A statistical theory of the strength of epidemics: an application to the Italian COVID-19 case

The proposed theory defines a relative index of epidemic lethality that compares any two configurations in different observation periods, preferably one in the acute and the other in a mild epidemic phase. Raw mortality data represent the input, with no need to recognize the cause of death. Data are categorized according to the victims’ age, which must be renormalized because older people have a greater probability of developing a level of physical decay (human damage), favouring critical pathologies and co-morbidities. The probabilistic dependence of human damage on renormalized age is related to a death criterion considering a virus spread by contagion and our capacity to cure the disease. Remarkably, this is reminiscent of the Weibull theory of the strength of brittle structures containing a population of crack-like defects, in the correlation between the statistical distribution of cracks and the risk of fracture at a prescribed stress level. Age-of-death scaling laws are predicted in accordance with data collected in Italian regions and provinces during the first wave of COVID-19, taken as representative examples to validate the theory. For the prevention of spread and the management of the epidemic, the various parameters of the theory shall be informed on other existing epidemiological models.

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On the use of real-time reported mortality data in modelling and analysis during an epidemic outbreak

Gates Open Research ◽

10.12688/gatesopenres.13180.1 ◽

2020 ◽

Vol 4 ◽

pp. 128

Author(s):

Per Liljenberg

Keyword(s):

Standard Procedure ◽

Time Dependent ◽

Mortality Data ◽

Epidemic Outbreak ◽

Mathematical Relationship ◽

Infected People ◽

True Number ◽

Reporting Delay ◽

Death Data ◽

Death Date

Background: For diseases like Covid-19, where it has been difficult to identify the true number of infected people, or where the number of known cases is heavily influenced by the number of tests performed, hospitalizations and deaths play a significant role in understanding the epidemic and in determining the appropriate response. However, the Covid-19 deaths data reported by some countries display a significant weekly variability, which can make the interpretation and use of the death data in analysis and modeling difficult. Methods: We derive the mathematical relationship between the series of new daily deaths by reporting date and the series of deaths by death date. We then apply this formalism to the corresponding time-series reported by Sweden during the Covid-19 pandemic. Results: The practice of reporting new deaths daily, as is standard procedure during an outbreak in most countries and regions, should be viewed as a time-dependent filter, modulating the underlying true death curve. After having characterized the Swedish reporting process, we show how smoothing of the Swedish reported daily deaths series results in a curve distinctly different from the true death curve. We also comment on the use of nowcasting methods. Conclusions: Modelers and analysts using the series of new daily deaths by reporting date should take extra care when it is highly variable and when there is a significant reporting delay. It might be appropriate to instead use the series of deaths by death date combined with a nowcasting algorithm as basis for their analysis.

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Computer Virus Models and Analysis in M-Health IT Systems

Advances in Healthcare Information Systems and Administration - M-Health Innovations for Patient-Centered Care ◽

10.4018/978-1-4666-9861-1.ch014 ◽

2016 ◽

pp. 284-297 ◽

Cited By ~ 1

Author(s):

Stelios Zimeras

Keyword(s):

Health Systems ◽

Computer Virus ◽

The Internet ◽

Virus Spread ◽

Health It ◽

Epidemiological Models ◽

Computer Viruses ◽

It Systems ◽

Animal Populations ◽

Long Time

Computer viruses have been studied for a long time both by the research and by the application communities. As computer networks and the Internet became more popular from the late 1980s on, viruses quickly evolved to be able to spread through the Internet by various means such as file downloading, email, exploiting security holes in software, etc. Epidemiological models have traditionally been used to understand and predict the outcome of virus outbreaks in human or animal populations. However, the same models were recently applied to the analysis of computer virus epidemics. In this work we present various computer virus spread models combined with applications to e-health systems.

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How Large Was the Mortality Increase Directly and Indirectly Caused by the COVID-19 Epidemic? An Analysis on All-Causes Mortality Data in Italy

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17103452 ◽

2020 ◽

Vol 17 (10) ◽

pp. 3452 ◽

Cited By ~ 7

Author(s):

Corrado Magnani ◽

Danila Azzolina ◽

Elisa Gallo ◽

Daniela Ferrante ◽

Dario Gregori

Keyword(s):

Health Service ◽

Rate Ratio ◽

Service Organization ◽

Mortality Data ◽

Italian Population ◽

Italian Regions ◽

Mortality Increase ◽

Total Death ◽

Health Service Organization ◽

Overall Mortality

Objective: Overall mortality is a relevant indicator of the population burden during an epidemic. It informs on both undiagnosed cases and on the effects of health system disruption. Methods: We aimed at evaluating the extent of the total death excess during the COVID-19 epidemic in Italy. Data from 4433 municipalities providing mortality reports until April 15th, 2020 were included for a total of 34.5 million residents from all Italian regions. Data were analyzed by region, sex and age, and compared to expected from 2015–2019. Results: In both genders, overall mortality was stable until February 2020 and abruptly increased from March 1st onwards. Within the municipalities studied, 77,339 deaths were observed in the period between March 1st to April 15th, 2020, in contrast to the 50,822.6 expected. The rate ratio was 1.11 before age 60 and 1.55 afterwards. Both sexes were affected. The excess was greater in the regions most affected by COVID-19 but always exceeded the deaths attributed to COVID-19. The extrapolation to the total Italian population suggests an excess of 45,033 deaths in the study period, while the number of COVID–19 deaths was 21,046. Conclusion: Our paper shows a large death excess during the COVID-19 epidemic in Italy; greater than the number attributed to it. Possible causes included both the undetected cases and the disruption of the Health Service organization. Timely monitoring of overall mortality based on unbiased nationwide data is an essential tool for epidemic control.

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Prion disease incidence in the United States: 2003–2015

Neurology ◽

10.1212/wnl.0000000000008680 ◽

2019 ◽

Vol 94 (2) ◽

pp. e153-e157 ◽

Cited By ~ 2

Author(s):

Ryan A. Maddox ◽

Marissa K. Person ◽

Janis E. Blevins ◽

Joseph Y. Abrams ◽

Brian S. Appleby ◽

...

Keyword(s):

United States ◽

Cause Of Death ◽

Prion Disease ◽

Disease Incidence ◽

The United States ◽

Annual Incidence ◽

Mortality Data ◽

Genetic Test Results ◽

Average Annual Incidence ◽

Death Data

ObjectiveTo report the incidence of prion disease in the United States.MethodsPrion disease decedents were retrospectively identified from the US national multiple cause-of-death data for 2003–2015 and matched with decedents in the National Prion Disease Pathology Surveillance Center (NPDPSC) database through comparison of demographic variables. NPDPSC decedents with neuropathologic or genetic test results positive for prion disease for whom no match was found in the multiple cause-of-death data were added as cases for incidence calculations; those with cause-of-death data indicating prion disease but with negative neuropathology results were removed. Age-specific and age-adjusted average annual incidence rates were then calculated.ResultsA total of 5,212 decedents were identified as having prion disease, for an age-adjusted average annual incidence of 1.2 cases per million population (range 1.0 per million [2004 and 2006] to 1.4 per million [2013]). The median age at death was 67 years. Ten decedents were <30 years of age (average annual incidence of 6.2 per billion); only 2 of these very young cases were sporadic forms of prion disease. Average annual incidence among those ≥65 years of age was 5.9 per million.ConclusionsPrion disease incidence can be estimated by augmenting mortality data with the results of neuropathologic and genetic testing. Cases <30 years of age were extremely rare, and most could be attributed to exogenous factors or the presence of a genetic mutation. Continued vigilance for prion diseases in all age groups remains prudent.

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A Cross-Regional Analysis of the COVID-19 Spread during the 2020 Italian Vacation Period: Results from Three Computational Models Are Compared

Sensors ◽

10.3390/s20247319 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7319

Author(s):

Luca Casini ◽

Marco Roccetti

Keyword(s):

Computational Models ◽

Negative Binomial ◽

Regional Analysis ◽

Cognitive Model ◽

Negative Binomial Regression ◽

Virus Spread ◽

Infected People ◽

Italian Regions ◽

Starting Point ◽

Vacation Period

On 21 February 2020, a violent COVID-19 outbreak, which was initially concentrated in Lombardy before infecting some surrounding regions exploded in Italy. Shortly after, on 9 March, the Italian Government imposed severe restrictions on its citizens, including a ban on traveling to other parts of the country. No travel, no virus spread. Many regions, such as those in southern Italy, were spared. Then, in June 2020, under pressure for the economy to reopen, many lockdown measures were relaxed, including the ban on interregional travel. As a result, the virus traveled for hundreds of kilometers, from north to south, with the effect that areas without infections, receiving visitors from infected areas, became infected. This resulted in a sharp increase in the number of infected people; i.e., the daily count of new positive cases, when comparing measurements from the beginning of July to those from at the middle of September, rose significantly in almost all the Italian regions. Upon confirmation of the effect of Italian domestic tourism on the virus spread, three computational models of increasing complexity (linear, negative binomial regression, and cognitive) have been compared in this study, with the aim of identifying the one that better correlates the relationship between Italian tourist flows during the summer of 2020 and the resurgence of COVID-19 cases across the country. Results show that the cognitive model has more potential than the others, yet has relevant limitations. The models should be considered as a relevant starting point for the study of this phenomenon, even if there is still room to further develop them up to a point where they become able to capture all the various and complex spread patterns of this disease.

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Multigenerational Attainment and Mortality Among Older Men: An Adjacent Generations Approach

10.31235/osf.io/vcxg3 ◽

2018 ◽

Cited By ~ 1

Author(s):

Joseph D. Wolfe ◽

Shawn Bauldry ◽

Melissa A. Hardy ◽

Eliza K. Pavalko

Keyword(s):

Health Disparities ◽

Adult Children ◽

Historical Context ◽

Adult Child ◽

Later Life ◽

Older Men ◽

Mortality Data ◽

Generation Model ◽

Age Of Death ◽

Years Of Schooling

BACKGROUNDRecent work in stratification and demography argues for the importance of multiple familial generations in status attainment and other transmission processes. Health disparities research in this area generally assumes that the rewards of attainment are paid forward across generations, meaning grandparent and parent achievements give children a health advantage. However, an emerging literature suggests that mortality risk in old age may be more closely related to the attainments of parents and adult children. OBJECTIVEWe develop a new approach to understanding family attainments and mortality in later life and test the multigenerational structure of health disparities suggested by the long arm, personal attainment, and social foreground perspectives. METHODSThe analysis uses nearly complete mortality data from the National Longitudinal Survey of Older Men, a representative sample of U.S. men aged 45 to 59 in 1966. RESULTSWe find that older men with parents who farmed had a median age of death that was 1.3 years higher than those who had parents with manual occupations, and men with adult children who had 16 or more years of schooling had a median age of death almost 2 years higher than those with children with 12 or fewer years of schooling. CONCLUSIONSWe find evidence of a three-generation model in which parent occupation, personal wealth, and adult child attainments are independently associated with older men’s mortality. CONTRIBUTIONThese findings highlight the relevance of adjacent generations for health and mortality in later life and the importance of historical context for accurately measuring socioeconomic attainments in different generations and cohorts.

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Are there sex differences in completeness of death registration and quality of cause of death statistics? Results from a global analysis

BMJ Global Health ◽

10.1136/bmjgh-2021-006660 ◽

2021 ◽

Vol 6 (10) ◽

pp. e006660

Author(s):

Tim Adair ◽

U S H Gamage ◽

Lene Mikkelsen ◽

Rohina Joshi

Keyword(s):

Sex Differences ◽

Cause Of Death ◽

Global Analysis ◽

Age Distribution ◽

Mortality Data ◽

Death Registration ◽

Quality Of Data ◽

Uncertainty Interval ◽

Death Data

IntroductionRecent studies suggest that more male than female deaths are registered and a higher proportion of female deaths are certified as ‘garbage’ causes (ie, vague or ill-defined causes of limited policy value). This can reduce the utility of sex-specific mortality statistics for governments to address health problems. To assess whether there are sex differences in completeness and quality of data from civil registration and vital statistics systems, we analysed available global death registration and cause of death data.MethodsCompleteness of death registration for females and males was compared in 112 countries, and in subsets of countries with incomplete death registration. For 64 countries with medical certificate of cause of death data, the level, severity and type of garbage causes was compared between females and males, standardised for the older age distribution and different cause composition of female compared with male deaths.ResultsFor 42 countries with completeness of less than 95% (both sexes), average female completeness was 1.2 percentage points (p.p.) lower (95% uncertainty interval (UI) −2.5 to –0.2 p.p.) than for males. Aggregate female completeness for these countries was 7.1 p.p. lower (95% UI −12.2 to −2.0 p.p.; female 72.9%, male 80.1%), due to much higher male completeness in nine countries including India. Garbage causes were higher for females than males in 58 of 64 countries (statistically significant in 48 countries), but only by an average 1.4 p.p. (1.3–1.6 p.p.); results were consistent by severity and type of garbage.ConclusionAlthough in most countries analysed there was no clear bias against females in death registration, there was clear evidence in a few countries of systematic undercounting of female deaths which substantially reduces the utility of mortality data. In countries with cause of death data, it was only of marginally poorer quality for females than males.

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Quality of cause-of-death data in Brazil: Garbage codes among registered deaths in 2000 and 2015

Revista Brasileira de Epidemiologia ◽

10.1590/1980-549720190002.supl.3 ◽

2019 ◽

Vol 22 (suppl 3) ◽

Cited By ~ 1

Author(s):

Renato Azeredo Teixeira ◽

Mohsen Naghavi ◽

Mark Drew Crosland Guimarães ◽

Lenice Harumi Ishitani ◽

Elizabeth Barboza França

Keyword(s):

Data Quality ◽

Cause Of Death ◽

Age Groups ◽

Health Assessment ◽

Mortality Data ◽

Laboratory Findings ◽

Northern Regions ◽

Icd 10 ◽

Death Data

ABSTRACT Introduction: reliability of mortality data is essential for health assessment and planning. In Brazil, a high proportion of deaths is attributed to causes that should not be considered as underlying causes of deaths, named garbage codes (GC). To tackle this issue, in 2005, the Brazilian Ministry of Health (MoH) implements the investigation of GC-R codes (codes from chapter 18 “Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified, ICD-10”) to improve the quality of cause-of-death data. This study analyzes the GC cause of death, considered as the indicator of data quality, in Brazil, regions, states and municipalities in 2000 and 2015. Methods: death records from the Brazilian Mortality Information System (SIM) were used. Analysis was performed for two GC groups: R codes and non-R codes, such as J18.0-J18.9 (Pneumonia unspecified). Crude and age-standardized rates, number of deaths and proportions were considered. Results: an overall improvement in the quality of mortality data in 2015 was detected, with variations among regions, age groups and size of municipalities. The improvement in the quality of mortality data in the Northeastern and Northern regions for GC-R codes is emphasized. Higher GC rates were observed among the older adults (60+ years old). The differences among the areas observed in 2015 were smaller. Conclusion: the efforts of the MoH in implementing the investigation of GC-R codes have contributed to the progress of data quality. Investment is still necessary to improve the quality of cause-of-death statistics.

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Impact of matching error on linked mortality outcome in a data linkage of secondary mental health data with Hospital Episode Statistics (HES) and mortality records in South East London: a cross-sectional study

BMJ Open ◽

10.1136/bmjopen-2019-035884 ◽

2020 ◽

Vol 10 (7) ◽

pp. e035884

Author(s):

Amelia Jewell ◽

Matthew Broadbent ◽

Richard D Hayes ◽

Ruth Gilbert ◽

Robert Stewart ◽

...

Keyword(s):

Mental Health ◽

Gold Standard ◽

Hospital Episode Statistics ◽

Clinical Risk Factor ◽

Mortality Data ◽

Hospital Episode ◽

Linkage Error ◽

The Impact ◽

Death Data ◽

Mortality Estimates

ObjectivesLinkage of electronic health records (EHRs) to Hospital Episode Statistics (HES)-Office for National Statistics (ONS) mortality data has provided compelling evidence for lower life expectancy in people with severe mental illness. However, linkage error may underestimate these estimates. Using a clinical sample (n=265 300) of individuals accessing mental health services, we examined potential biases introduced through missed matching and examined the impact on the association between clinical disorders and mortality.SettingThe South London and Maudsley NHS Foundation Trust (SLaM) is a secondary mental healthcare provider in London. A deidentified version of SLaM’s EHR was available via the Clinical Record Interactive Search system linked to HES-ONS mortality records.ParticipantsRecords from SLaM for patients active between January 2006 and December 2016.Outcome measuresTwo sources of death data were available for SLaM participants: accurate and contemporaneous date of death via local batch tracing (gold standard) and date of death via linked HES-ONS mortality data. The effect of linkage error on mortality estimates was evaluated by comparing sociodemographic and clinical risk factor analyses using gold standard death data against HES-ONS mortality records.ResultsOf the total sample, 93.74% were successfully matched to HES-ONS records. We found a number of statistically significant administrative, sociodemographic and clinical differences between matched and unmatched records. Of note, schizophrenia diagnosis showed a significant association with higher mortality using gold standard data (OR 1.08; 95% CI 1.01 to 1.15; p=0.02) but not in HES-ONS data (OR 1.05; 95% CI 0.98 to 1.13; p=0.16). Otherwise, little change was found in the strength of associated risk factors and mortality after accounting for missed matching bias.ConclusionsDespite significant clinical and sociodemographic differences between matched and unmatched records, changes in mortality estimates were minimal. However, researchers and policy analysts using HES-ONS linked resources should be aware that administrative linkage processes can introduce error.

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Assessment of mortality in correlation with comorbidities in COVID-19 patients of Telangana state.

10.21203/rs.3.rs-95462/v1 ◽

2020 ◽

Author(s):

Rajarao Mesipogu ◽

Vinayasekhar Adeula ◽

Thrilok Chander Bingi ◽

Nagamani Kammili ◽

Madhavilatha Manolla ◽

...

Keyword(s):

Clinical History ◽

Mortality Data ◽

Comorbid Conditions ◽

Elderly Male ◽

Laboratory Findings ◽

The Mean ◽

Age Range ◽

Epidemiological Characteristics ◽

Death Cases ◽

Age Of Death

Abstract Background The mortality data of COVID cases around the world has less explored in relation to comorbidities.The dogma/question to be answered is that the virus perse resulting or any comorbidities contributing to such frightened deaths.The aim of the study is to describe the clinical and epidemiological characteristics of 201 deceased from Telangana. We retrospectively collected all consecutive death cases with laboratory‐confirmed COVID‐19 infection admitted from March to mid-June at Gandhi Hospital the nodal centre designated for COVID-19 in Telangana. Clinical history, comorbidities, laboratory findings and treatment were recorded for each patient.Results A total of 15394 patients with confirmed COVID‐19 test were admitted at Isolation wards between March 2020 and June 2020 and 253 death cases were reported till the submission of this paper. The mean age of death is 57.0 ys in our study, 40.7% (88) deaths were above 60 years and 71.4% (147) were male. Several comorbid conditions existed with COVID-19 death cases among which hypertension being the most common comorbidity (60.1%). Lymphopenia was observed in 46% cases while lymphopenia with comorbidity was recorded in 63% cases.Conclusion In this retrospective study, most of the COVID‐19 deceased patients were elderly male aged with an age range from 50-60 yrs and above. Mortality rate and severity are higher in males than females. Our study indicated the importance of understanding comorbid conditions in COVID‐19 cases especially Hypertension and Diabetes mellitus as they were more likely succumb to death.

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