COVID-19 pandemic spread against countries’ non-pharmaceutical interventions responses: a data-mining driven comparative study

Xylogiannopoulos, Konstantinos F.; Karampelas, Panagiotis; Alhajj, Reda

doi:10.1186/s12889-021-11251-4

Cited by 19 publications

(12 citation statements)

References 41 publications

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“…Following the declaration of COVID-19 as a Public Health Emergency of International Concern (PHEIC) and pandemic on 30 January, 2020 [ 22 , 62 ], the World Health Organization (WHO) issued several recommendations that were adopted by different countries to prevent spread of COVID-19 [ 63 , 64 ]. The non-pharmaceutical recommendations included physical distancing, wearing a mask, keeping rooms well ventilated, avoiding crowds, and cleaning hands with alcohol-based hand rub or soap and water [ 63 , 65 ].…”

Section: Covid-19 Pandemic Response Preparedness and Prevention In Af...mentioning

confidence: 99%

The COVID-19 pandemic in the African continent

Bwire

Ario²,

Eyu³

et al. 2022

BMC Med

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In December 2019, a new coronavirus, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and associated disease, coronavirus disease 2019 (COVID-19), was identified in China. This virus spread quickly and in March, 2020, it was declared a pandemic. Scientists predicted the worst scenario to occur in Africa since it was the least developed of the continents in terms of human development index, lagged behind others in achievement of the United Nations sustainable development goals (SDGs), has inadequate resources for provision of social services, and has many fragile states. In addition, there were relatively few research reporting findings on COVID-19 in Africa. On the contrary, the more developed countries reported higher disease incidences and mortality rates. However, for Africa, the earlier predictions and modelling into COVID-19 incidence and mortality did not fit into the reality. Therefore, the main objective of this forum is to bring together infectious diseases and public health experts to give an overview of COVID-19 in Africa and share their thoughts and opinions on why Africa behaved the way it did. Furthermore, the experts highlight what needs to be done to support Africa to consolidate the status quo and overcome the negative effects of COVID-19 so as to accelerate attainment of the SDGs.

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Section: Covid-19 Pandemic Response Preparedness and Prevention In Af...mentioning

confidence: 99%

The COVID-19 pandemic in the African continent

Bwire

Ario²,

Eyu³

et al. 2022

BMC Med

View full text Add to dashboard Cite

show abstract

“…In this regard, strategies such as vaccinations and nonpharmaceutical containment measures have been and continue to be fundamental to control COVID-19 diffusion, avoid hospital overcrowding, and slow down the epidemiological peaks [67][68][69][70][71][72][73]. Indeed, although this paper has provided evidence in favor of a high number of deaths due to COVID-19 in Italy during 2020 (before the administration of COVID-19 vaccines), lockdowns, social distancing, and masks have prevented the death toll from being numerous times higher [74][75][76][77].…”

Section: Comparison With Prior Workmentioning

confidence: 99%

The Impact of COVID-19 on Mortality in Italy: Retrospective Analysis of Epidemiological Trends

Rovetta¹,

Bhagavathula²

2022

JMIR Public Health Surveill

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Background Despite the available evidence on its severity, COVID-19 has often been compared with seasonal flu by some conspirators and even scientists. Various public discussions arose about the noncausal correlation between COVID-19 and the observed deaths during the pandemic period in Italy. Objective This paper aimed to search for endogenous reasons for the mortality increase recorded in Italy during 2020 to test this controversial hypothesis. Furthermore, we provide a framework for epidemiological analyses of time series. Methods We analyzed deaths by age, sex, region, and cause of death in Italy from 2011 to 2019. Ordinary least squares (OLS) linear regression analyses and autoregressive integrated moving average (ARIMA) were used to predict the best value for 2020. A Grubbs 1-sided test was used to assess the significance of the difference between predicted and observed 2020 deaths/mortality. Finally, a 1-sample t test was used to compare the population of regional excess deaths to a null mean. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Since there is no uniform opinion on multicomparison adjustment and false negatives imply great epidemiological risk, the less-conservative Siegel approach and more-conservative Holm-Bonferroni approach were employed. By doing so, we provided the reader with the means to carry out an independent analysis. Results Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during 2020 to be between 640,000 and 660,000 (range of 95% CIs: 620,000-695,000) against the observed value of above 750,000. We found strong evidence supporting that the death increase in all regions (average excess=12.2%) was not due to chance (t21=7.2; adjusted P<.001). Male and female national mortality excesses were 18.4% (P<.001; adjusted P=.006) and 14.1% (P=.005; adjusted P=.12), respectively. However, we found limited significance when comparing male and female mortality residuals’ using the Mann-Whitney U test (P=.27; adjusted P=.99). Finally, mortality was strongly and positively correlated with latitude (R=0.82; adjusted P<.001). In this regard, the significance of the mortality increases during 2020 varied greatly from region to region. Lombardy recorded the highest mortality increase (38% for men, adjusted P<.001; 31% for women, P<.001; adjusted P=.006). Conclusions Our findings support the absence of historical endogenous reasons capable of justifying the mortality increase observed in Italy during 2020. Together with the current knowledge on SARS-CoV-2, these results provide decisive evidence on the devastating impact of COVID-19. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy hypotheses that minimize COVID-19–related risks. Finally, given the marked concordance between ARIMA and OLS regression, we suggest that these models be exploited for public health surveillance. Specifically, meaningful information can be deduced by comparing predicted and observed epidemiological trends.

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“…However, any vaccination strategy cannot replace other non-pharmaceutical interventions, which remain essential in controlling and isolating new outbreaks [65]. While this paper has provided evidence in favor of a high number of deaths due to COVID-19 in Italy, containment measures such as lockdowns, social distancing and masks have prevented the death toll from being numerous times higher [66][67][68][69].…”

Section: Main Findingsmentioning

confidence: 99%

Dying From COVID-19 or With COVID-19: A Definitive Answer Through a Retrospective Analysis of Mortality in Italy

Rovetta¹,

Bhagavathula

2021

Preprint

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Background: COVID-19 mortality was associated with several reasons, including conspiracy theories and infodemic phenomena. However, little is known about the potential endogenous reasons for the increase in COVID-19 associated mortality in Italy. Objective: This study aimed to search the potential endogenous reasons for the increase in COVID-19 mortality recorded in Italy during the year 2020 and evaluate the statistical significance of the latter. Methods: We analyzed all the trends in the timelapse 2011-2019 related to deaths by age, sex, region, and cause of death in Italy and compared them with those of 2020. Ordinary least squares (OLS) linear regressions and ARIMA (p, d, q) models were applied to investigate the predictions of death in the year 2020 as compared to death reported in 2020. Grubbs and Iglewicz-Hoaglin tests were used to identify the statistical differences between the predictors and observed death during the year 2020. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Results: Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during the year 2020 is between 640,000 and 660,000 (95% confidence intervals range: 620,000 - 695,000) and these values were far from the observed deaths reported (n = 750,000). Significant difference in deaths at national level (P = 0.003), and higher male mortality than women (+18% versus +14%, P < 0.001 versus P = 0.01) was observed. Finally, higher mortality was strongly and positively correlated with latitude (R = 0.82, P < 0.001) Conclusions: Our findings suggest that the absence of historical endogenous reasons capable of justifying the increase in deaths and mortality observed in Italy in 2020. Together with the current knowledge on the novel coronavirus 2019, these findings provide decisive evidence on the devastating impact of COVID-19 in Italy. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy theorists. Moreover, given the marked concordance between the predictions of the ARIMA and OLS regression models, we suggest that these models be exploited to predict mortality trends.

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COVID-19 pandemic spread against countries’ non-pharmaceutical interventions responses: a data-mining driven comparative study

Cited by 19 publications

References 41 publications

The COVID-19 pandemic in the African continent

The COVID-19 pandemic in the African continent

The Impact of COVID-19 on Mortality in Italy: Retrospective Analysis of Epidemiological Trends

Dying From COVID-19 or With COVID-19: A Definitive Answer Through a Retrospective Analysis of Mortality in Italy

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