Methods for Rapid Mobility Estimation to Support Outbreak Response

Telionis, Pyrros A.; Corbett, Patrick; Venkatramanan, Srinivasan; Lewis, Bryan

doi:10.1089/hs.2019.0101

Cited by 3 publications

(3 citation statements)

References 56 publications

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“…The population mobility rate (M) can be roughly estimated by the ratio of the population in unrestricted mobility, including medical personnel and administrative personnel, to the total population. 20,34 . Hospital response time (T1) and healing time (T2) can be estimated based on a certain number of cases.…”

Section: Discussionmentioning

confidence: 99%

Discrete simulation analysis of COVID-19 and prediction of isolation bed numbers

Cai

Ding

et al. 2020

Preprint

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Background. The outbreak of COVID-19 has been defined by the World Health Organization as a pandemic, and containment depends on traditional public health measures. However, the explosive growth of the number of infected cases in a short period of time has caused tremendous pressure on medical systems. Adequate isolation facilities are essential to control outbreaks, so this study aims to quickly estimate the demand and number of isolation beds. Methods. We established a discrete simulation model for epidemiology. By adjusting or fitting necessary epidemic parameters, the effects of the following indicators on the development of the epidemic and the occupation of medical resources were explained: (1) incubation period, (2) response speed and detection capacity of the hospital, (3) disease cure time, and (4) population mobility. Finally, a method for predicting the reasonable number of isolation beds was summarized through multiple linear regression. Results. Through simulation, we show that the incubation period, response speed and detection capacity of the hospital, disease cure time, degree of population mobility, and infectivity of cured patients have different effects on the infectivity, scale, and duration of the epidemic. Among them, (1) incubation period, (2) response speed and detection capacity of the hospital, (3) disease cure time, and (4) population mobility have a significant impact on the demand and number of isolation beds (P <0.05), which agrees with the following regression equation: N = P * (-0.273 + 0.009I +0.234M + 0.012T1 + 0.015T2) * (1+V).

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Section: Discussionmentioning

confidence: 99%

Discrete simulation analysis of COVID-19 and prediction of isolation bed numbers

Cai

Ding

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…Later, the number of infected cases increased to 28,652, where a total number of 11,325 of them died ( Bell, 2016 ; Poletto et al., 2014 ). In 2018, a new EVD epidemic was reported in Mbandaka, Democratic Republic of the Congo ( Gostin, 2018 ; Green, 2017 ; Leggiadro, 2019 ; Telionis et al., 2020 ).…”

Section: Past Epidemics and Technology Utilizedmentioning

confidence: 99%

IoMT amid COVID-19 pandemic: Application, architecture, technology, and security

Aman

Hassan

Sameen

et al. 2021

Journal of Network and Computer Applications

138

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In many countries, the Internet of Medical Things (IoMT) has been deployed in tandem with other strategies to curb the spread of COVID-19, improve the safety of front-line personnel, increase efficacy by lessening the severity of the disease on human lives, and decrease mortality rates. Significant inroads have been achieved in terms of applications and technology, as well as security which have also been magnified through the rapid and widespread adoption of IoMT across the globe. A number of on-going researches show the adoption of secure IoMT applications is possible by incorporating security measures with the technology. Furthermore, the development of new IoMT technologies merge with Artificial Intelligence, Big Data and Blockchain offers more viable solutions. Hence, this paper highlights the IoMT architecture, applications, technologies, and security developments that have been made with respect to IoMT in combating COVID-19. Additionally, this paper provides useful insights into specific IoMT architecture models, emerging IoMT applications, IoMT security measurements, and technology direction that apply to many IoMT systems within the medical environment to combat COVID-19.

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“…The population mobility rate (M) can be roughly estimated by the ratio of the population in unrestricted mobility, including medical personnel and administrative personnel, to the total population. (Telionis et al, 2020;Wu, Leung & Leung, 2020). Hospital response time (T1) and healing time (T2) can be estimated based on a certain number of cases.…”

Section: Multiple Regression Analysis and Parameter Prediction Of The Number Of Isolation Facilitiesmentioning

confidence: 99%

Discrete simulation analysis of COVID-19 and prediction of isolation bed numbers

Cai

Ding

et al. 2021

PeerJ

View full text Add to dashboard Cite

Background The outbreak of COVID-19 has been defined by the World Health Organization as a pandemic, and containment depends on traditional public health measures. However, the explosive growth of the number of infected cases in a short period of time has caused tremendous pressure on medical systems. Adequate isolation facilities are essential to control outbreaks, so this study aims to quickly estimate the demand and number of isolation beds. Methods We established a discrete simulation model for epidemiology. By adjusting or fitting necessary epidemic parameters, the effects of the following indicators on the development of the epidemic and the occupation of medical resources were explained: (1) incubation period, (2) response speed and detection capacity of the hospital, (3) disease healing time, and (4) population mobility. Finally, a method for predicting the number of isolation beds was summarized through multiple linear regression. This is a city level model that simulates the epidemic situation from the perspective of population mobility. Results Through simulation, we show that the incubation period, response speed and detection capacity of the hospital, disease healing time, degree of population mobility, and infectivity of cured patients have different effects on the infectivity, scale, and duration of the epidemic. Among them, (1) incubation period, (2) response speed and detection capacity of the hospital, (3) disease healing time, and (4) population mobility have a significant impact on the demand and number of isolation beds (P <0.05), which agrees with the following regression equation: N = P × (−0.273 + 0.009I + 0.234M + 0.012T1 + 0.015T2) × (1 + V).

show abstract

Methods for Rapid Mobility Estimation to Support Outbreak Response

Cited by 3 publications

References 56 publications

Discrete simulation analysis of COVID-19 and prediction of isolation bed numbers

Discrete simulation analysis of COVID-19 and prediction of isolation bed numbers

IoMT amid COVID-19 pandemic: Application, architecture, technology, and security

Discrete simulation analysis of COVID-19 and prediction of isolation bed numbers

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