The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19.

The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supply-constrained resource allocation strategies for controlling novel disease epidemics. To address the challenge of constrained resource optimization for managing diseases with complications like pre- and asymptomatic transmission, we develop an integro partial differential equation compartmental disease model which incorporates realistic latent, incubation, and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals. Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission. To analyze the influence of these realistic features on disease controllability, we find optimal strategies for reducing total infection sizes that allocate limited testing resources between 'clinical' testing, which targets symptomatic individuals, and 'non-clinical' testing, which targets non-symptomatic individuals. We apply our model not only to the original, delta, and omicron COVID-19 variants, but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions, which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness. We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies, while the relationship between incubation-latent mismatch, controllability, and optimal strategies is complicated. In particular, though greater degrees of presymptomatic transmission reduce disease controllability, they may increase or decrease the role of non-clinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length. Importantly, our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.

Comparing the incubation period, serial interval, and infectiousness profile between SARS-CoV-2 Omicron and Delta variants.

In January 2022, the SARS-CoV-2 Omicron variants initiated major outbreaks and dominated the transmissions in Hong Kong, displacing an earlier outbreak seeded by the Delta variants. To provide insight into the transmission potential of the emerging variants, we aimed to compare the epidemiological characteristics of the Omicron and Delta variants. We analyzed the line-list clinical and contact tracing data of the SARS-CoV-2 confirmed cases in Hong Kong. Transmission pairs were constructed based on the individual contact history. We fitted bias-controlled models to the data to estimate the serial interval, incubation period and infectiousness profile of the two variants. Viral load data were extracted and fitted to the random effect models to investigate the potential risk modifiers for the clinical viral shedding course. Totally 14 401 confirmed cases were reported between January 1 and February 15, 2022. The estimated mean serial interval (4.4 days vs. 5.8 days) and incubation period (3.4 days vs. 3.8 days) were shorter for the Omicron than the Delta variants. A larger proportion of presymptomatic transmission was observed for the Omicron (62%) compared to the Delta variants (48%). The Omicron cases had higher mean viral load over an infection course than the Delta cases, with the elder cases appearing more infectious than the younger cases for both variants. The epidemiological features of Omicron variants were likely an obstacle to contact tracing measures, imposed as a major intervention in settings like Hong Kong. Continuously monitoring the epidemiological feature for any emerging SARS-CoV-2 variants in the future is needed to assist officials in planning measures for COVID-19 control.

Inference of the SARS-CoV-2 generation time using UK household data.

The distribution of the generation time (the interval between individuals becoming infected and transmitting the virus) characterises changes in the transmission risk during SARS-CoV-2 infections. Inferring the generation time distribution is essential to plan and assess public health measures. We previously developed a mechanistic approach for estimating the generation time, which provided an improved fit to data from the early months of the COVID-19 pandemic (December 2019-March 2020) compared to existing models (Hart et al., 2021). However, few estimates of the generation time exist based on data from later in the pandemic. Here, using data from a household study conducted from March to November 2020 in the UK, we provide updated estimates of the generation time. We considered both a commonly used approach in which the transmission risk is assumed to be independent of when symptoms develop, and our mechanistic model in which transmission and symptoms are linked explicitly. Assuming independent transmission and symptoms, we estimated a mean generation time (4.2 days, 95% credible interval 3.3-5.3 days) similar to previous estimates from other countries, but with a higher standard deviation (4.9 days, 3.0-8.3 days). Using our mechanistic approach, we estimated a longer mean generation time (5.9 days, 5.2-7.0 days) and a similar standard deviation (4.8 days, 4.0-6.3 days). As well as estimating the generation time using data from the entire study period, we also considered whether the generation time varied temporally. Both models suggest a shorter mean generation time in September-November 2020 compared to earlier months. Since the SARS-CoV-2 generation time appears to be changing, further data collection and analysis is necessary to continue to monitor ongoing transmission and inform future public health policy decisions.

Pneumonia Patients Caused by Co-infection With SARS-CoV-2 and Human Adenovirus in China.

Objectives: To data, no patients with obvious epidemiological relationship co-infected with SARS-CoV-2 and other pathogens have been reported. Here, we investigated 10 patients caused by co-infection with SARS-CoV-2 and human adenovirus (HAdV), resulting in third-generation transmission. Materials and Methods: From Jan 15, 2020, we enrolled 10 patients with pneumonia in Hunan Province, China. Epidemiological, clinical, and laboratory investigation results from these patients were analyzed. An epidemiological investigation was performed to assess whether patient infections were linked using conventional methods and metagenomic sequencing. Results: The presence of co-infection with SARS-CoV-2 and HAdV was determined via RT-PCR and metagenomic sequencing. Phylogenetic analysis revealed that SARS-CoV-2 and HAdV genomes clustered together, with similar genetic relationships. The first patient likely became co-infected during meetings or travel in Wuhan. The patient transmitted the virus via dinners and meetings, which resulted in four second-generation cases. Then, a second-generation case transmitted the virus to her family members or relatives via presymptomatic transmission. Conclusions: This study described an example of co-infection with SARS-CoV-2 and HAdV in pneumonia patients, which caused third-generation cases and inter-regional transmission via meetings, household interactions, and dinner parties. We also observed the persistent and presymptomatic transmission of co-infection, which has the potential to make the continued control of the COVID-19 pandemic challenging. Continuous surveillance is needed to monitor the prevalence, infectivity, transmissibility, and pathogenicity of SARS-CoV-2 co-infection with other pathogens to evaluate its real risk.

High infectiousness immediately before COVID-19 symptom onset highlights the importance of continued contact tracing.

Background: Understanding changes in infectiousness during SARS-COV-2 infections is critical to assess the effectiveness of public health measures such as contact tracing. Methods: Here, we develop a novel mechanistic approach to infer the infectiousness profile of SARS-COV-2-infected individuals using data from known infector-infectee pairs. We compare estimates of key epidemiological quantities generated using our mechanistic method with analogous estimates generated using previous approaches. Results: The mechanistic method provides an improved fit to data from SARS-CoV-2 infector-infectee pairs compared to commonly used approaches. Our best-fitting model indicates a high proportion of presymptomatic transmissions, with many transmissions occurring shortly before the infector develops symptoms. Conclusions: High infectiousness immediately prior to symptom onset highlights the importance of continued contact tracing until effective vaccines have been distributed widely, even if contacts from a short time window before symptom onset alone are traced. Funding: Engineering and Physical Sciences Research Council (EPSRC).

The risk of a person with COVID-19 spreading the SARS-CoV-2 virus that causes it to others varies over the course of their infection. Transmission depends both on how much virus is in the infected person's airway and their behaviors, such as whether they wear a mask and how many people they have contact with. Learning more about when people are most infectious would help public health officials stop the spread of the virus. For example, officials can then introduce policies that ensure that people are isolated when they are most infectious. The majority of studies assessing when people with COVID-19 are most infectious so far have assumed that transmission is not linked to when symptoms appear. But that may not be true. After people develop symptoms, they may be more likely to stay home, avoid others, or take other measures that prevent transmission. Using computer modeling and data from previous studies of individuals who infected others with SARS-CoV-2, Hart et al. show that about 65% of virus transmission occurs before symptoms develop. In fact, the computational experiments show the risk of transmission is highest immediately before symptoms develop. This highlights the importance of identifying people exposed to someone infected with the virus and isolating potential recipients before they develop symptoms. This information may help public health officials develop more effective strategies to prevent the spread of SARS-CoV-2. It may also help scientists develop more accurate models to predict the spread of the virus. However, the computational experiments used data on infections early in the pandemic that may not reflect the current situation. Changes in public health policy, the behavior of individuals and the appearance of new strains of SARS-CoV-2, all affect the timing of transmission. As more recent data become available, Hart et al. plan to explore how characteristics of transmission have changed as the pandemic has progressed.

Transmission of SARS-CoV-2 before and after symptom onset: impact of nonpharmaceutical interventions in China.

Nonpharmaceutical interventions, such as contact tracing and quarantine, have been the primary means of controlling the spread of SARS-CoV-2; however, it remains uncertain which interventions are most effective at reducing transmission at the population level. Using serial interval data from before and after the rollout of nonpharmaceutical interventions in China, we estimate that the relative frequency of presymptomatic transmission increased from 34% before the rollout to 71% afterward. The shift toward earlier transmission indicates a disproportionate reduction in transmission post-symptom onset. We estimate that, following the rollout of nonpharmaceutical interventions, transmission post-symptom onset was reduced by 82% whereas presymptomatic transmission decreased by only 16%. The observation that only one-third of transmission was presymptomatic at baseline, combined with the finding that NPIs reduced presymptomatic transmission by less than 20%, suggests that the overall impact of NPIs was driven in large part by reductions in transmission following symptom onset. This implies that interventions which limit opportunities for transmission in the later stages of infection, such as contact tracing and isolation, are particularly important for control of SARS-CoV-2. Interventions which specifically reduce opportunities for presymptomatic transmission, such as quarantine of asymptomatic contacts, are likely to have smaller, but non-negligible, effects on overall transmission.

Presymptomatic viral shedding and infective ability of SARS-CoV-2; a case report.

Possible pre- or asymptomatic transmission has been reported, both from SARS-CoV and from MERS-CoV outbreaks, although this appears to be uncommon. In contrast, during the COVID-19 pandemic, an increasing number of studies and case reports indicate that pre- or asymptomatic transmission of SARS-CoV-2 is not only possible but also occurs frequently. We report repeated rRT-PCR detection of SARS-CoV-2 in a health care worker and demonstrate infective ability up to three days prior to mild COVID-19 symptoms. rRT-PCR indicated high viral levels approximately three days after exposure. Viral samples collected one and three days prior to symptoms exhibited infectivity on Vero E6 cells, confirmed by detection of double-stranded RNA by immunofluorescence, assessment of cytopathic effect (CPE) and rRT-PCR. SARS-CoV-2 specific IgM and IgG antibodies were detected by day 9 and 15, respectively, after symptom onset. We propose that this provides evidence for potential early presymptomatic transmission of SARS-CoV-2 and that infectivity may be manifest shortly after exposure.

A COVID-19 Outbreak in a Rheumatology Department Upon the Early Days of the Pandemic.

Objectives: To describe our experience with a coronavirus disease 2019 (COVID-19) outbreak within a large rheumatology department early in the pandemic. Methods: Symptomatic and asymptomatic healthcare workers (HCWs) had a naso-oropharyngeal swab for detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and were followed clinically. Reverse transcription polymerase-chain reaction (RT-PCR) was repeated to document cure, and serological response was assessed. Patients with risk contacts within the department in the 14 days preceding the outbreak were screened for COVID-19 symptoms. Results: 14/34 HCWs (41%; 40 ± 14 years, 71% female) tested positive for SARS-CoV-2, and 11/34 (32%) developed symptoms but were RT-PCR-negative. Half of RT-PCR-positive HCWs did not report fever, cough, or dyspnea before testing, which were absent in 3/14 cases (21%). Mild disease prevailed (79%), but 3 HCWs had moderate disease requiring further assessment, which excluded severe complications. Nevertheless, symptom duration (28 ± 18 days), viral shedding (31 ± 10 days post-symptom onset, range 15-51), and work absence (29 ± 28 days) were prolonged. 13/14 (93%) of RT-PCR-positive and none of the RT-PCR-negative HCWs had a positive humoral response Higher IgG indexes were observed in individuals over 50 years of age (14.5 ± 7.7 vs. 5.0 ± 4.4, p = 0.012). Of 617 rheumatic patients, 8 (1.3%) developed COVID-19 symptoms (1/8 hospitalization, 8/8 complete recovery), following a consultation/procedure with an asymptomatic (7/8) or mildly symptomatic (1/8) HCW. Conclusions: A COVID-19 outbreak can occur among HCWs and rheumatic patients, swiftly spreading over the presymptomatic stage. Mild disease without typical symptoms should be recognized and may evolve with delayed viral shedding, prolonged recovery, and adequate immune response in most individuals.

Epidemiological Parameters of COVID-19: Case Series Study.

BACKGROUND: The estimates of several key epidemiological parameters of the COVID-19 pandemic are often based on small sample sizes or are inaccurate for various reasons. OBJECTIVE: The aim of this study is to obtain more robust estimates of the incubation period, serial interval, frequency of presymptomatic transmission, and basic reproduction number (R0) of COVID-19 based on a large case series. METHODS: We systematically retrieved and screened 20,658 reports of laboratory-confirmed COVID-19 cases released by the health authorities of China, Japan, and Singapore. In addition, 9942 publications were retrieved from PubMed and China National Knowledge Infrastructure (CNKI) through April 8, 2020. To be eligible, a report had to contain individual data that allowed for accurate estimation of at least one parameter. Widely used models such as gamma distributions were fitted to the data sets and the results with the best-fitting values were presented. RESULTS: In total, 1591 cases were included for the final analysis. The mean incubation period (n=687) and mean serial interval (n=1015 pairs) were estimated to be 7.04 (SD 4.27) days and 6.49 (SD 4.90) days, respectively. In 40 cases (5.82%), the incubation period was longer than 14 days. In 32 infector-infectee pairs (3.15%), infectees' symptom onsets occurred before those of infectors. Presymptomatic transmission occurred in 129 of 296 infector-infectee pairs (43.58%). R0 was estimated to be 1.85 (95% CI 1.37-2.60). CONCLUSIONS: This study provides robust estimates of several epidemiological parameters of COVID-19. The findings support the current practice of 14-day quarantine of persons with potential exposure, but also suggest the need for additional measures. Presymptomatic transmission together with the asymptomatic transmission reported by previous studies highlight the importance of adequate testing, strict quarantine, and social distancing.

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.

Advances on presymptomatic or asymptomatic carrier transmission of COVID-19 / 中华流行病学杂志

COVID-19 is rapidly spreading. Patients in incubation period and healthy carriers are possible sources for transmission. However, such sources of infection cannot be effectively identified due to the symptoms absent. The research evidence is very lacking so far, although there are a few studies suggesting that presymptomatic or asymptomatic carrier may cause COVID-19 transmission. Nearly half of the literature is in the state of preprint without peer review. The question of "the degree to which presymptomatic or asymptomatic infections can transmit" is not fully understood. There is an urgent need to screen infected carriers in larger close contacts or in the general population, and assess their risk for transmission.