Understanding the outbreak dynamics of the COVID-19 pandemic has important implications for successful containment and mitigation strategies. Recent studies suggest that the population prevalence of SARS-CoV-2 antibodies, a proxy for the number of asymptomatic cases, could be an order of magnitude larger than expected from the number of reported symptomatic cases. Knowing the precise prevalence and contagiousness of asymptomatic transmission is critical to estimate the overall dimension and pandemic potential of COVID-19. However, at this stage, the effect of the asymptomatic population, its size, and its outbreak dynamics remain largely unknown. Here we use reported symptomatic case data in conjunction with antibody seroprevalence studies, a mathematical epidemiology model, and a Bayesian framework to infer the epidemiological characteristics of COVID-19. Our model computes, in real time, the time-varying contact rate of the outbreak, and projects the temporal evolution and credible intervals of the effective reproduction number and the symptomatic, asymptomatic, and recovered populations. Our study quantifies the sensitivity of the outbreak dynamics of COVID-19 to three parameters: the effective reproduction number, the ratio between the symptomatic and asymptomatic populations, and the infectious periods of both groups. For nine distinct locations, our model estimates the fraction of the population that has been infected and recovered by Jun 15, 2020 to 24.15% (95% CI: 20.48%-28.14%) for Heinsberg (NRW, Germany), 2.40% (95% CI: 2.09%-2.76%) for Ada County (ID, USA), 46.19% (95% CI: 45.81%-46.60%) for New York City (NY, USA), 11.26% (95% CI: 7.21%-16.03%) for Santa Clara County (CA, USA), 3.09% (95% CI: 2.27%-4.03%) for Denmark, 12.35% (95% CI: 10.03%-15.18%) for Geneva Canton (Switzerland), 5.24% (95% CI: 4.84%-5.70%) for the Netherlands, 1.53% (95% CI: 0.76%-2.62%) for Rio Grande do Sul (Brazil), and 5.32% (95% CI: 4.77%-5.93%) for Belgium. Our method traces the initial outbreak date in Santa Clara County back to January 20, 2020 (95% CI: December 29, 2019-February 13, 2020). Our results could significantly change our understanding and management of the COVID-19 pandemic: A large asymptomatic population will make isolation, containment, and tracing of individual cases challenging. Instead, managing community transmission through increasing population awareness, promoting physical distancing, and encouraging behavioral changes could become more relevant.
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http://dx.doi.org/10.1016/j.cma.2020.113410 | DOI Listing |
Emerg Microbes Infect
January 2025
Center for Influenza and Emerging Diseases, University of Missouri, Columbia, MO 652011, USA.
Influenza A viruses (IAVs) pose a major public health threat due to their wide host range and pandemic potential. Pigs have been proposed as "mixing vessels" for avian, swine, and human IAVs, significantly contributing to influenza ecology. In the United States, IAVs are enzootic in commercial swine farming operations, with numerous genetic and antigenic IAV variants having emerged in the past two decades.
View Article and Find Full Text PDFJ Community Health Nurs
January 2025
Department of Public Health, University of North Florida, Jacksonville, Florida.
Background: Previous research has underscored the efficacy of individual control strategies in mitigating influenza spread within communal settings; however, the unique dynamics of residential summer camps-characterized by close contact and high social interaction-present distinct challenges for outbreak management.
Purpose: The purpose of this study was to evaluate and compare the effectiveness of two targeted antiviral prophylaxis protocols using oseltamivir in controlling influenza outbreaks within a residential youth camp, aiming to provide evidence-based insights for optimizing outbreak management in communal settings with high social interaction.
Design: This retrospective study analyzed the progression of influenza outbreaks in a residential youth camp using two antiviral prophylaxis protocols with oseltamivir.
J Med Virol
January 2025
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
Mathematical models of viral dynamics are crucial in understanding infection trajectories. However, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load data often includes limited sparse observations with significant heterogeneity. This study aims to: (1) understand the impact of patient characteristics in shaping the temporal viral load trajectory and (2) establish a data collection protocol (DCP) to reliably reconstruct individual viral load trajectories.
View Article and Find Full Text PDFACS ES T Water
January 2025
Department of Statistics & Data Science, Dietrich College of Humanities and Social Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
Since the start of the coronavirus-19 pandemic, the use of wastewater-based epidemiology (WBE) for disease surveillance has increased throughout the world. Because wastewater measurements are affected by external factors, processing WBE data typically includes a normalization step in order to adjust wastewater measurements (e.g.
View Article and Find Full Text PDFInfect Dis Model
June 2025
Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Introduction: Social contact patterns significantly influence the transmission dynamics of respiratory pathogens. Previous surveys have quantified human social contact patterns, yielding heterogeneous results across different locations. However, significant gaps remain in understanding social contact patterns in rural areas of China.
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