Modelling presymptomatic infectiousness in COVID-19

This paper considers SEPIR, an extension of the well-known SEIR continuous simulation compartment model. Both models can be fitted to real data as they include parameters that can be estimated from the data. SEPIR deploys an additional presymptomatic infectious compartment, not modelled in SEIR but known to exist in COVID-19. This stage can also be fitted to data. We focus on how to fit SEPIR to a first wave of COVID. Both SEIR and SEPIR and the existing SEIR models assume a homogeneous mixing population with parameters fixed. Moreover, neither includes dynamically varying control strategies deployed against the virus. If either model is to represent more than just a single wave of the epidemic, then the parameters of the model would have to be time dependent. In view of this, we also show how reproduction numbers can be calculated to investigate the long-term overall outcome of an epidemic. © 2023 The Operational Research Society.

Transmission of COVID-19 through asymptomatic individuals

The rapid emergence of the coronavirus disease 2019 (COVID-19) has already taken on pandemic proportions, resulting thousands of deaths around the world. In the present manuscript, a mathematical model is proposed to investigate the current outbreak of the COVID-19. The model includes multiple transmission pathways and emphasises the role of asymptomatic and symptomatic infected population in the spread of this disease. To predict upcoming situation and a detail analysis of the spread of COVID-19 outbreak, basic reproduction number is calculated using publicly reported data from three different countries, where the outbreak is at its peak (USA), initial level (India) and controlled up to certain level (Japan). Analytical and numerical results of the model indicate that current on-going outbreak of COVID-19 would remain endemic if we do not proceed with extreme vigilance due to the serious risk it poses around the globe.

Follow-up investigation of asymptomatic COVID-19 cases at diagnosis in Busan, Korea.

OBJECTIVES: The objective of the study was to conduct a follow-up investigation of 10 asymptomatic patients at diagnosis among the 98 confirmed coronavirus disease 2019 (COVID-19) cases reported in Busan between February 21, 2020 and March 13, 2020 to determine whether asymptomatic infection and transmission during asymptomatic period are possible. METHODS: The study analyzed 10 asymptomatic, confirmed COVID-19 cases to determine whether asymptomatic infection is possible. We conducted in-depth interviews with patients and guardians; interviews with primary physicians; review of medical records and drug utilization review (DUR) reports; and base station-based location tracking. RESULTS: Among the 98, confirmed COVID-19 cases reported in Busan, the study analyzed 10 (10.2%) asymptomatic patients at diagnosis. The results confirmed that two (2.0%) patients reported to be asymptomatic during the initial epidemiological investigation, but turned symptomatic before diagnosis as per the in-depth interview results. Four cases (4.0%) of early detection led to confirmed diagnosis during the incubation period and presentation of symptoms after diagnosis. In addition, the remaining four patients (4.0%), having no subjective symptoms nor specific findings on chest radiography and computed tomography, remained asymptomatic until the isolation order was lifted. With regard to whether transmission during the asymptomatic period is possible, it was found that one out of 23 household contacts of the confirmed patients was identified as an additional confirmed case after coming in close contact with an index patient during the presymptomatic period. CONCLUSIONS: Among the 98 confirmed cases, asymptomatic infection was confirmed in four cases (4.0%). In addition, there was one additional confirmed case in which the patient was a family member who came in close contact with an index patient during the incubation period, thereby confirming that transmission during the asymptomatic period is possible. The possibility of transmission during the asymptomatic period has been confirmed; therefore, it is necessary to review the measures for expanding contact tracing that is currently being applied starting one day prior to the onset of symptoms.

A Discrete Model for the Evolution of Infection Prior to Symptom Onset

We consider a between-host model for a single epidemic outbreak of an infectious disease. According to the progression of the disease, hosts are classified in regard to the pathogen load. Specifically, we are assuming four phases: non-infectious asymptomatic phase, infectious asymptomatic phase (key-feature of the model where individuals show up mild or no symptoms), infectious symptomatic phase and finally an immune phase. The system takes the form of a non-linear Markov chain in discrete time where linear transitions are based on geometric (main model) or negative-binomial (enhanced model) probability distributions. The whole system is reduced to a single non-linear renewal equation. Moreover, after linearization, at least two meaningful definitions of the basic reproduction number arise: firstly as the expected secondary asymptomatic cases produced by an asymptomatic primary case, and secondly as the expected number of symptomatic individuals that a symptomatic individual will produce. We study the evolution of infection transmission before and after symptom onset. Provided that individuals can develop symptoms and die from the disease, we take disease-induced mortality as a measure of virulence and it is assumed to be positively correlated with a weighted average transmission rate. According to our findings, transmission rate of the infection is always higher in the symptomatic phase yet under a suitable condition, most of the infections take place prior to symptom onset.

A multi-strain model with asymptomatic transmission: Application to COVID-19 in the US.

COVID-19, induced by the SARS-CoV-2 infection, has caused an unprecedented pandemic in the world. New variants of the virus have emerged and dominated the virus population. In this paper, we develop a multi-strain model with asymptomatic transmission to study how the asymptomatic or pre-symptomatic infection influences the transmission between different strains and control strategies that aim to mitigate the pandemic. Both analytical and numerical results reveal that the competitive exclusion principle still holds for the model with the asymptomatic transmission. By fitting the model to the COVID-19 case and viral variant data in the US, we show that the omicron variants are more transmissible but less fatal than the previously circulating variants. The basic reproduction number for the omicron variants is estimated to be 11.15, larger than that for the previous variants. Using mask mandate as an example of non-pharmaceutical interventions, we show that implementing it before the prevalence peak can significantly lower and postpone the peak. The time of lifting the mask mandate can affect the emergence and frequency of subsequent waves. Lifting before the peak will result in an earlier and much higher subsequent wave. Caution should also be taken to lift the restriction when a large portion of the population remains susceptible. The methods and results obtained her e may be applied to the study of the dynamics of other infectious diseases with asymptomatic transmission using other control measures.

Incidence of asymptomatic SARS-CoV-2 infection in children undergoing elective otolaryngologic surgery throughout the COVID-19 pandemic.

Objective: Children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are less clinically affected than adults, with most cases presenting as asymptomatic or mildly symptomatic. However, true rates of asymptomatic SARS-CoV-2 infection in children remain unclear. We sought to examine rates of SARS-CoV-2 in asymptomatic children and the role of children in transmission. Methods: We performed a retrospective review of patients between 6 months and 17 years of age who underwent elective or semi-elective otolaryngologic surgery with physicians affiliated with Weill Cornell Medicine between May 15, 2020 and March 31, 2022. Patients were included if they received molecular assay testing for SARS-CoV-2 without SARS-CoV-2 symptoms within 5 days of scheduled surgery. SARS-CoV-2 infection status, exposure, clinical symptoms, demographic data, and insurance status were recorded. Results: 1047 patients met inclusion criteria. Thirteen positive cases (1.24%) were identified in the study population. Six cases occurred between December 2021 and February 2022 following the classification of the omicron variant as a variant of concern in November 2021. Five of the 13 cases occurred in children under 2 years of age. Seven patients were male, and five were female. Residences spanned all five boroughs of New York City and the surrounding metropolitan area. Conclusion: Throughout the pandemic, children have had a low rate of asymptomatic disease and likely pose a low risk of transmission of SARS-CoV-2 to the general population. Our results suggest that testing of asymptomatic children is a low-yield practice that is unlikely to influence rates of SARS-CoV-2 in the general population. Level of Evidence: 3.

Estimating the extent of asymptomatic covid-19 and its potential for community transmission: Systematic review and meta-analysis

Background: Knowing the prevalence of true asymptomatic coronavirus disease 2019 (COVID-19) cases is critical for designing mitigation measures against the pandemic. We aimed to synthesize all available research on asymptomatic cases and transmission rates. Methods: We searched PubMed, Embase, Cochrane COVID-19 trials, and Europe PMC for primary studies on asymptomatic prevalence in which (1) the sample frame includes at-risk populations, and;(2) follow-up was sufficient to identify pre-symptomatic cases. Meta-analysis used fixed-effects and random-effects models. We assessed risk of bias by combination of questions adapted from risk of bias tools for prevalence and diagnostic accuracy studies. Results: We screened 2,454 articles and included 13 low risk-of-bias studies from seven countries that tested 21,708 at-risk people, of which 663 were positive and 111 asymptomatic. Diagnosis in all studies was confirmed using a real-time reverse transcriptase–polymerase chain reaction test. The asymptomatic proportion ranged from 4% to 41%. Meta-analysis (fixed effects) found that the proportion of asymptomatic cases was 17% (95% CI 14% to 20%) overall and higher in aged care (20%;95% CI 14% to 27%) than in non-aged care (16%;95% CI 13% to 20%). The relative risk (RR) of asymptomatic transmission was 42% lower than that for symptomatic transmission (combined RR 0.58;95% CI 0.34 to 0.99, p = 0.047). Conclusions: Our one-in-six estimate of the prevalence of asymptomatic COVID-19 cases and asymptomatic transmission rates is lower than those of many highly publicized studies but still sufficient to warrant policy attention. Further robust epidemiological evidence is urgently needed, including in subpopulations such as children, to better understand how asymptomatic cases contribute to the pandemic.

How time-scale differences in asymptomatic and symptomatic transmission shape SARS-CoV-2 outbreak dynamics.

Asymptomatic and symptomatic SARS-CoV-2 infections can have different characteristic time scales of transmission. These time-scale differences can shape outbreak dynamics as well as bias population-level estimates of epidemic strength, speed, and controllability. For example, prior work focusing on the initial exponential growth phase of an outbreak found that larger time scales for asymptomatic vs. symptomatic transmission can lead to under-estimates of the basic reproduction number as inferred from epidemic case data. Building upon this work, we use a series of nonlinear epidemic models to explore how differences in asymptomatic and symptomatic transmission time scales can lead to changes in the realized proportion of asymptomatic transmission throughout an epidemic. First, we find that when asymptomatic transmission time scales are longer than symptomatic transmission time scales, then the effective proportion of asymptomatic transmission increases as total incidence decreases. Moreover, these time-scale-driven impacts on epidemic dynamics are enhanced when infection status is correlated between infector and infectee pairs (e.g., due to dose-dependent impacts on symptoms). Next we apply these findings to understand the impact of time-scale differences on populations with age-dependent assortative mixing and in which the probability of having a symptomatic infection increases with age. We show that if asymptomatic generation intervals are longer than corresponding symptomatic generation intervals, then correlations between age and symptoms lead to a decrease in the age of infection during periods of epidemic decline (whether due to susceptible depletion or intervention). Altogether, these results demonstrate the need to explore the role of time-scale differences in transmission dynamics alongside behavioral changes to explain outbreak features both at early stages (e.g., in estimating the basic reproduction number) and throughout an epidemic (e.g., in connecting shifts in the age of infection to periods of changing incidence).

Age-related model for estimating the symptomatic and asymptomatic transmissibility of COVID-19 patients.

Estimation of age-dependent transmissibility of COVID-19 patients is critical for effective policymaking. Although the transmissibility of symptomatic cases has been extensively studied, asymptomatic infection is understudied due to limited data. Using a dataset with reliably distinguished symptomatic and asymptomatic statuses of COVID-19 cases, we propose an ordinary differential equation model that considers age-dependent transmissibility in transmission dynamics. Under a Bayesian framework, multi-source information is synthesized in our model for identifying transmissibility. A shrinkage prior among age groups is also adopted to improve the estimation behavior of transmissibility from age-structured data. The added values of accounting for age-dependent transmissibility are further evaluated through simulation studies. In real-data analysis, we compare our approach with two basic models using the deviance information criterion (DIC) and its extension. We find that the proposed model is more flexible for our epidemic data. Our results also suggest that the transmissibility of asymptomatic infections is significantly lower (on average, 76.45% with a credible interval (27.38%, 88.65%)) than that of symptomatic cases. In both symptomatic and asymptomatic patients, the transmissibility mainly increases with age. Patients older than 30 years are more likely to develop symptoms with higher transmissibility. We also find that the transmission burden of asymptomatic cases is lower than that of symptomatic patients.

Symptomatic, Presymptomatic, and Asymptomatic Transmission of SARS-CoV-2 in a University Student Population, August-November 2020.

OBJECTIVES: The impact and risk of SARS-CoV-2 transmission from asymptomatic and presymptomatic hosts remains an open question. This study measured the secondary attack rates (SARs) and relative risk (RR) of SARS-CoV-2 transmission from asymptomatic and presymptomatic index cases as compared with symptomatic index cases. METHODS: We used COVID-19 test results, daily health check reports, and contact tracing data to measure SARs and corresponding RRs among close contacts of index cases in a cohort of 12 960 young adults at the University of Notre Dame in Indiana for 103 days, from August 10 to November 20, 2020. Further analysis included Fisher exact tests to determine the association between symptoms and COVID-19 infection and z tests to determine statistical differences between SARs. RESULTS: Asymptomatic rates of transmission of SARS-CoV-2 were higher (SAR = 0.19; 95% CI, 0.14-0.24) than was estimated in prior studies, producing an RR of 0.75 (95% CI, 0.54-1.07) when compared with symptomatic transmission. In addition, the transmission rate associated with presymptomatic cases (SAR = 0.25; 95% CI, 0.21-0.30) was approximately the same as that for symptomatic cases (SAR = 0.25; 95% CI, 0.19-0.31). Furthermore, different symptoms were associated with different transmission rates. CONCLUSIONS: Asymptomatic and presymptomatic hosts of SARS-CoV-2 are a risk for community spread of COVID-19, especially with new variants emerging. Moreover, typical symptom checks may easily miss people who are asymptomatic or presymptomatic but still infectious. Our study results may be used as a guide to analyze the spread of SARS-CoV-2 variants and help inform appropriate public health measures as they relate to asymptomatic and presymptomatic cases.

MOLECULAR EPIDEMIOLOGY of the COVID-19 PANDEMIC in CHICAGO

Background: The greater Chicagoland area has recorded over 10,000 COVID-related deaths and nearly 600,000 cases since the start of the COVID-19 pandemic in March of 2020. SARS-CoV-2, the causative agent of COVID-19, has continually changed over that time, with some variants evolving to become more transmissible or more resistant to neutralizing antibody responses. Methods: To better understand how viral genetic variation has contributed to differences in COVID-19 pathogenesis and patient outcome, we established a biobank of residual diagnostic samples from adult patients who tested positive for COVID-19 in a PCR-based test at Northwestern Memorial Hospital. Thus far, we have collected samples from 6448 out-patients and 632 in-patients. Of these, we have performed whole genome sequencing and viral load calculations on 1373 samples. Clinical and demographic information, including composite measures of disease severity, were extracted from available electronic health records. These data were assessed for longitudinal patterns and for specific association with viral lineage. Results: We found that the early epidemic in March of 2020 was defined by three distinct lineages reflecting the outbreaks in China (19B/A), Washington state (19B/A.1), and New York state (19A/B.1). By November of 2020, we saw a large increase in the number of confirmed cases, dominated by the 20G clade. This lineage remained predominant until March of 2021, when the Alpha and Gamma variants of concern became more established. These were recently supplanted by the Delta variant, which now accounts for over 90% of Chicago cases. At the height of the pandemic in November of 2020, case counts peaked at over 5000 cases per day, but hospitalizations, ICU admissions, and deaths over this period remained flat. Statistical testing revealed that the predominant clade at that time, 20G, was associated with better outcomes and less severe disease as measured by clinical measures of patient deterioration, even when controlling for patient demographics. These results suggest that a viral variant associated with less severe disease was predominant in late 2020 before the emergence of the more transmissible variants of concern. Conclusion: Current work is being done to determine if the less severe outcomes associated with this clade also contributed to more asymptomatic transmission, potentially contributing to the high case counts recorded over this period. These data emphasize the need for continued genomic surveillance of SARS-CoV-2 to character.

UNDERSTANDING the EPIDEMIOLOGY of COVID-19: A GLOBAL PERSPECTIVE

This keynote talk will provide an overview of the global epidemiology of COVID-19, mainly focusing on disparities in transmission, severity, and outcomes. It will also summarise the challenging and often misinterpreted but consequential epidemiological aspects such as asymptomatic transmission, changes in the severity of disease and transmissibility of variants, and the role of children in transmission dynamics, focusing on better ways to evaluate these areas going forward. The COVID-19 pandemic, with its myriad uncertainties, well-publicised retractions, shifting recommendations and over 300 thousand publications, has underscored the importance of carefully synthesising and translating the vast amount of data into evidence-based and actionable insights.

Parameter identifiability and optimal control of an SARS-CoV-2 model early in the pandemic.

We fit an SARS-CoV-2 model to US data of COVID-19 cases and deaths. We conclude that the model is not structurally identifiable. We make the model identifiable by prefixing some of the parameters from external information. Practical identifiability of the model through Monte Carlo simulations reveals that two of the parameters may not be practically identifiable. With thus identified parameters, we set up an optimal control problem with social distancing and isolation as control variables. We investigate two scenarios: the controls are applied for the entire duration and the controls are applied only for the period of time. Our results show that if the controls are applied early in the epidemic, the reduction in the infected classes is at least an order of magnitude higher compared to when controls are applied with 2-week delay. Further, removing the controls before the pandemic ends leads to rebound of the infected classes.

Mathematical modelling of COVID-19: A case study of Italy.

This manuscript describes a mathematical epidemiological model of COVID-19 to investigate the dynamics of this pandemic disease and we have fitted this model to the current COVID-19 cases in Italy. We have obtained the basic reproduction number which plays a crucial role on the stability of disease free equilibrium point. Backward bifurcation with respect to the cure rate of treatment occurs conditionally. It is clear from the sensitivity analysis that the developments of self immunities with proper maintaining of social distancing of the exposed and asymptomatic individuals play key role for controlling the disease. We have validated the model by considering the COVID-19 cases of Italy and the future situations of epidemicity in Italy have been predicted from the model. We have estimated the basic reproduction number for the COVID-19 outbreak in Italy and effective reproduction number has also been studied. Finally, an optimal control model has been formulated and solved to realize the positive impacts of adapting lock down by many countries for maintaining social distancing.

Inferring the reproduction number using the renewal equation in heterogeneous epidemics.

Real-time estimation of the reproduction number has become the focus of modelling groups around the world as the SARS-CoV-2 pandemic unfolds. One of the most widely adopted means of inference of the reproduction number is via the renewal equation, which uses the incidence of infection and the generation time distribution. In this paper, we derive a multi-type equivalent to the renewal equation to estimate a reproduction number which accounts for heterogeneity in transmissibility including through asymptomatic transmission, symptomatic isolation and vaccination. We demonstrate how use of the renewal equation that misses these heterogeneities can result in biased estimates of the reproduction number. While the bias is small with symptomatic isolation, it can be much larger with asymptomatic transmission or transmission from vaccinated individuals if these groups exhibit substantially different generation time distributions to unvaccinated symptomatic transmitters, whose generation time distribution is often well defined. The bias in estimate becomes larger with greater population size or transmissibility of the poorly characterized group. We apply our methodology to Ebola in West Africa in 2014 and the SARS-CoV-2 in the UK in 2020-2021.

Modeling the SARS-CoV-2 parallel transmission dynamics: Asymptomatic and symptomatic pathways.

Asymptomatic transmission of the coronavirus disease and the infected individual prediction has become very important in the COVID-19 outbreak study. The asymptomatic and symptomatic transmission studies are still ongoing to assess their impacts on disease monitoring and burden. However, there has been limited research on how asymptomatic and symptomatic transmissions together can affect the coronavirus disease outbreak. A mathematical model is therefore needed to be developed in order to assess the effect of these transmissions on the coronavirus disease dynamics. This paper develops a mathematical model concerning asymptomatic and symptomatic disease transmission processes in the COVID-19 outbreak. The model sensitivity has been analysed in terms of the variance of each parameter, and the local stability at two equilibrium points have been discussed in terms of the basic reproduction number (R0). It is found that the disease-free equilibrium gets stable for R0 < 1 whereas the endemic equilibrium becomes stable for R0 > 1 and unstable otherwise. The proportion of the effect of asymptomatic and symptomatic transmission rates on R0 is calculated to be approximately between 1 and 3. The results demonstrate that asymptomatic transmission has a significant impact compared to symptomatic transmission in the disease outbreak. Outcomes of this study will contribute to setting an effective control strategy for the COVID-19 outbreak.

Asymptomatic transmission in the COVID-19 pandemic: A systematic review and meta-analysis

Objective To determine the differences in the epidemiological characteristics of asymptomatic patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease 2019 (COVID-19) patients. Methods The relevant data of asymptomatic SARS-CoV-2 transmission literature in PubMed, Web of Science, CNKI, VIP medicine and Wanfang medical database were extracted as of August 1, 2020. Epidemiological information was screened and extracted according to pre-established inclusion and exclusion criteria. Age, gender, virus shedding duration and virual load of asymptomatic carriers were summarized and analyzed. Results A total of 38 articles met the criteria, 17 of which were asymptomatic case reports related to the virus shedding duration. Compared with symptomatic infected persons, asymptomatic individuals were younger [Weighted Mean Difference (WMD), WMD=-5.27, 95% CI: -9.78--0.76, P < 0.001] and the viral load was lower (WMD=2.36, 95% CI: 0.65-4.07, P=0.007). The virus shedding duration of asymptomatic individuals (median=11 days) was shorter than symptomatic patients (median=16 days). Conclusion Asymptomatic individuals with SARS-CoV-2 were younger, had a lower viral load and a shorter virus shedding duration than COVID-19 patients. © 2021, Publication Centre of Anhui Medical University. All rights reserved.

Asymptomatic infection and transmission of COVID-19 among clusters: systematic review and meta-analysis.

OBJECTIVES: Countries throughout the world are experiencing COVID-19 viral load in their populations, leading to potential transmission and infectivity of asymptomatic COVID-19 cases. The current systematic review and meta-analysis aims to investigate the role of asymptomatic infection and transmission reported in family clusters, adults, children and health care workers, globally. STUDY DESIGN: Systematic review and meta-analysis. METHODS: An online literature search of PubMed, Google Scholar, medRixv and BioRixv was performed using standard Boolean operators and included studies published up to 17 August 2021. For the systematic review, case reports, short communications and retrospective studies were included to ensure sufficient asymptomatic COVID-19 transmission data were reported. For the quantitative synthesis (meta-analysis), participant data from a collection of cohort studies focusing on groups of familial clusters, adults, children and health care workers were included. Inconsistency among studies was assessed using I2 statistics. The data synthesis was computed using the STATA 16.0 software. RESULTS: This study showed asymptomatic transmission among familial clusters, adults, children and health care workers of 15.72%, 29.48%, 24.09% and 0%, respectively. Overall, asymptomatic transmission was 24.51% (95% confidence interval [CI]: 14.38, 36.02) among all studied population groups, with a heterogeneity of I2 = 95.30% (P < 0.001). No heterogeneity was seen in the population subgroups of children and health care workers. The risk of bias in all included studies was assessed using the Newcastle Ottawa Scale. CONCLUSIONS: For minimising the spread of COVID-19 within the community, this study found that following the screening of asymptomatic cases and their close contacts for chest CT scan (for symptomatic patients), even after negative nucleic acid testing, it is essential to perform a rigorous epidemiological history, early isolation, social distancing and an increased quarantine period (a minimum of 14-28 days). This systematic review and meta-analysis supports the notion of asymptomatic COVID-19 infection and person-to-person transmission and suggests that this is dependent on the varying viral incubation period among individuals. Children, especially those of school age (i.e. <18 years), need to be monitored carefully and follow mitigation strategies (e.g. social distancing, hand hygiene, wearing face masks) to prevent asymptomatic community transmission of COVID-19.

Assessing the Impact of (Self)-Quarantine through a Basic Model of Infectious Disease Dynamics.

We introduce a system of differential equations to assess the impact of (self-)quarantine of symptomatic infectious individuals on disease dynamics. To this end we depart from using the classic bilinear infection process, but remain within the framework of the mass-action assumption. From the mathematical point of view, the model we propose is interesting due to the lack of continuous differentiability at disease-free steady states, which implies that the basic reproductive number cannot be computed following established mathematical approaches for certain parameter values. However, we parametrise our mathematical model using published values from the COVID-19 literature, and analyse the model simulations. We also contrast model simulations against publicly available COVID-19 test data, focusing on the first wave of the pandemic during March-July 2020 in the UK. Our simulations indicate that actual peak case numbers might have been as much as 200 times higher than the reported positive test cases during the first wave in the UK. We find that very strong adherence to self-quarantine rules yields (only) a reduction of 22% of peak numbers and delays the onset of the peak by approximately 30-35 days. However, during the early phase of the outbreak, the impact of (self)-quarantine is much more significant. We also take into account the effect of a national lockdown in a simplistic way by reducing the effective susceptible population size. We find that, in case of a 90% reduction of the effective susceptible population size, strong adherence to self-quarantine still only yields a 25% reduction of peak infectious numbers when compared to low adherence. This is due to the significant number of asymptomatic infectious individuals in the population.

Higher Viral Load Drives Infrequent Severe Acute Respiratory Syndrome Coronavirus 2 Transmission Between Asymptomatic Residence Hall Roommates.

BACKGROUND: The coronavirus disease 2019 pandemic spread to >200 countries in <6 months. To understand coronavirus spread, determining transmission rate and defining factors that increase transmission risk are essential. Most cases are asymptomatic, but people with asymptomatic infection have viral loads indistinguishable from those in symptomatic people, and they do transmit severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, asymptomatic cases are often undetected. METHODS: Given high residence hall student density, the University of Colorado Boulder established a mandatory weekly screening test program. We analyzed longitudinal data from 6408 students and identified 116 likely transmission events in which a second roommate tested positive within 14 days of the index roommate. RESULTS: Although the infection rate was lower in single-occupancy rooms (10%) than in multiple-occupancy rooms (19%), interroommate transmission occurred only about 20% of the time. Cases were usually asymptomatic at the time of detection. Notably, individuals who likely transmitted had an average viral load approximately 6.5-fold higher than individuals who did not (mean quantification cycle [Cq], 26.2 vs 28.9). Although students with diagnosed SARS-CoV-2 infection moved to isolation rooms, there was no difference in time to isolation between cases with or without interroommate transmission. CONCLUSIONS: This analysis argues that interroommate transmission occurs infrequently in residence halls and provides strong correlative evidence that viral load is proportional to transmission probability.