scholarly journals Inference on COVID-19 Epidemiological Parameters Using Bayesian Survival Analysis

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1262
Author(s):  
Chiara Bardelli
Keyword(s):  
Survival Analysis ◽  
Length Of Stay ◽  
Sir Model ◽  
Epidemiological Models ◽  
Age Factor ◽  
Epidemiological Parameters

The need to provide accurate predictions in the evolution of the COVID-19 epidemic has motivated the development of different epidemiological models. These models require a careful calibration of their parameters to capture the dynamics of the phenomena and the uncertainty in the data. This work analyzes different parameters related to the personal evolution of COVID-19 (i.e., time of recovery, length of stay in hospital and delay in hospitalization). A Bayesian Survival Analysis is performed considering the age factor and period of the epidemic as fixed predictors to understand how these features influence the evolution of the epidemic. These results can be easily included in the epidemiological SIR model to make prediction results more stable.

scholarly journals Illuminance affects epidemiological parameters of banana Yellow Sigatoka in Brazil

2019 ◽  
Author(s):  
Djalma M. Santana-Filho ◽  
Milene C. da Silva ◽  
Jorge T. de Souza ◽  
Zilton J. M. Cordeiro ◽  
Hermínio S. Rocha ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Survival Analysis ◽  
Cropping Systems ◽  
Leaf Age ◽  
Cropping System ◽  
Black Sigatoka ◽  
Light Interference ◽  
Leaf Spots ◽  
Leaf Necrosis ◽  
Epidemiological Parameters

ABSTRACTThe Sigatoka leaf spots are among the most important banana diseases. Although less damaging than black sigatoka, yellow sigatoka (Pseudocercospora musae) still prevails in some regions. This study aimed at testing the hypothesis of light interference in monocyclic parameters of yellow sigatoka epidemics. Grande Naine plantlets kept under contrasting shading conditions had their leaves 1 and 2 inoculated. Evaluations were performed for 60 days. For each inoculated leaf, the time until symptom onset (incubation), presence of infectious lesions (latency), and disease severity (extensive leaf necrosis) according to Stover’s scale modify per Gauhl (1994), called here only Stover’s scale, were registered. Logistic regression was used to assess the relative occurrence risk and survival analysis was used to check the effects of variables on relevant epidemiological parameters. The risks of sporulation and of reaching high severities were lower for plants kept under shading regardless of the acclimation conditions and no effect of leaf age was detected. The logistic regression showed symptoms appearing in both conditions (p=0,85), but have significance difference in occurrence of latent lesions (p=0,013) and necrosis (p<0,0001). The necrosis risk in non-shaded environment arrived 66%. The survival analysis showed significance difference in the time to appear the symptom evaluated in all tested variables (p<0,0001) in function of the cropping system. Lower illuminance negatively affected the incubation, latency and infectious periods, and severity. A shaded system could be tested to produce organic bananas in areas of high risk of occurrence of Yellow sigatoka disease.Significance and Impact of the StudyYellow Sigatoka (Pseudocercospora musae) is a banana disease that can cause severe damage if left uncontrolled. Its control is based mostly on fungicides.Our results show that shading downregulates the epidemiological parameters of that disease such as incubation, latent and infectious periods, and symptom’s severity. These results can be the basis for testing alternative cropping systems and producing organic bananas.

Length of Stay, Leave and Re-Offending by Patients from a Queensland Security Patients Hospital

1998 ◽  
Vol 32 (6) ◽  
pp. 839-847 ◽  
Author(s):  
Bob Green ◽  
Anthony J. Baglioni
Keyword(s):  
Survival Analysis ◽  
Length Of Stay ◽  
Empirical Data ◽  
International Research ◽  
Decision Makers ◽  
International Studies ◽  
Lower Range ◽  
Public Controversy ◽  
The Media ◽  
The Subject

Objective: The release of patients from a security patients hospital has been the subject of public controversy. The present study uses empirical data to examine the length of stay, leave, and re-offending of patients from a security hospital. Methods: Survival analysis was used to examine factors that may be predictive of length of stay and time under restriction, as well as time to first overnight leave. Data on re-offending were obtained from a variety of sources and were compared with seriousness of index offences. Results: Consistent with international research, patients with more serious offences had longer hospitalisations. Patients with more serious offences were also hospitalised for longer periods before leave was granted. Compared with international studies, re-offending was in the lower range. Conclusions: Despite concerns raised in the media regarding patient ‘dangerousness’, time spent in hospital and the granting of leave, patients with serious offences were more likely to be hospitalised longer, which suggests decision makers do take into account public safety.

scholarly journals Calibrating an SIR model for South Korea COVID-19 infections and predicting vaccination impact

2021 ◽  
Author(s):  
Brandon Pae
Keyword(s):  
South Korea ◽  
Vaccination Coverage ◽  
Herd Immunity ◽  
Sir Model ◽  
Transmission Dynamics ◽  
Effective Vaccine ◽  
Epidemiological Models ◽  
Policy Decisions

In the span of 1.5 years, COVID-19 has caused more than 4 million deaths worldwide. To prevent such a catastrophe from reoccurring, it is necessary to test and refine current epidemiological models that impact policy decisions. Thus, we developed a deterministic SIR model to examine the long-term transmission dynamics of COVID-19 in South Korea. Using this model, we analyzed how vaccines would affect the number of cases. We found that a 70% vaccination coverage with a 100% effective vaccine would effectively eliminate the number of cases and herd immunity would have been obtained approximately 85 days after February 15 had there not been a reintroduction of cases.

scholarly journals Reproducing country-wide COVID-19 dynamics can require the usage of a set of SIR systems

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10679
Author(s):  
Eugene B. Postnikov
Keyword(s):  
Sir Model ◽  
Epidemiological Models ◽  
Processing Data ◽  
Cumulative Amount

This work shows that simple compartmental epidemiological models may not reproduce actually reported country-wide statistics since the latter reflects the cumulative amount of infected persons, which in fact is a sum of outbreaks within different patched. It the same time, the multilogistic decomposition of such epidemiological curves reveals components, which are quite close to the solutions of the SIR model in logistic approximations characterised by different sets of parameters including time shifts. This line of reasoning is confirmed by processing data for Spain and Russia in details and, additionally, is illustrated for several other countries.

scholarly journals Parameter Estimation of Compartmental Epidemiological Model Using Harmony Search Algorithm and Its Variants

2021 ◽  
Vol 11 (3) ◽  
pp. 1138
Author(s):  
Kathiresan Gopal ◽  
Lai Soon Lee ◽  
Hsin-Vonn Seow
Keyword(s):  
Parameter Estimation ◽  
Search Algorithm ◽  
Harmony Search ◽  
Vital Role ◽  
Harmony Search Algorithm ◽  
Model Parameters ◽  
Data Sets ◽  
Epidemiological Models ◽  
Epidemiological Modeling ◽  
Epidemiological Parameters

Epidemiological models play a vital role in understanding the spread and severity of a pandemic of infectious disease, such as the COVID-19 global pandemic. The mathematical modeling of infectious diseases in the form of compartmental models are often employed in studying the probable outbreak growth. Such models heavily rely on a good estimation of the epidemiological parameters for simulating the outbreak trajectory. In this paper, the parameter estimation is formulated as an optimization problem and a metaheuristic algorithm is applied, namely Harmony Search (HS), in order to obtain the optimized epidemiological parameters. The application of HS in epidemiological modeling is demonstrated by implementing ten variants of HS algorithm on five COVID-19 data sets that were calibrated with the prototypical Susceptible-Infectious-Removed (SIR) compartmental model. Computational experiments indicated the ability of HS to be successfully applied to epidemiological modeling and as an efficacious estimator for the model parameters. In essence, HS is proposed as a potential alternative estimation tool for parameters of interest in compartmental epidemiological models.

scholarly journals The SIR and SEIR Epidemiological Models Revisited

2020 ◽  
Author(s):  
John P. Maassen
Keyword(s):  
Public Health ◽  
Monte Carlo ◽  
Stochastic Model ◽  
Monte Carlo Simulations ◽  
Sir Model ◽  
Epidemiological Models ◽  
Seir Model ◽  
Social Distancing

We review and assess the classic SIR and SEIR epidemiological models regarding possible applications to the COVID-19 pandemic. In spite of numerous more complicated models, we show how the qualitative features of the solution to the SIR and SEIR models continue to provide valuable public health insights in some scenarios. Using estimated COVID-19 data as of this date, the SEIR model shows that if it were possible to reduce R0 from 2.5 to 1.25 through social distancing and other measures, the maximum fraction of the population that would become infected at any particular time would drop from 17% to 4%, provided that all of the model assumptions are satisfied. Finally, we compare the classic SIR model with a recent stochastic model with favorable results. Since this comparison underscores the importance of underlying connectivity assumptions, we conclude with Monte-Carlo simulations with specific connectivity that reproduce the classical SIR model with standard incidence.

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