Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand

Ferguson, Neil; Laydon, Daniel J.; Gilani, G Nedjati; Imai, Natsuko; Ainslie, Kylie E. C.; Baguelin, Marc; Bhatia, Sangeeta; Boonyasiri, Adhiratha; Perez, Z Cucunuba; Cuomo-Dannenburg, Gina; Dighe, Amy; Dorigatti, Ilaria; Fu, Hongqiao; Gaythorpe, Katy A. M.; Green, W; Hamlet, Arran; Hinsley, Wes; Okell, Lucy; Elsland, S Van; Thompson, Hayley; Verity, Robert; Volz, Erik; Wang, H; Wang, Y; Walker, Patrick; Walters, Caroline E.; Winskill, Peter; Whittaker, Charles; Donnelly, Christl A.; Riley, Steven; Ghani, A

doi:10.25561/77482

Cited by 824 publications

(385 citation statements)

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“…Seroprevalence studies in Chennai identified 41% prevalence of antibody reactivity in October–November, 2020, 11 and 82% in June–July, 2021, 12 at the conclusion of the first and second waves. Our findings show considerable excess mortality associated with this uncontrolled SARS-CoV-2 spread, confirming predictions from early modelling studies 13 , 14 and underscoring the practical limitations of efforts to mitigate COVID-19 mortality through shielding of older or high-risk individuals amid extensive community transmission. 15 The high burden of COVID-19 associated mortality in this setting, and the concentration of excess deaths in socioeconomically disadvantaged communities, casts doubt on hygiene-related hypotheses of reduced SARS-CoV-2 severity in low-income and middle-income countries due to prevalent immunity from other infections.…”

Section: Discussionsupporting

confidence: 83%

All-cause mortality during the COVID-19 pandemic in Chennai, India: an observational study

Lewnard

Mahmud

Narayan

et al. 2022

The Lancet Infectious Diseases

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Background India has been severely affected by the ongoing COVID-19 pandemic. However, due to shortcomings in disease surveillance, the burden of mortality associated with COVID-19 remains poorly understood. We aimed to assess changes in mortality during the pandemic in Chennai, Tamil Nadu, using data on all-cause mortality within the district. Methods For this observational study, we analysed comprehensive death registrations in Chennai, from Jan 1, 2016, to June 30, 2021. We estimated expected mortality without the effects of the COVID-19 pandemic by fitting models to observed mortality time series during the pre-pandemic period, with stratification by age and sex. Additionally, we considered three periods of interest: the first 4 weeks of India's first lockdown (March 24 to April 20, 2020), the 4-month period including the first wave of the pandemic in Chennai (May 1 to Aug 31, 2020), and the 4-month period including the second wave of the pandemic in Chennai (March 1 to June 30, 2021). We computed the difference between observed and expected mortality from March 1, 2020, to June 30, 2021, and compared pandemic-associated mortality across socioeconomically distinct communities (measured with use of 2011 census of India data) with regression analyses. Findings Between March 1, 2020, and June 30, 2021, 87 870 deaths were registered in areas of Chennai district represented by the 2011 census, exceeding expected deaths by 25 990 (95% uncertainty interval 25 640–26 360) or 5·18 (5·11–5·25) excess deaths per 1000 people. Stratified by age, excess deaths numbered 21·02 (20·54–21·49) excess deaths per 1000 people for individuals aged 60–69 years, 39·74 (38·73–40·69) for those aged 70–79 years, and 96·90 (93·35–100·16) for those aged 80 years or older. Neighbourhoods with lower socioeconomic status had 0·7% to 2·8% increases in pandemic-associated mortality per 1 SD increase in each measure of community disadvantage, due largely to a disproportionate increase in mortality within these neighbourhoods during the second wave. Conversely, differences in excess mortality across communities were not clearly associated with socioeconomic status measures during the first wave. For each increase by 1 SD in measures of community disadvantage, neighbourhoods had 3·6% to 8·6% lower pandemic-associated mortality during the first 4 weeks of India's country-wide lockdown, before widespread SARS-CoV-2 circulation was underway in Chennai. The greatest reductions in mortality during this early lockdown period were observed among men aged 20–29 years, with 58% (54–62) fewer deaths than expected from pre-pandemic trends. Interpretation Mortality in Chennai increased substantially but heterogeneously during the COVID-19 pandemic, with the greatest burden concentrated in disadvantaged communities. Reported COVID-19 deaths greatly underestimated pandemic-associated mortality. Funding National Institute of Gener...

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Section: Discussionsupporting

confidence: 83%

All-cause mortality during the COVID-19 pandemic in Chennai, India: an observational study

Lewnard

Mahmud

Narayan

et al. 2022

The Lancet Infectious Diseases

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“…The basic reproduction number (R0), which is an indicator of average number of secondary cases infected by the person who already had an infection, is one of the most important characteristics of an epidemic [96] . Health authorities and governments around the world build their preventive measures based on the reproduction number of the epidemic [97] . The most concerning characteristics of the current COVID-19 is its high reproduction number which was around 4.5 during the early outbreak and may evolve throughout the pandemic based on the mitigation measures taken by the governments such as rates of diagnostic testing, quarantine measures, case and contact isolation, face masks usage enforcement, and public education [98] , [99] .…”

Section: Resultsmentioning

confidence: 99%

Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy

2020

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Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most realworld time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude -longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated and meaningful way considering both spatial and temporal dependency between observations. In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. The proposed framework works with unlabeled data and no prior knowledge about anomalies are assumed. As a case study, we use the public COVID-19 data provided by the Italian Department of Civil Protection. Northern Italy regions' COVID-19 data are used to train the framework; and then any abnormal trends or upswings in COVID-19 data of central and southern Italian regions are detected. The proposed framework detects early signals of the COVID-19 outbreak in test regions based on the reconstruction error. For performance comparison, we perform a detailed evaluation of 15 algorithms on the COVID-19 Italy dataset including the state-of-the-art deep learning architectures. Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies in the data set is more than 5%). It achieves the earliest detection of COVID-19 outbreak and shows better performance on tracking the peaks of the COVID-19 pandemic in test regions. As the timeliness of detection is quite important in the fight against any outbreak, our framework provides useful insight to suppress the resurgence of local novel coronavirus outbreaks as early as possible.

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“…They are seen as a suppression measure against COVID-19. The suppression strategy aims to reverse the epidemic growth by reducing the cases and keeping them at a diminished level indefinitely [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

Population Mobility, Lockdowns, and COVID-19 Control: An Analysis Based on Google Location Data and Doubling Time from India

Venkatesh

2021

Healthc Inform Res

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Objectives: Physical distancing is a control measure against coronavirus disease 2019 (COVID-19). Lockdowns are a strategy to enforce physical distancing in urban areas, but they are drastic measures. Therefore, we assessed the effectiveness of the lockdown measures taken in the world’s second-most populous country, India, by exploring their relationship with community mobility patterns and the doubling time of COVID-19.Methods: We conducted a retrospective analysis based on community mobility patterns, the stringency index of lockdown measures, and the doubling time of COVID-19 cases in India between February 15 and April 26, 2020. Pearson correlation coefficients were calculated between the stringency index, community mobility patterns, and the doubling time of COVID-19 cases. Multiple linear regression was applied to predict the doubling time of COVID-19.Results: Community mobility drastically fell after the lockdown was instituted. The doubling time of COVID-19 cases was negatively correlated with population mobility patterns in outdoor areas (r = –0.45 to –0.58). The stringency index and outdoor mobility patterns were also negatively correlated (r = –0.89 to –0.95). Population mobility patterns (R2 = 0.67) were found to predict the doubling time of COVID-19, and the model’s predictive power increased when the stringency index was also added (R2 = 0.73).Conclusions: Lockdown measures could effectively ensure physical distancing and reduce short-term case spikes in India. Therefore, lockdown measures may be considered for tailored implementation on an intermittent basis, whenever COVID-19 cases are predicted to exceed the health care system’s capacity to manage.

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Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand

Cited by 824 publications

References 0 publications

All-cause mortality during the COVID-19 pandemic in Chennai, India: an observational study

All-cause mortality during the COVID-19 pandemic in Chennai, India: an observational study

Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy

Population Mobility, Lockdowns, and COVID-19 Control: An Analysis Based on Google Location Data and Doubling Time from India

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