Dataset Information

A Bayesian inferential approach to quantify the transmission intensity of disease outbreak.

ABSTRACT:

Background

Emergence of infectious diseases like influenza pandemic (H1N1) 2009 has become great concern, which posed new challenges to the health authorities worldwide. To control these diseases various studies have been developed in the field of mathematical modelling, which is useful tool for understanding the epidemiological dynamics and their dependence on social mixing patterns.

Method

We have used Bayesian approach to quantify the disease outbreak through key epidemiological parameter basic reproduction number (R0), using effective contacts, defined as sum of the product of incidence cases and probability of generation time distribution. We have estimated R0 from daily case incidence data for pandemic influenza A/H1N1 2009 in India, for the initial phase.

Result

The estimated R0 with 95% credible interval is consistent with several other studies on the same strain. Through sensitivity analysis our study indicates that infectiousness affects the estimate of R0.

Conclusion

Basic reproduction number R0 provides the useful information to the public health system to do some effort in controlling the disease by using mitigation strategies like vaccination, quarantine, and so forth.

SUBMITTER: Kadi AS

PROVIDER: S-EPMC4345055 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

A Bayesian inferential approach to quantify the transmission intensity of disease outbreak.

Kadi Adiveppa S AS Avaradi Shivakumari R SR

Computational and mathematical methods in medicine 20150215

<h4>Background</h4>Emergence of infectious diseases like influenza pandemic (H1N1) 2009 has become great concern, which posed new challenges to the health authorities worldwide. To control these diseases various studies have been developed in the field of mathematical modelling, which is useful tool for understanding the epidemiological dynamics and their dependence on social mixing patterns.<h4>Method</h4>We have used Bayesian approach to quantify the disease outbreak through key epidemiologica ...[more]

PMID: 25784956

Similar Datasets

Project description:BackgroundAsymptomatic carriage of malaria parasites is common in high transmission intensity areas and confounds clinical case definitions for research studies. This is important for investigations that aim to identify immune correlates of protection from clinical malaria. The proportion of fevers attributable to malaria parasites is widely used to define different thresholds of parasite density associated with febrile episodes. The varying intensity of malaria transmission was investigated to check whether it had a significant impact on the parasite density thresholds. The same dataset was used to explore an alternative statistical approach, using the probability of developing fevers as a choice over threshold cut-offs. The former has been reported to increase predictive power.MethodsData from children monitored longitudinally between 2005 and 2017 from Junju and Chonyi in Kilifi, Kenya were used. Performance comparison of Bayesian-latent class and logistic power models in estimating malaria attributable fractions and probabilities of having fever given a parasite density with changing malaria transmission intensity was done using Junju cohort. Zero-inflated beta regressions were used to assess the impact of using probabilities to evaluate anti-merozoite antibodies as correlates of protection, compared with multilevel binary regression using data from Chonyi and Junju.ResultsMalaria transmission intensity declined from over 49% to 5% between 2006 and 2017, respectively. During this period, malaria attributable fraction varied between 27-59% using logistic regression compared to 10-36% with the Bayesian latent class approach. Both models estimated similar patterns of fevers attributable to malaria with changing transmission intensities. The Bayesian latent class model performed well in estimating the probabilities of having fever, while the latter was efficient in determining the parasite density threshold. However, compared to the logistic power model, the Bayesian algorithm yielded lower estimates for both attributable fractions and probabilities of fever. In modelling the association of merozoite antibodies and clinical malaria, both approaches resulted in comparable estimates, but the utilization of probabilities had a better statistical fit.ConclusionsMalaria attributable fractions, varied with an overall decline in the malaria transmission intensity in this setting but did not significantly impact the outcomes of analyses aimed at identifying immune correlates of protection. These data confirm the statistical advantage of using probabilities over binary data.

Dataset Information

A Bayesian inferential approach to quantify the transmission intensity of disease outbreak.

Background

Method

Result

Conclusion

Publications

A Bayesian inferential approach to quantify the transmission intensity of disease outbreak.

Similar Datasets

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