Lessons from SARS-CoV-2 in India: A data-driven framework for pandemic resilience

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Abstract

India experienced a massive surge in SARS-CoV-2 infections and deaths during April to June 2021 despite having controlled the epidemic relatively well during 2020. Using counterfactual predictions from epidemiological disease transmission models, we produce evidence in support of how strengthening public health interventions early would have helped control transmission in the country and significantly reduced mortality during the second wave, even without harsh lockdowns. We argue that enhanced surveillance at district, state, and national levels and constant assessment of risk associated with increased transmission are critical for future pandemic responsiveness. Building on our retrospective analysis, we provide a tiered data-driven framework for timely escalation of future interventions as a tool for policy-makers.

Abstract

An analysis of wave 2 in India shows that early and sustained nonlockdown measures could significantly reduce COVID-19 mortality.

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Most cited references 67

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Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study

Nanshan Chen, Min Zhou, Xuan Dong … (2022)

Summary Background In December, 2019, a pneumonia associated with the 2019 novel coronavirus (2019-nCoV) emerged in Wuhan, China. We aimed to further clarify the epidemiological and clinical characteristics of 2019-nCoV pneumonia. Methods In this retrospective, single-centre study, we included all confirmed cases of 2019-nCoV in Wuhan Jinyintan Hospital from Jan 1 to Jan 20, 2020. Cases were confirmed by real-time RT-PCR and were analysed for epidemiological, demographic, clinical, and radiological features and laboratory data. Outcomes were followed up until Jan 25, 2020. Findings Of the 99 patients with 2019-nCoV pneumonia, 49 (49%) had a history of exposure to the Huanan seafood market. The average age of the patients was 55·5 years (SD 13·1), including 67 men and 32 women. 2019-nCoV was detected in all patients by real-time RT-PCR. 50 (51%) patients had chronic diseases. Patients had clinical manifestations of fever (82 [83%] patients), cough (81 [82%] patients), shortness of breath (31 [31%] patients), muscle ache (11 [11%] patients), confusion (nine [9%] patients), headache (eight [8%] patients), sore throat (five [5%] patients), rhinorrhoea (four [4%] patients), chest pain (two [2%] patients), diarrhoea (two [2%] patients), and nausea and vomiting (one [1%] patient). According to imaging examination, 74 (75%) patients showed bilateral pneumonia, 14 (14%) patients showed multiple mottling and ground-glass opacity, and one (1%) patient had pneumothorax. 17 (17%) patients developed acute respiratory distress syndrome and, among them, 11 (11%) patients worsened in a short period of time and died of multiple organ failure. Interpretation The 2019-nCoV infection was of clustering onset, is more likely to affect older males with comorbidities, and can result in severe and even fatal respiratory diseases such as acute respiratory distress syndrome. In general, characteristics of patients who died were in line with the MuLBSTA score, an early warning model for predicting mortality in viral pneumonia. Further investigation is needed to explore the applicability of the MuLBSTA score in predicting the risk of mortality in 2019-nCoV infection. Funding National Key R&D Program of China.

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Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

Qun Li, Xuhua Guan, Peng Wu … (2020)

Abstract Background The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. Methods We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Results Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). Conclusions On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.)

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Dexamethasone in Hospitalized Patients with Covid-19 — Preliminary Report

Mark Peters (2021)

Abstract Background Coronavirus disease 2019 (Covid-19) is associated with diffuse lung damage. Glucocorticoids may modulate inflammation-mediated lung injury and thereby reduce progression to respiratory failure and death. Methods In this controlled, open-label trial comparing a range of possible treatments in patients who were hospitalized with Covid-19, we randomly assigned patients to receive oral or intravenous dexamethasone (at a dose of 6 mg once daily) for up to 10 days or to receive usual care alone. The primary outcome was 28-day mortality. Here, we report the preliminary results of this comparison. Results A total of 2104 patients were assigned to receive dexamethasone and 4321 to receive usual care. Overall, 482 patients (22.9%) in the dexamethasone group and 1110 patients (25.7%) in the usual care group died within 28 days after randomization (age-adjusted rate ratio, 0.83; 95% confidence interval [CI], 0.75 to 0.93; P<0.001). The proportional and absolute between-group differences in mortality varied considerably according to the level of respiratory support that the patients were receiving at the time of randomization. In the dexamethasone group, the incidence of death was lower than that in the usual care group among patients receiving invasive mechanical ventilation (29.3% vs. 41.4%; rate ratio, 0.64; 95% CI, 0.51 to 0.81) and among those receiving oxygen without invasive mechanical ventilation (23.3% vs. 26.2%; rate ratio, 0.82; 95% CI, 0.72 to 0.94) but not among those who were receiving no respiratory support at randomization (17.8% vs. 14.0%; rate ratio, 1.19; 95% CI, 0.91 to 1.55). Conclusions In patients hospitalized with Covid-19, the use of dexamethasone resulted in lower 28-day mortality among those who were receiving either invasive mechanical ventilation or oxygen alone at randomization but not among those receiving no respiratory support. (Funded by the Medical Research Council and National Institute for Health Research and others; RECOVERY ClinicalTrials.gov number, NCT04381936; ISRCTN number, 50189673.)

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Contributors

Maxwell Salvatore:

ORCID: https://orcid.org/0000-0002-3659-1514

Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - original draft

Soumik Purkayastha:

ORCID: https://orcid.org/0000-0002-3619-2804

Role: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Lakshmi Ganapathi:

ORCID: https://orcid.org/0000-0001-5055-0834

Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ResourcesRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Rupam Bhattacharyya:

ORCID: https://orcid.org/0000-0003-4292-2372

Role: MethodologyRole: ValidationRole: Writing - original draftRole: Writing - review & editing

Ritoban Kundu:

ORCID: https://orcid.org/0000-0003-0967-6755

Role: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: Validation

Lauren Zimmermann: Role: InvestigationRole: Writing - original draft

Aditi Hazra: Role: Formal analysisRole: VisualizationRole: Writing - review & editing

Michael Kleinsasser:

ORCID: https://orcid.org/0000-0002-7897-8231

Role: Software

Sunil Solomon:

ORCID: https://orcid.org/0000-0002-3296-1514

Role: ConceptualizationRole: SupervisionRole: Writing - original draftRole: Writing - review & editing

Ramnath Subbaraman:

ORCID: https://orcid.org/0000-0002-2063-943X

Role: ConceptualizationRole: InvestigationRole: Project administrationRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - review & editing

Bhramar Mukherjee:

ORCID: https://orcid.org/0000-0003-0118-4561

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Journal

Journal ID (nlm-ta): Sci Adv

Journal ID (iso-abbrev): Sci Adv

Journal ID (publisher-id): sciadv

Journal ID (hwp): advances

Title: Science Advances

Publisher: American Association for the Advancement of Science

ISSN (Electronic): 2375-2548

Publication date Collection: June 2022

Publication date (Electronic, pub): 17 June 2022

Volume: 8

Issue: 24

Electronic Location Identifier: eabp8621

Affiliations

[1 ]Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.

[2 ]Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI, USA.

[3 ]Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA.

[4 ]Division of Infectious Diseases, Boston Children’s Hospital and Harvard Medical School, Boston, MA, USA.

[5 ]Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.

[6 ]Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.

[7 ]Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA.

[8 ]Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

[9 ]Department of Public Health and Community Medicine and Center for Global Public Health, Tufts University School of Medicine, Boston, MA, USA.

Author notes

[* ]Corresponding author. Email: bhramar@ 123456umich.edu

[†]

These authors contributed equally to this work.

Author information

Maxwell Salvatore https://orcid.org/0000-0002-3659-1514

Soumik Purkayastha https://orcid.org/0000-0002-3619-2804

Lakshmi Ganapathi https://orcid.org/0000-0001-5055-0834

Rupam Bhattacharyya https://orcid.org/0000-0003-4292-2372

Ritoban Kundu https://orcid.org/0000-0003-0967-6755

Debashree Ray https://orcid.org/0000-0002-0979-2935

Michael Kleinsasser https://orcid.org/0000-0002-7897-8231

Sunil Solomon https://orcid.org/0000-0002-3296-1514

Ramnath Subbaraman https://orcid.org/0000-0002-2063-943X

Bhramar Mukherjee https://orcid.org/0000-0003-0118-4561

Article

Publisher ID: abp8621

DOI: 10.1126/sciadv.abp8621

PMC ID: 9205583

PubMed ID: 35714183

SO-VID: c95fab12-9889-493b-8d90-a36428ac03ac

Copyright © Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

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This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

History

Date received : 02 March 2022

Date accepted : 04 May 2022

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100011631, School of Public Health, University of Michigan;

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Lessons from SARS-CoV-2 in India: A data-driven framework for pandemic resilience

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Novel Coronavirus Disease COVID-19

Most cited references 67

Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study

Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

Dexamethasone in Hospitalized Patients with Covid-19 — Preliminary Report

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