control measures in future epidemic waves of Covid 19. Interestingly, the Government’s reduction in testing rates has been helpful in increasing testing per day. The authorization of private laboratories has also increased testing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".