Persistent SARS-CoV-2 RNA Shedding Without Evidence of Infectiousness: A Cohort Study of Individuals With COVID-19.

BACKGROUND: To better understand severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shedding and infectivity, we estimated SARS-CoV-2 RNA shedding duration, described participant characteristics associated with the first negative rRT-PCR test (resolution), and determined if replication-competent viruses was recoverable ≥10 days after symptom onset. METHODS: We collected serial nasopharyngeal specimens from 109 individuals with rRT-PCR-confirmed COVID-19 in Utah and Wisconsin. We calculated viral RNA shedding resolution probability using the Kaplan-Meier estimator and evaluated characteristics associated with shedding resolution using Cox proportional hazards regression. We attempted viral culture for 35 rRT-PCR-positive nasopharyngeal specimens collected ≥10 days after symptom onset. RESULTS: The likelihood of viral RNA shedding resolution at 10 days after symptom onset was approximately 3%. Time to shedding resolution was shorter among participants aged <18 years (adjusted hazards ratio [aHR], 3.01; 95% confidence interval [CI], 1.6-5.6) and longer among those aged ≥50 years (aHR, 0.50; 95% CI, .3-.9) compared to participants aged 18-49 years. No replication-competent viruses were recovered. CONCLUSIONS: Although most patients were positive for SARS-CoV-2 for ≥10 days after symptom onset, our findings suggest that individuals with mild to moderate COVID-19 are unlikely to be infectious ≥10 days after symptom onset.

Reconstructing the household transmission of influenza in the suburbs of Tokyo based on clinical cases.

BACKGROUND: Influenza is a public health issue that needs to be addressed strategically. The assessment of detailed infectious profiles is an important part of this effort. Household transmission data play a key role in estimating such profiles. We used diagnostic and questionnaire-based data on influenza patients at a Japanese clinic to estimate the detailed infectious period (as well as incubation period, symptomatic and infectious periods, and extended infectious period after recovery) and the secondary attack ratio (SAR) of influenza for households of various sizes based on a modified Cauchemez-type model. RESULTS: The data were from enrolled patients with confirmed influenza who were treated at the Hirotsu Clinic (Kawasaki, Japan) with a neuraminidase inhibitor (NAI) during six northern hemisphere influenza seasons between 2010 and 2016. A total of 2342 outpatients, representing 1807 households, were included. For influenza type A, the average incubation period was 1.43 days (95% probability interval, 0.03-5.32 days). The estimated average symptomatic and infective period was 1.76 days (0.33-4.62 days); the extended infective period after recovery was 0.25 days. The estimated SAR rose from 20 to 32% as household size increased from 3 to 5. For influenza type B, the average incubation period, average symptomatic and infective period, and extended infective period were estimated as 1.66 days (0.21-4.61), 2.62 days (0.54-5.75) and 1.00 days, respectively. The SAR increased from 12 to 21% as household size increased from 3 to 5. CONCLUSION: All estimated periods of influenza type B were longer than the corresponding periods for type A. However, the SAR for type B was less than that for type A. These results may reflect Japanese demographics and treatment policy. Understanding the infectious profiles of influenza is necessary for assessing public health measures.

Correlation Between the Interval of Influenza Virus Infectivity and Results of Diagnostic Assays in a Ferret Model.

BACKGROUND: The relationship between influenza virus infectivity and virus shedding, based on different diagnostic methods, has not been defined. METHODS: Three donor ferrets infected with 2009 pandemic influenza A(H1N1) underwent daily quantitative culture, antigen-detection testing, and real-time reverse transcription-polymerase chain reaction (RT-PCR). Eight contacts were sequentially cohoused with each of the donors for 24 hours during days 3-10 after inoculation. RESULTS: Transmission was observed until day 5 after inoculation, corresponding to high culture titers and positive results of antigen-detection tests. Real-time RT-PCR showed no relation to the cessation of transmission. CONCLUSIONS: Antigen-detection testing and virus culture but not real-time RT-PCR identified the end of the infectious period.

Detection of Measles Virus RNA in Air and Surface Specimens in a Hospital Setting.

Measles virus (MeV) is known to be highly contagious, with an infectious period lasting from 4 days before to 4 days after rash onset. An unvaccinated, young, female patient with measles confirmed by direct epidemiologic link was hospitalized on day 5 after rash onset. Environmental samples were collected over the 4-day period of hospitalization in a single room. MeV RNA was detectable in air specimens, on surface specimens, and on respirators on days 5-8 after rash onset. This is the first report of environmental surveillance for MeV, and the results suggest that MeV-infected fomites may be present in healthcare settings.

A pulmonary tuberculosis outbreak in a long-term care facility.

In long-term care facilities (LTCFs), the elderly are apt to be infected because those with latent tuberculosis infections (LTBIs) are at an increased risk for reactivation and post-primary TB disease. We report an outbreak of TB in staff and residents in a LTCF. An outbreak investigation was conducted after two TB cases were reported from the LTCF. A tuberculin skin test (TST), bacteriological examination and chest radiograph were administered to all facility staff and residents. An outbreak is defined as at least two epidemiologically linked cases that have identical Mycobacterium tuberculosis genotype isolates. This outbreak infected eight residents and one staff member, who were confirmed to have TB in a LTCF between September 2011 and October 2012. Based on the Becker method, the latent and infectious periods were estimated at 223·6 and 55·9 days. Two initial TST-negative resident contacts were diagnosed as TB cases through comprehensive TB screening. Observing elderly people who have a negative TST after TB screening appears to be necessary, given the long latent period for controlling a TB outbreak in a LTCF. It is important to consider providing LTBI treatment for elderly contacts.

Dynamics of Multi-stage Infections on Networks.

This paper investigates the dynamics of infectious diseases with a non-exponentially distributed infectious period. This is achieved by considering a multi-stage infection model on networks. Using pairwise approximation with a standard closure, a number of important characteristics of disease dynamics are derived analytically, including the final size of an epidemic and a threshold for epidemic outbreaks, and it is shown how these quantities depend on disease characteristics, as well as the number of disease stages. Stochastic simulations of dynamics on networks are performed and compared to output of pairwise models for several realistic examples of infectious diseases to illustrate the role played by the number of stages in the disease dynamics. These results show that a higher number of disease stages results in faster epidemic outbreaks with a higher peak prevalence and a larger final size of the epidemic. The agreement between the pairwise and simulation models is excellent in the cases we consider.

Transmission rates of veterinary and clinically important antibiotic resistant Escherichia coli: A meta- ANALYSIS.

The transmission rate per hour between hosts is a key parameter for simulating transmission dynamics of antibiotic-resistant bacteria, and might differ for antibiotic resistance genes, animal species, and antibiotic usage. We conducted a Bayesian meta-analysis of resistant Escherichia coli (E. coli) transmission in broilers and piglets to obtain insight in factors determining the transmission rate, infectious period, and reproduction ratio. We included blaCTX-M-1, blaCTX-M-2, blaOXA-162, catA1, mcr-1, and fluoroquinolone resistant E. coli. The Maximum a Posteriori (MAP) transmission rate in broilers without antibiotic treatment ranged from 0.4∙10-3 to 2.5∙10-3 depending on type of broiler (SPF vs conventional) and inoculation strains. For piglets, the MAP in groups without antibiotic treatment were between 0.7∙10-3 and 0.8∙10-3, increasing to 0.9∙10-3 in the group with antibiotic treatment. In groups without antibiotic treatment, the transmission rate of resistant E. coli in broilers was almost twice the transmission rate in piglets. Amoxicillin increased the transmission rate of E. coli carrying blaCTX-M-2 by three-fold. The MAP infectious period of resistant E. coli in piglets with and without antibiotics is between 971 and 1065 hours (40 - 43 days). The MAP infectious period of resistant E. coli in broiler without antibiotics is between 475 and 2306 hours (20 - 96 days). The MAP infectious period of resistant E. coli in broiler with antibiotics is between 2702 and 3462 hours (113 - 144 days) which means a lifelong colonization. The MAP basic reproduction ratio in piglets of infection with resistant E. coli when using antibiotics is 27.70, which is higher than MAP in piglets without antibiotics between 15.65 and 18.19. The MAP basic reproduction ratio in broilers ranges between 3.46 and 92.38. We consider three possible explanations for our finding that in the absence of antibiotics the transmission rate is higher among broilers than among piglets: i) due to the gut microbiome of animals, ii) fitness costs of bacteria, and iii) differences in experimental set-up between the studies. Regarding infectious period and reproduction ratio, the effect of the resistance gene, antibiotic treatment, and animal species are inconclusive due to limited data.

The effects of disease control measures on the reproduction number of COVID-19 in British Columbia, Canada.

We propose a new method to estimate the change of the effective reproduction number with time, due to either disease control measures or seasonally varying transmission rate. We validate our method using a simulated epidemic curve and show that our method can effectively estimate both sudden changes and gradual changes in the reproduction number. We apply our method to the COVID-19 case counts in British Columbia, Canada in 2020, and we show that strengthening control measures had a significant effect on the reproduction number, while relaxations in May (business reopening) and September (school reopening) had significantly increased the reproduction number from around 1 to around 1.7 at its peak value. Our method can be applied to other infectious diseases, such as pandemics and seasonal influenza.

A generalized ODE susceptible-infectious-susceptible compartmental model with potentially periodic behavior.

Differential equation compartmental models are crucial tools for forecasting and analyzing disease trajectories. Among these models, those dealing with only susceptible and infectious individuals are particularly useful as they offer closed-form expressions for solutions, namely the logistic equation. However, the logistic equation has limited ability to describe disease trajectories since its solutions must converge monotonically to either the disease-free or endemic equilibrium, depending on the parameters. Unfortunately, many diseases exhibit periodic cycles, and thus, do not converge to equilibria. To address this limitation, we developed a generalized susceptible-infectious-susceptible compartmental model capable of accurately incorporating the duration of infection distribution and describing both periodic and non-periodic disease trajectories. We characterized how our model's parameters influence its behavior and applied the model to predict gonorrhea incidence in the US, using Akaike Information Criteria to inform on its merit relative to the traditional SIS model. The significance of our work lies in providing a novel susceptible-infected-susceptible model whose solutions can have closed-form expressions that may be periodic or non-periodic depending on the parameterization. Our work thus provides disease modelers with a straightforward way to investigate the potential periodic behavior of many diseases and thereby may aid ongoing efforts to prevent recurrent outbreaks.

Anchoring the mean generation time in the SEIR to mitigate biases in ℜ0 estimates due to uncertainty in the distribution of the epidemiological delays.

The basic reproduction number, ℜ0, is of paramount importance in the study of infectious disease dynamics. Primarily, ℜ0 serves as an indicator of the transmission potential of an emerging infectious disease and the effort required to control the invading pathogen. However, its estimates from compartmental models are strongly conditioned by assumptions in the model structure, such as the distributions of the latent and infectious periods (epidemiological delays). To further complicate matters, models with dissimilar delay structures produce equivalent incidence dynamics. Following a simulation study, we reveal that the nature of such equivalency stems from a linear relationship between ℜ0 and the mean generation time, along with adjustments to other parameters in the model. Leveraging this knowledge, we propose and successfully test an alternative parametrization of the SEIR model that produces accurate ℜ0 estimates regardless of the distribution of the epidemiological delays, at the expense of biases in other quantities deemed of lesser importance. We further explore this approach's robustness by testing various transmissibility levels, generation times and data fidelity (overdispersion). Finally, we apply the proposed approach to data from the 1918 influenza pandemic. We anticipate that this work will mitigate biases in estimating ℜ0.

Inter-Country COVID-19 Contagiousness Variation in Eight African Countries.

The estimates of contiguousness parameters of an epidemic have been used for health-related policy and control measures such as non-pharmaceutical control interventions (NPIs). The estimates have varied by demographics, epidemic phase, and geographical region. Our aim was to estimate four contagiousness parameters: basic reproduction number (R0), contact rate, removal rate, and infectious period of coronavirus disease 2019 (COVID-19) among eight African countries, namely Angola, Botswana, Egypt, Ethiopia, Malawi, Nigeria, South Africa, and Tunisia using Susceptible, Infectious, or Recovered (SIR) epidemic models for the period 1 January 2020 to 31 December 2021. For reference, we also estimated these parameters for three of COVID-19's most severely affected countries: Brazil, India, and the USA. The basic reproduction number, contact and remove rates, and infectious period ranged from 1.11 to 1.59, 0.53 to 1.0, 0.39 to 0.81; and 1.23 to 2.59 for the eight African countries. For the USA, Brazil, and India these were 1.94, 0.66, 0.34, and 2.94; 1.62, 0.62, 0.38, and 2.62, and 1.55, 0.61, 0.39, and 2.55, respectively. The average COVID-19 related case fatality rate for 8 African countries in this study was estimated to be 2.86%. Contact and removal rates among an affected African population were positively and significantly associated with COVID-19 related deaths (p-value < 0.003). The larger than one estimates of the basic reproductive number in the studies of African countries indicate that COVID-19 was still being transmitted exponentially by the 31 December 2021, though at different rates. The spread was even higher for the three countries with substantial COVID-19 outbreaks. The lower removal rates in the USA, Brazil, and India could be indicative of lower death rates (a proxy for good health systems). Our findings of variation in the estimate of COVID-19 contagiousness parameters imply that countries in the region may implement differential COVID-19 containment measures.

[COVID-19-associated Guillain-Barré syndrome in infectious period: a case report].

A 75-year-old man with a history of hypertension developed weakness and sensory disturbance in the extremities 1 week after upper respiratory tract infection and faced difficulty walking. Screening at the time of hospital admission revealed an incidental positive SARS-CoV-2 PCR test, and COVID-19 was diagnosed. Neurological findings showed dysarthria, dysphagia, absence of deep tendon reflexes in the extremities, distal-dominant muscle weakness, sensory disturbance, urinary retention and constipation. Nerve conduction studies showed prolonged distal latency, decreased conduction velocity, and poor F-wave response, leading to a diagnosis of COVID-19-associated Guillain-Barré syndrome (GBS). The patient was treated with intravenous immunoglobulin, and his neurological symptoms improved without the need of a ventilator. Anti-ganglioside autoantibodies were negative. The patient developed GBS during the infectious period of SARS-CoV-2 and was treated in the isolation ward by clinical staff with personal protective equipment. Because COVID-19-associated GBS can develop during the infectious period of SARS-CoV-2, it is important for neurologists to consider GBS and other neurological disorders as being potentially COVID-19-related, and to treat patients with COVID-19 accordingly.

Estimation of R(t) based on illness onset data: An analysis of 1907-1908 smallpox epidemic in Tokyo.

R(t), the actual average number of secondary cases per primary case at calendar time t, is epidemiologically useful in assessing transmission dynamics in a population with varying susceptibility levels. However, a technical limitation of existing methods of estimating R(t) is the reliance on the daily number of cases with illness onset and the distribution of the serial interval, although the estimator of R(t) should be calculated as the ratio of newly infected cases at time t to the total number of potentially infectious people at the same time. Using historical data of a smallpox outbreak in Tokyo City, Japan, approximately 100 years ago, we propose a new method to compute R(t) that can be estimated using information on illness onset. Our method decomposes the mechanism of transmission into two distinct pieces of information: the frequency of secondary transmission relative to disease age and the probability density function of the incubation period. Employing a piecewise constant model, our maximum likelihood estimates of R(t) dropped below unity (0.6; 95% confidence interval: 0.5-0.7) for the period from Day 64 to Day 79, indicating that the epidemic was under control in this period. R(t) was continuously below one through the remaining days. The model prediction captured the overall observed pattern of the epidemic well. Our method is appropriate for acute infectious diseases other than smallpox for which variations in infectivity relative to disease age should be considered to correctly estimate the transmission potential, such as the ongoing global epidemic of coronavirus disease 2019 (COVID-19).

Using Proper Mean Generation Intervals in Modeling of COVID-19.

In susceptible-exposed-infectious-recovered (SEIR) epidemic models, with the exponentially distributed duration of exposed/infectious statuses, the mean generation interval (GI, time lag between infections of a primary case and its secondary case) equals the mean latent period (LP) plus the mean infectious period (IP). It was widely reported that the GI for COVID-19 is as short as 5 days. However, many works in top journals used longer LP or IP with the sum (i.e., GI), e.g., >7 days. This discrepancy will lead to overestimated basic reproductive number and exaggerated expectation of infection attack rate (AR) and control efficacy. We argue that it is important to use suitable epidemiological parameter values for proper estimation/prediction. Furthermore, we propose an epidemic model to assess the transmission dynamics of COVID-19 for Belgium, Israel, and the United Arab Emirates (UAE). We estimated a time-varying reproductive number [R0(t)] based on the COVID-19 deaths data and we found that Belgium has the highest AR followed by Israel and the UAE.

Two prolonged viremic SARS-CoV-2 infections with conserved viral genome for two months.

We document two cases of viremic and prolonged active infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) where the viral genome was conserved for two months, but infection was with little or no symptoms. The first infection persisted for 80 days and the second for 62 days. Clearance of infection occurred 40 and 41 days, respectively, after development of detectable antibodies. Both cases were identified incidentally in an investigation of reinfection in a cohort of 133,266 laboratory-confirmed infected persons.

A generalized differential equation compartmental model of infectious disease transmission.

For decades, mathematical models of disease transmission have provided researchers and public health officials with critical insights into the progression, control, and prevention of disease spread. Of these models, one of the most fundamental is the SIR differential equation model. However, this ubiquitous model has one significant and rarely acknowledged shortcoming: it is unable to account for a disease's true infectious period distribution. As the misspecification of such a biological characteristic is known to significantly affect model behavior, there is a need to develop new modeling approaches that capture such information. Therefore, we illustrate an innovative take on compartmental models, derived from their general formulation as systems of nonlinear Volterra integral equations, to capture a broader range of infectious period distributions, yet maintain the desirable formulation as systems of differential equations. Our work illustrates a compartmental model that captures any Erlang distributed duration of infection with only 3 differential equations, instead of the typical inflated model sizes required by traditional differential equation compartmental models, and a compartmental model that captures any mean, standard deviation, skewness, and kurtosis of an infectious period distribution with 4 differential equations. The significance of our work is that it opens up a new class of easy-to-use compartmental models to predict disease outbreaks that do not require a complete overhaul of existing theory, and thus provides a starting point for multiple research avenues of investigation under the contexts of mathematics, public health, and evolutionary biology.

A simple, SIR-like but individual-based epidemic model: Application in comparison of COVID-19 in New York City and Wuhan.

In this study, an individual-based epidemic model, considering latent-infectious-recovery periods, is presented. The analytic solution of the model in the form of recursive formulae with a time-dependent transmission coefficient is derived and implanted in Excel. The simulated epidemic curves from the model fit very well with the daily reported cases of COVID-19 in Wuhan, China and New York City (NYC), USA. These simulations show that the transmission rate of NYC's COVID-19 is nearly 30% greater than the transmission rate of Wuhan's COVID-19, and that the actual number of cumulative infected people in NYC is around 9 times the reported number of cumulative COVID-19 cases in NYC. Results from this study also provide important information about latent period, infectious period and lockdown efficiency.

Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic Models.

Background: Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China, in December 2019 and quickly spread throughout China and the rest of the world. Many mathematical models have been developed to understand and predict the infectiousness of COVID-19. We aim to summarize these models to inform efforts to manage the current outbreak. Methods: We searched PubMed, Web of science, EMBASE, bioRxiv, medRxiv, arXiv, Preprints, and National Knowledge Infrastructure (Chinese database) for relevant studies published between 1 December 2019 and 21 February 2020. References were screened for additional publications. Crucial indicators were extracted and analysed. We also built a mathematical model for the evolution of the epidemic in Wuhan that synthesised extracted indicators. Results: Fifty-two articles involving 75 mathematical or statistical models were included in our systematic review. The overall median basic reproduction number (R0) was 3.77 [interquartile range (IQR) 2.78-5.13], which dropped to a controlled reproduction number (Rc) of 1.88 (IQR 1.41-2.24) after city lockdown. The median incubation and infectious periods were 5.90 (IQR 4.78-6.25) and 9.94 (IQR 3.93-13.50) days, respectively. The median case-fatality rate (CFR) was 2.9% (IQR 2.3-5.4%). Our mathematical model showed that, in Wuhan, the peak time of infection is likely to be March 2020 with a median size of 98,333 infected cases (range 55,225-188,284). The earliest elimination of ongoing transmission is likely to be achieved around 7 May 2020. Conclusions: Our analysis found a sustained Rc and prolonged incubation/ infectious periods, suggesting COVID-19 is highly infectious. Although interventions in China have been effective in controlling secondary transmission, sustained global efforts are needed to contain an emerging pandemic. Alternative interventions can be explored using modelling studies to better inform policymaking as the outbreak continues.

Time from Symptom Onset to Hospitalisation of Coronavirus Disease 2019 (COVID-19) Cases: Implications for the Proportion of Transmissions from Infectors with Few Symptoms.

Interventions targeting symptomatic hosts and their contacts were successful in bringing the 2003 SARS pandemic under control. In contrast, the COVID-19 pandemic has been harder to contain, partly because of its wide spectrum of symptoms in infectious hosts. Current evidence suggests that individuals can transmit the novel coronavirus while displaying few symptoms. Here, we show that the proportion of infections arising from hosts with few symptoms at the start of an outbreak can, in combination with the basic reproduction number, indicate whether or not interventions targeting symptomatic hosts are likely to be effective. However, as an outbreak continues, the proportion of infections arising from hosts with few symptoms changes in response to control measures. A high proportion of infections from hosts with few symptoms after the initial stages of an outbreak is only problematic if the rate of new infections remains high. Otherwise, it can simply indicate that symptomatic transmissions are being prevented successfully. This should be considered when interpreting estimates of the extent of transmission from hosts with few COVID-19 symptoms.

An exact and implementable computation of the final outbreak size distribution under Erlang distributed infectious period.

This paper deals with a stochastic SIR (Susceptible-Infected-Recovered) model with Erlang(k,µ) distributed infectious period commonly referred as SIkR model. We show that using the total number of remaining Erlang stages as the state variable, we do not need to keep track of the stages of individual infections, and can employ a first step analysis to efficiently obtain quantities of interest. We study the distribution of the total number of recovered individuals and the distribution of the maximum number of individuals who are simultaneously infected until the end of the disease. In the literature, final outbreak size is calculated only for a small population size exactly and derivations of approximate analytic solutions from asymptotic results are suggested for larger population sizes. We numerically demonstrate that our methods are implementable on large size problem instances.