A Data-Informed Approach for Analysis, Validation, and Identification of COVID-19 Models

Olmez, S. Yagiz; Mori, Jameson; Miehling, Erik; Başar, Tamer; Smith, Rebecca L.; West, Matthew; Mehta, Prashant G.

doi:10.23919/acc50511.2021.9483102

Cited by 2 publications

(3 citation statements)

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“…In the literature, in order to model epidemics, population is partitioned into groups called compartments . One such example is the SIR model used in [ 106 ] with the compartments susceptible (S), infected (I), and recovered (R) which has been further developed by adding the states hospitalized (H), and death (D) in [ 107 ]. In these epidemic models, the transitions between the compartments are assumed to be Markovian.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…In these epidemic models, the transitions between the compartments are assumed to be Markovian. In [ 107 ], with epidemiological data, the delay distributions for the infected (I) to hospitalized (H), and infected (I) to death (D) are well approximated by exponential and gamma distributions, respectively. However, due to the lack of data availability the delay distribution for infected (I) to recovered (R) is modeled with gamma distribution with higher tolerance.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…In our work, we modeled infection and recovery times, i.e., the delays between recovered (R) to infected (I) and infected (I) to recovered (R) with exponential distributions. Therefore, more realistic infection tracking models can be developed by considering gamma distributions as observed in [ 107 ]. This more realistic model corresponds to the problem of real-time timely tracking of a binary Markov source in a serially connected network.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

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Using Timeliness in Tracking Infections

Bastopcu

Ulukuş

2022

Entropy

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We consider real-time timely tracking of infection status (e.g., COVID-19) of individuals in a population. In this work, a health care provider wants to detect both infected people and people who have recovered from the disease as quickly as possible. In order to measure the timeliness of the tracking process, we use the long-term average difference between the actual infection status of the people and their real-time estimate by the health care provider based on the most recent test results. We first find an analytical expression for this average difference for given test rates, infection rates and recovery rates of people. Next, we propose an alternating minimization-based algorithm to find the test rates that minimize the average difference. We observe that if the total test rate is limited, instead of testing all members of the population equally, only a portion of the population may be tested in unequal rates calculated based on their infection and recovery rates. Next, we characterize the average difference when the test measurements are erroneous (i.e., noisy). Further, we consider the case where the infection status of individuals may be dependent, which occurs when an infected person spreads the disease to another person if they are not detected and isolated by the health care provider. In addition, we consider an age of incorrect information-based error metric where the staleness metric increases linearly over time as long as the health care provider does not detect the changes in the infection status of the people. Through extensive numerical results, we observe that increasing the total test rate helps track the infection status better. In addition, an increased population size increases diversity of people with different infection and recovery rates, which may be exploited to spend testing capacity more efficiently, thereby improving the system performance. Depending on the health care provider’s preferences, test rate allocation can be adjusted to detect either the infected people or the recovered people more quickly. In order to combat any errors in the test, it may be more advantageous for the health care provider to not test everyone, and instead, apply additional tests to a selected portion of the population. In the case of people with dependent infection status, as we increase the total test rate, the health care provider detects the infected people more quickly, and thus, the average time that a person stays infected decreases. Finally, the error metric needs to be chosen carefully to meet the priorities of the health care provider, as the error metric used greatly influences who will be tested and at what test rate.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Conclusion and Discussionmentioning

confidence: 99%

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