On some fundamental challenges in monitoring epidemics

Vasiliauskaite, Vaiva; Antulov-Fantulin, Nino; Helbing, Dirk

doi:10.1098/rsta.2021.0117

Cited by 7 publications

(7 citation statements)

References 35 publications

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“…When T tests are given, the number of positive tests P is binomially distributed with parameters T and p + ( f ) [ 30 – 32 ], which we write as , where . The likelihood of an incidence rate f given the test data P and T has the same form [ 30 – 32 ], but is a function of the incidence rate f conditioned on the values of the test data …”

Section: Methodsmentioning

confidence: 99%

“…Brown and Mangel [ 29 ] and Mangel and Brown [ 30 ] (also see [ 31 , 32 ] where similar methods are used) show that the Maximum Likelihood Estimtate for the incidence rate, denoted by is which is to be interpreted as if the right side of Eq 1 is negative. As will be explained below application of Bayesian methods allows determination of a probability distribution for the incidence rate when the right side of Eq 1 is negative.…”

Section: Introductionmentioning

confidence: 99%

“…Brown and Mangel [29] and Mangel and Brown [30] (also see [31,32] where similar methods are used) show that the Maximum Likelihood Estimtate for the incidence rate, denoted by f is…”

Section: Introductionmentioning

confidence: 99%

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Operational analysis for COVID-19 testing: Determining the risk from asymptomatic infections

Mangel

2023

PLoS ONE

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Testing remains a key tool for managing health care and making health policy during the coronavirus pandemic, and it will probably be important in future pandemics. Because of false negative and false positive tests, the observed fraction of positive tests—the surface positivity—is generally different from the fraction of infected individuals (the incidence rate of the disease). In this paper a previous method for translating surface positivity to a point estimate for incidence rate, then to an appropriate range of values for the incidence rate consistent with the model and data (the test range), and finally to the risk (the probability of including one infected individual) associated with groups of different sizes is illustrated. The method is then extended to include asymptomatic infections. To do so, the process of testing is modeled using both analysis and Monte Carlo simulation. Doing so shows that it is possible to determine point estimates for the fraction of infected and symptomatic individuals, the fraction of uninfected and symptomatic individuals, and the ratio of infected asymptomatic individuals to infected symptomatic individuals. Inclusion of symptom status generalizes the test range from an interval to a region in the plane determined by the incidence rate and the ratio of asymptomatic to symptomatic infections; likelihood methods can be used to determine the contour of the rest region. Points on this contour can be used to compute the risk (defined as the probability of including one asymptomatic infected individual) in groups of different sizes. These results have operational implications that include: positivity rate is not incidence rate; symptom status at testing can provide valuable information about asymptomatic infections; collecting information on time since putative virus exposure at testing is valuable for determining point estimates and test ranges; risk is a graded (rather than binary) function of group size; and because the information provided by testing becomes more accurate with more tests but at a decreasing rate, it is possible to over-test fixed spatial regions. The paper concludes with limitations of the method and directions for future work.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Operational analysis for COVID-19 testing: Determining the risk from asymptomatic infections

Mangel

2023

PLoS ONE

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“…Certain factors (testing policies, case definitions, reporting procedures, etc.,) may differ from country to country. This makes it difficult both to evaluate the number of cases and deaths and to make comparisons between countries ( 5 ). In this case, better-standardized indicators and criteria are required to assess the impact accurately and to minimize the difficulties on country comparisons.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Excess Deaths Associated With the COVID-19 Pandemic in Istanbul, Turkey

Uçar

Arslan²

2022

Front. Public Health

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Background and ObjectivesThe official number of daily cases and deaths are the most prominent indicators used to plan actions against the COVID-19 pandemic but are insufficient to see the real impact. Official numbers vary due to testing policy, reporting methods, etc. Therefore, critical interventions are likely to lose their effectiveness and better-standardized indicators like excess deaths/mortality are needed. In this study, excess deaths in Istanbul were examined and a web-based monitor was developed.MethodsDaily all-cause deaths data between January 1, 2015- November 11, 2021 in Istanbul is used to estimate the excess deaths. Compared to the pre-pandemic period, the % increase in the number of deaths was calculated as the ratio of excess deaths to expected deaths (P-Scores). The ratio of excess deaths to official figures (T) was also examined.ResultsThe total number of official and excess deaths in Istanbul are 24.218 and 37.514, respectively. The ratio of excess deaths to official deaths is 1.55. During the first three death waves, maximum P-Scores were 71.8, 129.0, and 116.3% respectively.ConclusionExcess mortality in Istanbul is close to the peak scores in Europe. 38.47% of total excess deaths could be considered as underreported or indirect deaths. To re-optimize the non-pharmaceutical interventions there is a need to monitor the real impact beyond the official figures. In this study, such a monitoring tool was created for Istanbul. The excess deaths are more reliable than official figures and it can be used as a gold standard to estimate the impact more precisely.

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“…In [ 9 ], Dirk Helbing and colleagues pointed out the fundamental challenges in using SIR-type compartmental models to monitor epidemics because of the neglect of stochastic and network effects, and the role of the measurement process. They combined such compartmental models with a measurement model of the testing process and examined the errors and biased sampling.…”

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confidence: 99%

Data science approaches to infectious disease surveillance

Zhang

2021

Phil. Trans. R. Soc. A.

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Novel data science approaches are needed to confront large-scale infectious disease epidemics such as COVID-19, human immunodeficiency viruses, African swine flu and Ebola. Human beings are now equipped with richer data and more advanced data analytics methodologies, many of which have become available only in the last decade. The theme issue Data Science Approaches to Infectious Diseases Surveillance reports the latest interdisciplinary research on developing novel data science methodologies to capitalize on the rich ‘big data’ of human behaviours to confront infectious diseases, with a particular focus on combating the ongoing COVID-19 pandemic. Compared to conventional public health research, articles in this issue present innovative data science approaches that were not possible without the growing human behaviour data and the recent advances in information and communications technology. This issue has 12 research papers and one review paper from a strong lineup of contributors from multiple disciplines, including data science, computer science, computational social sciences, applied maths, statistics, physics and public health. This introductory article provides a brief overview of the issue and discusses the future of this emerging field. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

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On some fundamental challenges in monitoring epidemics

Cited by 7 publications

References 35 publications

Operational analysis for COVID-19 testing: Determining the risk from asymptomatic infections

Operational analysis for COVID-19 testing: Determining the risk from asymptomatic infections

Estimation of Excess Deaths Associated With the COVID-19 Pandemic in Istanbul, Turkey

Data science approaches to infectious disease surveillance

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