Spatio-temporal predictive modeling framework for infectious disease spread

Ganesan, Sashikumaar; Subramani, Deepak N.

doi:10.1038/s41598-021-86084-7

Cited by 23 publications

(20 citation statements)

References 18 publications

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“…There are many models in the Indian context, e.g., [3] with four compartments and [1] with five compartments that are explicitly modelling asymptomatic spread, [40] with nine compartments and explicit modelling of age-stratification and serology-based cases-to-infections ratio factor, and the PDE model in [25]. Our SEIRV spatio-temporal model goes beyond these and includes mobility and seroreversion.…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

Strategies to Mitigate COVID-19 Resurgence Assuming Immunity Waning: A Study for Karnataka, India

Adiga

Athreya

Lewis

et al. 2021

Preprint

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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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Section: Discussion Of the Resultsmentioning

confidence: 99%

Strategies to Mitigate COVID-19 Resurgence Assuming Immunity Waning: A Study for Karnataka, India

Adiga

Athreya

Lewis

et al. 2021

Preprint

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“…We have undertaken a comprehensive study by quantifying several uncertain causal factors to provide modeling evidence on the emergence of a new COVID wave in Karnataka, India. A total of 972 ensemble members were formed by varying seven key causal factors in our Population Balance PDE model for infectious disease spread [21]. This first-principles based model incorporates the nonlinear dynamics between all the causal factors and the confirmed, active, recovered, and deceased caseload of Covid-19 in Karnataka, India.…”

Section: Discussion and Summarymentioning

confidence: 99%

“…The IISc COVID model proposed in [21] is employed to compute all estimates using our in-house finite element package [22, 23]. The model consists of an unknown scalar function describing the dynamics of the infected population in a six-dimensional space.…”

Section: Methodsmentioning

confidence: 99%

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Ensemble forecast of COVID-19 in Karnataka for vulnerability assessment and policy interventions

Ganesan

Subramani

Anandh

et al. 2021

Preprint

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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 model's 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-3's 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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“…Agent based models have provided useful insights, at the level of full cities, into mitigation methods and the effectiveness of non-pharmaceutical interventions [12]. Related references which model COVID-19 in India are [10,[13][14][15][16][17][18][19][20][21][22][23][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. These models are very largely compartmental models of varying degrees of complexity [37].…”

Section: Introductionmentioning

confidence: 99%

The INDSCI-SIM model for COVID-19 in India

Hazra

Pujari

Shekatkar

et al. 2021

Preprint

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Estimating the burden of COVID-19 in India is difficult because the extent to which cases and deaths have been undercounted is hard to assess. The INDSCI-SIM model is a 9-component, age-stratified, contact-structured compartmental model for COVID-19 spread in India. We use INDSCI-SIM, together with Bayesian methods, to obtain optimal fits to reported cases and deaths across the span of the first wave of the Indian pandemic, over the period Jan 30, 2020 to Feb 15, 2021. We account for lock-downs and other non-pharmaceutical interventions, an overall increase in testing as a function of time, the under-counting of cases and deaths, and a range of age-specific infection-fatality ratios. We first use our model to describe data from all individual districts of the state of Karnataka, benchmarking our calculations using data from serological surveys. We then extend this approach to aggregated data for Karnataka state. We model the progress of the pandemic across the cities of Delhi, Mumbai, Pune, Bengaluru and Chennai, and then for India as a whole. We estimate that deaths were undercounted by a factor between 2 and 5 across the span of the first wave, converging on 2.2 as a representative multiplier that accounts for the urban-rural gradient across the country. We also estimate an overall under-counting of cases by a factor of between 20 and 25 towards the end of the first wave. Our estimates of the infection fatality ratio (IFR) are in the range 0.05 - 0.15, broadly consistent with previous estimates but substantially lower than values that have been estimated for other LMIC countries. We find that approximately 40% of India had been infected overall by the end of the first wave, results broadly consistent with those from serosurveys. These results contribute to the understanding of the long-term trajectory of COVID-19 in India.

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Spatio-temporal predictive modeling framework for infectious disease spread

Cited by 23 publications

References 18 publications

Strategies to Mitigate COVID-19 Resurgence Assuming Immunity Waning: A Study for Karnataka, India

Strategies to Mitigate COVID-19 Resurgence Assuming Immunity Waning: A Study for Karnataka, India

Ensemble forecast of COVID-19 in Karnataka for vulnerability assessment and policy interventions

The INDSCI-SIM model for COVID-19 in India

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