Advancing infection profiling under data uncertainty through contagion potential
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| Publicado en: | PLoS One vol. 20, no. 8 (Aug 2025), p. e0329828 |
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| Autor principal: | |
| Otros Autores: | , |
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Public Library of Science
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 3239016524 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0329828 |2 doi | |
| 035 | |a 3239016524 | ||
| 045 | 2 | |b d20250801 |b d20250831 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Roy, Satyaki | |
| 245 | 1 | |a Advancing infection profiling under data uncertainty through contagion potential | |
| 260 | |b Public Library of Science |c Aug 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a During the COVID-19 pandemic, the prevalence of asymptomatic cases challenged the reliability of epidemiological statistics in policymaking. To address this, we introduced contagion potential (CP) as a continuous metric derived from sociodemographic and epidemiological data to quantify the infection risk posed by the asymptomatic within a region. However, CP estimation is hindered by incomplete or biased incidence data, where underreporting and testing constraints make direct estimation infeasible. To overcome this limitation, we employ a hypothesis-testing approach to infer CP from sampled data, allowing for robust estimation despite missing information. Even within the sample collected from spatial contact data, individuals possess partial knowledge of their neighborhoods, as their awareness is restricted to interactions captured by available tracking data. We introduce an adjustment factor that calibrates the sample CPs so that the sample is a reasonable estimate of the population CP. Further complicating estimation, biases in epidemiological and mobility data arise from heterogeneous reporting rates and sampling inconsistencies, which we address through inverse probability weighting to enhance reliability. Using a spatial model for infection spread through social mixing and an optimization framework based on the SIRS epidemic model, we analyze real infection datasets from Italy, Germany, and Austria. Our findings demonstrate that statistical methods can achieve high-confidence CP estimates while accounting for variations in sample size, confidence level, mobility models, and viral strains. By assessing the effects of bias, social mixing, and sampling frequency, we propose statistical corrections to improve CP prediction accuracy. Finally, we discuss how reliable CP estimates can inform outbreak mitigation strategies despite the inherent uncertainties in epidemiological data. | |
| 653 | |a Pandemics | ||
| 653 | |a Infections | ||
| 653 | |a Statistics | ||
| 653 | |a COVID-19 vaccines | ||
| 653 | |a Bias | ||
| 653 | |a Hypothesis testing | ||
| 653 | |a Epidemiology | ||
| 653 | |a Asymptomatic | ||
| 653 | |a Social networks | ||
| 653 | |a Contact tracing | ||
| 653 | |a Samples | ||
| 653 | |a COVID-19 | ||
| 653 | |a Sampling | ||
| 653 | |a Statistical methods | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Immunization | ||
| 653 | |a Uncertainty | ||
| 653 | |a Survival analysis | ||
| 653 | |a Mobility | ||
| 653 | |a Spatial data | ||
| 653 | |a Confidence intervals | ||
| 653 | |a Pharmaceuticals | ||
| 653 | |a Reliability | ||
| 653 | |a Epidemic models | ||
| 653 | |a Epidemics | ||
| 653 | |a Sociodemographics | ||
| 653 | |a Estimates | ||
| 653 | |a Natural language processing | ||
| 653 | |a Coronaviruses | ||
| 653 | |a Health risks | ||
| 653 | |a Disease transmission | ||
| 653 | |a Social | ||
| 700 | 1 | |a Biswas, Preetom | |
| 700 | 1 | |a Ghosh, Preetam | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 8 (Aug 2025), p. e0329828 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3239016524/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3239016524/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3239016524/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |