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We estimated the incubation period for mpox during an outbreak in Pereira, Colombia, using data from 11 confirmed cases. Mean incubation period was 7.1 (95% CI 4.9-9.9) days, consistent with previous outbreaks. Accurately estimating the incubation period provides insights into transmission dynamics, informing public health interventions and surveillance strategies.

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BACKGROUND: The Surveillance Outbreak Response Management and Analysis System (SORMAS) contains a management module to support countries in their epidemic response. It consists of the documentation, linkage, and follow-up of cases, contacts, and events. To allow SORMAS users to visualize data, compute essential surveillance indicators, and estimate epidemiological parameters from such network data in real-time, we developed the SORMAS Statistics (SORMAS-Stats) application. OBJECTIVE: This study aims to describe the essential visualizations, surveillance indicators, and epidemiological parameters implemented in the SORMAS-Stats application and illustrate the application of SORMAS-Stats in response to the COVID-19 outbreak. METHODS: Based on findings from a rapid review and SORMAS user requests, we included the following visualization and estimation of parameters in SORMAS-Stats: transmission network diagram, serial interval (SI), time-varying reproduction number R(t), dispersion parameter k, and additional surveillance indicators presented in graphs and tables. We estimated SI by fitting lognormal, gamma, and Weibull distributions to the observed distribution of the number of days between symptom onset dates of infector-infectee pairs. We estimated k by fitting a negative binomial distribution to the observed number of infectees per infector. Furthermore, we applied the Markov Chain Monte Carlo approach and estimated R(t) using the incidence data and the observed SI computed from the transmission network data. RESULTS: Using COVID-19 contact-tracing data of confirmed cases reported between July 31 and October 29, 2021, in the Bourgogne-Franche-Comté region of France, we constructed a network diagram containing 63,570 nodes. The network comprises 1.75% (1115/63,570) events, 19.59% (12,452/63,570) case persons, and 78.66% (50,003/63,570) exposed persons, including 1238 infector-infectee pairs and 3860 transmission chains with 24.69% (953/3860) having events as the index infector. The distribution with the best fit to the observed SI data was a lognormal distribution with a mean of 4.30 (95% CI 4.09-4.51) days. We estimated a dispersion parameter k of 21.11 (95% CI 7.57-34.66) and an effective reproduction number R of 0.9 (95% CI 0.58-0.60). The weekly estimated R(t) values ranged from 0.80 to 1.61. CONCLUSIONS: We provide an application for real-time estimation of epidemiological parameters, which is essential for informing outbreak response strategies. The estimates are commensurate with findings from previous studies. The SORMAS-Stats application could greatly assist public health authorities in the regions using SORMAS or similar tools by providing extensive visualizations and computation of surveillance indicators.

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BACKGROUND AND OBJECTIVE: The incubation period of COVID-19 helps to determine the optimal duration of the quarantine and inform predictive models of incidence curves. Several emerging studies have produced varying results; this systematic review aims to provide a more accurate estimate of the incubation period of COVID-19. METHODS: For this systematic review, a literature search was conducted using Pubmed, Scopus/EMBASE, and the Cochrane Library databases, covering all observational and experimental studies reporting the incubation period and published from 1 January 2020 to 21 March 2020.We estimated the mean and 95th percentile of the incubation period using meta-analysis, taking into account between-study heterogeneity, and the analysis with moderator variables. RESULTS: We included seven studies (n=792) in the meta-analysis. The heterogeneity (I2 83.0%, p<0.001) was significantly decreased when we included the study quality and the statistical model used as moderator variables (I2 15%). The mean incubation period ranged from 5.6 (95% CI: 5.2-6.0) to 6.7 days (95% CI: 6.0-7.4) according to the statistical model. The 95th percentile was 12.5 days when the mean age of patients was 60 years, increasing 1 day for every 10 years. CONCLUSION: Based on the published data reporting the incubation period of COVID-19, the mean time between exposure and onset of clinical symptoms depended on the statistical model used, and the 95th percentile depended on the mean age of the patients. It is advisable to record sex and age when collecting data in order to analyze possible differential patterns.

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BACKGROUND: The aim of our study was to determine through a systematic review and meta-analysis the incubation period of COVID-19. It was conducted based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA). Criteria for eligibility were all published population-based primary literature in PubMed interface and the Science Direct, dealing with incubation period of COVID-19, written in English, since December 2019 to December 2020. We estimated the mean of the incubation period using meta-analysis, taking into account between-study heterogeneity, and the analysis with moderator variables. RESULTS: This review included 42 studies done predominantly in China. The mean and median incubation period were of maximum 8 days and 12 days respectively. In various parametric models, the 95th percentiles were in the range 10.3-16 days. The highest 99th percentile would be as long as 20.4 days. Out of the 10 included studies in the meta-analysis, 8 were conducted in China, 1 in Singapore, and 1 in Argentina. The pooled mean incubation period was 6.2 (95% CI 5.4, 7.0) days. The heterogeneity (I2 77.1%; p < 0.001) was decreased when we included the study quality and the method of calculation used as moderator variables (I2 0%). The mean incubation period ranged from 5.2 (95% CI 4.4 to 5.9) to 6.65 days (95% CI 6.0 to 7.2). CONCLUSIONS: This work provides additional evidence of incubation period for COVID-19 and showed that it is prudent not to dismiss the possibility of incubation periods up to 14 days at this stage of the epidemic.

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Antecedentes y objetivo El período de incubación de la COVID-19 ayuda a determinar la duración óptima del período de cuarentena y a crear modelos predictivos de curvas de incidencia. Se han reportado resultados variables en recientes estudios y, por ello, el objetivo de esta revisión sistemática es proporcionar una estimación más precisa del período de incubación de la COVID-19. Métodos Se realizó una búsqueda bibliográfica en las bases de datos de Pubmed, Scopus/EMBASE y la Cochrane Library, incluyendo todos los estudios observacionales y experimentales que reportaban un período de incubación y que se habían publicado entre el 1 de enero y el 21 de marzo de 2020. Se estimó la media y el percentil 95 del período de incubación mediante metaanálisis, teniendo en cuenta la heterogeneidad entre los estudios y el análisis con variables moderadoras. Resultados Se incluyeron siete estudios (n = 792) en el metaanálisis. La heterogeneidad (I2 83,0%, p < 0,001) disminuyó significativamente cuando se tuvo en cuenta la calidad del estudio y el modelo estadístico utilizado como variables moderadoras (I2 15%). El período medio de incubación oscilaba entre 5,6 (IC 95%: 5,2 a 6,0) y 6,7 días (IC 95%: 6,0 a 7,4), según el modelo estadístico utilizado. El percentil 95 fue de 12,5 días cuando la edad media de los pacientes era de 60 años, aumentando un día por cada 10 años de edad. Conclusión Según los datos publicados sobre el período de incubación de la COVID-19, el tiempo medio entre la exposición y la aparición de los síntomas clínicos depende del modelo estadístico utilizado y el percentil 95, de la edad media de los pacientes. Se recomienda registrar el sexo y la edad en la recogida de los datos para poder analizar los posibles patrones diferenciales (AU)

Background and objective The incubation period of COVID-19 helps to determine the optimal duration of the quarantine and inform predictive models of incidence curves. Several emerging studies have produced varying results; this systematic review aims to provide a more accurate estimate of the incubation period of COVID-19. Methods For this systematic review, a literature search was conducted using Pubmed, Scopus/EMBASE, and the Cochrane Library databases, covering all observational and experimental studies reporting the incubation period and published from 1 January 2020 to 21 March 2020.We estimated the mean and 95th percentile of the incubation period using meta-analysis, taking into account between-study heterogeneity, and the analysis with moderator variables. Results We included seven studies (n = 792) in the meta-analysis. The heterogeneity (I2 83.0%, p < 0.001) was significantly decreased when we included the study quality and the statistical model used as moderator variables (I2 15%). The mean incubation period ranged from 5.6 (95% CI: 5.2 to 6.0) to 6.7 days (95% CI: 6.0 to 7.4) according to the statistical model. The 95th percentile was 12.5 days when the mean age of patients was 60 years, increasing 1 day for every 10 years. Conclusion Based on the published data reporting the incubation period of COVID-19, the mean time between exposure and onset of clinical symptoms depended on the statistical model used, and the 95th percentile depended on the mean age of the patients. It is advisable to record sex and age when collecting data in order to analyze possible differential patterns (AU)

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BACKGROUND AND OBJECTIVE: The incubation period of COVID-19 helps to determine the optimal duration of the quarantine and inform predictive models of incidence curves. Several emerging studies have produced varying results; this systematic review aims to provide a more accurate estimate of the incubation period of COVID-19. METHODS: For this systematic review, a literature search was conducted using Pubmed, Scopus/EMBASE, and the Cochrane Library databases, covering all observational and experimental studies reporting the incubation period and published from 1 January 2020 to 21 March 2020.We estimated the mean and 95th percentile of the incubation period using meta-analysis, taking into account between-study heterogeneity, and the analysis with moderator variables. RESULTS: We included seven studies (n = 792) in the meta-analysis. The heterogeneity (I2 83.0%, p < 0.001) was significantly decreased when we included the study quality and the statistical model used as moderator variables (I2 15%). The mean incubation period ranged from 5.6 (95% CI: 5.2 to 6.0) to 6.7 days (95% CI: 6.0 to 7.4) according to the statistical model. The 95th percentile was 12.5 days when the mean age of patients was 60 years, increasing 1 day for every 10 years. CONCLUSION: Based on the published data reporting the incubation period of COVID-19, the mean time between exposure and onset of clinical symptoms depended on the statistical model used, and the 95th percentile depended on the mean age of the patients. It is advisable to record sex and age when collecting data in order to analyze possible differential patterns.

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BACKGROUND AND OBJECTIVE: The incubation period of COVID-19 helps to determine the optimal duration of the quarantine and inform predictive models of incidence curves. Several emerging studies have produced varying results; this systematic review aims to provide a more accurate estimate of the incubation period of COVID-19. METHODS: For this systematic review, a literature search was conducted using Pubmed, Scopus/EMBASE, and the Cochrane Library databases, covering all observational and experimental studies reporting the incubation period and published from 1 January 2020 to 21 March 2020.We estimated the mean and 95th percentile of the incubation period using meta-analysis, taking into account between-study heterogeneity, and the analysis with moderator variables. RESULTS: We included seven studies (n = 792) in the meta-analysis. The heterogeneity (I2 83.0%, p < 0.001) was significantly decreased when we included the study quality and the statistical model used as moderator variables (I2 15%). The mean incubation period ranged from 5.6 (95% CI: 5.2 to 6.0) to 6.7 days (95% CI: 6.0 to 7.4) according to the statistical model. The 95th percentile was 12.5 days when the mean age of patients was 60 years, increasing 1 day for every 10 years. CONCLUSION: Based on the published data reporting the incubation period of COVID-19, the mean time between exposure and onset of clinical symptoms depended on the statistical model used, and the 95th percentile depended on the mean age of the patients. It is advisable to record sex and age when collecting data in order to analyze possible differential patterns.

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Based on the practical application, this paper introduced the basic calculation conditions, methods and epidemiological significance of incubation period. The real data were used for calculations of the incubation period by lognormal, gamma, Weibull and Erlang distribution methods. Both of the complete and incomplete observation data were demonstrated.

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OBJECTIVES: The distribution of the transmission onset of COVID-19 relative to the symptom onset is a key parameter for infection control. It is often not easy to study the transmission onset time, as it is difficult to know who infected whom exactly when. METHODS: We inferred transmission onset time from 72 infector-infectee pairs in South Korea, either with known or inferred contact dates, utilizing the incubation period. Combining this data with known information of the infector's symptom onset, we could generate the transmission onset distribution of COVID-19, using Bayesian methods. Serial interval distribution could be automatically estimated from our data. RESULTS: We estimated the median transmission onset to be 1.31 days (standard deviation, 2.64 days) after symptom onset with a peak at 0.72 days before symptom onset. The pre-symptomatic transmission proportion was 37% (95% credible interval [CI], 16-52%). The median incubation period was estimated to be 2.87 days (95% CI, 2.33-3.50 days), and the median serial interval to be 3.56 days (95% CI, 2.72-4.44 days). CONCLUSIONS: Considering that the transmission onset distribution peaked with the symptom onset and the pre-symptomatic transmission proportion is substantial, the usual preventive measures might be too late to prevent SARS-CoV-2 transmission.

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Controversy remains over whether the novel coronavirus 2019 (COVID-19) virus may have infectivity during the incubation period before the onset of symptoms. The author had the opportunity to examine the infectivity of COVID-19 during the incubation period by conducting an epidemiological survey on a confirmed patient who had visited Jeju Island during the incubation period. The epidemiological findings support the claim that the COVID-19 virus does not have infectivity during the incubation period.

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BACKGROUND: A sputum culture is the most reliable indicator of the infectiousness of pulmonary tuberculosis (PTB); however, a spontaneous sputum specimen may not be suitable. The aim of this study was to evaluate the infectious period in patients with non-drug-resistant (DR) PTB receiving adequate standard chemotherapy, using induced sputum (IS) specimens. METHODS: We evaluated the duration of infectiousness of PTB using a retrospective cohort design. RESULTS: Among the 35 patients with PTB, 22 were smear-positive. The rates of IS culture positivity from baseline to the sixth week of anti-tuberculosis medication in the smear-positive PTB group were 100%, 100%, 91%, 73%, 36%, and 18%, respectively. For smear-positive PTB cases, the median time of conversion to culture negativity was 35.0 days (range, 28.0-42.0 days). In the smear-negative PTB group (n=13), the weekly rates of positive IS culture were 100%, 77%, 39%, 8%, 0%, and 0%, respectively, and the median time to conversion to culture-negative was 21.0 days (range, 17.5-28.0 days). CONCLUSION: The infectiousness of PTB, under adequate therapy, may persist longer than previously reported, even in patients with non-DR PTB.

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BACKGROUND: A sputum culture is the most reliable indicator of the infectiousness of pulmonary tuberculosis (PTB); however, a spontaneous sputum specimen may not be suitable. The aim of this study was to evaluate the infectious period in patients with non–drug-resistant (DR) PTB receiving adequate standard chemotherapy, using induced sputum (IS) specimens. METHODS: We evaluated the duration of infectiousness of PTB using a retrospective cohort design. RESULTS: Among the 35 patients with PTB, 22 were smear-positive. The rates of IS culture positivity from baseline to the sixth week of anti-tuberculosis medication in the smear-positive PTB group were 100%, 100%, 91%, 73%, 36%, and 18%, respectively. For smear-positive PTB cases, the median time of conversion to culture negativity was 35.0 days (range, 28.0–42.0 days). In the smear-negative PTB group (n=13), the weekly rates of positive IS culture were 100%, 77%, 39%, 8%, 0%, and 0%, respectively, and the median time to conversion to culture-negative was 21.0 days (range, 17.5–28.0 days). CONCLUSION: The infectiousness of PTB, under adequate therapy, may persist longer than previously reported, even in patients with non-DR PTB.

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In a novel approach, the standard birth-death process is extended to incorporate a fundamental mechanism undergone by intracellular bacteria, phagocytosis. The model accounts for stochastic interaction between bacteria and cells of the immune system and heterogeneity in susceptibility to infection of individual hosts within a population. Model output is the dose-response relation and the dose-dependent distribution of time until response, where response is the onset of symptoms. The model is thereafter parametrized with respect to the highly virulent Schu S4 strain of Francisella tularensis, in the first such study to consider a biologically plausible mathematical model for early human infection with this bacterium. Results indicate a median infectious dose of about 23 organisms, which is higher than previously thought, and an average incubation period of between 3 and 7 days depending on dose. The distribution of incubation periods is right-skewed up to about 100 organisms and symmetric for larger doses. Moreover, there are some interesting parallels to the hypotheses of some of the classical dose-response models, such as independent action (single-hit model) and individual effective dose (probit model). The findings of this study support experimental evidence and postulations from other investigations that response is, in fact, influenced by both in-host and between-host variability.

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