scholarly journals A Hybrid Modeling Technique of Epidemic Outbreaks with Application to COVID-19 Dynamics in West Africa

Biology ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 365
Author(s):  
Chénangnon Frédéric Tovissodé ◽  
Jonas Têlé Doumatè ◽  
Romain Glèlè Kakaï
Keyword(s):  
West Africa ◽  
Growth Curve ◽  
Exponential Growth ◽  
Latency Period ◽  
Growth Curves ◽  
Exponential Growth Phase ◽  
Model Framework ◽  
Logistic Regression Models ◽  
Containment Measures ◽  
Reporting Data

The widely used logistic model for epidemic case reporting data may be either restrictive or unrealistic in presence of containment measures when implemented after an epidemic outbreak. For flexibility in epidemic case reporting data modeling, we combined an exponential growth curve for the early epidemic phase with a flexible growth curve to account for the potential change in growth pattern after implementation of containment measures. We also fitted logistic regression models to recoveries and deaths from the confirmed positive cases. In addition, the growth curves were integrated into a SIQR (Susceptible, Infective, Quarantined, Recovered) model framework to provide an overview on the modeled epidemic wave. We focused on the estimation of: (1) the delay between the appearance of the first infectious case in the population and the outbreak (“epidemic latency period”); (2) the duration of the exponential growth phase; (3) the basic and the time-varying reproduction numbers; and (4) the peaks (time and size) in confirmed positive cases, active cases and new infections. The application of this approach to COVID-19 data from West Africa allowed discussion on the effectiveness of some containment measures implemented across the region.

scholarly journals A Hybrid Modelling Technique of Epidemic Outbreaks With Application to COVID-19 Dynamics in West Africa

2021 ◽  
Author(s):  
Chénangnon Frédéric Tovissodé ◽  
Jonas Têlé Doumatè ◽  
Romain Glèlè Kakaï
Keyword(s):  
West Africa ◽  
Growth Curve ◽  
Exponential Growth ◽  
Latency Period ◽  
Growth Curves ◽  
Exponential Growth Phase ◽  
Model Framework ◽  
Logistic Regression Models ◽  
Containment Measures ◽  
Reporting Data

The widely used logistic model for epidemic case reporting data may be either restrictive or unrealistic in presence of containment measures when implemented after an epidemic outbreak. For flexibility in epidemic case reporting data modelling, we combined an exponential growth curve for the early epidemic phase with a exible growth curve to account for the potential change in growth pattern after implementation of containment measures. We also fitted logistic regression models to recoveries and deaths from the confirmed positive cases. In addition, the growth curves were integrated in a SIQKU (Susceptible, Infective, Quarantined, Known recovered, Unknown recovered) model framework to provide an overview on the modelled epidemic wave. We focused on the estimation of: (1) the delay between the appearance of the first infectious case in the population and the outbreak ("epidemic latency period"); (2) the duration of the exponential growth phase; (3) the basic and the time-varying reproduction numbers; and (4) the peaks (time and size) in confirmed positive cases, active cases and new infections. The application of this approach to COVID-19 data from West Africa allowed to discuss the eectiveness of some containment measures implemented across the region.

scholarly journals A Hybrid Modelling Technique of Epidemic Outbreaks With Application to COVID-19 Dynamics in West Africa

2021 ◽  
Author(s):  
Chénangnon Frédéric Tovissodé ◽  
Jonas Têlé Doumatè ◽  
Romain Glèlè Kakaï
Keyword(s):  
West Africa ◽  
Growth Curve ◽  
Exponential Growth ◽  
Latency Period ◽  
Growth Curves ◽  
Exponential Growth Phase ◽  
Model Framework ◽  
Logistic Regression Models ◽  
Containment Measures ◽  
Reporting Data

The widely used logistic model for epidemic case reporting data may be either restrictive or unrealistic in presence of containment measures when implemented after an epidemic outbreak. For flexibility in epidemic case reporting data modelling, we combined an exponential growth curve for the early epidemic phase with a flexible growth curve to account for the potential change in growth pattern after implementation of containment measures. We also fitted logistic regression models to recoveries and deaths from the confirmed positive cases. In addition, the growth curves were integrated into a SIQR (Susceptible, Infective, Quarantined, Recovered) model framework to provide an overview on the modelled epidemic wave. We focused on the estimation of: (1) the delay between the appearance of the first infectious case in the population and the outbreak (“epidemic latency period"); (2) the duration of the exponential growth phase; (3) the basic and the time-varying reproduction numbers; and (4) the peaks (time and size) in confirmed positive cases, active cases and new infections. The application of this approach to COVID-19 data from West Africa allowed to discuss the effectiveness of some containment measures implemented across the region.

scholarly journals Trends of SARS-Cov-2 infection in 67 countries: Role of climate zone, temperature, humidity and curve behavior of cumulative frequency on duplication time

2020 ◽  
Author(s):  
Jaime Berumen ◽  
Max Schmulson ◽  
Guadalupe Guerrero ◽  
Elizabeth Barrera ◽  
Jorge Larriva-Sahd ◽  
...  
Keyword(s):  
Growth Curve ◽  
Growth Curves ◽  
Cumulative Frequency ◽  
Climate Zone ◽  
First Case ◽  
Containment Measures ◽  
The Difference ◽  
Lineal Regression

Summary Objective. To analyze the role of temperature, humidity, date of first case diagnosed (DFC) and the behavior of the growth-curve of cumulative frequency (CF) [number of days to rise (DCS) and reach the first 100 cases (D100), and the difference between them (ΔDD)] with the doubling time (Td) of Covid-19 cases in 67 countries grouped by climate zone. Design. Retrospective incident case study. Setting. WHO based register of cumulative incidence of Covid-19 cases. Participants. 1,706,914 subjects diagnosed between 12-29-2019 and 4-15-2020. Exposures. SARS-Cov-2 virus, ambient humidity, temperature and climate areas (temperate, tropical/subtropical). Main outcome measures. Comparison of DCS, D100, ΔDD, DFC, humidity, temperature, Td for the first (Td10) and second (Td20) ten days of the CF growth-curve between countries according to climate zone, and identification of factors involved in Td, as well as predictors of CF using lineal regression models. Results. Td10 and Td20 were ≥3 days longer in tropical/subtropical vs. temperate areas (2.8[plusmn]1.2 vs. 5.7[plusmn]3.4; p=1.41E-05 and 4.6[plusmn]1.8 vs. 8.6[plusmn]4.2; p=9.7E-05, respectively). The factors involved in Td10 (DFC and ΔDD) were different than those in Td20 (Td10 and climate areas). After D100, the fastest growth-curves during the first 10 days, were associated with Td10<2 and Td10<3 in temperate and tropical/subtropical countries, respectively. The fold change Td20/Td10 >2 was associated with earlier flattening of the growth-curve. In multivariate models, Td10, DFC and ambient temperature were negatively related with CF and explained 44.7% (r2 = 0.447) of CF variability at day 20 of the growth-curve, while Td20 and DFC were negatively related with CF and explained 63.8% (r2 = 0.638) of CF variability towards day 30 of the growth-curve. Conclusions. The larger Td in tropical/subtropical countries is positively related to DFC and temperature. Td and environmental factors explain 64% of CF variability in the best of cases. Therefore, other factors, such as pandemic containment measures, would explain the remaining variability.

scholarly journals Estimation of the Number of Cumulative COVID-19 Cases by Day in Thailand Based on a Flattened Curve Policy

2021 ◽  
Vol 18 (13) ◽  
Author(s):  
Yupaporn AREEPONG ◽  
Rapin SUNTHORNWAT
Keyword(s):  
Growth Curve ◽  
Exponential Growth ◽  
Herd Immunity ◽  
Growth Curves ◽  
Least Square Method ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Logistic Growth ◽  
Thai Government

Since December 2019, the world has been facing an emerging infectious disease named coronavirus disease 2019. Thailand has also been affected by the spread of the coronavirus. The Thai government have announced policies to protect people, based on the emergency decree and curfew law for flattening the curve of the number of the coronavirus disease 2019 cases without vaccination in Thailand. This research estimated of the number of total infectious cases of coronavirus disease 2019 in Thailand. Two growth curves, including an exponential growth curve under a non-flattened curve policy (herd immunity policy without vaccination), and a logistic growth curve under a flattened curve policy without vaccination, were selected to estimate the parameters of the curves by the least square method to represent the number of the total infectious cases in Thailand. Moreover, the maximum infectious cases of coronavirus disease 2019 and the speed of spreading for coronavirus disease 2019 in Thailand were also explored. Based on the number of the total infectious cases of coronavirus disease 2019 in Thailand, the findings demonstrated that the coefficient of determination of the logistic growth curve was greater than the exponential growth curve and the root means squared percentage error of the logistic growth curve was less than the exponential growth curve. These results suggest that the logistic growth curve is suitable for describing the number of total infectious cases of coronavirus disease 2019 in Thailand under the fattened curve policy. GRAPHICAL ABSTRACT

scholarly journals A new regression model for the forecasting of COVID-19 outbreak evolution: an application to Italian data

2020 ◽  
Author(s):  
Davide Sisti ◽  
Ettore Rocchi ◽  
Sara Peluso ◽  
Stefano Amatori ◽  
Margherita Carletti
Keyword(s):  
Growth Curve ◽  
Exponential Growth ◽  
Growth Patterns ◽  
Expected Time ◽  
Straight Line ◽  
Italian Regions ◽  
Containment Measures ◽  
Empirical Curve ◽  
Novel Coronavirus ◽  
The Impact

Abstract The novel coronavirus SARS-CoV-2 was first identified in China in December 2019. In just over five months, the virus affected over 4 million people and caused about 300,000 deaths. This study aimed to model new COVID-19 cases in Italy using a new curve. A new empirical curve is proposed to model the number of new cases of COVID-19. It resembles a known exponential growth curve which has a straight line as an exponent, but in the growth curve proposed, the exponent is a logistic curve multiplied for a straight line. This curve shows an initial phase, the expected exponential growth; then rises to the maximum value and finally reaches zero. We characterized the epidemic growth patterns for the entire Italian nation and for each of the 20 Italian regions. The estimated growth curve has been used to calculate the expected time of the beginning, the time related to peak, and the end of the epidemics. Our analysis explores the development of the epidemics in Italy and the impact of the containment measures. Data obtained are useful to forecast future scenario and the possible end of the outbreak.

scholarly journals sparse-growth-curve: a Computational Pipeline for Parsing Cellular Growth Curves with Low Temporal Resolution

2021 ◽  
Vol 10 (19) ◽  
Author(s):  
Chuankai Cheng ◽  
J. Cameron Thrash
Keyword(s):  
Temporal Resolution ◽  
Growth Curve ◽  
Growth Rates ◽  
Exponential Growth ◽  
Software Package ◽  
Growth Curves ◽  
Cellular Growth ◽  
Computational Pipeline ◽  
Growth Phases ◽  
Time Data

ABSTRACT Here, we introduce a Python-based repository, sparse-growth-curve, a software package designed for parsing cellular growth curves with low temporal resolution. The repository uses cell density and time data as the input, automatically separates different growth phases, calculates the exponential growth rates, and produces multiple graphs to aid in interpretation.

scholarly journals Chasing the ghost of infection past: identifying thresholds of change during the COVID-19 infection in Spain

2020 ◽  
Vol 148 ◽  
Author(s):  
Luis Santamaría ◽  
Joaquín Hortal
Keyword(s):  
Public Health ◽  
Growth Rate ◽  
Health Communication ◽  
Communication Strategies ◽  
Growth Curves ◽  
Personal Hygiene ◽  
Exponential Growth Rate ◽  
Public Health Communication ◽  
Containment Measures ◽  
Key Events

Abstract One of the largest nationwide bursts of the first COVID-19 outbreak occurred in Spain, where infection expanded in densely populated areas through March 2020. We analyse the cumulative growth curves of reported cases and deaths in all Spain and two highly populated regions, Madrid and Catalonia, identifying changes and sudden shifts in their exponential growth rate through segmented Poisson regressions. We associate these breakpoints with a timeline of key events and containment measures, and data on policy stringency and citizen mobility. Results were largely consistent for infections and deaths in all territories, showing four major shifts involving 19–71% reductions in growth rates originating from infections before 3 March and on 5–8, 10–12 and 14–18 March, but no identifiable effect of the strengthened lockdown of 29–30 March. Changes in stringency and mobility were only associated to the latter two shifts, evidencing an early deceleration in COVID-19 spread associated to personal hygiene and social distancing recommendations, followed by a stronger decrease when lockdown was enforced, leading to the contention of the outbreak by mid-April. This highlights the importance of combining public health communication strategies and hard confinement measures to contain epidemics.

scholarly journals Depressive Symptoms in Swiss University Students during the COVID-19 Pandemic and Their Correlates

2021 ◽  
Vol 18 (4) ◽  
pp. 1458 ◽  
Author(s):  
Thomas Volken ◽  
Annina Zysset ◽  
Simone Amendola ◽  
Anthony Klein Swormink ◽  
Marion Huber ◽  
...  
Keyword(s):  
Mental Health ◽  
Depressive Symptoms ◽  
University Students ◽  
Mental Health Problems ◽  
Binary Logistic Regression ◽  
Cross Sectional ◽  
Logistic Regression Models ◽  
National Population ◽  
Student Cohorts ◽  
Containment Measures

Background: COVID-19 containment measures and the uncertainties associated with the pandemic may have contributed to changes in mental health risks and mental health problems in university students. Due to the high burden of the disease, depression is of particular concern. However, knowledge about the prevalence of depressive symptoms in Swiss university students during the pandemic is limited. We therefore assessed the prevalence of depressive symptoms and their change during the COVID-19 pandemic in a large sample of Swiss university students. Methods: We assessed depressive symptoms in two cross-sectional cohorts of university students (n = 3571) in spring and autumn 2020 during the COVID-19 pandemic and compared them with a matched sample of the Swiss national population (n = 2328). Binary logistic regression models estimated prevalence with corresponding 95% confidence intervals (95% CI). Results: Adjusted prevalence of depressive symptoms in female (30.8% (95% CI: 28.6–33.0)) and male students (24.8% (95% CI: 21.7–28.1)) was substantially higher than in the matching female (10.9% (95% CI: 8.9–13.2)) and male (8.5% (6.6–11.0)) pre-pandemic national population. Depressive symptoms in the two consecutive student cohorts did not significantly differ. Conclusions: More than a quarter of Swiss university students reported depressive symptoms during the COVID-19 pandemic, which was substantially higher as compared to the matched general population. Universities should introduce measures to support students in such times of crisis and gain an understanding of the factors impacting mental health positively or negatively and related to university structures and procedures.

scholarly journals Growth in achondroplasia, from birth to adulthood, analysed by the JPA-2 model

2020 ◽  
Vol 33 (12) ◽  
pp. 1589-1595
Author(s):  
Mariana del Pino ◽  
Virginia Fano ◽  
Paula Adamo
Keyword(s):  
General Population ◽  
Growth Curve ◽  
Growth Velocity ◽  
Adult Height ◽  
Growth Curves ◽  
Peak Height ◽  
Height Growth ◽  
Individual Growth ◽  
Height Velocity ◽  
Peak Height Velocity

AbstractObjectivesIn general population, there are three phases in the human growth curve: infancy, childhood and puberty, with different main factors involved in their regulation and mathematical models to fit them. Achondroplasia children experience a fast decreasing growth during infancy and an “adolescent growth spurt”; however, there are no longitudinal studies that cover the analysis of the whole post-natal growth. Here we analyse the whole growth curve from infancy to adulthood applying the JPA-2 mathematical model.MethodsTwenty-seven patients, 17 girls and 10 boys with achondroplasia, who reached adult size, were included. Height growth data was collected from birth until adulthood. Individual growth curves were estimated by fitting the JPA-2 model to each individual’s height for age data.ResultsHeight growth velocity curves show that after a period of fast decreasing growth velocity since birth, with a mean of 9.7 cm/year at 1 year old, the growth velocity is stable in late preschool years, with a mean of 4.2 cm/year. In boys, age and peak height velocity in puberty were 13.75 years and 5.08 cm/year and reach a mean adult height of 130.52 cm. In girls, the age and peak height velocity in puberty were 11.1 years and 4.32 cm/year and reach a mean adult height of 119.2 cm.ConclusionsThe study of individual growth curves in achondroplasia children by the JPA-2 model shows the three periods, infancy, childhood and puberty, with a similar shape but lesser in magnitude than general population.

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