Estimating pre-symptomatic transmission potential of influenza A and B viruses in household transmission studies

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Fang, Kwok Hung Chan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7701955/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jan, 2026 Read the published version in Nature Health → Version 1 posted You are reading this latest preprint version Abstract The effectiveness of case isolation and contact quarantine depends critically on the extent of pre-symptomatic transmission. However, the capacity of influenza viruses for transmission before symptom onset remains uncertain. We developed an individual-based household transmission model that explicitly accounts for transmission occurring before symptom onset by marginalizing unobserved infection times. We analyzed influenza household transmission studies of influenza A and B viruses in Hong Kong between 2008 and 2017 (493 and 255 households respectively), estimating pathogen-specific pre-symptomatic transmission proportions and identifying factors affecting individual susceptibility and infectiousness. We estimated that 9.6% (95% credible interval (CrI): 5.9%, 14.7%) of influenza A transmission occurred > 0.5 days earlier than infectors’ symptom onset, while influenza B showed no evidence of pre-symptomatic transmission. Younger age was associated with an increased susceptibility and infectiousness for both influenza A and B viruses. Influenza A infectiousness was positively associated with viral load and fever. Using an additional SARS-CoV-2 Omicron dataset in Hong Kong, our analysis confirmed substantial pre-symptomatic transmission (65.5%, 95% CrI: 52.7%, 78.9%), validating our methodology. Our modeling framework provides a robust approach for quantifying pre-symptomatic transmission across respiratory pathogens in households, offering a methodological template for resolving similar empirical uncertainties in infectious disease transmission. Our findings suggest symptom-based control measures may be feasible for influenza, though quarantine of asymptomatic contacts would provide limited additional benefit. Health sciences/Diseases/Infectious diseases/Influenza virus Health sciences/Medical research/Epidemiology Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Influenza represents a major global health threat, causing an estimated 300,000 deaths annually through seasonal circulation worldwide 1 , 2 . During the post-pandemic era, influenza and SARS-CoV-2 continue to circulate and remain important public health concerns 3 . Case Isolation and contact quarantine are available strategies for controlling respiratory infectious disease outbreaks, having proven effective during the 2003 SARS pandemic. However, the effectiveness of these symptom-based interventions depends critically on the extent of transmission occurring before symptom onset 4 , 5 . Pre-symptomatic transmission, i.e. occurring before the onset of clinical illness poses a fundamental challenge to symptom-based control measures, yet quantitative evidence for influenza viruses remains limited and conflicting. Patients infected by influenza virus can shed the virus before illness onset 6 – 8 . Some studies reported possible pre-symptomatic transmission of influenza A during the 2009 pandemic 9 – 11 and in later seasons 12 , 13 . However, other investigations found no evidence of transmission before symptom onset 14 , 15 . For influenza B, the capacity for pre-symptomatic transmission has received virtually no systematic investigation. This uncertainty has important implications for outbreak control strategies, as the proportion of pre-symptomatic transmission directly determines the potential effectiveness of case isolation and contact tracing 4 , 5 . The conflicting empirical evidence likely reflects methodological limitations in existing approaches, particularly the challenge of determining true infection times when only symptom onset dates are observable 16 . In contrast to the uncertain evidence for influenza, SARS-CoV-2 provides a useful comparison case where substantial pre-symptomatic transmission is well-established, with multiple studies documenting 40–70% of transmission occurring during the pre-symptomatic phase 16 – 23 . This established transmission pattern makes SARS-CoV-2 an ideal validation case for methodological approaches designed to quantify pre-symptomatic transmission. To address these knowledge gaps, we developed a new individual-based household transmission model that explicitly accounts for transmission occurring before symptom onset while properly handling unobserved infection times. We applied this framework to reconstruct transmission dynamics using household studies of influenza A and B virus (2008–2017) in Hong Kong, and validated our approach using SARS-CoV-2 data where transmission patterns are established. Our primary aim was to provide quantitative evidence of influenza pre-symptomatic transmission potential, resolving the conflicting literature and informing evidence-based design of pathogen-specific control strategies. RESULTS Overview of the study This study developed an individual-based household transmission model to estimate household transmission dynamics and quantify pre-symptomatic transmission of influenza and SARS-CoV-2 (Fig. 1 ). Our approach addresses the challenge of unobserved infection times by calculating likelihoods across all possible combinations of infection times for each case. The model defines infectiousness profiles starting before symptom onset and adaptively handles varying incubation periods through profile re-normalization. By combining input incubation period distributions with estimated infectiousness profiles, we estimate the proportion of pre-symptomatic transmissions, defined as the proportion of transmission occurring before the day of symptom onset, (effectively > 0.5 days before symptoms) in our discrete-time framework (Methods). To ensure computational feasibility, we introduced a probability threshold mechanism that excludes highly improbable (probability below 0.0001) infection time combinations while maintaining estimation accuracy. The framework accommodates tertiary transmissions, community-acquired infections, and households with multiple co-index cases. Using Bayesian inference, we jointly estimated pre-symptomatic transmission proportions and associated risk factors, with model comparison used to identify significant transmission determinants 24 . We applied this model to household transmission studies of influenza A, B and SARS-CoV-2 in Hong Kong, with a more detailed analysis of transmission factors for influenza due to richer data availability. Household transmission study of influenza in Hong Kong In this study, 839 index cases with PCR-confirmed influenza infections and 2484 household contacts were recruited from 2008 to 2017 25,26 . Among all index cases, 559 were infected by influenza A virus and 280 by influenza B. We excluded 91 households: 32 with all members testing negative for influenza, 12 with fewer than the required minimum of 3 household members, 29 with incomplete data, and 18 with unidentified influenza A virus subtypes. Finally, 493 influenza A index cases with 1451 household contacts and 255 influenza B cases with 795 contacts were included in the analysis (Fig. 2 ). In 101 households, at least one household contact tested positive at the first home visit in addition to the index cases. For influenza A, the study included 170 seasonal A(H1N1), 211 seasonal A(H3N2), and 112 pandemic A(H1N1) index cases (hereafter denoted as sH1N1, sH3N2 and pH1N1 respectively). For influenza B, the study included 44 Victoria, 71 Yamagata, and 140 unsubtypable index cases. Characteristics of primary cases and household contacts among subtypes were largely similar. However, we observed different uptake for antiviral treatment and vaccination coverage among infected household contacts (Appendix Table 1, p \(\:<0.01\) , Fisher’s test). Unsubtypable influenza B index cases had fewer secondary infections (Appendix Table 2, p \(\:<0.01\) , Fisher’s test). To compare the levels of viral shedding, we adopted a log-linear mixed-effect censored regression model to estimate the complete viral shedding trajectories and peak viral load at symptom onset (Appendix Fig. 1; Appendix Table 3–4) 27–31 . We found that pandemic A(H1N1) patients had a lower predicted viral load at onset (Appendix Table 1, p \(\:<0.01\) , Kruskal-Wallis test) among primary cases and infected contacts including co-index cases. Age, viral shedding and fever were interdependent for both influenza A and B (Appendix Table 5–6) 30 . Compared to adults, children had higher viral loads at symptom onset (p \(\:<0.01\) , Kruskal-Wallis test), and had more frequent fevers (p \(\:<0.01\) , Fisher’s test), while febrile cases showed increased viral load (p \(\:<0.01\) , Kruskal-Wallis test). Pre-symptomatic transmission potential and transmission dynamics of influenza A We assumed the incubation period of influenza A followed a discretized log-normal distribution with a median of 1.4 days (Fig. 2 ) 32 . We fitted models assuming the infectiousness of cases started from 0 to 4 days before symptom onset, and model comparison supported that infectiousness started 4 days before symptom onset, and estimated that 9.6% (95% CrI: 5.9%, 14.7%) of infections occurred before the day of symptom onset of infectors (Fig. 2 ; Appendix Fig. 2 ; Appendix Table 7). The model assuming no pre-symptomatic infectiousness performed substantially worse ( \(\:\varDelta\:\text{D}\text{I}\text{C}>5\) ). Based on the best model, we estimated that the person-to-person transmission probabilities for seasonal H1N1, pandemic H1N1 and seasonal H3N2 subtypes were 7.4% (95% CrI: 4.5%, 11.3%), 7.3% (95% CrI: 4.3%, 11.6%) and 9.0% (95% CrI: 5.7%, 13.5%), which were similar. Younger age was associated with increased susceptibility to infection, as pre-school children (age below 6 years) and school-age children (age 6–17 years) were 261% (95% CrI: 137%, 423%) and 105% (95% CrI: 48%, 180%) more susceptible than adults aged between 18 and 50 years, while old household members (age above 50 years) were 53% (95% CrI: 25%, 74%) less susceptible than younger adults (Fig. 3 ). Younger age, the onset of fever symptoms and higher viral load were associated with higher infectiousness. Pre-school children and school-age children had 172% (95% CrI: 78%, 320%) and 56% (95% CrI: 7%, 132%) higher infectiousness compared to adults, while febrile cases were 81% (95% CrI: 28%, 156%) more infectious than patients without fever symptoms. Predicted peak viral load was added to the model as a standardized continuous variable, therefore, one standard deviation (0.686 \(\:{\text{l}\text{o}\text{g}}_{10}\) copies/mL) higher for viral load at symptom onset was associated with 19% (95% CrI: 2%, 37%) higher infectiousness. Antiviral treatment was associated with 24% (95% CrI: 3%, 41%) lower infectiousness. Compared to households with 3 members, infectiousness of cases in households with 4–5 members and 6 members was associated with 36% (95% CrI: 15%, 52%) and 42% (95% CrI: 9%, 64%) lower infectiousness (Fig. 3 ). The estimated associations between factors and susceptibility/infectiousness were robust to the assumption about infectiousness starting date (0 to 4 days before symptom onset) (Appendix Fig. 3 ). We compared models with different combinations of age, fever, individual viral load to quantify their interplays 30 . We found that younger age, fever and higher viral load were associated with increased infectiousness in all models (Fig. 4 ; Appendix Table 8). The model including age, fever, and viral load had the lowest DIC, while it was comparable with the model that included age and fever only (DIC difference = 4.36) (Appendix Table 8). Also, other models performed substantially worse. Model adequacy, validation and sensitivity analyses In a simulation study with 10,000 epidemics, the predicted final size distribution was consistent with the observed one (Appendix Section 5; Appendix Table 9), suggesting the model fit was adequate. Besides, to assess the validation of our model, we fitted models to 50 simulated epidemics, 78% – 100% of estimated 95% credible intervals covered corresponding actual values. Our inference approach could estimate the model parameters with no systematic bias (Appendix Section 4; Appendix Table 10). In a sensitivity analysis that used Poisson distribution instead of Gamma distribution for infectiousness profile, the best fit was obtained for the model assuming that infectiousness started 1 day prior to symptom onset (Appendix Fig. 4; Appendix Table 11), and the estimated proportion of pre-symptomatic transmission was similar to the main model (Appendix Fig. 5). Model comparisons suggested that except for the one assuming no pre-symptomatic transmission, models using the Poisson distribution as infectiousness profile performed similarly to the main model ( \(\:\varDelta\:\) DIC \(\:<\) 5). Also, the estimates of factors affecting susceptibility and infectiousness remained similar (Appendix Fig. 6). Sensitivity analyses testing probability thresholds from 0.00001 to 0.005 (vs. 0.0001 in main analysis) yield similar estimates of factors affecting transmission and pre-symptomatic infectiousness (Appendix Fig. 5, 7; Appendix Table 12) suggesting our method could provide unbiased estimates. Pre-symptomatic transmission potential and transmission dynamics of influenza B We assumed a shorter incubation period with a median of 0.6 days for influenza B (Fig. 2 ) 32 . Models assuming infectiousness starting 0 to 4 days before symptom onset were fitted. The goodness-of-fit between models with and without pre-symptomatic transmission was not substantial ( \(\:\varDelta\:\text{D}\text{I}\text{C}<5\) ). Therefore, influenza B showed no evidence of pre-symptomatic transmission. (Fig. 2 ; Appendix Fig. 2 ; Appendix Table 13). Model selection based on DIC values identified age as the key infectiousness factor, with viral load and fever providing minimal improvement in fit (Appendix Table 14). Higher viral load was associated with increased infectiousness in univariate analysis (Fig. 4 ; Appendix Table 14). Pre-school and school-age children were 232% (95% CrI: 55%, 543%) and 289% (95% CrI: 144%, 527%) more susceptible than adults under 50, with pre-school children also 248% (95% CrI: 68%, 696%) more infectious. Patients in households with > 5 members were 59% (95% CrI: 4%, 84%) less infectious than those in 3-member households (Fig. 3 ). These estimates were robust for models assuming different infectiousness starting dates (Appendix Fig. 8). Simulation studies confirmed the model provided predictions consistent with observations and unbiased parameter estimates, as 82% – 100% of 95% credible intervals of model parameters covered actual values (Appendix Table 15–16). Models adopting Poisson-distributed infectiousness profiles also supported that the goodness-of-fits between models with and without pre-symptomatic transmission were not substantial (Appendix Fig. 4), with similar estimates for factors affecting transmissions (Appendix Fig. 9; Appendix Table 17). Changing the probability threshold did not affect parameter estimates (Appendix Fig. 10). Sensitivity analyses using the influenza A incubation period (median: 1.4 days) yielded consistent results (Appendix Fig. 4, 11; Appendix Table 18). Pre-symptomatic transmission potential and transmission dynamics of SARS-CoV-2 Based on the government reporting system that collected data with mandatory reporting of SARS-CoV-2 tested positive and their household contacts’ information during March 2022 to December 2022, we randomly selected 100 households including 327 members reporting from 9 to 31 December 2022, when the Hong Kong government eased isolation and quarantine policies (Fig. 2 ; Appendix Table 19). We assumed the incubation period for the SARS-CoV-2 Omicron variant followed a discretized log-normal distribution with a median of 3.04 (Fig. 2 ) 33 . We fitted models assuming the infectiousness started from 0 to 7 days before symptom onset and model comparison supported that infectiousness started 5 days before symptom onset. We estimated that 65.5% (95% CrI: 52.7%, 78.9%) of SARS-CoV-2 transmission occurred during the pre-symptomatic phase (Fig. 2 ; Appendix Table 20). Similarly, the model assuming no pre-symptomatic transmission performed substantially worse (Appendix Fig. 2 ; \(\:\varDelta\:\text{D}\text{I}\text{C}>5\) ). The person-to-person transmission probability was estimated to be 30.2% (95% CrI: 16.8%, 49.8%). Cases in households with \(\:>\) 3 members were associated with 44% (95% CrI: 6%, 66%) lower infectiousness than those living in smaller households (Fig. 3 ). The estimated associations between factors and susceptibility/infectiousness were robust to the assumption about the infectiousness starting date (Appendix Fig. 12). We conducted the same simulation study for model adequacy checking and found that the predicted final size distribution was consistent with the observed one (Appendix Table 21). Also, we conducted the same simulation study for model validation, and our approach could also provide unbiased estimates for SARS-CoV-2 as 90% – 100% of 95% credible intervals of model parameters covered their actual values (Appendix Table 22). Similarly, in a sensitivity analysis we found that the best model using the Poisson distribution for infectiousness profile was comparable with the main model using the Gamma distributions (Appendix Fig. 4; \(\:\varDelta\:\) DIC \(\:<\) 5). The estimates of the proportion of pre-symptomatic transmission and factors affecting transmission were also similar to the main model (Appendix Fig. 5, 13; Appendix Table 23). Moreover, estimates for model parameters and the proportion of pre-symptomatic transmission were consistent when using low probability thresholds (0.001 in primary analyses) for infection time scenarios (Appendix Fig. 5, 14; Appendix Table 24). DISCUSSION Understanding pre-symptomatic transmission potential is crucial for designing effective outbreak control strategies, yet quantitative evidence for influenza viruses has remained limited and conflicting. By developing a novel individual-based hazard model that considers infections prior to the illness onset of infectors, we reconstructed household transmission dynamics in Hong Kong and provided evidence of pre-symptomatic transmission potential. We estimated that influenza A had limited transmission before symptom onset (9.6%), influenza B showed no pre-symptomatic transmission. These findings offer additional context for understanding longstanding uncertainty in the literature, where some studies reported possible pre-symptomatic transmission of influenza A virus during the 2009 pandemic 9 – 11 , while other cohorts found no evidence of transmission before symptom onset 14 , 15 , demonstrating limited pre-symptomatic transmission potential, and influenza B transmission patterns remained essentially unstudied. By providing systematic quantitative evidence across both major influenza virus types, our work establishes a clear understanding of influenza transmission dynamics that has important implications for outbreak control strategies. Our estimate of 9.6% pre-symptomatic transmission for influenza A is consistent with emerging evidence from recent household studies 9 – 11 , 13 , 34 , collectively suggesting that pre-symptomatic transmission occurs at low but measurable levels, intermediate between studies finding no transmission 14 , 15 , and estimates of up to 25% 12 . These transmission patterns reflect underlying viral shedding dynamics and incubation periods. Viral shedding of both influenza and SARS-CoV-2 peaked around symptom onset 6–8,35−37 , the timing of shedding onset relative to symptoms differs between pathogens. Influenza shows constrained pre-symptomatic shedding due to rapid symptom development and shorter incubation periods 6 – 8 , 32 . The differential patterns likely reflect differences in immune recognition timing, as influenza viruses trigger rapid innate immune responses through RIG-I and TLR7 pathways 38 – 40 , while SARS-CoV-2 employs immune evasion strategies that delay symptom onset relative to peak viral shedding 35 , 37 , 41 – 43 . This biological basis aligns with serial interval studies showing that influenza had longer serial intervals than their incubation periods 44 , while the length of incubation period and serial interval for SARS-CoV-2 were found to be similar 23 , 45 , 46 . Our SARS-CoV-2 analysis (65.5% pre-symptomatic transmission) validates our methodological approach by confirming established estimates of 40–70% from multiple studies documenting viral shedding and viable virus isolation before clinical illness onset 16 – 18 , 22 , 35 – 37 and previous estimates using serial interval imputation 19 – 21 , 23 , stochastic transmission models 47 as well as a recent work developing a multi-scale model which considers both within- and between-host dynamics 48 . This validation suggests that our framework can be applied to various pathogens with distinct transmission patterns, from no pre-symptomatic transmission (influenza B) to limited transmission (influenza A) to substantial transmission (SARS-CoV-2). The consistency with known SARS-CoV-2 patterns strengthens confidence in our influenza findings. Our findings provide valuable insights into the effectiveness of symptom-based control measures for influenza and SARS-CoV-2 outbreaks 4 . The limited pre-symptomatic transmission of influenza A (9.6%) and absence of such transmission for influenza B suggest possible benefit to control influenza outbreaks by symptom-based control measures like rapid case detection and efficient case isolation 5 , however, the overall effectiveness is challenged by the high proportion of asymptomatic influenza infections 6 , 49 . Besides, the low potential of transmission before clinical illness in turn implicated that the quarantine of asymptomatic contacts only provides limited benefit. In contrast, SARS-CoV-2’s substantial pre-symptomatic transmission rendered symptom-based approaches insufficient 50 , 51 , necessitating additional control measures including social distancing 18 , 20 , 50 , 52 , and enhanced prevention measures like universal masking that could block pre-symptomatic transmission 17 , 52 . Our findings also reinforce the importance of vaccination programmes, as the best available approach to contain respiratory infectious diseases burden, particularly for SARS-CoV-2 due to the suboptimal effectiveness of symptom-based non-pharmaceutical interventions. An important consideration is that while our approach successfully quantified the proportion of pre-symptomatic transmission, model comparison could not determine precise infectiousness starting dates. Models assuming infectiousness beginning 1–4 days before symptoms showed similar goodness-of-fit, indicating that our framework reliably identifies the presence and magnitude of pre-symptomatic transmission rather than exact timing. Importantly, both Gamma and Poisson distributions converged on the same practical conclusion: most pre-symptomatic transmission occurred within one day of symptom onset. This suggests that while the precise infectious period duration remains uncertain, the key public health insight, the proportion of transmission occurring before symptoms, is robust across different modelling assumptions. Our analysis revealed key transmission factors that provide important biological and clinical insights. For influenza A, the associations between younger age, higher viral load, and fever symptoms with increased infectiousness likely reflect both behavioral factors (closer contact patterns among children) and biological differences (enhanced viral replication in naive immune systems) 28 , 30 , 53 – 55 . These patterns suggest that clinical severity indicators, particularly fever and viral load, can serve as practical markers for stratifying transmission risk and guiding isolation decisions in healthcare settings 7 , 28 , 56 , 57 . The viral load-infectiousness relationship validates theoretical frameworks linking viral shedding to transmission potential and supports mechanistic transmission models. Influenza B showed similar age-related patterns. The universal finding that infectiousness decreased in larger households across all pathogens confirms contact dilution theory 58 , where transmission probability decreases as infectious opportunities become distributed among more household members. Vaccination was not associated with influenza transmission in our study; however, this likely reflects vaccine-strain mismatches, immunity waning, and data limitations rather than vaccine ineffectiveness. We lacked data on vaccine types, administration timing, and strain concordance, limiting our model to a simple dichotomous vaccination variable. These results should not be interpreted as evidence of vaccine ineffectiveness. Our findings should be interpreted within Hong Kong's specific epidemiological context. As a subtropical city with high population density, Hong Kong experiences unique influenza dynamics with two annual epidemic peaks 59 , 60 , winter peaks likely driven by low temperature and humidity, and summer peaks potentially related to high humidity conditions. These climatic and demographic factors may influence transmission patterns, and our pre-symptomatic transmission estimates should be considered alongside these contextual factors, including population differences, healthcare systems, policy contexts and household compositions, when applying findings to other settings with different climatic conditions or population densities. This approach can be readily applied to other respiratory pathogens to empirically determine pre-symptomatic transmission potential. As new respiratory threats continue to emerge 61 , 62 , this approach provides a standardized tool for rapid characterization of pre-symptomatic transmission potential during the critical early weeks of outbreak response 4 . We recommend integrating household transmission monitoring into pandemic preparedness infrastructure to enable evidence-based calibration of control strategies 63 . Future research should examine how these patterns vary across populations and how viral evolution may alter transmission dynamics. There were several limitations in our study. First, our model required substantial computational time to calculate marginal likelihoods across all possible infection time combinations, and computational constraints necessitated a smaller SARS-CoV-2, increasing uncertainty in parameter estimates. Second, our household transmission model assumed continuous contacts among household members throughout the study period, however, normal social activities were restricted during the SARS-CoV-2 study period due to public health measures, potentially resulting in higher household contact rates than typical circumstances and overestimating baseline household transmission patterns. However, our results align well with SARS-CoV-2 studies 19 – 21 , 23 , 47 . Third, influenza index cases were recruited from outpatients while SARS-CoV-2 index cases were collected through self-reporting, meaning asymptomatic and mild index cases were unlikely to be included. 30,57 . In conclusion, this study provides robust quantitative evidence that influenza viruses have limited (influenza A: 9.6%) to no (influenza B) pre-symptomatic transmission, contrasting sharply with SARS-CoV-2's substantial pre-symptomatic transmission (65.5%). Our methodological framework offers a template for characterizing pre-symptomatic transmission across respiratory pathogens, enabling evidence-based design of pathogen-specific control strategies. As new respiratory threats continue to emerge, quantifying pre-symptomatic transmission potential will be essential for developing effective, proportionate public health responses that appropriately balance epidemic control with societal impact. METHODS Data sources Household transmission study of influenza in Hong Kong Between 2008 and 2017, we conducted community-based large studies investigating household transmission of influenza virus 25 , 26 . We enrolled outpatients with acute respiratory illness within 2 days of onset, who lived with at least two household members without symptoms in the past 14 days as index cases. These participants were tested by the QuickVue Influenza A + B test (Quidel, San Diego, CA). Those with positive results on the rapid test and their household contacts were followed up with three home visits over about 7 days. In each home visit, nose and throat swab specimens were collected from all individuals regardless of symptoms. Symptoms were recorded daily in symptom diaries. Participants recruited between January 2008 and June 2009 were part of a randomized controlled trial of enhanced hand hygiene with or without surgical face masks, assigned at the household level 25 . Those recruited from summer 2009 onwards participated in a study on influenza transmission dynamics in households, with all households receiving basic hand hygiene intervention 26 . Our analysis focused exclusively on households with index cases with PCR-confirmed influenza A and B virus infection. Laboratory procedures were summarized in previous publications 25 , 64 – 66 . In brief, paired nasal and throat swabs were combined immediately after collection in a viral transport medium and transported to the laboratory for cryopreservation at -70°C within 24 hours. Total nucleic acid extraction was performed using the NucliSens easy MAG extraction system (bioMerieux, Boxtel, The Netherlands) following the manufacturer's guidelines. Complementary DNA was generated using 12 microliters of extracted nucleic acid with a random primer and the Invitrogen Superscript III kit (Invitrogen) 65 . The identification of influenza virus was carried out in a PCR assay incorporating a reference standard created from pCRII-TOPO vector (Invitrogen, San Diego) containing the target viral sequences 64 . Melting-curve analysis was performed on the PCR products to confirm the assay's specificity. The lower limit of detection (LLOD) for the PCR assay was 900 virus gene copies per milliliter. We defined influenza virus infection as a positive influenza PCR result from at least one specimen collected during the follow-up. Symptom onset for PCR-confirmed infections was defined as the first day when at least 2 of 7 common symptoms (fever, runny nose, cough, sore throat, headache, phlegm, myalgia) 67 were reported. Household transmission study of SARS-CoV-2 From March 2022 to January 2023, Hong Kong implemented a government-mandated reporting system for SARS-CoV-2 cases and their household contacts. This system required individuals who tested positive for COVID-19 (through either PCR or rapid antigen tests) to register their results via the Centre for Health Protection's online declaration platform, where they provided personal information, details of household contacts, symptom status, and living environment characteristics. Upon registration, confirmed cases received isolation orders, while their household contacts were subject to mandatory quarantine orders, both initially set at 14 days with possible early release for individuals with at least two vaccine doses. On December 8, 2022, this policy changed significantly: isolation periods for infected persons were reduced to 5 days (with early discharge possible after negative RAT results on both Days 4 and 5), and quarantine for close contacts was likewise shortened to 5 days (requiring negative daily rapid antigen tests for release). The system facilitated epidemiological surveillance by capturing household transmission data. We analyzed data from this system specifically from December 9 to 31, 2022, a period following significant relaxation of public health and social measures in Hong Kong, when transmission patterns began to reflect more endemic circulation rather than epidemic spread, providing insights into household transmission dynamics under conditions approaching normalcy. In this period, 53,167 households were included, and 100 households were randomly selected. Influenza viral shedding trajectories To estimate individual viral shedding trajectories, a log-linear mixed-effect regression model considering censored responses was fitted (Appendix Section 1; Appendix Fig. 1) 27 – 31 . For each subtype, we fitted two separate models for children and adults, to account for their potential difference in viral shedding (Appendix Table 1). The models assumed that viral load peaked at symptom onset, and we adopted the estimated viral load at symptom onset as the proxy of the level of viral shedding for infected participants. Household transmission model The household transmission dynamics of influenza and SARS-CoV-2 virus was modelled by individual-based hazard models (Appendix Section 2) 30,58,67,68 . The model described the risk of infection for each susceptible household member at each time point, considering the hazard of being infected by other infected household members (person-to-person hazard), as well as the hazard of infection from outside the household (community). In the model, we accounted for pre-symptomatic transmission by specifying the pre-symptom and post-symptom phases in the individual infectiousness profile. With an assumed incubation period, we estimated the complete infectiousness profile across both phases. We assumed the incubation period of influenza A and B followed discretized log-normal distributions with medians of 1.4 and 0.6 days 32 . The incubation period of SARS-CoV-2 Omicron variant was assumed to follow a log-normal distribution with a median of 3.04 days (Appendix Section 2.2) 33 . For influenza, age, vaccination status were included as factors affecting susceptibility, and age, standardized viral load at symptom onset, fever symptoms, use of antiviral treatment, virus subtypes and household size were included as factors affecting infectiousness 30 , 57 . Given the interdependency among age, viral load and fever symptoms, we systematically evaluated influenza models incorporating various combinations of these three factors as covariates affecting infectiousness. For SARS-CoV-2, only age and vaccination were included as factors affecting susceptibility, while age and household size were included as factors affecting infectiousness (Appendix Section 2.3). Since our data collected at day interval, we employed a discrete-time framework where each day represents a continuous interval from − 0.5 to + 0.5 days relative to that day's midpoint. Thus, day 0 (symptom onset) encompasses the interval [-0.5, + 0.5) days relative to true symptom onset, day − 1 represents [-1.5, -0.5) days before onset, and so forth. Under this framework, pre-symptomatic transmission captures events occurring before the symptom onset day interval begins (effectively > 12 hours before symptoms). The infectiousness profile described the probability that infections occurred in each day in the infectious period, following a discretized Gamma or Poisson distribution (Appendix Section 2.4). It depended on the day of symptom onset, the length of the incubation period and the starting day of the infectious period. To maintain biological plausibility, we set the infectiousness to 0 for any time prior to actual infection and then re-normalized the probability mass for all post-infection time points. Hence, the proportion of pre-symptomatic transmission was defined as the proportion of transmission that occurred on and before 1 day prior to symptom onset, and could be estimated by integrating the incubation period distribution with the estimated infectiousness profile (Appendix Section 2.4). To derive the likelihood of each household, we considered all combinations of infection times of cases in the households, whose probabilities were calculated according to their symptom onset times and the distribution of the incubation period. The marginal likelihood for each combination was calculated. To reduce the computational burden, if the probability of a combination was lower than the fixed threshold \(\:\gamma\:\) , which was set to be 0.0001 in the main analysis, the corresponding likelihood was set to be 0 (Appendix Section 2.7). We conducted a sensitivity analysis to investigate the robustness of model inference for different probability thresholds. Model parameters were estimated by the Bayesian Markov Chain Monte Carlo (MCMC) metropolis-hasting algorithm (Appendix Section 2.8), and the goodness-of-fit was evaluated by the Deviance Information Criterion (DIC) value (Appendix Section 3) 24 . Model adequacy and validation For model validation, we applied the proposed inference approach to simulated datasets, with parameters set to be those estimated from the real data. This approach allowed us to assess whether our inference methods produced unbiased estimates of parameters (Appendix Section 4). Model adequacy was evaluated by utilizing the posterior distribution to simulate datasets and comparing the predicted and observed numbers of infections in households (Appendix Section 5). Declarations Potential conflicts of interest. BJC reports honoraria from AstraZeneca, Fosun Pharma, GSK, Haleon, Moderna, Pfizer, Roche and Sanofi Pasteur. All other authors report no other potential conflicts of interest. Acknowledgements The authors thank Julie Au for assistance. This project was supported by the Theme-based Research Scheme (Project No. T11-712/19-N) of the Research Grants Council of the Hong Kong SAR Government, and the Research Grants Council of the Hong Kong Special Administrative Region, China (GRF 17104220). S.C. acknowledges support from the European Commission under the EU4Health program 2021–2027, Grant Agreement-Project: 101102733—DURABLE, the Investissement d’Avenir program, the Laboratoire d’Excellence Integrative Biology of Emerging Infectious Diseases program (grant ANR-10-LABX-62-IBEID), AXARF and the INCEPTION project (PIA/ANR-16-CONV-0005). References Iuliano, A.D., et al. Estimates of global seasonal influenza-associated respiratory mortality: a modelling study. Lancet 391, 1285–1300 (2018). Uyeki, T.M., Hui, D.S., Zambon, M., Wentworth, D.E. & Monto, A.S. Influenza. Lancet 400, 693–706 (2022). Murray, C.J.L. Findings from the Global Burden of Disease Study 2021. Lancet 403, 2259–2262 (2024). Fraser, C., Riley, S., Anderson, R.M. & Ferguson, N.M. Factors that make an infectious disease outbreak controllable. Proc Natl Acad Sci U S A 101, 6146–6151 (2004). Peak, C.M., Childs, L.M., Grad, Y.H. & Buckee, C.O. 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The effect of variation of individual infectiousness on SARS-CoV-2 transmission in households. eLife 12, e82611 (2023). Ali, S.T., et al. Influenza seasonality and its environmental driving factors in mainland China and Hong Kong. Sci Total Environ 818, 151724 (2022). Chong, K.C., Goggins, W., Zee, B.C. & Wang, M.H. Identifying meteorological drivers for the seasonal variations of influenza infections in a subtropical city – Hong Kong. Int J Environ Res Public Health 12, 1560–1576 (2015). Jones, K.E., et al. Global trends in emerging infectious diseases. Nature 451, 990–993 (2008). WHO Regional Office for the Eastern Mediterranean. Zoonotic disease: emerging public health threats in the Region. WHO. Stronger household transmission investigations for Pandemic Special Studies. (2022). Chan, K.H., Peiris, J.S., Lim, W., Nicholls, J.M. & Chiu, S.S. Comparison of nasopharyngeal flocked swabs and aspirates for rapid diagnosis of respiratory viruses in children. J Clin Virol 42, 65–69 (2008). Peiris, J.S., et al. Children with respiratory disease associated with metapneumovirus in Hong Kong. Emerg Infect Dis 9, 628–633 (2003). Xu, C., et al. Comparative Epidemiology of Influenza B Yamagata- and Victoria-Lineage Viruses in Households. American Journal of Epidemiology 182, 705–713 (2015). Tsang, T.K., et al. Association between antibody titers and protection against influenza virus infection within households. J Infect Dis 210, 684–692 (2014). Cauchemez, S., et al. Household transmission of 2009 pandemic influenza A (H1N1) virus in the United States. N Engl J Med 361, 2619–2627 (2009). Additional Declarations Yes there is potential Competing Interest. BJC reports honoraria from AstraZeneca, Fosun Pharma, GSK, Haleon, Moderna, Pfizer, Roche and Sanofi Pasteur. All other authors report no other potential conflicts of interest. Supplementary Files AppendixFigure3.pdf Appendix Figure 3 AppendixFigure7.pdf Appendix Figure 7 AppendixFigure4.pdf Appendix Figure 4 AppendixFigure8.pdf Appendix Figure 8 AppendixFigure13.pdf Appendix Figure 13 AppendixFigure12.pdf Appendix Figure 12 AppendixFigure14.pdf Appendix Figure 14 AppendixFigure1.pdf Appendix Figure 1 AppendixFigure2.pdf Appendix Figure 2 AppendixFigure10.pdf Appendix Figure 10 AppendixFigure11.pdf Appendix Figure 11 AppendixFigure6.pdf Appendix Figure 6 AppendixFigure9.pdf Appendix Figure 9 appenidxpresymptomatic0806.docx Supplementary Material (Appendix) AppendixFigure5.pdf Appendix Figure 5 Cite Share Download PDF Status: Published Journal Publication published 28 Jan, 2026 Read the published version in Nature Health → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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08:43:20","extension":"html","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150822,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/321cb8776213810972468d6a.html"},{"id":97131632,"identity":"1273d055-79cf-495f-b98b-40736a78217f","added_by":"auto","created_at":"2025-12-01 08:43:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":819915,"visible":true,"origin":"","legend":"\u003cp\u003eA: Flowchart of our modelling framework. We adopted the household transmission model to reconstruct transmission dynamics using data in household studies (blue), with fixed model parameters (green), to estimate effects of factors affecting susceptibility and infectiousness, the infectiousness profile distribution and the proportion of pre-symptomatic transmission (yellow). The framework also included methods to impute viral shedding trajectories, conduct model comparison, assess model adequacy and validation, and conduct sensitivity analysis (red). B: Infectiousness profile and pre-symptomatic transmission. When the infector had a longer incubation period that the infection time t\u003csub\u003ei\u003c/sub\u003e' was earlier than the transmission starting time t\u003csub\u003ei\u003c/sub\u003e-δ, pre-symptomatic transmission was defined as transmission occurring between t\u003csub\u003ei\u003c/sub\u003e-δ and the time of symptom onset t\u003csub\u003ei\u003c/sub\u003e; C: When the infector had a shorter incubation period that the infection time t\u003csub\u003ei\u003c/sub\u003e' was later than the transmission starting time t\u003csub\u003ei\u003c/sub\u003e-δ, the infectiousness profile densities were re-normalized and pre-symptomatic transmission was defined as transmission occurring between infection t\u003csub\u003ei\u003c/sub\u003e' and the time of symptom onset t\u003csub\u003ei\u003c/sub\u003e.\u003c/p\u003e","description":"","filename":"Figure1291.png","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/8bbeb7dfb051ef811f65456c.png"},{"id":97141919,"identity":"a492d2a6-adf3-4321-a5bd-26218640f047","added_by":"auto","created_at":"2025-12-01 10:07:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":167583,"visible":true,"origin":"","legend":"\u003cp\u003eA: Date or period of recruitment for households in influenza and SARS-CoV-2 datasets; B: Probability density functions of assumed incubation periods of influenza A, influenza B and SARS-CoV-2; C – D: Estimated infectiousness profiles by the length of incubation period, following discretized Gamma distributions; E: Median of probabilities that transmission occurred on each day before (red area) or after (blue area) the symptom onset of the infector, and the proportion of pre-symptomatic transmission.\u003c/p\u003e","description":"","filename":"Figure1292.png","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/a1355c7060210450f6709a0f.png"},{"id":97141883,"identity":"e1148cdd-f7e6-42f6-a8c3-472a2710e41b","added_by":"auto","created_at":"2025-12-01 10:07:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":110711,"visible":true,"origin":"","legend":"\u003cp\u003eResults of model inference and estimates of effects of covariates affecting susceptibility and infectiousness.\u003c/p\u003e","description":"","filename":"Figure1293.png","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/843b0d77bde56a2a5aeba68d.png"},{"id":97141836,"identity":"e9773ef0-3488-4f70-831a-88e2971b4122","added_by":"auto","created_at":"2025-12-01 10:07:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35925,"visible":true,"origin":"","legend":"\u003cp\u003eRisk ratios of age, predicted viral load at symptom onset and the presence of fever symptoms as factors affecting influenza A and B individual infectiousness, estimated by models using Poisson-distributed infectiousness profiles, but with different combinations of the three mentioned covariates.\u003c/p\u003e","description":"","filename":"Figure1294.png","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/01302f1374b9609ff9d14fc9.png"},{"id":101391527,"identity":"1c661462-3f38-465b-9bce-978b20b1bdd4","added_by":"auto","created_at":"2026-01-29 08:33:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1805591,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/a81a9d3e-c519-4f11-b4ea-6dfff360f31b.pdf"},{"id":97142494,"identity":"7856d006-b664-4ab4-b48b-6d924838bda9","added_by":"auto","created_at":"2025-12-01 10:07:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12857,"visible":true,"origin":"","legend":"Appendix Figure 3","description":"","filename":"AppendixFigure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/05b182d1a3cd2e609d19bbc3.pdf"},{"id":97131636,"identity":"d84e9dbf-c566-4434-8d6e-7c85bdc0301d","added_by":"auto","created_at":"2025-12-01 08:43:20","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14165,"visible":true,"origin":"","legend":"Appendix Figure 7","description":"","filename":"AppendixFigure7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/0649c05c2b08c94a4530703e.pdf"},{"id":97131634,"identity":"ce3e7d6f-e76e-40b9-aa67-e9e1091236dc","added_by":"auto","created_at":"2025-12-01 08:43:20","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":6952,"visible":true,"origin":"","legend":"Appendix Figure 4","description":"","filename":"AppendixFigure4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/a62ac51542e5ee2366cae8f4.pdf"},{"id":97142687,"identity":"1aa51cf1-ca4b-42b2-8aff-211bdd1b00a7","added_by":"auto","created_at":"2025-12-01 10:07:51","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":11687,"visible":true,"origin":"","legend":"Appendix Figure 8","description":"","filename":"AppendixFigure8.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/dbda1b2e32ed60edb699815a.pdf"},{"id":97131640,"identity":"b057da1c-890c-4773-b5c9-660656839b35","added_by":"auto","created_at":"2025-12-01 08:43:20","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":9182,"visible":true,"origin":"","legend":"Appendix Figure 13","description":"","filename":"AppendixFigure13.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/398ef42f13e156137e56c63a.pdf"},{"id":97131644,"identity":"c4cb149e-0db6-4063-b5f1-797c6b54e979","added_by":"auto","created_at":"2025-12-01 08:43:20","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":8645,"visible":true,"origin":"","legend":"Appendix Figure 12","description":"","filename":"AppendixFigure12.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/8098b48ddbbb38c6aad4c572.pdf"},{"id":97142472,"identity":"98c4f451-6bed-42df-b39a-e017557e1c77","added_by":"auto","created_at":"2025-12-01 10:07:38","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":8087,"visible":true,"origin":"","legend":"Appendix Figure 14","description":"","filename":"AppendixFigure14.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/19c0439eaec5817abe2b172a.pdf"},{"id":97131653,"identity":"31748a3d-454d-48ec-ae82-aa0f63ccce5c","added_by":"auto","created_at":"2025-12-01 08:43:20","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":155382,"visible":true,"origin":"","legend":"Appendix Figure 1","description":"","filename":"AppendixFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/be9e06235acc33ca4d6c3dce.pdf"},{"id":97131648,"identity":"e2f8de1e-d9e7-4fd7-a821-f676a017a7ed","added_by":"auto","created_at":"2025-12-01 08:43:20","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":6353,"visible":true,"origin":"","legend":"Appendix Figure 2","description":"","filename":"AppendixFigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/4652dd6712ce48931e55384d.pdf"},{"id":97142011,"identity":"03ae35eb-14f5-43c6-ac77-fd9d605c2ac8","added_by":"auto","created_at":"2025-12-01 10:07:16","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":12637,"visible":true,"origin":"","legend":"Appendix Figure 10","description":"","filename":"AppendixFigure10.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/b601d5a5fb6ca23ea15445f8.pdf"},{"id":97142834,"identity":"46296b77-046f-47df-b3c2-0d5d00c4c55c","added_by":"auto","created_at":"2025-12-01 10:07:59","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":13052,"visible":true,"origin":"","legend":"Appendix Figure 11","description":"","filename":"AppendixFigure11.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/1bc8e5fff15f404c846025a3.pdf"},{"id":97131650,"identity":"aafa30c8-5cfe-444b-b9f9-98bf6a7c9d74","added_by":"auto","created_at":"2025-12-01 08:43:20","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":14223,"visible":true,"origin":"","legend":"Appendix Figure 6","description":"","filename":"AppendixFigure6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/c64b19439e9f1ff9dd9be045.pdf"},{"id":97142053,"identity":"3718b868-48a5-454f-ac4c-bec20ce1056d","added_by":"auto","created_at":"2025-12-01 10:07:18","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":12752,"visible":true,"origin":"","legend":"Appendix Figure 9","description":"","filename":"AppendixFigure9.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/73ceb443d52358ab9298fe40.pdf"},{"id":97131662,"identity":"1628b6ff-d404-48a3-a1e5-ce92680abde4","added_by":"auto","created_at":"2025-12-01 08:43:20","extension":"docx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":150103,"visible":true,"origin":"","legend":"Supplementary Material (Appendix)","description":"","filename":"appenidxpresymptomatic0806.docx","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/6719656d2cd36a9aee9db1ff.docx"},{"id":97142320,"identity":"1de82632-ea69-4b6a-9fab-7d7120e584c5","added_by":"auto","created_at":"2025-12-01 10:07:30","extension":"pdf","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":6688,"visible":true,"origin":"","legend":"Appendix Figure 5","description":"","filename":"AppendixFigure5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7701955/v1/1f6dcd17fb1a9de002737980.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nBJC reports honoraria from AstraZeneca, Fosun Pharma, GSK, Haleon, Moderna, Pfizer, Roche and Sanofi Pasteur. All other authors report no other potential conflicts of interest.","formattedTitle":"Estimating pre-symptomatic transmission potential of influenza A and B viruses in household transmission studies","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eInfluenza represents a major global health threat, causing an estimated 300,000 deaths annually through seasonal circulation worldwide \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. During the post-pandemic era, influenza and SARS-CoV-2 continue to circulate and remain important public health concerns \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Case Isolation and contact quarantine are available strategies for controlling respiratory infectious disease outbreaks, having proven effective during the 2003 SARS pandemic. However, the effectiveness of these symptom-based interventions depends critically on the extent of transmission occurring before symptom onset \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePre-symptomatic transmission, i.e. occurring before the onset of clinical illness poses a fundamental challenge to symptom-based control measures, yet quantitative evidence for influenza viruses remains limited and conflicting. Patients infected by influenza virus can shed the virus before illness onset \u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Some studies reported possible pre-symptomatic transmission of influenza A during the 2009 pandemic \u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and in later seasons \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, other investigations found no evidence of transmission before symptom onset \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. For influenza B, the capacity for pre-symptomatic transmission has received virtually no systematic investigation. This uncertainty has important implications for outbreak control strategies, as the proportion of pre-symptomatic transmission directly determines the potential effectiveness of case isolation and contact tracing \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe conflicting empirical evidence likely reflects methodological limitations in existing approaches, particularly the challenge of determining true infection times when only symptom onset dates are observable \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In contrast to the uncertain evidence for influenza, SARS-CoV-2 provides a useful comparison case where substantial pre-symptomatic transmission is well-established, with multiple studies documenting 40\u0026ndash;70% of transmission occurring during the pre-symptomatic phase \u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21 CR22\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This established transmission pattern makes SARS-CoV-2 an ideal validation case for methodological approaches designed to quantify pre-symptomatic transmission.\u003c/p\u003e\u003cp\u003eTo address these knowledge gaps, we developed a new individual-based household transmission model that explicitly accounts for transmission occurring before symptom onset while properly handling unobserved infection times. We applied this framework to reconstruct transmission dynamics using household studies of influenza A and B virus (2008\u0026ndash;2017) in Hong Kong, and validated our approach using SARS-CoV-2 data where transmission patterns are established. Our primary aim was to provide quantitative evidence of influenza pre-symptomatic transmission potential, resolving the conflicting literature and informing evidence-based design of pathogen-specific control strategies.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eOverview of the study\u003c/h2\u003e\u003cp\u003eThis study developed an individual-based household transmission model to estimate household transmission dynamics and quantify pre-symptomatic transmission of influenza and SARS-CoV-2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Our approach addresses the challenge of unobserved infection times by calculating likelihoods across all possible combinations of infection times for each case. The model defines infectiousness profiles starting before symptom onset and adaptively handles varying incubation periods through profile re-normalization. By combining input incubation period distributions with estimated infectiousness profiles, we estimate the proportion of pre-symptomatic transmissions, defined as the proportion of transmission occurring before the day of symptom onset, (effectively\u0026thinsp;\u0026gt;\u0026thinsp;0.5 days before symptoms) in our discrete-time framework (Methods).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo ensure computational feasibility, we introduced a probability threshold mechanism that excludes highly improbable (probability below 0.0001) infection time combinations while maintaining estimation accuracy. The framework accommodates tertiary transmissions, community-acquired infections, and households with multiple co-index cases. Using Bayesian inference, we jointly estimated pre-symptomatic transmission proportions and associated risk factors, with model comparison used to identify significant transmission determinants \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. We applied this model to household transmission studies of influenza A, B and SARS-CoV-2 in Hong Kong, with a more detailed analysis of transmission factors for influenza due to richer data availability.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eHousehold transmission study of influenza in Hong Kong\u003c/h3\u003e\n\u003cp\u003eIn this study, 839 index cases with PCR-confirmed influenza infections and 2484 household contacts were recruited from 2008 to 2017 \u003csup\u003e25,26\u003c/sup\u003e. Among all index cases, 559 were infected by influenza A virus and 280 by influenza B. We excluded 91 households: 32 with all members testing negative for influenza, 12 with fewer than the required minimum of 3 household members, 29 with incomplete data, and 18 with unidentified influenza A virus subtypes. Finally, 493 influenza A index cases with 1451 household contacts and 255 influenza B cases with 795 contacts were included in the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In 101 households, at least one household contact tested positive at the first home visit in addition to the index cases.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor influenza A, the study included 170 seasonal A(H1N1), 211 seasonal A(H3N2), and 112 pandemic A(H1N1) index cases (hereafter denoted as sH1N1, sH3N2 and pH1N1 respectively). For influenza B, the study included 44 Victoria, 71 Yamagata, and 140 unsubtypable index cases. Characteristics of primary cases and household contacts among subtypes were largely similar. However, we observed different uptake for antiviral treatment and vaccination coverage among infected household contacts (Appendix Table\u0026nbsp;1, p \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;0.01\\)\u003c/span\u003e\u003c/span\u003e, Fisher\u0026rsquo;s test). Unsubtypable influenza B index cases had fewer secondary infections (Appendix Table\u0026nbsp;2, p \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;0.01\\)\u003c/span\u003e\u003c/span\u003e, Fisher\u0026rsquo;s test).\u003c/p\u003e\u003cp\u003eTo compare the levels of viral shedding, we adopted a log-linear mixed-effect censored regression model to estimate the complete viral shedding trajectories and peak viral load at symptom onset (Appendix Fig.\u0026nbsp;1; Appendix Table\u0026nbsp;3\u0026ndash;4) \u003csup\u003e27\u0026ndash;31\u003c/sup\u003e. We found that pandemic A(H1N1) patients had a lower predicted viral load at onset (Appendix Table\u0026nbsp;1, p \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;0.01\\)\u003c/span\u003e\u003c/span\u003e, Kruskal-Wallis test) among primary cases and infected contacts including co-index cases.\u003c/p\u003e\u003cp\u003eAge, viral shedding and fever were interdependent for both influenza A and B (Appendix Table\u0026nbsp;5\u0026ndash;6) \u003csup\u003e30\u003c/sup\u003e. Compared to adults, children had higher viral loads at symptom onset (p \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;0.01\\)\u003c/span\u003e\u003c/span\u003e, Kruskal-Wallis test), and had more frequent fevers (p \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;0.01\\)\u003c/span\u003e\u003c/span\u003e, Fisher\u0026rsquo;s test), while febrile cases showed increased viral load (p \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;0.01\\)\u003c/span\u003e\u003c/span\u003e, Kruskal-Wallis test).\u003c/p\u003e\n\u003ch3\u003ePre-symptomatic transmission potential and transmission dynamics of influenza A\u003c/h3\u003e\n\u003cp\u003eWe assumed the incubation period of influenza A followed a discretized log-normal distribution with a median of 1.4 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. We fitted models assuming the infectiousness of cases started from 0 to 4 days before symptom onset, and model comparison supported that infectiousness started 4 days before symptom onset, and estimated that 9.6% (95% CrI: 5.9%, 14.7%) of infections occurred before the day of symptom onset of infectors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Appendix Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Appendix Table\u0026nbsp;7). The model assuming no pre-symptomatic infectiousness performed substantially worse (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\text{D}\\text{I}\\text{C}\u0026gt;5\\)\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBased on the best model, we estimated that the person-to-person transmission probabilities for seasonal H1N1, pandemic H1N1 and seasonal H3N2 subtypes were 7.4% (95% CrI: 4.5%, 11.3%), 7.3% (95% CrI: 4.3%, 11.6%) and 9.0% (95% CrI: 5.7%, 13.5%), which were similar. Younger age was associated with increased susceptibility to infection, as pre-school children (age below 6 years) and school-age children (age 6\u0026ndash;17 years) were 261% (95% CrI: 137%, 423%) and 105% (95% CrI: 48%, 180%) more susceptible than adults aged between 18 and 50 years, while old household members (age above 50 years) were 53% (95% CrI: 25%, 74%) less susceptible than younger adults (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eYounger age, the onset of fever symptoms and higher viral load were associated with higher infectiousness. Pre-school children and school-age children had 172% (95% CrI: 78%, 320%) and 56% (95% CrI: 7%, 132%) higher infectiousness compared to adults, while febrile cases were 81% (95% CrI: 28%, 156%) more infectious than patients without fever symptoms. Predicted peak viral load was added to the model as a standardized continuous variable, therefore, one standard deviation (0.686 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{l}\\text{o}\\text{g}}_{10}\\)\u003c/span\u003e\u003c/span\u003ecopies/mL) higher for viral load at symptom onset was associated with 19% (95% CrI: 2%, 37%) higher infectiousness. Antiviral treatment was associated with 24% (95% CrI: 3%, 41%) lower infectiousness. Compared to households with 3 members, infectiousness of cases in households with 4\u0026ndash;5 members and 6 members was associated with 36% (95% CrI: 15%, 52%) and 42% (95% CrI: 9%, 64%) lower infectiousness (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The estimated associations between factors and susceptibility/infectiousness were robust to the assumption about infectiousness starting date (0 to 4 days before symptom onset) (Appendix Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe compared models with different combinations of age, fever, individual viral load to quantify their interplays \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. We found that younger age, fever and higher viral load were associated with increased infectiousness in all models (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Appendix Table\u0026nbsp;8). The model including age, fever, and viral load had the lowest DIC, while it was comparable with the model that included age and fever only (DIC difference\u0026thinsp;=\u0026thinsp;4.36) (Appendix Table\u0026nbsp;8). Also, other models performed substantially worse.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eModel adequacy, validation and sensitivity analyses\u003c/h3\u003e\n\u003cp\u003eIn a simulation study with 10,000 epidemics, the predicted final size distribution was consistent with the observed one (Appendix Section 5; Appendix Table\u0026nbsp;9), suggesting the model fit was adequate. Besides, to assess the validation of our model, we fitted models to 50 simulated epidemics, 78% \u0026ndash; 100% of estimated 95% credible intervals covered corresponding actual values. Our inference approach could estimate the model parameters with no systematic bias (Appendix Section 4; Appendix Table\u0026nbsp;10).\u003c/p\u003e\u003cp\u003eIn a sensitivity analysis that used Poisson distribution instead of Gamma distribution for infectiousness profile, the best fit was obtained for the model assuming that infectiousness started 1 day prior to symptom onset (Appendix Fig.\u0026nbsp;4; Appendix Table\u0026nbsp;11), and the estimated proportion of pre-symptomatic transmission was similar to the main model (Appendix Fig.\u0026nbsp;5). Model comparisons suggested that except for the one assuming no pre-symptomatic transmission, models using the Poisson distribution as infectiousness profile performed similarly to the main model (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003eDIC \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e 5). Also, the estimates of factors affecting susceptibility and infectiousness remained similar (Appendix Fig.\u0026nbsp;6).\u003c/p\u003e\u003cp\u003eSensitivity analyses testing probability thresholds from 0.00001 to 0.005 (vs. 0.0001 in main analysis) yield similar estimates of factors affecting transmission and pre-symptomatic infectiousness (Appendix Fig.\u0026nbsp;5, 7; Appendix Table\u0026nbsp;12) suggesting our method could provide unbiased estimates.\u003c/p\u003e\n\u003ch3\u003ePre-symptomatic transmission potential and transmission dynamics of influenza B\u003c/h3\u003e\n\u003cp\u003eWe assumed a shorter incubation period with a median of 0.6 days for influenza B (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Models assuming infectiousness starting 0 to 4 days before symptom onset were fitted. The goodness-of-fit between models with and without pre-symptomatic transmission was not substantial (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\text{D}\\text{I}\\text{C}\u0026lt;5\\)\u003c/span\u003e\u003c/span\u003e). Therefore, influenza B showed no evidence of pre-symptomatic transmission. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Appendix Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Appendix Table\u0026nbsp;13).\u003c/p\u003e\u003cp\u003eModel selection based on DIC values identified age as the key infectiousness factor, with viral load and fever providing minimal improvement in fit (Appendix Table\u0026nbsp;14). Higher viral load was associated with increased infectiousness in univariate analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Appendix Table\u0026nbsp;14). Pre-school and school-age children were 232% (95% CrI: 55%, 543%) and 289% (95% CrI: 144%, 527%) more susceptible than adults under 50, with pre-school children also 248% (95% CrI: 68%, 696%) more infectious. Patients in households with \u0026gt;\u0026thinsp;5 members were 59% (95% CrI: 4%, 84%) less infectious than those in 3-member households (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These estimates were robust for models assuming different infectiousness starting dates (Appendix Fig.\u0026nbsp;8).\u003c/p\u003e\u003cp\u003eSimulation studies confirmed the model provided predictions consistent with observations and unbiased parameter estimates, as 82% \u0026ndash; 100% of 95% credible intervals of model parameters covered actual values (Appendix Table\u0026nbsp;15\u0026ndash;16). Models adopting Poisson-distributed infectiousness profiles also supported that the goodness-of-fits between models with and without pre-symptomatic transmission were not substantial (Appendix Fig.\u0026nbsp;4), with similar estimates for factors affecting transmissions (Appendix Fig.\u0026nbsp;9; Appendix Table\u0026nbsp;17). Changing the probability threshold did not affect parameter estimates (Appendix Fig.\u0026nbsp;10). Sensitivity analyses using the influenza A incubation period (median: 1.4 days) yielded consistent results (Appendix Fig.\u0026nbsp;4, 11; Appendix Table\u0026nbsp;18).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePre-symptomatic transmission potential and transmission dynamics of SARS-CoV-2\u003c/h2\u003e\u003cp\u003eBased on the government reporting system that collected data with mandatory reporting of SARS-CoV-2 tested positive and their household contacts\u0026rsquo; information during March 2022 to December 2022, we randomly selected 100 households including 327 members reporting from 9 to 31 December 2022, when the Hong Kong government eased isolation and quarantine policies (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Appendix Table\u0026nbsp;19).\u003c/p\u003e\u003cp\u003eWe assumed the incubation period for the SARS-CoV-2 Omicron variant followed a discretized log-normal distribution with a median of 3.04 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. We fitted models assuming the infectiousness started from 0 to 7 days before symptom onset and model comparison supported that infectiousness started 5 days before symptom onset. We estimated that 65.5% (95% CrI: 52.7%, 78.9%) of SARS-CoV-2 transmission occurred during the pre-symptomatic phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Appendix Table\u0026nbsp;20). Similarly, the model assuming no pre-symptomatic transmission performed substantially worse (Appendix Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\text{D}\\text{I}\\text{C}\u0026gt;5\\)\u003c/span\u003e\u003c/span\u003e). The person-to-person transmission probability was estimated to be 30.2% (95% CrI: 16.8%, 49.8%). Cases in households with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026gt;\\)\u003c/span\u003e\u003c/span\u003e3 members were associated with 44% (95% CrI: 6%, 66%) lower infectiousness than those living in smaller households (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The estimated associations between factors and susceptibility/infectiousness were robust to the assumption about the infectiousness starting date (Appendix Fig.\u0026nbsp;12).\u003c/p\u003e\u003cp\u003eWe conducted the same simulation study for model adequacy checking and found that the predicted final size distribution was consistent with the observed one (Appendix Table\u0026nbsp;21). Also, we conducted the same simulation study for model validation, and our approach could also provide unbiased estimates for SARS-CoV-2 as 90% \u0026ndash; 100% of 95% credible intervals of model parameters covered their actual values (Appendix Table\u0026nbsp;22). Similarly, in a sensitivity analysis we found that the best model using the Poisson distribution for infectiousness profile was comparable with the main model using the Gamma distributions (Appendix Fig.\u0026nbsp;4; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003eDIC \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e 5). The estimates of the proportion of pre-symptomatic transmission and factors affecting transmission were also similar to the main model (Appendix Fig.\u0026nbsp;5, 13; Appendix Table\u0026nbsp;23). Moreover, estimates for model parameters and the proportion of pre-symptomatic transmission were consistent when using low probability thresholds (0.001 in primary analyses) for infection time scenarios (Appendix Fig.\u0026nbsp;5, 14; Appendix Table\u0026nbsp;24).\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eUnderstanding pre-symptomatic transmission potential is crucial for designing effective outbreak control strategies, yet quantitative evidence for influenza viruses has remained limited and conflicting. By developing a novel individual-based hazard model that considers infections prior to the illness onset of infectors, we reconstructed household transmission dynamics in Hong Kong and provided evidence of pre-symptomatic transmission potential. We estimated that influenza A had limited transmission before symptom onset (9.6%), influenza B showed no pre-symptomatic transmission. These findings offer additional context for understanding longstanding uncertainty in the literature, where some studies reported possible pre-symptomatic transmission of influenza A virus during the 2009 pandemic \u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, while other cohorts found no evidence of transmission before symptom onset \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, demonstrating limited pre-symptomatic transmission potential, and influenza B transmission patterns remained essentially unstudied. By providing systematic quantitative evidence across both major influenza virus types, our work establishes a clear understanding of influenza transmission dynamics that has important implications for outbreak control strategies.\u003c/p\u003e\u003cp\u003eOur estimate of 9.6% pre-symptomatic transmission for influenza A is consistent with emerging evidence from recent household studies \u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, collectively suggesting that pre-symptomatic transmission occurs at low but measurable levels, intermediate between studies finding no transmission \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, and estimates of up to 25% \u003csup\u003e12\u003c/sup\u003e. These transmission patterns reflect underlying viral shedding dynamics and incubation periods. Viral shedding of both influenza and SARS-CoV-2 peaked around symptom onset \u003csup\u003e6\u0026ndash;8,35\u0026minus;37\u003c/sup\u003e, the timing of shedding onset relative to symptoms differs between pathogens. Influenza shows constrained pre-symptomatic shedding due to rapid symptom development and shorter incubation periods \u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The differential patterns likely reflect differences in immune recognition timing, as influenza viruses trigger rapid innate immune responses through RIG-I and TLR7 pathways \u003csup\u003e\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, while SARS-CoV-2 employs immune evasion strategies that delay symptom onset relative to peak viral shedding \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. This biological basis aligns with serial interval studies showing that influenza had longer serial intervals than their incubation periods \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, while the length of incubation period and serial interval for SARS-CoV-2 were found to be similar\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur SARS-CoV-2 analysis (65.5% pre-symptomatic transmission) validates our methodological approach by confirming established estimates of 40\u0026ndash;70% from multiple studies documenting viral shedding and viable virus isolation before clinical illness onset \u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e and previous estimates using serial interval imputation \u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, stochastic transmission models \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e as well as a recent work developing a multi-scale model which considers both within- and between-host dynamics \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. This validation suggests that our framework can be applied to various pathogens with distinct transmission patterns, from no pre-symptomatic transmission (influenza B) to limited transmission (influenza A) to substantial transmission (SARS-CoV-2). The consistency with known SARS-CoV-2 patterns strengthens confidence in our influenza findings.\u003c/p\u003e\u003cp\u003eOur findings provide valuable insights into the effectiveness of symptom-based control measures for influenza and SARS-CoV-2 outbreaks\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The limited pre-symptomatic transmission of influenza A (9.6%) and absence of such transmission for influenza B suggest possible benefit to control influenza outbreaks by symptom-based control measures like rapid case detection and efficient case isolation\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, however, the overall effectiveness is challenged by the high proportion of asymptomatic influenza infections\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Besides, the low potential of transmission before clinical illness in turn implicated that the quarantine of asymptomatic contacts only provides limited benefit. In contrast, SARS-CoV-2\u0026rsquo;s substantial pre-symptomatic transmission rendered symptom-based approaches insufficient \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, necessitating additional control measures including social distancing \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, and enhanced prevention measures like universal masking that could block pre-symptomatic transmission \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Our findings also reinforce the importance of vaccination programmes, as the best available approach to contain respiratory infectious diseases burden, particularly for SARS-CoV-2 due to the suboptimal effectiveness of symptom-based non-pharmaceutical interventions.\u003c/p\u003e\u003cp\u003eAn important consideration is that while our approach successfully quantified the proportion of pre-symptomatic transmission, model comparison could not determine precise infectiousness starting dates. Models assuming infectiousness beginning 1\u0026ndash;4 days before symptoms showed similar goodness-of-fit, indicating that our framework reliably identifies the presence and magnitude of pre-symptomatic transmission rather than exact timing. Importantly, both Gamma and Poisson distributions converged on the same practical conclusion: most pre-symptomatic transmission occurred within one day of symptom onset. This suggests that while the precise infectious period duration remains uncertain, the key public health insight, the proportion of transmission occurring before symptoms, is robust across different modelling assumptions.\u003c/p\u003e\u003cp\u003eOur analysis revealed key transmission factors that provide important biological and clinical insights. For influenza A, the associations between younger age, higher viral load, and fever symptoms with increased infectiousness likely reflect both behavioral factors (closer contact patterns among children) and biological differences (enhanced viral replication in naive immune systems) \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. These patterns suggest that clinical severity indicators, particularly fever and viral load, can serve as practical markers for stratifying transmission risk and guiding isolation decisions in healthcare settings \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. The viral load-infectiousness relationship validates theoretical frameworks linking viral shedding to transmission potential and supports mechanistic transmission models. Influenza B showed similar age-related patterns. The universal finding that infectiousness decreased in larger households across all pathogens confirms contact dilution theory \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, where transmission probability decreases as infectious opportunities become distributed among more household members. Vaccination was not associated with influenza transmission in our study; however, this likely reflects vaccine-strain mismatches, immunity waning, and data limitations rather than vaccine ineffectiveness. We lacked data on vaccine types, administration timing, and strain concordance, limiting our model to a simple dichotomous vaccination variable. These results should not be interpreted as evidence of vaccine ineffectiveness.\u003c/p\u003e\u003cp\u003eOur findings should be interpreted within Hong Kong's specific epidemiological context. As a subtropical city with high population density, Hong Kong experiences unique influenza dynamics with two annual epidemic peaks \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, winter peaks likely driven by low temperature and humidity, and summer peaks potentially related to high humidity conditions. These climatic and demographic factors may influence transmission patterns, and our pre-symptomatic transmission estimates should be considered alongside these contextual factors, including population differences, healthcare systems, policy contexts and household compositions, when applying findings to other settings with different climatic conditions or population densities.\u003c/p\u003e\u003cp\u003eThis approach can be readily applied to other respiratory pathogens to empirically determine pre-symptomatic transmission potential. As new respiratory threats continue to emerge \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, this approach provides a standardized tool for rapid characterization of pre-symptomatic transmission potential during the critical early weeks of outbreak response \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. We recommend integrating household transmission monitoring into pandemic preparedness infrastructure to enable evidence-based calibration of control strategies \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Future research should examine how these patterns vary across populations and how viral evolution may alter transmission dynamics.\u003c/p\u003e\u003cp\u003eThere were several limitations in our study. First, our model required substantial computational time to calculate marginal likelihoods across all possible infection time combinations, and computational constraints necessitated a smaller SARS-CoV-2, increasing uncertainty in parameter estimates. Second, our household transmission model assumed continuous contacts among household members throughout the study period, however, normal social activities were restricted during the SARS-CoV-2 study period due to public health measures, potentially resulting in higher household contact rates than typical circumstances and overestimating baseline household transmission patterns. However, our results align well with SARS-CoV-2 studies \u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Third, influenza index cases were recruited from outpatients while SARS-CoV-2 index cases were collected through self-reporting, meaning asymptomatic and mild index cases were unlikely to be included. \u003csup\u003e30,57\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn conclusion, this study provides robust quantitative evidence that influenza viruses have limited (influenza A: 9.6%) to no (influenza B) pre-symptomatic transmission, contrasting sharply with SARS-CoV-2's substantial pre-symptomatic transmission (65.5%). Our methodological framework offers a template for characterizing pre-symptomatic transmission across respiratory pathogens, enabling evidence-based design of pathogen-specific control strategies. As new respiratory threats continue to emerge, quantifying pre-symptomatic transmission potential will be essential for developing effective, proportionate public health responses that appropriately balance epidemic control with societal impact.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eData sources\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003eHousehold transmission study of influenza in Hong Kong\u003c/h2\u003e\u003cp\u003eBetween 2008 and 2017, we conducted community-based large studies investigating household transmission of influenza virus \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. We enrolled outpatients with acute respiratory illness within 2 days of onset, who lived with at least two household members without symptoms in the past 14 days as index cases. These participants were tested by the QuickVue Influenza A\u0026thinsp;+\u0026thinsp;B test (Quidel, San Diego, CA). Those with positive results on the rapid test and their household contacts were followed up with three home visits over about 7 days. In each home visit, nose and throat swab specimens were collected from all individuals regardless of symptoms. Symptoms were recorded daily in symptom diaries.\u003c/p\u003e\u003cp\u003eParticipants recruited between January 2008 and June 2009 were part of a randomized controlled trial of enhanced hand hygiene with or without surgical face masks, assigned at the household level \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Those recruited from summer 2009 onwards participated in a study on influenza transmission dynamics in households, with all households receiving basic hand hygiene intervention \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Our analysis focused exclusively on households with index cases with PCR-confirmed influenza A and B virus infection.\u003c/p\u003e\u003cp\u003eLaboratory procedures were summarized in previous publications \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. In brief, paired nasal and throat swabs were combined immediately after collection in a viral transport medium and transported to the laboratory for cryopreservation at -70\u0026deg;C within 24 hours. Total nucleic acid extraction was performed using the NucliSens easy MAG extraction system (bioMerieux, Boxtel, The Netherlands) following the manufacturer's guidelines. Complementary DNA was generated using 12 microliters of extracted nucleic acid with a random primer and the Invitrogen Superscript III kit (Invitrogen) \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. The identification of influenza virus was carried out in a PCR assay incorporating a reference standard created from pCRII-TOPO vector (Invitrogen, San Diego) containing the target viral sequences \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Melting-curve analysis was performed on the PCR products to confirm the assay's specificity. The lower limit of detection (LLOD) for the PCR assay was 900 virus gene copies per milliliter.\u003c/p\u003e\u003cp\u003eWe defined influenza virus infection as a positive influenza PCR result from at least one specimen collected during the follow-up. Symptom onset for PCR-confirmed infections was defined as the first day when at least 2 of 7 common symptoms (fever, runny nose, cough, sore throat, headache, phlegm, myalgia) \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e were reported.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eHousehold transmission study of SARS-CoV-2\u003c/h2\u003e\u003cp\u003eFrom March 2022 to January 2023, Hong Kong implemented a government-mandated reporting system for SARS-CoV-2 cases and their household contacts. This system required individuals who tested positive for COVID-19 (through either PCR or rapid antigen tests) to register their results via the Centre for Health Protection's online declaration platform, where they provided personal information, details of household contacts, symptom status, and living environment characteristics. Upon registration, confirmed cases received isolation orders, while their household contacts were subject to mandatory quarantine orders, both initially set at 14 days with possible early release for individuals with at least two vaccine doses. On December 8, 2022, this policy changed significantly: isolation periods for infected persons were reduced to 5 days (with early discharge possible after negative RAT results on both Days 4 and 5), and quarantine for close contacts was likewise shortened to 5 days (requiring negative daily rapid antigen tests for release).\u003c/p\u003e\u003cp\u003eThe system facilitated epidemiological surveillance by capturing household transmission data. We analyzed data from this system specifically from December 9 to 31, 2022, a period following significant relaxation of public health and social measures in Hong Kong, when transmission patterns began to reflect more endemic circulation rather than epidemic spread, providing insights into household transmission dynamics under conditions approaching normalcy. In this period, 53,167 households were included, and 100 households were randomly selected.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eInfluenza viral shedding trajectories\u003c/h2\u003e\u003cp\u003eTo estimate individual viral shedding trajectories, a log-linear mixed-effect regression model considering censored responses was fitted (Appendix Section 1; Appendix Fig.\u0026nbsp;1) \u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. For each subtype, we fitted two separate models for children and adults, to account for their potential difference in viral shedding (Appendix Table\u0026nbsp;1). The models assumed that viral load peaked at symptom onset, and we adopted the estimated viral load at symptom onset as the proxy of the level of viral shedding for infected participants.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eHousehold transmission model\u003c/h2\u003e\u003cp\u003eThe household transmission dynamics of influenza and SARS-CoV-2 virus was modelled by individual-based hazard models (Appendix Section 2) \u003csup\u003e30,58,67,68\u003c/sup\u003e. The model described the risk of infection for each susceptible household member at each time point, considering the hazard of being infected by other infected household members (person-to-person hazard), as well as the hazard of infection from outside the household (community). In the model, we accounted for pre-symptomatic transmission by specifying the pre-symptom and post-symptom phases in the individual infectiousness profile. With an assumed incubation period, we estimated the complete infectiousness profile across both phases.\u003c/p\u003e\u003cp\u003eWe assumed the incubation period of influenza A and B followed discretized log-normal distributions with medians of 1.4 and 0.6 days \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The incubation period of SARS-CoV-2 Omicron variant was assumed to follow a log-normal distribution with a median of 3.04 days (Appendix Section 2.2) \u003csup\u003e33\u003c/sup\u003e. For influenza, age, vaccination status were included as factors affecting susceptibility, and age, standardized viral load at symptom onset, fever symptoms, use of antiviral treatment, virus subtypes and household size were included as factors affecting infectiousness \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Given the interdependency among age, viral load and fever symptoms, we systematically evaluated influenza models incorporating various combinations of these three factors as covariates affecting infectiousness. For SARS-CoV-2, only age and vaccination were included as factors affecting susceptibility, while age and household size were included as factors affecting infectiousness (Appendix Section 2.3).\u003c/p\u003e\u003cp\u003eSince our data collected at day interval, we employed a discrete-time framework where each day represents a continuous interval from \u0026minus;\u0026thinsp;0.5 to +\u0026thinsp;0.5 days relative to that day's midpoint. Thus, day 0 (symptom onset) encompasses the interval [-0.5, +\u0026thinsp;0.5) days relative to true symptom onset, day \u0026minus;\u0026thinsp;1 represents [-1.5, -0.5) days before onset, and so forth. Under this framework, pre-symptomatic transmission captures events occurring before the symptom onset day interval begins (effectively\u0026thinsp;\u0026gt;\u0026thinsp;12 hours before symptoms).\u003c/p\u003e\u003cp\u003eThe infectiousness profile described the probability that infections occurred in each day in the infectious period, following a discretized Gamma or Poisson distribution (Appendix Section 2.4). It depended on the day of symptom onset, the length of the incubation period and the starting day of the infectious period. To maintain biological plausibility, we set the infectiousness to 0 for any time prior to actual infection and then re-normalized the probability mass for all post-infection time points. Hence, the proportion of pre-symptomatic transmission was defined as the proportion of transmission that occurred on and before 1 day prior to symptom onset, and could be estimated by integrating the incubation period distribution with the estimated infectiousness profile (Appendix Section 2.4).\u003c/p\u003e\u003cp\u003eTo derive the likelihood of each household, we considered all combinations of infection times of cases in the households, whose probabilities were calculated according to their symptom onset times and the distribution of the incubation period. The marginal likelihood for each combination was calculated. To reduce the computational burden, if the probability of a combination was lower than the fixed threshold \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\gamma\\:\\)\u003c/span\u003e\u003c/span\u003e, which was set to be 0.0001 in the main analysis, the corresponding likelihood was set to be 0 (Appendix Section 2.7). We conducted a sensitivity analysis to investigate the robustness of model inference for different probability thresholds. Model parameters were estimated by the Bayesian Markov Chain Monte Carlo (MCMC) metropolis-hasting algorithm (Appendix Section 2.8), and the goodness-of-fit was evaluated by the Deviance Information Criterion (DIC) value (Appendix Section 3) \u003csup\u003e24\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eModel adequacy and validation\u003c/h2\u003e\u003cp\u003eFor model validation, we applied the proposed inference approach to simulated datasets, with parameters set to be those estimated from the real data. This approach allowed us to assess whether our inference methods produced unbiased estimates of parameters (Appendix Section 4). Model adequacy was evaluated by utilizing the posterior distribution to simulate datasets and comparing the predicted and observed numbers of infections in households (Appendix Section 5).\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003ePotential conflicts of interest.\u003c/h2\u003e\u003cp\u003eBJC reports honoraria from AstraZeneca, Fosun Pharma, GSK, Haleon, Moderna, Pfizer, Roche and Sanofi Pasteur. All other authors report no other potential conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors thank Julie Au for assistance. This project was supported by the Theme-based Research Scheme (Project No. T11-712/19-N) of the Research Grants Council of the Hong Kong SAR Government, and the Research Grants Council of the Hong Kong Special Administrative Region, China (GRF 17104220). S.C. acknowledges support from the European Commission under the EU4Health program 2021\u0026ndash;2027, Grant Agreement-Project: 101102733\u0026mdash;DURABLE, the Investissement d\u0026rsquo;Avenir program, the Laboratoire d\u0026rsquo;Excellence Integrative Biology of Emerging Infectious Diseases program (grant ANR-10-LABX-62-IBEID), AXARF and the INCEPTION project (PIA/ANR-16-CONV-0005).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIuliano, A.D., et al. Estimates of global seasonal influenza-associated respiratory mortality: a modelling study. Lancet 391, 1285\u0026ndash;1300 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUyeki, T.M., Hui, D.S., Zambon, M., Wentworth, D.E. \u0026amp; Monto, A.S. Influenza. Lancet 400, 693\u0026ndash;706 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurray, C.J.L. Findings from the Global Burden of Disease Study 2021. 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N Engl J Med 361, 2619\u0026ndash;2627 (2009).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7701955/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7701955/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe effectiveness of case isolation and contact quarantine depends critically on the extent of pre-symptomatic transmission. However, the capacity of influenza viruses for transmission before symptom onset remains uncertain. We developed an individual-based household transmission model that explicitly accounts for transmission occurring before symptom onset by marginalizing unobserved infection times. We analyzed influenza household transmission studies of influenza A and B viruses in Hong Kong between 2008 and 2017 (493 and 255 households respectively), estimating pathogen-specific pre-symptomatic transmission proportions and identifying factors affecting individual susceptibility and infectiousness. We estimated that 9.6% (95% credible interval (CrI): 5.9%, 14.7%) of influenza A transmission occurred\u0026thinsp;\u0026gt;\u0026thinsp;0.5 days earlier than infectors\u0026rsquo; symptom onset, while influenza B showed no evidence of pre-symptomatic transmission. Younger age was associated with an increased susceptibility and infectiousness for both influenza A and B viruses. Influenza A infectiousness was positively associated with viral load and fever. Using an additional SARS-CoV-2 Omicron dataset in Hong Kong, our analysis confirmed substantial pre-symptomatic transmission (65.5%, 95% CrI: 52.7%, 78.9%), validating our methodology. Our modeling framework provides a robust approach for quantifying pre-symptomatic transmission across respiratory pathogens in households, offering a methodological template for resolving similar empirical uncertainties in infectious disease transmission. Our findings suggest symptom-based control measures may be feasible for influenza, though quarantine of asymptomatic contacts would provide limited additional benefit.\u003c/p\u003e","manuscriptTitle":"Estimating pre-symptomatic transmission potential of influenza A and B viruses in household transmission studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:43:15","doi":"10.21203/rs.3.rs-7701955/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-health","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nathealth","sideBox":"Learn more about [Nature Health](https://www.nature.com/naturehealth/)","snPcode":"44360","submissionUrl":"https://mts-nathealth.nature.com","title":"Nature Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6393caf1-e7fa-41c5-907b-717711fcf68a","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":58570531,"name":"Health sciences/Diseases/Infectious diseases/Influenza virus"},{"id":58570532,"name":"Health sciences/Medical research/Epidemiology"}],"tags":[],"updatedAt":"2026-01-29T08:33:36+00:00","versionOfRecord":{"articleIdentity":"rs-7701955","link":"https://doi.org/10.1038/s44360-025-00049-y","journal":{"identity":"nature-health","isVorOnly":false,"title":"Nature Health"},"publishedOn":"2026-01-28 05:00:00","publishedOnDateReadable":"January 28th, 2026"},"versionCreatedAt":"2025-12-01 08:43:15","video":"","vorDoi":"10.1038/s44360-025-00049-y","vorDoiUrl":"https://doi.org/10.1038/s44360-025-00049-y","workflowStages":[]},"version":"v1","identity":"rs-7701955","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7701955","identity":"rs-7701955","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}