Effect of sampling bias for random social mixing: (a) Mean transition probability across 60 runs based on the HCMM mobility model; and (b) Confidence interval of estimated mean CP based on sample mean CP.

Effect of sampling bias for random social mixing: (a) Mean transition probability across 60 runs based on the HCMM mobility model; and (b) Confidence interval of estimated mean CP based on sample mean CP.

<p>Effect of sampling bias for random social mixing: (a) Mean transition probability across 60 runs based on the HCMM mobility model; and (b) Confidence interval of estimated mean CP based on sample mean CP.</p>

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Bibliographic Details
Main Author:	Satyaki Roy (9557585) (author)
Other Authors:	Preetom Biswas (16733955) (author), Preetam Ghosh (3441398) (author)
Published:	2025
Subjects:	Medicine Biotechnology Ecology Cancer Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified optimization framework based heterogeneous reporting rates continuous metric derived contagion potential </ infection risk posed advancing infection profiling biased incidence data available tracking data sirs epidemic model propose statistical corrections asymptomatic cases challenged reliable cp estimates spatial contact data mobility data arise div >< p confidence cp estimates spatial model infection spread sampled data data uncertainty statistical methods mobility models confidence level asymptomatic within epidemiological data population cp infer cp cp estimation viral strains testing approach social mixing sampling inconsistencies sampling frequency reasonable estimate interactions captured inherent uncertainties findings demonstrate even within complicating estimation adjustment factor achieve high 19 pandemic
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