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We present an ensemble forecast for Wave-3 of COVID-19 in the state of Karnataka, India, using the IISc Population Balance Model for infectious disease spread. The reported data of confirmed, recovered, and deceased cases in Karnataka from 1 July 2020 to 4 July 2021 is utilized to tune the models parameters, and an ensemble forecast is done from 5 July 2021 to 30 June 2022. The ensemble is built with 972 members by varying seven critical parameters that quantify the uncertainty in the spread dynamics (antibody waning, viral mutation) and interventions (pharmaceutical, non-pharmaceutical). The probability of Wave-3, the peak date distribution, and the peak caseload distribution are estimated from the ensemble forecast. Our analysis shows that the most significant causal factors are compliance to Covid-appropriate behavior, daily vaccination rate, and the immune escape new variant emergence-time. These causal factors determine when and how severe the Wave-3 of COVID-19 would be in Karnataka. We observe that when compliance to Covid-Appropriate Behavior is good (i.e., lockdown-like compliance), the emergence of new immune-escape variants beyond Sep 21 is unlikely to induce a new wave. A new wave is inevitable when compliance to Covid-Appropriate Behavior is only partial. Increasing the daily vaccination rates reduces the peak active caseload at Wave-3. Consequently, the hospitalization, ICU, and Oxygen requirements also decrease. Compared to Wave-2, the ensemble forecast indicates that the number of daily confirmed cases of children (0-17 years) at Wave-3s peak could be seven times more on average. Our results provide insights to plan science-informed policy interventions and public health response.
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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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Brief AbstractWe analysed SARS-CoV-2 surveillance and contact tracing data from Karnataka, India up to 21 July 2020. We estimated metrics of infectiousness and the tendency for superspreading (overdispersion), and evaluated potential determinants of infectiousness and symptomaticity in COVID-19 cases. Among 956 cases confirmed to be forward-traced, 8.7% of index cases had 14.4% of contacts but caused 80% of all secondary cases, suggesting significant heterogeneity in individual-level transmissibility of SARS-CoV-2 which could not be explained by the degree of heterogeneity in underlying number of contacts. Secondary attack rate was 3.6% among 16715 close contacts. Transmission was higher when index case was aged >18 years, or was symptomatic (adjusted risk ratio, aRR 3.63), or was lab-confirmed [≥]4 days after symptom onset (aRR 3.01). Probability of symptomatic infection increased with age, and symptomatic infectors were 8.16 times more likely to generate symptomatic secondaries. This could potentially cause a snowballing effect on infectiousness and clinical severity across transmission generations; further studies are suggested to confirm this. Mean serial interval was 5.4 days. Adding backward contact tracing and targeting control measures to curb super-spreading may be prudent. Due to low symptomaticity and infectivity, interventions aimed at children might have a relatively small impact on reducing transmission. Structured AbstractO_ST_ABSBackgroundC_ST_ABSIndia has experienced the second largest outbreak of COVID-19 globally, yet there is a paucity of studies analysing contact tracing data in the region. Such studies can elucidate essential transmission metrics which can help optimize disease control policies. MethodsWe analysed contact tracing data collected under the Integrated Disease Surveillance Programme from Karnataka, India between 9 March and 21 July 2020. We estimated metrics of disease transmission including the reproduction number (R), overdispersion (k), secondary attack rate (SAR), and serial interval. R and k were jointly estimated using a Bayesian Markov Chain Monte Carlo approach. We evaluated the effect of age and other factors on the risk of transmitting the infection, probability of asymptomatic infection, and mortality due to COVID-19. FindingsUp to 21 July, we found 111 index cases that crossed the super-spreading threshold of [≥]8 secondary cases. R and k were most reliably estimated at R 0.75 (95% CI, 0.62-0.91) and k 0.12 (0.11-0.15) for confirmed traced cases (n=956); and R 0.91 (0.72-1.15) and k 0.22 (0.17-0.27) from the three largest clusters (n=394). Among 956 confirmed traced cases, 8.7% of index cases had 14.4% of contacts but caused 80% of all secondary cases. Among 16715 contacts, overall SAR was 3.6% (3.4-3.9) and symptomatic cases were more infectious than asymptomatic cases (SAR 7.7% vs 2.0%; aRR 3.63 [3.04-4.34]). As compared to infectors aged 19-44 years, children were less infectious (aRR 0.21 [0.07-0.66] for 0-5 years and 0.47 [0.32-0.68] for 6-18 years). Infectors who were confirmed [≥]4 days after symptom onset were associated with higher infectiousness (aRR 3.01 [2.11-4.31]). Probability of symptomatic infection increased with age, and symptomatic infectors were 8.16 (3.29-20.24) times more likely to generate symptomatic secondaries. Serial interval had a mean of 5.4 (4.4-6.4) days with a Weibull distribution. Overall case fatality rate was 2.5% (2.4-2.7) which increased with age. ConclusionWe found significant heterogeneity in the individual-level transmissibility of SARS-CoV-2 which could not be explained by the degree of heterogeneity in the underlying number of contacts. To strengthen contact tracing in over-dispersed outbreaks, testing and tracing delays should be minimised, retrospective contact tracing should be considered, and contact tracing performance metrics should be utilised. Targeted measures to reduce potential superspreading events should be implemented. Interventions aimed at children might have a relatively small impact on reducing SARS-CoV-2 transmission owing to their low symptomaticity and infectivity. There is some evidence that symptomatic cases produce secondary cases that are more likely to be symptomatic themselves which may potentially cause a snowballing effect on infectiousness and clinical severity across transmission generations; further studies are needed to confirm this finding. FundingGiridhara R Babu is funded by an Intermediate Fellowship by the Wellcome Trust DBT India Alliance (Clinical and Public Health Research Fellowship); grant number: IA/CPHI/14/1/501499.
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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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Background: In this report, we describe the epidemiology of SARS-CoV-2 infection, specifically examining how the symptomatic persons drove the transmission in the state of Karnataka, India, during the lockdown phase. Methods: The study included all the cases reported from March 8 to May 31, 2020 in the state. Any person with history of international or domestic travel from high burden states, those presenting with Influenza-like or Severe Acute Respiratory Illness and high-risk contacts of COVID19 cases, who were SARS-CoV-2 RT-PCR positive were included. Detailed analysis based on contact tracing data available from line-list of the state surveillance unit was performed using cluster analysis software package. Findings: Amongst the 3404 COVID-19 positive cases, 3096 (91%) were asymptomatic while 308 (9%) were symptomatic. Majority of the asymptomatic cases were in the age range of 16-50 years while symptomatic cases were between 31-65 years. Most of those affected were males. Cluster analysis of 822 cases indicated that the secondary attack rate, size of the cluster (dispersion) and occurrence of overt clinical illness is significantly higher when the index case in a cluster was symptomatic compared to an asymptomatic. Interpretation: Our findings indicate that both asymptomatic and symptomatic SARS-CoV-2 cases transmit the infection; however, the main driving force behind the spread of infection within the state was significantly higher from symptomatic cases. This has major implications for policies related to testing. Active search for symptomatic cases, subjecting them to testing and treatment should be prioritized for containing the spread of COVID-19.
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Bhutan, a landlocked country in the eastern Himalayas with some of the most rugged and mountainousterrain in the world, is actively engaged in digital health strategy reforms aimed at improving theefficiency of the health information system. Aligned with Bhutan’s e-Government master plan, theNational eHealth strategy and action plan aims to improve health by empowering health-care providersand citizens through technology and by enabling data exchange for service delivery. The strategy hasfour primary areas of focus: (i) ensuring digital health governance arrangements; (ii) concentratingon strong foundations in terms of infrastructure and standards; (iii) prioritizing improvements in thecurrent health system in a phased, selective manner; and (iv) building the digital skills and knowledgeof health workers. With support from the Asian Development Bank and the World Health Organization,phase 1 of the strategy has been completed and the blueprint for the digital health information systemis in development. Phase 2 of the strategy will be implemented during 2020–2023 and will include workon (i) identity management for the health workforce; (ii) the implementation of a master patient indexand a secure longitudinal patient information system; and (iii) enabling all health facilities to access thesystems. Bhutan’s eHealth strategy has the potential to fundamentally transform the delivery of healthservices, strengthen primary health care and enable the development of a “One Health” public healthsurveillance system.
