scholarly journals 2367. Selecting Testing Frequency for Estimation of Incubation Periods: A Simulation Study Based on Clostridioides difficile Observations

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S816-S816
Author(s):  
Brigid Wilson ◽  
Mustafa S Ascha ◽  
Justin O’Hagan ◽  
Curtis Donskey
Keyword(s):  
Incubation Period ◽  
Simulation Study ◽  
Symptom Onset ◽  
Pathogen Transmission ◽  
Sample Sizes ◽  
Clostridioides Difficile ◽  
Testing Frequency ◽  
Incubation Periods ◽  
Interval Censored ◽  
The Impact

Abstract Background Estimates of the incubation period (time between pathogen transmission and symptom onset) for an infection inform infection control and prevention measures. However, observation of the exact transmission and onset times rarely occurs and “coarse,” or doubly interval-censored, data about these exact times are typically used for estimation. The effect of coarseness on the required number of symptomatic cases and the uncertainty of the estimates is unknown, prompting a simulation study informed by data from an investigation of the incubation period of Clostridioides difficile. Methods We simulated incubation period data assuming a log-normal distribution, a true median incubation period of 7 days, and a standard deviation of 1 day for sample sizes of 50 to 300 symptomatic cases. For each sample size, we simulated 1000 datasets and examined the impact of testing frequencies, considering intervals between tests of 0.25 to 2 times the median incubation period (1.75 to 14 days) about both transmission and symptom onset times. With these doubly interval-censored observed values, we fit accelerated failure time models to estimate the median incubation time and its 95% confidence interval (CI). Comparing the coverage of the true median and the widths of the CIs, we summarized simulation results across sample sizes and testing frequencies. Results Model results from all combinations of sample sizes and testing frequencies yielded median incubation period CIs close to the target 95% coverage level (Figure 1). The width of the 95% CI about the median decreased with larger sample sizes and shorter times between tests (Figure 2). Thus, similar estimates and confidence intervals would be observed from 100 symptomatic cases with a testing frequency of 3.5 days as from 200 symptomatic cases tested every 14 days. Conclusion The frequency of testing is a key factor in planning studies to estimate incubation periods for infectious diseases. To achieve a desired degree of certainty in estimation, increased frequency of testing can reduce the number of symptomatic cases required. We showed that simulations can assist in planning natural history studies, and these methods could be extended to include population data (e.g., transmission incidence) and cost constraints. Disclosures All authors: No reported disclosures.

scholarly journals Longer incubation periods of SARS-CoV-2 infection in infants

2021 ◽  
Author(s):  
Char Leung
Keyword(s):  
Incubation Period ◽  
Symptom Onset ◽  
Nasal Congestion ◽  
Large Body ◽  
Likelihood Estimation ◽  
Infants And Children ◽  
Web Pages ◽  
Health Authorities ◽  
Incubation Periods ◽  
Interval Censored

Objective: A large body of research has described the incubation period of SARS-CoV-2 infection, an important metric for assessing the risk of developing a disease as well as surveillance. While longer incubation periods for elderly have been found, it remains elusive whether this also holds true for infants and children, partly due to the lack of data. The present work clarified the incubation periods of COVID-19 for infants and children. Methods: Using the data released by the Chinese health authorities and municipal offices, statistical comparisons of clinical features were made between infants (aged below 1 year) and children (aged between 1 and 17 years). An age-varying incubation period distribution period was modeled using maximum likelihood estimation modified for interval censored exposure time and age. Discussion: Reported in 56 web pages, a total of 65 cases from 20 provinces dated between January and June 2020, including 18 infants and 47 children, were eligible for inclusion. Infants appeared to bear more severe clinical courses, as demonstrated by the higher prevalence of breathing difficulty as well as nasal congestion. In contrast, fever was less prominent in infants than in children. The incubation period was found to decrease with age, with infants appearing to have longer incubation periods. Conclusion: Fever remained to be one of the most commonly seen symptoms in infants and children with SARS-CoV-2 infection and have continued to determine the time of symptom onset. While shorter incubation periods should be seen in patients with weaker immune system due to weaker antiviral response that is beneficial for viral growth, the longer incubation period in infants may be due to their weaker febrile response to the virus, leading to prolonged symptom onset.

scholarly journals Association between incubation period and clinical characteristics of patients with COVID-19

2020 ◽  
Vol 48 (9) ◽  
pp. 030006052095683
Author(s):  
Yeyu Cai ◽  
Jiayi Liu ◽  
Haitao Yang ◽  
Mian Wang ◽  
Qingping Guo ◽  
...  
Keyword(s):  
Incubation Period ◽  
Disease Severity ◽  
Symptom Onset ◽  
Clinical Characteristics ◽  
Characteristic Curve ◽  
Severe Disease ◽  
Receiver Operator Characteristic Curve ◽  
Hunan Province ◽  
Laboratory Findings ◽  
Incubation Periods

Purpose To investigate associations between the clinical characteristics and incubation periods of patients infected with coronavirus disease 2019 (COVID-19) in Wuhan, China. Methods Complete clinical and epidemiological data from 149 patients with COVID-19 at a hospital in Hunan Province, China, were collected and retrospectively analyzed. Results Analysis of the distribution and receiver operator characteristic curve of incubation periods showed that 7 days was the optimal cut-off value to assess differences in disease severity between groups. Patients with shorter (≤7 days) incubation periods (n = 79) had more severe disease, longer durations of hospitalization, longer times from symptom onset to discharge, more abnormal laboratory findings, and more severe radiological findings than patients with longer (>7 days) incubation periods. Regression and correlation analyses also showed that a shorter incubation period was associated with longer times from symptom onset to discharge. Conclusion The associations between the incubation periods and clinical characteristics of COVID-19 patients suggest that the incubation period may be a useful marker of disease severity and prognosis.

scholarly journals Incubation periods impact the spatial predictability of cholera and Ebola outbreaks in Sierra Leone

2020 ◽  
Vol 117 (9) ◽  
pp. 5067-5073 ◽  
Author(s):  
Rebecca Kahn ◽  
Corey M. Peak ◽  
Juan Fernández-Gracia ◽  
Alexandra Hill ◽  
Amara Jambai ◽  
...  
Keyword(s):  
Sierra Leone ◽  
Incubation Period ◽  
Population Connectivity ◽  
Disease Spread ◽  
Long Distance ◽  
Spatial Spread ◽  
Transmission Parameters ◽  
Incubation Periods ◽  
Using Data ◽  
The Impact

Forecasting the spatiotemporal spread of infectious diseases during an outbreak is an important component of epidemic response. However, it remains challenging both methodologically and with respect to data requirements, as disease spread is influenced by numerous factors, including the pathogen’s underlying transmission parameters and epidemiological dynamics, social networks and population connectivity, and environmental conditions. Here, using data from Sierra Leone, we analyze the spatiotemporal dynamics of recent cholera and Ebola outbreaks and compare and contrast the spread of these two pathogens in the same population. We develop a simulation model of the spatial spread of an epidemic in order to examine the impact of a pathogen’s incubation period on the dynamics of spread and the predictability of outbreaks. We find that differences in the incubation period alone can determine the limits of predictability for diseases with different natural history, both empirically and in our simulations. Our results show that diseases with longer incubation periods, such as Ebola, where infected individuals can travel farther before becoming infectious, result in more long-distance sparking events and less predictable disease trajectories, as compared to the more predictable wave-like spread of diseases with shorter incubation periods, such as cholera.

scholarly journals Incubation periods impact the spatial predictability of outbreaks: analysis of cholera and Ebola outbreaks in Sierra Leone

2019 ◽  
Author(s):  
Rebecca Kahn ◽  
Corey M. Peak ◽  
Juan Fernández-Gracia ◽  
Alexandra Hill ◽  
Amara Jambai ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Sierra Leone ◽  
Incubation Period ◽  
Population Connectivity ◽  
Disease Spread ◽  
Long Distance ◽  
Spatial Spread ◽  
Transmission Parameters ◽  
Incubation Periods ◽  
The Impact

AbstractForecasting the spatiotemporal spread of infectious diseases during an outbreak is an important component of epidemic response. However, it remains challenging both methodologically and with respect to data requirements as disease spread is influenced by numerous factors, including the pathogen’s underlying transmission parameters and epidemiological dynamics, social networks and population connectivity, and environmental conditions. Here, using data from Sierra Leone we analyze the spatiotemporal dynamics of recent cholera and Ebola outbreaks and compare and contrast the spread of these two pathogens in the same population. We develop a simulation model of the spatial spread of an epidemic in order to examine the impact of a pathogen’s incubation period on the dynamics of spread and the predictability of outbreaks. We find that differences in the incubation period alone can determine the limits of predictability for diseases with different natural history, both empirically and in our simulations. Our results show that diseases with longer incubation periods, such as Ebola, where infected individuals can travel further before becoming infectious, result in more long-distance sparking events and less predictable disease trajectories, as compared to the more predictable wave-like spread of diseases with shorter incubation periods, such as cholera.Significance statementUnderstanding how infectious diseases spread is critical for preventing and containing outbreaks. While advances have been made in forecasting epidemics, much is still unknown. Here we show that the incubation period – the time between exposure to a pathogen and onset of symptoms – is an important factor in predicting spatiotemporal spread of disease and provides one explanation for the different trajectories of the recent Ebola and cholera outbreaks in Sierra Leone. We find that outbreaks of pathogens with longer incubation periods, such as Ebola, tend to have less predictable spread, whereas pathogens with shorter incubation periods, such as cholera, spread in a more predictable, wavelike pattern. These findings have implications for the scale and timing of reactive interventions, such as vaccination campaigns.

Test stewardship, frequency and fidelity: Impact on reported hospital-onset Clostridioides difficile

2019 ◽  
Vol 40 (6) ◽  
pp. 710-712 ◽  
Author(s):  
Michele S. Fleming ◽  
Olivia Hess ◽  
Heather L. Albert ◽  
Emily Styslinger ◽  
Michelle Doll ◽  
...  
Keyword(s):  
Decision Support ◽  
Medical Record ◽  
Critical Review ◽  
Medical Center ◽  
Academic Medical Center ◽  
Clostridioides Difficile ◽  
Testing Frequency ◽  
Academic Medical ◽  
Support Matrix ◽  
The Impact

AbstractWe assessed the impact of an embedded electronic medical record decision-support matrix (Cerner software system) for the reduction of hospital-onset Clostridioides difficile. A critical review of 3,124 patients highlighted excessive testing frequency in an academic medical center and demonstrated the impact of decision support following a testing fidelity algorithm.

scholarly journals Demand for longer quarantine period among common and uncommon COVID-19 infections: a scoping review

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhi-Yao Li ◽  
Yu Zhang ◽  
Liu-Qing Peng ◽  
Rong-Rong Gao ◽  
Jia-Rui Jing ◽  
...  
Keyword(s):  
Incubation Period ◽  
Symptom Onset ◽  
Failure Time ◽  
Accelerated Failure Time Model ◽  
China National Knowledge Infrastructure ◽  
Active Monitoring ◽  
Time Model ◽  
Infected People ◽  
Interval Censored ◽  
Coarse Information

Abstract Background As one of the non-pharmacological interventions to control the transmission of COVID-19, determining the quarantine duration is mainly based on the accurate estimates of the incubation period. However, patients with coarse information of the exposure date, as well as infections other than the symptomatic, were not taken into account in previously published studies. Thus, by using the statistical method dealing with the interval-censored data, we assessed the quarantine duration for both common and uncommon infections. The latter type includes the presymptomatic, the asymptomatic and the recurrent test positive patients. Methods As of 10 December 2020, information on cases have been collected from the English and Chinese databases, including Pubmed, Google scholar, CNKI (China National Knowledge Infrastructure) and Wanfang. Official websites and medias were also searched as data sources. All data were transformed into doubly interval-censored and the accelerated failure time model was applied. By estimating the incubation period and the time-to-event distribution of worldwide COVID-19 patients, we obtain the large percentiles for determining and suggesting the quarantine policies. For symptomatic and presymptomatic COVID-19 patients, the incubation time is the duration from exposure to symptom onset. For the asymptomatic, we substitute the date of first positive result of nucleic acid testing for that of symptom onset. Furthermore, the time from hospital discharge or getting negative test result to the positive recurrence has been calculated for recurrent positive patients. Results A total of 1920 laboratory confirmed COVID-19 cases were included. Among all uncommon infections, 34.1% (n = 55) of them developed symptoms or were identified beyond fourteen days. Based on all collected cases, the 95th and 99th percentiles were estimated to be 16.2 days (95% CI 15.5–17.0) and 22.9 days (21.7‒24.3) respectively. Besides, we got similar estimates based on merely symptomatic and presymptomatic infections as 15.1 days (14.4‒15.7) and 21.1 days (20.0‒22.2). Conclusions There are a certain number of infected people who require longer quarantine duration. Our findings well support the current practice of the extended active monitoring. To further prevent possible transmissions induced and facilitated by such infectious outliers after the 14-days quarantine, properly prolonging the quarantine duration could be prudent for high-risk scenarios and in regions with insufficient test resources.

scholarly journals Assessing the Goodness of Fit of the Gompertz Model in the Presence of Right and Interval Censored Data with Covariate

2020 ◽  
Vol 49 (3) ◽  
pp. 57-71
Author(s):  
Azid Maarof Nur Niswah Naslina ◽  
Arasan Jayanthi ◽  
Zulkafli Hani Syahida ◽  
Adam Mohd Bakri
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Censored Data ◽  
Simulation Study ◽  
Standard Error ◽  
Goodness Of Fit ◽  
Gompertz Model ◽  
Mean Square ◽  
Sample Sizes ◽  
Interval Censored

This research focuses on assessing the goodness of fit for the Gompertz model in the presence of right and interval censored data with covariate. The performance of the maximum likelihood estimates was evaluated via a simulation study at various censoring proportions and sample sizes. The conclusions were drawn based on the results of bias, standard error and root mean square error at different settings. Following that, another simulation study was carried out to compare the performance of the proposed modifications to the Cox-Snell residuals for both censored and uncensored observations at different combinations of sample sizes and censoring levels. The results show that standard error and root mean square error values of the parameter estimates increase with the increase in censoring proportions and decrease in the number of sample size. This indicates that the estimates perform better when sample sizes are larger and censoring proportions are lower. The performance of the proposed modifications of the Cox-Snell residuals showed that they perform slightly better than existing method.

scholarly journals Estimation of incubation period distribution of COVID-19 using disease onset forward time: a novel cross-sectional and forward follow-up study

2020 ◽  
Author(s):  
Jing Qin ◽  
Chong You ◽  
Qiushi Lin ◽  
Taojun Hu ◽  
Shicheng Yu ◽  
...  
Keyword(s):  
Incubation Period ◽  
Symptom Onset ◽  
Prevention And Control ◽  
Recall Bias ◽  
Cross Sectional ◽  
Follow Up Study ◽  
Incubation Periods ◽  
Current Outbreak ◽  
And Control

SummaryBackgroundThe current outbreak of coronavirus disease 2019 (COVID-19) has quickly spread across countries and become a global crisis. However, one of the most important clinical characteristics in epidemiology, the distribution of the incubation period, remains unclear. Different estimates of the incubation period of COVID-19 were reported in recent published studies, but all have their own limitations. In this study, we propose a novel low-cost and accurate method to estimate the incubation distribution.MethodsWe have conducted a cross-sectional and forward follow-up study by identifying those asymptomatic individuals at their time of departure from Wuhan and then following them until their symptoms developed. The renewal process is hence adopted by considering the incubation period as a renewal and the duration between departure and symptom onset as a forward recurrence time. Under mild assumptions, the observations of selected forward times can be used to consistently estimate the parameters in the distribution of the incubation period. Such a method enhances the accuracy of estimation by reducing recall bias and utilizing the abundant and readily available forward time data.FindingsThe estimated distribution of forward time fits the observations in the collected data well. The estimated median of incubation period is 8·13 days (95% confidence interval [CI]: 7·37-8·91), the mean is 8·62 days (95% CI: 8·02-9·28), the 90th percentile is 14·65 days (95% CI: 14·00-15·26), and the 99th percentile is 20·59 days (95% CI: 19·47, 21·62). Compared with results in other studies, the incubation period estimated in this study is longer.InterpretationBased on the estimated incubation distribution in this study, about 10% of patients with COVID-19 would not develop symptoms until 14 days after infection. Further study of the incubation distribution is warranted to directly estimate the proportion with long incubation periods.FundingThis research is supported by National Natural Science Foundation of China grant 8204100362 and Zhejiang University special scientific research fund for COVID-19 prevention and control.Research in contextEvidence before this studyBefore the current outbreak of coronavirus disease (COVID-19) in China, there were two other coronaviruses that have caused major global epidemics over the last two decades. Severe acute respiratory syndrome (SARS) spread to 37 countries and caused 8424 cases and 919 deaths in 2002-03, while Middle East respiratory syndrome (MERS) spread to 27 countries, causing 2494 cases and 858 deaths worldwide to date. Precise knowledge of the incubation period is crucial for the prevention and control of these diseases. We have searched PubMed and preprint archives for articles published as of February 22, 2020, which contain information about these diseases by using the key words of “COVID-19”, “SARS”, “MERS”, “2019-nCoV”, “coronavirus”, and “incubation”. We have found 15 studies that estimated the distribution of the incubation period. There are four articles focused on COVID-19, five on MERS, and six on SARS. Most of these studies had limited sample sizes and were potentially influenced by recall bias. The estimates for mean, median, and percentiles of the incubation period from these articles are summarized in Table 1.Added value of this studyIn the absence of complete and robust contact-tracing data, we have inferred the distribution of the incubation period of COVID-19 from the durations between departure from Wuhan and symptom onset for the confirmed cases. More than 1000 cases were collected from publicly available data. The proposed approach has a solid theoretical foundation and enhances the accuracy of estimation by reducing recall bias and utilizing a large pool of samples.Implications of all the available evidenceBased on our model, about 10% of patients with COVID-19 do not develop symptoms until 14 days after infection. Further study of individuals with long incubation periods is warranted.

scholarly journals 216. Are Automatic Antimicrobial Stop Orders an Effective Stewardship Tool for Urinary Tract and Intra-Abdominal Infections?

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S110-S110
Author(s):  
Christina Maguire ◽  
Dusten T Rose ◽  
Theresa Jaso
Keyword(s):  
Urinary Tract ◽  
Antimicrobial Therapy ◽  
Source Control ◽  
Clostridioides Difficile ◽  
Days Of Therapy ◽  
Before And After ◽  
The Difference ◽  
Tract Infections ◽  
Length Of Therapy ◽  
The Impact

Abstract Background Automatic antimicrobial stop orders (ASOs) are a stewardship initiative used to decrease days of therapy, prevent resistance, and reduce drug costs. Limited evidence outside of the perioperative setting exists on the effects of ASOs on broad spectrum antimicrobial use, discharge prescription duration, and effects of missed doses. This study aims to evaluate the impact of an ASO policy across a health system of adult academic and community hospitals for treatment of intra-abdominal (IAI) and urinary tract infections (UTI). ASO Outcome Definitions ASO Outcomes Methods This multicenter retrospective cohort study compared patients with IAI and UTI treated before and after implementation of an ASO. Patients over the age of 18 with a diagnosis of UTI or IAI and 48 hours of intravenous (IV) antimicrobial administration were included. Patients unable to achieve IAI source control within 48 hours or those with a concomitant infection were excluded. The primary outcome was the difference in sum length of antimicrobial therapy (LOT). Secondary endpoints include length and days of antimicrobial therapy (DOT) at multiple timepoints, all cause in hospital mortality and readmission, and adverse events such as rates of Clostridioides difficile infection. Outcomes were also evaluated by type of infection, hospital site, and presence of infectious diseases (ID) pharmacist on site. Results This study included 119 patients in the pre-ASO group and 121 patients in the post-ASO group. ASO shortened sum length of therapy (LOT) (12 days vs 11 days respectively; p=0.0364) and sum DOT (15 days vs 12 days respectively; p=0.022). This finding appears to be driven by a decrease in outpatient LOT (p=0.0017) and outpatient DOT (p=0.0034). Conversely, ASO extended empiric IV LOT (p=0.005). All other secondary outcomes were not significant. Ten patients missed doses of antimicrobials due to ASO. Subgroup analyses suggested that one hospital may have influenced outcomes and reduction in LOT was observed primarily in sites without an ID pharmacist on site (p=0.018). Conclusion While implementation of ASO decreases sum length of inpatient and outpatient therapy, it may not influence inpatient length of therapy alone. Moreover, ASOs prolong use of empiric intravenous therapy. Hospitals without an ID pharmacist may benefit most from ASO protocols. Disclosures All Authors: No reported disclosures

scholarly journals Quantity and Quality of Aquaculture Enrichments Influence Disease Epidemics and Provide Ecological Alternatives to Antibiotics

Antibiotics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 335
Author(s):  
Anssi Karvonen ◽  
Ville Räihä ◽  
Ines Klemme ◽  
Roghaieh Ashrafi ◽  
Pekka Hyvärinen ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Bacterial Infections ◽  
Environmental Heterogeneity ◽  
Pathogen Transmission ◽  
Central Component ◽  
Flavobacterium Columnare ◽  
Disease Epidemics ◽  
The Impact ◽  
Environmental Microbes

Environmental heterogeneity is a central component influencing the virulence and epidemiology of infectious diseases. The number and distribution of susceptible hosts determines disease transmission opportunities, shifting the epidemiological threshold between the spread and fadeout of a disease. Similarly, the presence and diversity of other hosts, pathogens and environmental microbes, may inhibit or accelerate an epidemic. This has important applied implications in farming environments, where high numbers of susceptible hosts are maintained in conditions of minimal environmental heterogeneity. We investigated how the quantity and quality of aquaculture enrichments (few vs. many stones; clean stones vs. stones conditioned in lake water) influenced the severity of infection of a pathogenic bacterium, Flavobacterium columnare, in salmonid fishes. We found that the conditioning of the stones significantly increased host survival in rearing tanks with few stones. A similar effect of increased host survival was also observed with a higher number of unconditioned stones. These results suggest that a simple increase in the heterogeneity of aquaculture environment can significantly reduce the impact of diseases, most likely operating through a reduction in pathogen transmission (stone quantity) and the formation of beneficial microbial communities (stone quality). This supports enriched rearing as an ecological and economic way to prevent bacterial infections with the minimal use of antimicrobials.

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