Multiple studies have been conducted to predict the impact and duration of the current COVID-19 epidemics. Most of those studies rely on parameter calibration using the published number of confirmed cases. Unfortunately, this number is usually incomplete and biased due to the lack of testing capacities, and varying testing protocols. An essential requirement for better monitoring is the evaluation of the number of undiagnosed infected individuals. This number is crucial for the determination of transmission prevention strategies and it provides statistics on the epidemic dynamics. To estimate the number of undiagnosed infected individuals, we studied the relation between the fraction of diagnosed infected out of all infected, and the fraction of infected with known contaminator out of all diagnosed infected. We simulated multiple models currently used to study the COVID-19 pandemic and computed the relation between these two fractions in all those models . Across most models currently used and for most realistic model parameters, the relation between the two fractions is consistently linear and model independent. This relation can be used to estimate the number of undiagnosed infected, with no explicit epidemiological model. We apply this method to measure the number of undiagnosed infected in Israel. Since the fraction of confirmed cases with a known source can be obtained from epidemiological investigations in any country, one can estimate the total number of infected individuals in the same country .
In times of outbreaks, an essential requirement for better monitoring is the evaluation of the number of undiagnosed infected individuals. An accurate estimate of this fraction is crucial for the assessment of the situation and the establishment of protective measures. In most current studies using epidemics models, the total number of infected is either approximated by the number of diagnosed individuals or is dependent on the model parameters and assumptions, which are often debated. We here study the relationship between the fraction of diagnosed infected out of all infected, and the fraction of infected with known contaminator out of all diagnosed infected. We show that those two are approximately the same in exponential models and across most models currently used in the study of epidemics, independently of the model parameters. As an application, we compute an estimate of the effective number of infected by the COVID-19 virus in Israel .
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Multiple studies have been conducted to predict the impact and duration of the current COVID-19 epidemics. Most of those studies rely on parameter calibration using the published number of confirmed cases. Unfortunately, this number is usually incomplete and biased due to the lack of testing capacities, and varying testing protocols. An
In times of outbreaks, an
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This number
An accurate estimate of this fraction
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determination of transmission prevention strategies and it provides statistics on the epidemic dynamics. To estimate the number of undiagnosed infected individuals, we studied the relation
assessment of the situation and the establishment of protective measures. In most current studies using epidemics models, the total number of infected is either approximated by the number of diagnosed individuals or is dependent on the model parameters and assumptions, which are often debated. We here study the relationship
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simulated multiple models currently used to study the COVID-19 pandemic and computed the relation between these two fractions in all those models . Across
show that those two are approximately the same in exponential models and across
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and for most realistic model parameters, the relation between the two fractions is consistently linear and model independent. This relation can be used to estimate the number of undiagnosed infected, with no explicit epidemiological model. We apply this method to measure the number of undiagnosed infected in Israel. Since the fraction of confirmed cases with a known source can be obtained from epidemiological investigations in any country, one can estimate
in the study of epidemics, independently of
R
total
model parameters. As an application, we compute an estimate of the effective
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individuals in the same country
by the COVID-19 virus in Israel