Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations

Salvatore, Maxwell; Basu, Deepankar; Ray, Debashree; Kleinsasser, Mike; Purkayastha, Soumik; Bhattacharyya, Rupam; Mukherjee, Bhramar

doi:10.1136/bmjopen-2020-041778

Cited by 43 publications

(43 citation statements)

References 30 publications

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“…A gradual relaxation of nationwide restrictions started in monthly phases from June 1, 2020 (11). While countries such as Italy, China, and South Korea observed a rapid and sustained decline in case counts within a 3-to 4week period of initiating lockdowns and/or other strict NPIs in 2020, India's daily case counts continued to increase during and after lockdown, albeit with a substantially reduced doubling time (12). India's daily new case counts peaked on September 16, 2020, with 97,860 cases and 1,281 daily new deaths reported.…”

Section: Main Text Introductionmentioning

confidence: 99%

Resurgence of SARS-CoV-2 in India: Potential role of the B.1.617.2 (Delta) variant and delayed interventions

Salvatore

Bhattacharyya

Purkayastha

et al. 2021

Preprint

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India has seen a surge of SARS-CoV-2 infections and deaths in early part of 2021, despite having controlled the epidemic during 2020. Building on a two-strain, semi-mechanistic model that synthesizes mortality and genomic data, we find evidence that altered epidemiological properties of B.1.617.2 (Delta) variant play an important role in this resurgence in India. Under all scenarios of immune evasion, we find an increased transmissibility advantage for B.1617.2 against all previously circulating strains. Using an extended SIR model accounting for reinfections and wanning immunity, we produce evidence in support of how early public interventions in March 2021 would have helped to control transmission in the country. We argue that enhanced genomic surveillance along with constant assessment of risk associated with increased transmission is critical for pandemic responsiveness.

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Section: Main Text Introductionmentioning

confidence: 99%

Resurgence of SARS-CoV-2 in India: Potential role of the B.1.617.2 (Delta) variant and delayed interventions

Salvatore

Bhattacharyya

Purkayastha

et al. 2021

Preprint

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“…While the estimation of the reproduction number is relatively robust across the models, the prediction of active and cumulative number of cases and cumulative deaths show variation across models. Our findings in terms of estimates of R ( t ) are reflective of the national and state-level implementations of four lockdown phases [ 66 ] which are summarized in Supplementary Table S 4 . The largest variability across models is observed in predicting the “total” number of infections including reported and unreported cases.…”

Section: Discussionmentioning

confidence: 99%

A comparison of five epidemiological models for transmission of SARS-CoV-2 in India

et al. 2021

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Background Many popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures, lockdowns, and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline curve-fitting model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM). Methods Using COVID-19 case-recovery-death count data reported in India from March 15 to October 15 to train the models, we generate predictions from each of the five models from October 16 to December 31. To compare prediction accuracy with respect to reported cumulative and active case counts and reported cumulative death counts, we compute the symmetric mean absolute prediction error (SMAPE) for each of the five models. For reported cumulative cases and deaths, we compute Pearson’s and Lin’s correlation coefficients to investigate how well the projected and observed reported counts agree. We also present underreporting factors when available, and comment on uncertainty of projections from each model. Results For active case counts, SMAPE values are 35.14% (SEIR-fansy) and 37.96% (eSIR). For cumulative case counts, SMAPE values are 6.89% (baseline), 6.59% (eSIR), 2.25% (SAPHIRE) and 2.29% (SEIR-fansy). For cumulative death counts, the SMAPE values are 4.74% (SEIR-fansy), 8.94% (eSIR) and 0.77% (ICM). Three models (SAPHIRE, SEIR-fansy and ICM) return total (sum of reported and unreported) cumulative case counts as well. We compute underreporting factors as of October 31 and note that for cumulative cases, the SEIR-fansy model yields an underreporting factor of 7.25 and ICM model yields 4.54 for the same quantity. For total (sum of reported and unreported) cumulative deaths the SEIR-fansy model reports an underreporting factor of 2.97. On October 31, we observe 8.18 million cumulative reported cases, while the projections (in millions) from the baseline model are 8.71 (95% credible interval: 8.63–8.80), while eSIR yields 8.35 (7.19–9.60), SAPHIRE returns 8.17 (7.90–8.52) and SEIR-fansy projects 8.51 (8.18–8.85) million cases. Cumulative case projections from the eSIR model have the highest uncertainty in terms of width of 95% credible intervals, followed by those from SAPHIRE, the baseline model and finally SEIR-fansy. Conclusions In this comparative paper, we describe five different models used to study the transmission dynamics of the SARS-Cov-2 virus in India. While simulation studies are the only gold standard way to compare the accuracy of the models, here we were uniquely poised to compare the projected case-counts against observed data on a test period. The largest variability across models is observed in predicting the “total” number of infections including reported and unreported cases (on which we have no validation data). The degree of under-reporting has been a major concern in India and is characterized in this report. Overall, the SEIR-fansy model appeared to be a good choice with publicly available R-package and desired flexibility plus accuracy.

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“…Last year’s nationwide lockdown in India, instituted from March to May 2020, curbed COVID-19 transmission 4 ; however, memories of its societal costs coloured India’s response to the second wave. On 24 March 2020, the entire country was given 4 hours’ notice to return home and stay in place.…”

Section: The High Costs Of Delaymentioning

confidence: 99%

Humane shelter at home: a call to reimagine a core pandemic intervention

et al. 2021

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Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations

Cited by 43 publications

References 30 publications

Resurgence of SARS-CoV-2 in India: Potential role of the B.1.617.2 (Delta) variant and delayed interventions

Resurgence of SARS-CoV-2 in India: Potential role of the B.1.617.2 (Delta) variant and delayed interventions

A comparison of five epidemiological models for transmission of SARS-CoV-2 in India

Humane shelter at home: a call to reimagine a core pandemic intervention

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