A phenomenological estimate of the true scale of CoViD-19 from primary data

Palatella, Luigi; Vanni, Fabio; Lambert, David

doi:10.1016/j.chaos.2021.110854

Cited by 7 publications

(6 citation statements)

References 24 publications

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“…As regard with the epidemiological side, and following Palatella et al [54], Vanni et al [67], the reproduction number is a function of s t which is the share of susceptible population (not immunized individuals), R t 0 is the reproduction number at the beginning of the observed period, and B t is the transmission rate function which depends on variables which describe the interaction frequency of contacts and the rate of becoming infected after a contact. Furthermore it also depends on the infectious age of individuals in the contagion process.…”

Section: Model and Resultsmentioning

confidence: 99%

On the regularity of human mobility patterns at times of a pandemic

Vanni,

Lambert

2021

Preprint

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The study of human mobility patterns is a crucially important research eld for its impact on several socioeconomic aspects and, in particular, the measure of regularity patters of human mobility can provide a acrossthe-board view of many social distancing variables in epidemics such as: human movement trends, physical interpersonal distances and population density. We will show that the notion of information entropy is also strongly related to demographic and economic trends by the use and analysis of real-time data. In the present research paper we address three di erent problems. First, we provide an evidence-based analytical approach which relates the human mobility patterns, social distancing attitudes and population density, with entropic measures which depict for erraticity of human contact behaviors. Second, we investigate the correlations between the aggregated mobility and entropic measures versus ve external economic indicators. Finally, we show how entropic measures represents a useful tool for testing the limitations of typical assumptions in epidemiological and mobility models.

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Section: Model and Resultsmentioning

confidence: 99%

On the regularity of human mobility patterns at times of a pandemic

Vanni,

Lambert

2021

Preprint

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“…Other methodological statistical studies have tried to estimate the true number of COVID-19-infected people indirectly. Palatella et al [ 4 ] tried to estimate the number of cases based on PCR test alone and Noh and Danuser [ 68 ] based on government daily counts of confirmed cases. However, these estimates could underestimate the real proportion since the data are related to the specific population.…”

Section: Discussionmentioning

confidence: 99%

“…Such cases are mostly asymptomatic or paucisymptomatic, as their lack or scarcity of symptoms does not reach the attention of the healthcare system. Undocumented cases have been found to expose a higher proportion of the population due to the lack of quarantine measures [ 4 ] and to be hard to recognize, as asymptomatic or mildly symptomatic patients often do not seek medical attention due to a lack of symptoms [ 5 ]. While challenging, the prevalence estimation for asymptomatic and mildly symptomatic cases is very important given the highly contagious nature of the virus.…”

Section: Introductionmentioning

confidence: 99%

Using Social Networks to Estimate the Number of COVID-19 Cases: The Incident (Hidden COVID-19 Cases Network Estimation) Study Protocol

Ocagli

Azzolina

Lorenzoni

et al. 2021

IJERPH

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Recent literature has reported a high percentage of asymptomatic or paucisymptomatic cases in subjects with COVID-19 infection. This proportion can be difficult to quantify; therefore, it constitutes a hidden population. This study aims to develop a proof-of-concept method for estimating the number of undocumented infections of COVID-19. This is the protocol for the INCIDENT (Hidden COVID-19 Cases Network Estimation) study, an online, cross-sectional survey with snowball sampling based on the network scale-up method (NSUM). The original personal network size estimation method was based on a fixed-effects maximum likelihood estimator. We propose an extension of previous Bayesian estimation methods to estimate the unknown network size using the Markov chain Monte Carlo algorithm. On 6 May 2020, 1963 questionnaires were collected, 1703 were completed except for the random questions, and 1652 were completed in all three sections. The algorithm was initialized at the first iteration and applied to the whole dataset. Knowing the number of asymptomatic COVID-19 cases is extremely important for reducing the spread of the virus. Our approach reduces the number of questions posed. This allows us to speed up the completion of the questionnaire with a subsequent reduction in the nonresponse rate.

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“…In this context, it's important to note that informational entropy serves as a crucial variable in determining the effective reproduction number of an epidemic. The reproduction number R t has been estimated via a renewal epidemic model as developed in [59,60]. The human mobility regularity pattern influences the epidemiological evolution as follows: Proposition 3.…”

Section: Economic Activity Mobility Patterns and Epidemiological Evol...mentioning

confidence: 99%

On an Aggregated Estimate for Human Mobility Regularities through Movement Trends and Population Density

Vanni,

Lambert

2024

Preprint

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This article introduces an analytical framework that interprets individual measures of entropy-based mobility derived from mobile phone data. We explore and analyze two widely recognized entropy metrics: random entropy and uncorrelated Shannon entropy. These metrics are estimated through collective variables of human mobility, including movement trends and population density. By employing a collisional model, we establish statistical relationships between entropy measures and mobility variables. Furthermore, our research addresses three primary objectives: firstly, validating the model; secondly, exploring correlations between aggregated mobility and entropy measures in comparison to five economic indicators; and finally, demonstrating the utility of entropy measures. Specifically, we provide an effective population density estimate that offers a more realistic understanding of social interactions. This estimation takes into account both movement regularities and intensity, utilizing real-time data analysis conducted during the peak period of the Covid-19 pandemic.

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A phenomenological estimate of the true scale of CoViD-19 from primary data

Cited by 7 publications

References 24 publications

On the regularity of human mobility patterns at times of a pandemic

On the regularity of human mobility patterns at times of a pandemic

Using Social Networks to Estimate the Number of COVID-19 Cases: The Incident (Hidden COVID-19 Cases Network Estimation) Study Protocol

On an Aggregated Estimate for Human Mobility Regularities through Movement Trends and Population Density

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