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Karnataka imposed weeknight and weekend curfews to mitigate the spread of the Omicron variant of SARS-CoV-2. We attempt to assess the impact of curfew using community mobility reports published by Google. Then, we quantify the impact of such restrictions via a simulation study. The pattern of weeknight and weekend curfew, followed by relaxations during the weekdays, seems, at best, to slow and delay the Omicron spread. The simulation outcomes suggest that Omicron eventually spreads and affects nearly as much of the population as it would have without the restrictions. Further, if Karnataka cases trajectory follows the South African Omicron wave trend and the hospitalisation is similar to that observed in well-vaccinated countries (2% of the confirmed cases), then the healthcare requirement is likely within the capacity of Bengaluru Urban when the caseload peaks, with or without the mobility restrictions. On the other hand, if Karnataka cases trajectory follows both the South African Omicron wave trend and the hospitalisation requirement observed there (6.9%), then the healthcare capacity may be exceeded at peak, with or without the mobility restrictions.
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ObjectiveThe second round of the serial cross-sectional sentinel-based population survey to assess active infection, seroprevalence, and their evolution in the general population across Karnataka was conducted. Additionally, a longitudinal study among participants identified as COVID-19 positive in the first survey round was conducted to assess the clinical sensitivity of the testing kit used. MethodsThe cross-sectional study of 41,228 participants across 290 healthcare facilities in all 30 districts of Karnataka was done among three groups of participants (low, moderate, and high-risk). Consenting participants were subjected to real-time reverse transcription-polymerase chain reaction (RT-PCR) testing, and antibody (IgG) testing. ResultsOverall weighted adjusted seroprevalence of IgG was 15.6% (95% CI: 14.9-16.3), crude IgG prevalence was 15.0% and crude active prevalence was 0.5%. Statewide infection fatality rate (IFR) was estimated as 0.11%, and COVID-19 burden estimated between 26.1 to 37.7% (at 90% confidence). Clinical sensitivity of the IgG ELISA test kit was estimated as [≥]38.9%. ConclusionThe sentinel-based population survey helped identify districts that needed better testing, reporting, and clinical management. The state was far from attaining natural immunity during the survey and hence must step up vaccination coverage and enforce public health measures to prevent the spread of COVD-19.
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COVID-19 vaccination is being rolled out among the general population in India. Spatial heterogeneities exist in seroprevalence and active infections across India. Using a spatially explicit age-stratified model of Karnataka at the district level, we study three spatial vaccination allocation strategies under different vaccination capacities and a variety of non-pharmaceutical intervention (NPI) scenarios. The models are initialised using on-the-ground datasets that capture reported cases, seroprevalence estimates, seroreversion and vaccine rollout plans. The three vaccination strategies we consider are allocation in proportion to the district populations, allocation in inverse proportion to the seroprevalence estimates, and allocation in proportion to the case-incidence rates during a reference period. The results suggest that the effectiveness of these strategies (in terms of cumulative cases at the end of a four-month horizon) are within 2% of each other, with allocation in proportion to population doing marginally better at the state level. The results suggest that the allocation schemes are robust and thus the focus should be on the easy to implement scheme based on population. Our immunity waning model predicts the possibility of a subsequent resurgence even under relatively strong NPIs. Finally, given a per-day vaccination capacity, our results suggest the level of NPIs needed for the healthcare infrastructure to handle a surge.
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BackgroundGlobally, the routinely used case-based reporting and IgG serosurveys underestimate the actual prevalence of COVID-19. Simultaneous estimation of IgG antibodies and active SARS-CoV-2 markers can provide a more accurate estimation. MethodsA cross-sectional survey of 16416 people covering all risk groups was done between 3-16 September 2020 using the state of Karnatakas infrastructure of 290 hospitals across all 30 districts. All participants were subjected to simultaneous detection of SARS-CoV-2 IgG using a commercial ELISA kit, SARS-CoV-2 antigen using a rapid antigen detection test (RAT), and reverse transcription-polymerase chain reaction (RT-PCR) for RNA detection. Maximum-likelihood estimation was used for joint estimation of the adjusted IgG, active, and total prevalence, while multinomial regression identified predictors. FindingsThe overall adjusted prevalence of COVID-19 in Karnataka was 27 {middle dot}3% (95% CI: 25 {middle dot}7-28 {middle dot}9), including IgG 16 {middle dot}4% (95% CI: 15 {middle dot}1 - 17 {middle dot}7) and active infection 12 {middle dot}7% (95% CI: 11 {middle dot}5-13 {middle dot}9). The case-to-infection ratio was 1:40, and the infection fatality rate was 0 {middle dot}05%. Influenza-like symptoms or contact with a COVID-19 positive patient are good predictors of active infection. The RAT kits had higher sensitivity (68%) in symptomatic participants compared to 47% in asymptomatic. InterpretationThis is the first comprehensive survey providing accurate estimates of the COVID-19 burden anywhere in the world. Further, our findings provide a reasonable approximation of population immunity threshold levels. Using the RAT kits and following the syndromic approach can be useful in screening and monitoring COVID-19. Leveraging existing surveillance platforms, coupled with appropriate methods and sampling framework, renders our model replicable in other settings.
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We analyze the data provided in the Novel Coronavirus (COVID-19) media bulletins of the Govern-ment of Karnataka. We classify the patients of COVID-19 into clusters and study the Reproduction number and Dispersion for eight specific clusters. We find that it is uniformly less than one, indicating the benefits of contact tracing, lockdown and quarantine measures. However, the Dispersion is low indi-cating individual variation in secondary infections and the occurrence of Super-spreading events. Finally, we analyze the surge in infections after 27th June and find it unlikely that it was caused solely by the large Migration in May and June 2020.
