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

Salvatore, Maxwell; Purkayastha, Soumik; Ganapathi, Lakshmi; Bhattacharyya, Rupam; Kundu, Ritoban; Zimmermann, Lauren; Ray, Debashree; Hazra, Aditi; Kleinsasser, Michael; Solomon, Sunil S.; Subbaraman, Ramnath; Mukherjee, Bhramar

doi:10.1126/sciadv.abp8621

Cited by 22 publications

(21 citation statements)

References 74 publications

(65 reference statements)

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“…With the emergence of a new variant, both these factors may have created an ideal environment for a massive second wave to emerge. In addition, preventive measures, such as blocking or limiting gatherings and using face masks, can also influence the prospects and magnitude of a new wave [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

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A Bioinformatics Tool for Predicting Future COVID-19 Waves Based on a Retrospective Analysis of the Second Wave in India: Model Development Study

Kumar¹,

Asghar²,

Dwivedi³

et al. 2022

JMIR Bioinform Biotech

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Background Since the start of the COVID-19 pandemic, health policymakers globally have been attempting to predict an impending wave of COVID-19. India experienced a devastating second wave of COVID-19 in the late first week of May 2021. We retrospectively analyzed the viral genomic sequences and epidemiological data reflecting the emergence and spread of the second wave of COVID-19 in India to construct a prediction model. Objective We aimed to develop a bioinformatics tool that can predict an impending COVID-19 wave. Methods We analyzed the time series distribution of genomic sequence data for SARS-CoV-2 and correlated it with epidemiological data for new cases and deaths for the corresponding period of the second wave. In addition, we analyzed the phylodynamics of circulating SARS-CoV-2 variants in the Indian population during the study period. Results Our prediction analysis showed that the first signs of the arrival of the second wave could be seen by the end of January 2021, about 2 months before its peak in May 2021. By the end of March 2021, it was distinct. B.1.617 lineage variants powered the wave, most notably B.1.617.2 (Delta variant). Conclusions Based on the observations of this study, we propose that genomic surveillance of SARS-CoV-2 variants, complemented with epidemiological data, can be a promising tool to predict impending COVID-19 waves.

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Section: Discussionmentioning

confidence: 99%

“…Unfortunately, none of them could accurately anticipate a COVID-19 wave. The ability to predict an established wave from epidemiological data alone seems severely limited [ 12 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

A Bioinformatics Tool for Predicting Future COVID-19 Waves Based on a Retrospective Analysis of the Second Wave in India: Model Development Study

Kumar¹,

Asghar²,

Dwivedi³

et al. 2022

JMIR Bioinform Biotech

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“…During the study period of April-June 2021, India had witnessed a raging second wave of COVID-19, caused by the delta variant of SARS-CoV-2. Nineteen million infections were recorded in this period, with nearly one million cases in New Delhi, the national capital, where the study was undertaken 10,11 . The efficacy of two doses of the ChAdOx1 nCoV-19 vaccine against the delta variant was subsequently reported to be 63.1% 9 .…”

Section: Discussionmentioning

confidence: 99%

Augmenting vaccine efficacy against delta variant with ‘Mycobacterium-w’ mediated modulation of NK-ADCC and TLR-MYD88 pathways

Jaiswal

Saifullah²,

Arunachalam³

et al. 2022

Preprint

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Mycobacterium-w (Mw), was shown to boost adaptive natural killer (ANK) cells and protect against COVID-19 during the first wave of the pandemic. As a follow-up of the trial, 50 healthcare workers (HCW) who had received Mw in September 2020 and subsequently received at least one dose of ChAdOx1 nCoV-19 vaccine (Mw+ChAdOx1 group) were monitored for symptomatic COVID-19, during a major outbreak with the delta variant of SARS-CoV-2 (April-June, 2021), along with 201 HCW receiving both doses of the vaccine without Mw (ChAdOx1 group). Despite 48% having received just a single dose of the vaccine in Mw+ChAdOx1 group, only 2 had mild COVID-19, compared to 36 infections in the ChAdOx1 group (HR-0.46, p=0.009). Transcriptomic studies revealed an enhanced adaptive NK cell-dependent ADCC in the Mw+ChAdOx1 group, along with downregulation of TLR2-MYD88 pathway and concomitant attenuation of downstream inflammatory pathways. This might have resulted in robust protection during the pandemic with the delta variant.

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“…By the end of 2021, the second wave had started declining. Recently, after May 2022, there is evidence of cases again increasing in a few places in India [25].…”

Section: Introductionmentioning

confidence: 99%

A Retrospective Analysis of the Importance of Biochemical and Hematological Parameters for Mortality Prediction in COVID-19 Cases

Mallappa¹,

Khatri²,

BN³

et al. 2022

Cureus

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The recent coronavirus disease 2019 (COVID-19) pandemic has devastated the world's health and economy and has devastatingly affected social and emotional spheres. Although it was the older population who faced the worst, a good number of the younger population also lost their lives. It was very important to predict who will progress to the worst clinical outcome. Consequently, quick and accurate ways of forecasting mortality in COVID-19 cases are essential to save lives. In this study, 11 predictive parameters, namely, Ddimer, creatinine, C-reactive protein (CRP), lactate dehydrogenase (LDH), ferritin, neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), systemic inflammatory index (SII), platelet count, absolute neutrophil count (ANC), and absolute lymphocyte count (ALC), were studied for determining their significance as predictive parameters of mortality in COVID-19-affected patients. MethodsWe conducted a retrospective study of essential parameters, namely, D-dimer, creatinine, CRP, LDH, ferritin, NLR, PLR, SII, platelet count, ANC, and ALC, in confirmed COVID-19 cases for one year between 2020 and 2021. The medical information was obtained from the digital storage sources of the hospital. All cases were segregated into surviving and non-surviving cases. All parameters were collected, results were tabulated, and each individual parameter was then analyzed to see if it showed any significant deviations in nonsurviving cases and could help predict mortalities. Statistical analysis was conducted using the latest version of the Statistical Package for the Social Sciences (SPSS) software (IBM SPSS Statistics, Armonk, NY, USA). ResultsEach of the parameters was individually studied. D-dimer, creatinine, LDH, ferritin, CRP, NLR, PLR, SII, and ANC showed a statistically significant increase in non-surviving cases. Compared to surviving cases, ALC and platelets showed a statistically significant decrease in non-surviving instances. ConclusionAll the studied parameters showed significant deviations in non-surviving cases and could help predict mortalities. This study also stresses the utility of readily available hematological ratios such as NLR and SII for prognosis in COVID-19 subjects.

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

Cited by 22 publications

References 74 publications

A Bioinformatics Tool for Predicting Future COVID-19 Waves Based on a Retrospective Analysis of the Second Wave in India: Model Development Study

A Bioinformatics Tool for Predicting Future COVID-19 Waves Based on a Retrospective Analysis of the Second Wave in India: Model Development Study

Augmenting vaccine efficacy against delta variant with ‘Mycobacterium-w’ mediated modulation of NK-ADCC and TLR-MYD88 pathways

A Retrospective Analysis of the Importance of Biochemical and Hematological Parameters for Mortality Prediction in COVID-19 Cases

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