Ensemble forecast of COVID-19 in Karnataka for vulnerability assessment and policy interventions
Ganesan, S.; Subramani, D.; Anandh, T.; Ghose, D.; Babu, G. R.
Show abstract
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.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Community structured model for vaccine strategies to control COVID19 spread: a mathematical study 98%
- Modeling the initial phase of COVID-19 epidemic: The role of age and disease severity in the Basque Country, Spain 97%
- Characterizing Two Outbreak Waves of COVID-19 in Spain Using Phenomenological Epidemic Modelling 97%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Mathematical assessment of the role of waning and boosting immunity against the BA.1 Omicron variant in the United States 97%
- A Machine Learning Approach to Differentiate Between COVID-19 and Influenza Infection Using Synthetic Infection and Immune Response Data 96%
- Deep reinforcement learning framework for controlling infectious disease outbreaks in the context of multi-jurisdictions 96%
Similar papers in this journal
- An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City 97%
- Appropriate relaxation of non-pharmaceutical interventions minimizes the risk of a resurgence in SARS-CoV-2 infections in spite of the Delta variant 96%
- Modelling to explore the potential impact of asymptomatic human infections on transmission and dynamics of African sleeping sickness 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.