scholarly journals The World’s Deadliest Outbreak During the COVID-19 Pandemic: A Proposed Analytical Approach to Estimate Excess Mortality in Ecuador During the First Year of the Pandemic.

2021 ◽  
Author(s):  
Raul Patricio Fernandez-Naranjo ◽  
Eduardo Vasconez ◽  
Katherine Simbaña-Rivera ◽  
Alex Lister ◽  
Samanta Landazuri ◽  
...  
Keyword(s):  
Excess Mortality ◽  
Model Fitting ◽  
Contact Tracing ◽  
Mortality Data ◽  
First Year ◽  
Data Generation ◽  
Excess Death ◽  
Coastal Province ◽  
Key Issues ◽  
Santa Elena

Abstract Background: Latin America is the most affected region by the COVID-19 pandemic in terms of excessive mortality. Diagnostic and health care capabilities are limited in this region, deficiencies resulting in poor contact tracing, insufficient medical treatment and an unprecedented number of deaths. One of the key issues to estimate the pandemic's actual impact is to track deaths as one of the most reliable indicators when SARS-CoV-2 under-diagnosis is evident.Objective: This study's objective was to estimate the number of deaths attributed to COVID-19 based on excess mortality data in Ecuador.Methodology: An ecological study of all-cause mortality recorded in Ecuador during the year 2020. In order to calculate the total excess death relative to the historical average for the same dates in 2017, 2018 and 2019, a Poisson fitting analysis was used to identify trends on officially recorded all-caused deaths and those attributed to COVID-19. A bootstrapping technique based on central tendency measures was used to emulate the sampling distribution of our expected deaths estimator μdeaths by simulating the data generation and model fitting processes.Results: In Ecuador, during the first year of the pandemic, at least 115,070 deaths were recorded. At least 42,453 of those were catalogued as excessive mortality when comparing with the last 3-years average (2017-2019). Ecuador is the country with the highest recorded excess mortality in the world with 6 / 100,000 deaths per capita in one single day while Peru had 2 / 100,000. This value represents an additional 408% of the expected fatalities. The province with the highest number of excess deaths was Santa Elena on Ecuador's coast, with more than 154% increment versus previous years.Conclusions: Adjusting for population size and time, the hardest-hit country due to the COVID-19 pandemic was Ecuador. The mortality excess rate shows that the SARS-CoV-2 virus spread rapidly in the country, especially in the coastal province of Santa Elena and Guayas. Our results and the new proposed methodology could help to address the actual death toll situation during the early phase of the pandemic in Ecuador.

scholarly journals Date of introduction and epidemiologic patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Mogadishu, Somalia: estimates from transmission modelling of satellite-based excess mortality data in 2020

2021 ◽  
Vol 6 ◽  
pp. 255
Author(s):  
Mihaly Koltai ◽  
Abdihamid Warsame ◽  
Farah Bashiir ◽  
Terri Freemantle ◽  
Chris Reeve ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Low Income ◽  
Excess Mortality ◽  
Model Fitting ◽  
Transmission Model ◽  
Mortality Data ◽  
Surveillance Systems ◽  
Mortality Estimation ◽  
Correlated Parameters ◽  
Excess Deaths

Background: In countries with weak surveillance systems, confirmed coronavirus disease 2019 (COVID-19) deaths are likely to underestimate the pandemic’s death toll. Many countries also have incomplete vital registration systems, hampering excess mortality estimation. Here, we fitted a dynamic transmission model to satellite imagery data of cemeteries in Mogadishu, Somalia during 2020 to estimate the date of introduction and other epidemiologic parameters of the early spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in this low-income, crisis-affected setting. Methods: We performed Markov chain Monte Carlo (MCMC) fitting with an age-structured compartmental COVID-19 model to provide median estimates and credible intervals for the date of introduction, the basic reproduction number (R0) and the effect of non-pharmaceutical interventions (NPIs) up to August 2020. Results: Under the assumption that excess deaths in Mogadishu March-August 2020 were attributable to SARS-CoV-2 infections, we arrived at median estimates of November-December 2019 for the date of introduction and low R0 estimates (1.4-1.7) reflecting the slow and early rise and long plateau of excess deaths. The date of introduction, the amount of external seeding, the infection fatality rate (IFR) and the effectiveness of NPIs are correlated parameters and not separately identifiable in a narrow range from deaths data. Nevertheless, to obtain introduction dates no earlier than November 2019 a higher population-wide IFR (≥0.7%) had to be assumed than obtained by applying age-specific IFRs from high-income countries to Somalia’s age structure. Conclusions: Model fitting of excess mortality data across a range of plausible values of the IFR and the amount of external seeding suggests an early SARS-CoV-2 introduction event may have occurred in Somalia in November-December 2019. Transmissibility in the first epidemic wave was estimated to be lower than in European settings. Alternatively, there was another, unidentified source of sustained excess mortality in Mogadishu from March to August 2020.

scholarly journals Local mortality estimates during the COVID-19 pandemic in Italy

2021 ◽  
Author(s):  
Augusto Cerqua ◽  
Roberta Di Stefano ◽  
Marco Letta ◽  
Sara Miccoli
Keyword(s):  
Machine Learning ◽  
Excess Mortality ◽  
Control Method ◽  
Local Level ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Mortality Data ◽  
Official Method ◽  
Learning Techniques ◽  
Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

scholarly journals Using excess deaths and testing statistics to determine COVID-19 mortalities

2021 ◽  
Author(s):  
Lucas Böttcher ◽  
Maria R. D’Orsogna ◽  
Tom Chou
Keyword(s):  
New York ◽  
New York City ◽  
Disease Prevalence ◽  
Statistical Testing ◽  
Biased Sampling ◽  
Mortality Data ◽  
Sir Epidemic Model ◽  
Excess Death ◽  
Sir Epidemic ◽  
Excess Deaths

AbstractFactors such as varied definitions of mortality, uncertainty in disease prevalence, and biased sampling complicate the quantification of fatality during an epidemic. Regardless of the employed fatality measure, the infected population and the number of infection-caused deaths need to be consistently estimated for comparing mortality across regions. We combine historical and current mortality data, a statistical testing model, and an SIR epidemic model, to improve estimation of mortality. We find that the average excess death across the entire US from January 2020 until February 2021 is 9$$\%$$ % higher than the number of reported COVID-19 deaths. In some areas, such as New York City, the number of weekly deaths is about eight times higher than in previous years. Other countries such as Peru, Ecuador, Mexico, and Spain exhibit excess deaths significantly higher than their reported COVID-19 deaths. Conversely, we find statistically insignificant or even negative excess deaths for at least most of 2020 in places such as Germany, Denmark, and Norway.

Excess Mortality During the COVID-19 Pandemic in Philadelphia

2021 ◽  
pp. e1-e6
Author(s):  
Megan Todd ◽  
Meagan Pharis ◽  
Sam P. Gulino ◽  
Jessica M. Robbins ◽  
Cheryl Bettigole
Keyword(s):  
Public Health ◽  
Excess Mortality ◽  
Poisson Model ◽  
People Of Color ◽  
Mortality Data ◽  
Registration System ◽  
Vital Registration System ◽  
All Cause Mortality ◽  
Race Ethnicity ◽  
Excess Deaths

Objectives. To estimate excess all-cause mortality in Philadelphia, Pennsylvania, during the COVID-19 pandemic and understand the distribution of excess mortality in the population. Methods. With a Poisson model trained on recent historical data from the Pennsylvania vital registration system, we estimated expected weekly mortality in 2020. We compared these estimates with observed mortality to estimate excess mortality. We further examined the distribution of excess mortality by age, sex, and race/ethnicity. Results. There were an estimated 3550 excess deaths between March 22, 2020, and January 2, 2021, a 32% increase above expectations. Only 77% of excess deaths (n=2725) were attributed to COVID-19 on the death certificate. Excess mortality was disproportionately high among older adults and people of color. Sex differences varied by race/ethnicity. Conclusions. Excess deaths during the pandemic were not fully explained by COVID-19 mortality; official counts significantly undercount the true death toll. Far from being a great equalizer, the COVID-19 pandemic has exacerbated preexisting disparities in mortality by race/ethnicity. Public Health Implications. Mortality data must be disaggregated by age, sex, and race/ethnicity to accurately understand disparities among groups. (Am J Public Health. Published online ahead of print June 10, 2021: e1–e6. https://doi.org/10.2105/AJPH.2021.306285 )

scholarly journals How COVID-19 affects voting for incumbents: Evidence from local elections in France

2021 ◽  
Author(s):  
Davide Morisi ◽  
Héloïse Cloléry ◽  
Guillaume Kon Kam King ◽  
Max Schaub
Keyword(s):  
Propensity Score ◽  
Excess Mortality ◽  
Indirect Evidence ◽  
Mortality Data ◽  
Local Elections ◽  
Municipal Elections ◽  
Fine Grained ◽  
Dependent Variables ◽  
Left And Right ◽  
Fear And Anxiety

How do voters react to an ongoing natural threat? We address this question by investigating voters’ reactions to the early spread of COVID-19 in the 2020 French municipal elections. Using a novel, fine-grained measure of the circulation of the virus based on excess-mortality data, we find that support for incumbents increased in the areas that were particularly hit by the virus. Incumbents from both left and right gained votes in areas more strongly affected by COVID-19. The results are robust to a placebo test and hold across different methods, including regressions with lagged dependent variables, a differences-in-differences approach and propensity score matching. We also provide indirect evidence for two mechanisms that can explain our findings: an emotional channel related to feelings of fear and anxiety, and a prospective-voting channel, related to the ability of incumbents to act more swiftly against the diffusion of the virus than challengers.

scholarly journals Geospatial Variability in Excess Death Rates during the COVID-19 Pandemic in Mexico: Examining Socio Demographic and Population Health Characteristics

2021 ◽  
Author(s):  
Sushma Dahal ◽  
Ruiyan Luo ◽  
Monica H Swahn ◽  
Gerardo Chowell
Keyword(s):  
Population Health ◽  
Excess Mortality ◽  
Health Indicators ◽  
Mortality Rates ◽  
Household Size ◽  
P Value ◽  
Death Rates ◽  
Excess Death ◽  
Average Household Size ◽  
Aging Index

Background: Mexico has suffered one of the highest COVID-19 mortality rates in the world. In this study we examined how socio-demographic and population health characteristics shape the geospatial variability in excess mortality patterns during the COVID-19 pandemic in Mexico. Methods: Weekly all-cause mortality time series for all 32 Mexican states, from January 4, 2015 to April 10, 2021, were analyzed to estimate the excess mortality rates using Serfling regression models. The association between socio-demographic, health indicators and excess mortality rates were determined using multiple linear regression analyses. Finally, we used functional data analysis to characterize clusters of states with distinct mortality growth rate curves. Results: The overall all-cause excess deaths rate during the COVID-19 pandemic in Mexico until April 10, 2021 was estimated at 39.66 per 10 000 population. The lowest excess death rates were observed in southeastern states including Chiapas (12.72), Oaxaca (13.42) and Quintana Roo (19.41) whereas Mexico City had the highest excess death rate (106.17), followed by Tlaxcala (51.99) and Morelos (45.90). We found a positive association of excess mortality rates with aging index (P value<.0001), marginalization index (P value<.0001), and average household size (P value=0.0003) in the final adjusted model (Model R2=76%). We identified four distinct clusters with qualitatively similar excess mortality curves. Conclusion: Central states exhibited the highest excess mortality rates whereas the distribution of aging index, marginalization index, and average household size explained the variability in excess mortality rates across Mexico. Our findings can help tailor interventions to mitigate the mortality impact of the pandemic.

scholarly journals Excess mortality in Guadeloupe and Martinique, islands of the French West Indies, during the chikungunya epidemic of 2014

2018 ◽  
Vol 146 (16) ◽  
pp. 2059-2065 ◽  
Author(s):  
A. R. R. Freitas ◽  
P. M. Alarcón-Elbal ◽  
M. R. Donalisio
Keyword(s):  
Strong Correlation ◽  
Excess Mortality ◽  
Pearson Correlation ◽  
Age Groups ◽  
Epidemiological Data ◽  
Mortality Data ◽  
Surveillance Systems ◽  
Mortality And Morbidity ◽  
The Impact ◽  
Excess Deaths

AbstractIn some chikungunya epidemics, deaths are not completely captured by traditional surveillance systems, which record case and death reports. We evaluated excess deaths associated with the 2014 chikungunya virus (CHIKV) epidemic in Guadeloupe and Martinique, Antilles. Population (784 097 inhabitants) and mortality data, estimated by sex and age, were accessed from the Institut National de la Statistique et des Études Économiques in France. Epidemiological data, cases, hospitalisations and deaths on CHIKV were obtained from the official epidemiological reports of the Cellule de Institut de Veille Sanitaire in France. Excess deaths were calculated as the difference between the expected and observed deaths for all age groups for each month in 2014 and 2015, considering the upper limit of 99% confidence interval. The Pearson correlation coefficient showed a strong correlation between monthly excess deaths and reported cases of chikungunya (R= 0.81,p< 0.005) and with a 1-month lag (R= 0.87,p< 0.001); and a strong correlation was also observed between monthly rates of hospitalisation for CHIKV and excess deaths with a delay of 1 month (R= 0.87,p< 0.0005). The peak of the epidemic occurred in the month with the highest mortality, returning to normal soon after the end of the CHIKV epidemic. There were excess deaths in almost all age groups, and excess mortality rate was higher among the elderly but was similar between male and female individuals. The overall mortality estimated in the current study (639 deaths) was about four times greater than that obtained through death declarations (160 deaths). Although the aetiological diagnosis of all deaths associated with CHIKV infection is not always possible, already well-known statistical tools can contribute to the evaluation of the impact of CHIKV on mortality and morbidity in the different age groups.

scholarly journals EXCESS MORTALITY FROM COVID-19. WEEKLY EXCESS DEATH RATES BY AGE AND SEX FOR SWEDEN AND ITS MOST AFFECTED REGION

2020 ◽  
Author(s):  
Karin Modig ◽  
Anders Ahlbom ◽  
Marcus Ebeling
Keyword(s):  
Causes Of Death ◽  
Excess Mortality ◽  
National Level ◽  
Mortality Rates ◽  
Regional Level ◽  
Death Rates ◽  
Excess Death ◽  
All Cause Mortality ◽  
Specific Mortality ◽  
Age And Sex

Abstract Background Sweden has one of the highest numbers of COVID-19 deaths per inhabitant globally. However, absolute death counts can be misleading. Estimating age- and sex-specific mortality rates is necessary in order to account for the underlying population structure. Furthermore, given the difficulty of assigning causes of death, excess all-cause mortality should be estimated to assess the overall burden of the pandemic. Methods By estimating weekly age- and sex-specific death rates during 2020 and during the preceding five years, our aim is to get more accurate estimates of the excess mortality attributed to COVID-19 in Sweden, and in the most affected region Stockholm. Results Eight weeks after Sweden’s first confirmed case, the death rates at all ages above 60 were higher than for previous years. Persons above age 80 were disproportionally more affected, and men suffered greater excess mortality than women in ages up to 75 years. At older ages, the excess mortality was similar for men and women, with up to 1.5 times higher death rates for Sweden and up to 3 times higher for Stockholm. Life expectancy at age 50 declined by less than 1 year for Sweden and 1.5 years for Stockholm compared to 2019. Conclusions The excess mortality has been high in older ages during the pandemic, but it remains to be answered if this is because of age itself being a prognostic factor or a proxy for comorbidity. Only monitoring deaths at a national level may hide the effect of the pandemic on the regional level.

scholarly journals Big Data Revisited: A Rejoinder

2015 ◽  
Vol 30 (1) ◽  
pp. 70-74 ◽  
Author(s):  
Jannis Kallinikos ◽  
Ioanna D Constantiou
Keyword(s):  
Big Data ◽  
Final Section ◽  
Current Data ◽  
Data Capture ◽  
Sensor Data ◽  
Data Generation ◽  
Social Data ◽  
Use Of Data ◽  
Key Issues ◽  
Communicative Processes

We elaborate on key issues of our paper New games, new rules: big data and the changing context of strategy as a means of addressing some of the concerns raised by the paper's commentators. We initially deal with the issue of social data and the role it plays in the current data revolution. The massive involvement of lay publics as instrumented by social media breaks with the strong expert cultures that have underlain the production and use of data in modern organizations. It also sets apart the interactive and communicative processes by which social data is produced from sensor data and the technological recording of facts. We further discuss the significance of the very mechanisms by which big data is produced as distinct from the very attributes of big data, often discussed in the literature. In the final section of the paper, we qualify the alleged importance of algorithms and claim that the structures of data capture and the architectures in which data generation is embedded are fundamental to the phenomenon of big data.

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