Two biases in incubation time estimation related to exposure

BACKGROUND

Estimation of the SARS-CoV-2 incubation time distribution is hampered by incomplete data about infection. We discuss two biases that may result from incorrect handling of such data. Notified cases may recall recent exposures more precisely (differential recall). This creates bias if the analysis is restricted to observations with well-defined exposures, as longer incubation times are more likely to be excluded. Another bias occurred in the initial estimates based on data concerning travellers from Wuhan. Only individuals who developed symptoms after their departure were included, leading to under-representation of cases with shorter incubation times (left truncation). This issue was not addressed in the analyses performed in the literature.

METHODS

We performed simulations and provide a literature review to investigate the amount of bias in estimated percentiles of the SARS-CoV-2 incubation time distribution.

RESULTS

Depending on the rate of differential recall, restricting the analysis to a subset of narrow exposure windows resulted in underestimation in the median and even more in the 95th percentile. Failing to account for left truncation led to an overestimation of multiple days in both the median and the 95th percentile.

CONCLUSION

We examined two overlooked sources of bias concerning exposure information that the researcher engaged in incubation time estimation needs to be aware of.

Biomarkers of early SARS-CoV-2 infection before the onset of respiratory symptoms

OBJECTIVES

Currently, limited data exist regarding the pathological changes occurring during the incubation phase of SARS-CoV-2 infection. We utilized proteomic analysis to explore changes in the circulatory host response in individuals with SARS-CoV-2 infection before the onset of symptoms.

METHODS

Participants were individuals from a randomized clinical trial of prophylaxis for COVID-19 in a workers' dormitory. Proteomic signatures of blood samples collected within 7 days before symptom onset (incubation group) were compared with those collected >21 days (non-incubation group) to derive candidate biomarkers of incubation. Candidate biomarkers were assessed by comparing levels in the incubation group with both infected individuals (positive controls) and non-infected individuals (negative controls).

RESULTS

The study included men (mean age 34.2 years and standard deviation 7.1) who were divided into three groups: an incubation group consisting of 44 men, and two control groups-positive (n = 56) and negative (n = 67) controls. Through proteomic analysis, we identified 49 proteins that, upon pathway analyses, indicated an upregulation of the renin-angiotensin and innate immune systems during the virus incubation period. Biomarker analyses revealed increased concentrations of plasma angiotensin II (mean 731 vs. 139 pg/mL), angiotensin (1-7) (302 vs. 9 pg/mL), CXCL10 (423 vs. 85 pg/mL), CXCL11 (82.7 vs. 32.1 pg/mL), interferon-gamma (0.49 vs. 0.20 pg/mL), legumain (914 vs. 743 pg/mL), galectin-9 (1443 vs. 836 pg/mL), and tumour necrosis factor (20.3 vs. 17.0 pg/mL) during virus incubation compared with non-infected controls (all p < 0.05). Plasma angiotensin (1-7) exhibited a significant increase before the onset of symptoms when compared with uninfected controls (area under the curve 0.99, sensitivity 0.97, and specificity 0.99).

DISCUSSION

Angiotensin (1-7) could play a crucial role in the progression of symptomatic COVID-19 infection, and its assessment could help identify individuals who would benefit from enhanced monitoring and early antiviral intervention.

Improved time-varying reproduction numbers using the generation interval for COVID-19

Estimating key epidemiological parameters, such as incubation period, serial interval (SI), generation interval (GI) and latent period, is essential to quantify the transmissibility and effects of various interventions of COVID-19. These key parameters play a critical role in quantifying the basic reproduction number. With the hard work of epidemiological investigators in South Korea, estimating these key parameters has become possible based on infector-infectee surveillance data of COVID-19 between February 2020 and April 2021. Herein, the mean incubation period was estimated to be 4.9 days (95% CI: 4.2, 5.7) and the mean generation interval was estimated to be 4.3 days (95% CI: 4.2, 4.4). The mean serial interval was estimated to be 4.3, with a standard deviation of 4.2. It is also revealed that the proportion of presymptomatic transmission was ~57%, which indicates the potential risk of transmission before the disease onset. We compared the time-varying reproduction number based on GI and SI and found that the time-varying reproduction number based on GI may result in a larger estimation of Rt, which refers to the COVID-19 transmission potential around the rapid increase of cases. This highlights the importance of considering presymptomatic transmission and generation intervals when estimating the time-varying reproduction number.

Infectious diseases and predominant travel-related syndromes among long-term expatriates living in low-and middle- income countries: a scoping review

Introduction

Expatriates working in low-and middle-income countries have unique health problems. Migration leads not only to an increase in individual health risk but also a risk of global impact, such as pandemics. Expatriates with no prior experience living in tropical settings have expressed greatest concern about infectious diseases and appropriate peri-travel consultation is essential to expatriates. The objective of this review is to describe infections and travel-related syndromes among expatriates living in low-and middle-income countries.

Methods

MEDLINE database since the year 2000 was searched for relevant literature. Search terms were “long-term travel”, “expatriate”, and “health problems”. The additional references were obtained from hand-searching of selected articles.

Results

Up to 80% of expatriates suffered from gastrointestinal problems followed by dermatologic problems (up to 40%), and febrile systemic infection/vector-borne/parasitic infection (up to 34%) Expatriates living in Southeast Asia were at risk of vector-borne diseases including dengue and non-Plasmodium falciparum (pf) malaria while expatriates living in South Asia had a high prevalence of acute and chronic diarrhea. Staying long-term in Africa was related to an elevated risk for pf malaria and gastrointestinal infection. In Latin America, dermatologic problems were commonly reported illnesses among expatriates.

Conclusion

Certain health risks for expatriates who are going to depart to specific regions should be the focus of pre-travel consultation. Specific health preparations may reduce the risk of disease throughout their time abroad. Disease and symptom awareness is essential for screening, early diagnosis, and better health outcomes for ill-expatriates.

Incubation Period of COVID-19 Caused by Unique SARS-CoV-2 Strains: A Systematic Review and Meta-analysis

IMPORTANCE

Several studies were conducted to estimate the average incubation period of COVID-19; however, the incubation period of COVID-19 caused by different SARS-CoV-2 variants is not well described.

OBJECTIVE

To systematically assess the incubation period of COVID-19 and the incubation periods of COVID-19 caused by different SARS-CoV-2 variants in published studies.

DATA SOURCES

PubMed, EMBASE, and ScienceDirect were searched between December 1, 2019, and February 10, 2022.

STUDY SELECTION

Original studies of the incubation period of COVID-19, defined as the time from infection to the onset of signs and symptoms.

DATA EXTRACTION AND SYNTHESIS

Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline, 3 reviewers independently extracted the data from the eligible studies in March 2022. The parameters, or sufficient information to facilitate calculation of those values, were derived from random-effects meta-analysis.

MAIN OUTCOMES AND MEASURES

The mean estimate of the incubation period and different SARS-CoV-2 strains.

RESULTS

A total of 142 studies with 8112 patients were included. The pooled incubation period was 6.57 days (95% CI, 6.26-6.88) and ranged from 1.80 to 18.87 days. The incubation period of COVID-19 caused by the Alpha, Beta, Delta, and Omicron variants were reported in 1 study (with 6374 patients), 1 study (10 patients), 6 studies (2368 patients) and 5 studies (829 patients), respectively. The mean incubation period of COVID-19 was 5.00 days (95% CI, 4.94-5.06 days) for cases caused by the Alpha variant, 4.50 days (95% CI, 1.83-7.17 days) for the Beta variant, 4.41 days (95% CI, 3.76-5.05 days) for the Delta variant, and 3.42 days (95% CI, 2.88-3.96 days) for the Omicron variant. The mean incubation was 7.43 days (95% CI, 5.75-9.11 days) among older patients (ie, aged over 60 years old), 8.82 days (95% CI, 8.19-9.45 days) among infected children (ages 18 years or younger), 6.99 days (95% CI, 6.07-7.92 days) among patients with nonsevere illness, and 6.69 days (95% CI, 4.53-8.85 days) among patients with severe illness.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that SARS-CoV-2 has evolved and mutated continuously throughout the COVID-19 pandemic, producing variants with different enhanced transmission and virulence. Identifying the incubation period of different variants is a key factor in determining the isolation period.

Borderline microscopic organism and lockdown impacted across the borders-global shakers

Viruses are the potential cause of several diseases including novel corona virus-19, flu, small pox, chicken pox, acquired immunodeficiency syndrome, severe acute respiratory syndrome etc. The objectives of this review article are to summarize the reasons behind the epidemics caused by several emerging viruses and bacteria, how to control the infection and preventive strategies. We have explained the causes of epidemics along with their preventive measures, the impact of lockdown on the health of people and the economy of a country. Several reports have revealed the transmission of infection during epidemic from the contact of an infected person to the public that can be prevented by implementing the lockdown by the government of a country. Though lockdown has been considered as one of the significant parameters to control the diseases, however, it has some negative consequences on the health of people as they can be more prone to other ailments like obesity, diabetes, cardiac problems etc. and drastic decline in the economy of a country. Therefore, the transmission of diseases can be prevented by warning the people about the severity of diseases, avoiding their public transportation, keeping themselves isolated, strictly following the guidelines of lockdown and encouraging regular exercise.

The Incubation Period of COVID-19: Current Understanding and Modeling Technique

This chapter aims to answer the following questions regarding the incubation period of COVID-19. Why is understanding the incubation period of COVID-19 important? How long is the incubation time, and what are the associating factors? How should the incubation period be modeled given the current pandemic situation? Where should we go from here? As a critical epidemiological metric, the incubation period is of public health and clinical importance. While the incubation time of COVID-19 is generally similar to that of SARS and MERS, recent studies identifying factors that impact the incubation period of COVID-19, travel history, for example, only tell part of the story. Therefore, in addition to reviewing current findings, this chapter also explores the modeling technique and future research directions of the incubation period of COVID-19.

Preparedness for self-isolation or quarantine and lockdown in South Africa: results from a rapid online survey

BACKGROUND

The World Health Organization (WHO) declared the COVID-19 pandemic a public health emergency of international concern. South Africa, like many other countries, initiated a multifaceted national response to the pandemic. Self-isolation and quarantine are essential components of the public health response in the country. This paper examined perceptions and preparedness for self-isolation or quarantine during the initial phase of the pandemic in South Africa.

METHODS

The analysis used data obtained from an online quantitative survey conducted in all nine provinces using a data-free platform. Descriptive statistics and multivariable logistic regression models were used to analyse the data.

RESULTS

Of 55,823 respondents, 40.1% reported that they may end up in self-isolation or quarantine, 32.6% did not think that they would and 27.4% were unsure. Preparedness for self-isolation or quarantine was 59.0% for self, 53.8% for child and 59.9% for elderly. The odds of perceived possibility for self-isolation or quarantine were significantly higher among Coloureds, Whites, and Indians/Asians than Black Africans, and among those with moderate or high self-perceived risk of contracting COVID-19 than those with low risk perception. The odds were significantly lower among older age groups than those aged 18-29 years, and those unemployed than fully employed. The odds of preparedness for self-isolation or quarantine were significantly less likely among females than males. Preparedness for self, child and elderly isolation or quarantine was significantly more likely among other population groups than Black Africans and among older age groups than those aged 18-29 years. Preparedness for self, child and elderly isolation or quarantine was significantly less likely among those self-employed than fully employed and those residing in informal dwellings than formal dwellings. In addition, preparedness for self-isolation or quarantine was significantly less likely among those with moderate and high self-perceived risk of contracting COVID-19 than low risk perception.

CONCLUSION

The findings highlight the challenge of implementing self-isolation or quarantine in a country with different and unique social contexts. There is a need for public awareness regarding the importance of self-isolation or quarantine as well as counter measures against contextual factors inhibiting this intervention, especially in impoverished communities.

The incubation period of COVID-19: A meta-analysis

Objectives

A valid measurement of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incubation period is needed for case definitions and for adapting appropriate isolation measures but is challenging in an emergency context. Our objective was to systematically review recent literature reporting estimates of the distribution of the incubation period of SARS-CoV-2 and describe the distribution and its variability and dispersion through a meta-analysis.

Methods

A systematic review was carried out on studies published from 1 January 2020 to 10 January 2021 reporting the SARS-CoV-2 incubation period. Individual mean and standard deviation were used to produce the pooled estimate. Sources of heterogeneity were explored by age, gender and study design using a meta-regression.

Results

In total, 99 studies were eligible for analysis in our meta-analysis. The pooled estimate of the mean incubation period across the studies was 6.38 days, 95% CI (5.79; 6.97).

Conclusion

Calculation of the mean incubation period will help with the identification of time of exposure, however, determinants of its variations/range might be explored for potential links with the clinical outcome or pathogenic steps at the early stage of infection. A real-time meta-analysis, named the InCoVid Lyon, is proposed following this initial analysis.

Incubation period of typhoidal salmonellosis: a systematic review and meta-analysis of outbreaks and experimental studies occurring over the last century

BACKGROUND

Salmonella Typhi is a human pathogen that causes typhoid fever. It is a major cause of morbidity and mortality in developing countries and is responsible for several outbreaks in developed countries. Studying certain parameters of the pathogen, such as the incubation period, provides a better understanding of its pathophysiology and its characteristics within a population. Outbreak investigations and human experimental studies provide an avenue to study these relevant parameters.

METHODS

In this study, the authors have undertaken a systematic review of outbreak investigation reports and experimental studies, extracted reported data, tested for heterogeneity, identified subgroups of studies with limited evidence of heterogeneity between them and identified factors that may contribute to the distribution of incubation period. Following identification of relevant studies, we extracted both raw and summary incubation data. We tested for heterogeneity by deriving the value of I2 and conducting a KS-test to compare the distribution between studies. We performed a linear regression analysis to identify the factors associated with incubation period and using the resulting p-values from the KS-test, we conducted a hierarchical cluster analysis to classify studies with limited evidence of heterogeneity into subgroups.

RESULTS

We identified thirteen studies to be included in the review and extracted raw incubation period data from eleven. The value of I2 was 84% and the proportion of KS test p-values that were less than 0.05 was 63.6% indicating high heterogeneity not due to chance. We identified vaccine history and attack rates as factors that may be associated with incubation period, although these were not significant in the multivariable analysis (p-value: 0.1). From the hierarchical clustering analysis, we classified the studies into five subgroups. The mean incubation period of the subgroups ranged from 9.7 days to 21.2 days. Outbreaks reporting cases with previous vaccination history were clustered in a single subgroup and reported the longest incubation period.

CONCLUSIONS

We identified attack rate and previous vaccination as possible associating factors, however further work involving analyses of individual patient data and developing mathematical models is needed to confirm these as well as examine additional factors that have not been included in our study.

Identifying determinants of heterogeneous transmission dynamics of the Middle East respiratory syndrome (MERS) outbreak in the Republic of Korea, 2015: a retrospective epidemiological analysis

OBJECTIVES

To investigate the heterogeneous transmission patterns of Middle East respiratory syndrome (MERS) in the Republic of Korea, with a particular focus on epidemiological characteristics of superspreaders.

DESIGN

Retrospective epidemiological analysis.

SETTING

Multiple healthcare facilities of secondary and tertiary care centres in an urban setting.

PARTICIPANTS

A total of 185 laboratory-confirmed cases with partially known dates of illness onset and most likely sources of infection.

PRIMARY AND SECONDARY OUTCOME MEASURES

Superspreaders were identified using the transmission tree. The reproduction number, that is, the average number of secondary cases produced by a single primary case, was estimated as a function of time and according to different types of hosts.

RESULTS

A total of five superspreaders were identified. The reproduction number throughout the course of the outbreak was estimated at 1.0 due to reconstruction of the transmission tree, while the variance of secondary cases generated by a primary case was 52.1. All of the superspreaders involved in this outbreak appeared to have generated a substantial number of contacts in multiple healthcare facilities (association: p<0.01), generating on average 4.0 (0.0-8.6) and 28.6 (0.0-63.9) secondary cases among patients who visited multiple healthcare facilities and others. The time-dependent reproduction numbers declined substantially below the value of 1 on and after 13 June 2015.

CONCLUSIONS

Superspreaders who visited multiple facilities drove the epidemic by generating a disproportionate number of secondary cases. Our findings underscore the need to limit the contacts in healthcare settings. Contact tracing efforts could assist early laboratory testing and diagnosis of suspected cases.

Assessing the risk of observing multiple generations of Middle East respiratory syndrome (MERS) cases given an imported case

To guide risk assessment, expected numbers of cases and generations were estimated, assuming a case importation of Middle East respiratory syndrome (MERS). Our analysis of 36 importation events yielded the risk of observing secondary transmission events at 22.7% (95% confidence interval: 19.3–25.1). The risks of observing generations 2, 3 and 4 were estimated at 10.5%, 6.1% and 3.9%, respectively. Countries at risk should be ready for highly variable outcomes following an importation of MERS.

Probabilistic differential diagnosis of Middle East respiratory syndrome (MERS) using the time from immigration to illness onset among imported cases

Middle East respiratory syndrome (MERS) has spread worldwide since 2012. As the clinical symptoms of MERS tend to be non-specific, the incubation period has been shown to complement differential diagnosis, especially to rule out influenza. However, because an infection event is seldom directly observable, the present study aims to construct a diagnostic model that predicts the probability of MERS diagnosis given the time from immigration to illness onset among imported cases which are suspected of MERS. Addressing censoring by considering the transmission dynamics in an exporting country, we demonstrate that the illness onset within 2 days from immigration is suggestive of influenza. Two exceptions to suspect MERS even for those with illness onset within 2 days since immigration are (i) when we observe substantial community transmissions of MERS and (ii) when the cases are at high risk of MERS (e.g. cases with close contact in hospital or household). It is vital to collect the information of the incubation period upon emergence of a novel infectious disease, and moreover, in our model, the fundamental transmission dynamics including the initial growth rate has to be explored to differentiate the disease diagnoses with non-specific symptoms.

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Clinical symptoms of MERS tend to be non-specific.

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The incubation period complements differential diagnosis, ruling out influenza.

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The time from immigration to illness in imported cases also informs diagnosis.

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Illness onset within 2 days from immigration is suggestive of influenza.

The impact of model building on the transmission dynamics under vaccination: observable (symptom-based) versus unobservable (contagiousness-dependent) approaches

Background

The way we formulate a mathematical model of an infectious disease to capture symptomatic and asymptomatic transmission can greatly influence the likely effectiveness of vaccination in the presence of vaccine effect for preventing clinical illness. The present study aims to assess the impact of model building strategy on the epidemic threshold under vaccination.

Methodology/Principal Findings

We consider two different types of mathematical models, one based on observable variables including symptom onset and recovery from clinical illness (hereafter, the “observable model”) and the other based on unobservable information of infection event and infectiousness (the “unobservable model”). By imposing a number of modifying assumptions to the observable model, we let it mimic the unobservable model, identifying that the two models are fully consistent only when the incubation period is identical to the latent period and when there is no pre-symptomatic transmission. We also computed the reproduction numbers with and without vaccination, demonstrating that the data generating process of vaccine-induced reduction in symptomatic illness is consistent with the observable model only and examining how the effective reproduction number is differently calculated by two models.

Conclusions

To explicitly incorporate the vaccine effect in reducing the risk of symptomatic illness into the model, it is fruitful to employ a model that directly accounts for disease progression. More modeling studies based on observable epidemiological information are called for.

A novel coronavirus capable of lethal human infections: an emerging picture

In September 2012, a novel coronavirus was isolated from a patient in Saudi Arabia who had died of an acute respiratory illness and renal failure. The clinical presentation was reminiscent of the outbreak caused by the SARS-coronavirus (SARS-CoV) exactly ten years ago that resulted in over 8000 cases. Sequence analysis of the new virus revealed that it was indeed a member of the same genus as SARS-CoV. By mid-February 2013, 12 laboratory-confirmed cases had been reported with 6 fatalities. The first 9 cases were in individuals resident in the Middle East, while the most recent 3 cases were in family members resident in the UK. The index case in the UK family cluster had travel history to Pakistan and Saudi Arabia. Although the current evidence suggests that this virus is not highly transmissible among humans, there is a real danger that it may spread to other parts of the world. Here, a brief review of the events is provided to summarize the rapidly emerging picture of this new virus.