Using a Negative Binomial Regression Model for Early Warning at the Start of a Hand Foot Mouth Disease Epidemic in Dalian, Liaoning Province, China

An, Qiuling; Wu, Jun; Xue-song, Fan; Pan, Liyang; Sun, Wei

doi:10.1371/journal.pone.0157815

Cited by 8 publications

(6 citation statements)

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“…The Poisson regression model is inappropriate for handling such over-dispersed data, and the negative binomial (NB) regression model performs proper rigour with the estimation procedure in the presence of overdispersion [ 44 – 46 ]. This model has been extensively used by researchers [ 47 – 49 ] to manage over-dispersed count data attributable to rare events, such as different types of infectious and non-infectious diseases. The NB regression model, a generalisation of the Poisson regression model, incorporates overdispersion in the estimation procedure through the inclusion of an additional parameter, which is termed the overdispersion parameter [ 50 , 51 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Modelling Approachmentioning

confidence: 99%

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Heat health risk assessment analysing heatstroke patients in Fukuoka City, Japan

Toosty

Hagishima

Tanaka³

2021

PLoS ONE

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Background Climate change, as a defining issue of the current time, is causing severe heat-related illness in the context of extremely hot weather conditions. In Japan, the remarkable temperature increase in summer caused by an urban heat island and climate change has become a threat to public health in recent years. Methods This study aimed to determine the potential risk factors for heatstroke by analysing data extracted from the records of emergency transport to the hospital due to heatstroke in Fukuoka City, Japan. In this regard, a negative binomial regression model was used to account for overdispersion in the data. Age-structure analyses of heatstroke patients were also embodied to identify the sub-population of Fukuoka City with the highest susceptibility. Results The daily maximum temperature and wet-bulb globe temperature (WBGT), along with differences in both the mean temperature and time-weighted temperature from those of the consecutive past days were detected as significant risk factors for heatstroke. Results indicated that there was a positive association between the resulting risk factors and the probability of heatstroke occurrence. The elderly of Fukuoka City aged 70 years or older were found to be the most vulnerable to heatstroke. Most of the aforementioned risk factors also encountered significant and positive associations with the risk of heatstroke occurrence for the group with highest susceptibility. Conclusion These results can provide insights for health professionals and stakeholders in designing their strategies to reduce heatstroke patients and to secure the emergency transport systems in summer.

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Section: Methodsmentioning

confidence: 99%

Section: Modelling Approachmentioning

confidence: 99%

Heat health risk assessment analysing heatstroke patients in Fukuoka City, Japan

Toosty

Hagishima

Tanaka³

2021

PLoS ONE

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“…A hybrid model combining a seasonal autoregressive integrated moving average (ARIMA) model and a nonlinear autoregressive neural network (NARNN) was proposed to predict the expected incidence of cases in Shenzhen city from December 2012 to May 2013 (Yu et al 2014). In Dalian city, a negative binomial regression model was used to estimate the weekly baseline number of HFMD cases and identified the optimal alerting threshold between different tested threshold values during the epidemic and nonepidemic year (An et al 2016). Most of these early warning models use historical case data to forecast an imminent outbreak; in fact, these models use only retrospective research (Edmond et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Using a Bayesian belief network model for early warning of death and severe risk of HFMD in Hunan province, China

Liao

Liu

et al. 2018

Stoch Environ Res Risk Assess

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Hand, foot, and mouth disease (HFMD) is a global infectious disease resulting in millions of cases and even hundreds of deaths. Although a newly developed formalin-inactivated EV71 (FI-EV71) vaccine is effective against EV71, which is a major pathogen for HFMD, no vaccine against HFMD itself has yet been developed. Therefore, establishing a sensitive and accurate early warning system for HFMD is important. The early warning model for HFMD in the China Infectious Disease Automated-alert and Response System combines control chart and spatial statistics models to detect spatiotemporal abnormal aggregations of morbidity. However, that type of early warning for HFMD just involves retrospective analysis. In this study, we apply a Bayesian belief network (BBN) to estimate the increased risk of death and severe HFMD in the next month based on pathogen detection and environmental factors. Hunan province, one of the regions with the highest prevalence of HFMD in China, was selected as the study area. The results showed that compared with the traditional early warning model for HFMD, the proposed method can achieve a very high performance evaluation (the average AUC tests were more than 0.92). The model is also simple and easy to operate. Once the structure of the BBN is established, the increased risk of death and severe HFMD in the next month can be estimated based on any one node in the BBN. Keywords Hand, foot, and mouth disease Á Early warning Á Spatiotemporal abnormal aggregation Á Moving percentile method Á Bayesian belief network

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“…The existing epidemic modelling literature has recognized the need for overdispersed distributions to deal with erratic incidence counts [ 29 , 66 , 67 ]. Thus, the study in [ 67 ] shows that use of a negative binomial distribution is more appropriate than the Poisson for describing emerging infections with overdispersed case distributions due to superspreading events.…”

Section: Discussionmentioning

confidence: 99%

Mid-Epidemic Forecasts of COVID-19 Cases and Deaths: A Bivariate Model Applied to the UK

Congdon

2021

Interdisciplinary Perspectives on Infectious Diseases

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Background. The evolution of the COVID-19 epidemic has been accompanied by efforts to provide comparable international data on new cases and deaths. There is also accumulating evidence on the epidemiological parameters underlying COVID-19. Hence, there is potential for epidemic models providing mid-term forecasts of the epidemic trajectory using such information. The effectiveness of lockdown or lockdown relaxation can also be assessed by modelling later epidemic stages, possibly using a multiphase epidemic model. Methods. Commonly applied methods to analyse epidemic trajectories or make forecasts include phenomenological growth models (e.g., the Richards family of densities) and variants of the susceptible-infected-recovered (SIR) compartment model. Here, we focus on a practical forecasting approach, applied to interim UK COVID data, using a bivariate Reynolds model (for cases and deaths), with implementation based on Bayesian inference. We show the utility of informative priors in developing and estimating the model and compare error densities (Poisson-gamma, Poisson-lognormal, and Poisson-log-Student) for overdispersed data on new cases and deaths. We use cross validation to assess medium-term forecasts. We also consider the longer-term postlockdown epidemic profile to assess epidemic containment, using a two-phase model. Results. Fit to interim mid-epidemic data show better fit to training data and better cross-validation performance for a Poisson-log-Student model. Estimation of longer-term epidemic data after lockdown relaxation, characterised by protracted slow downturn and then upturn in cases, casts doubt on effective containment. Conclusions. Many applications of phenomenological models have been to complete epidemics. However, evaluation of such models based simply on their fit to observed data may give only a partial picture, and cross validation against actual trends is also valuable. Similarly, it may be preferable to model incidence rather than cumulative data, although this raises questions about suitable error densities for modelling often erratic fluctuations. Hence, there may be utility in evaluating alternative error assumptions.

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Using a Negative Binomial Regression Model for Early Warning at the Start of a Hand Foot Mouth Disease Epidemic in Dalian, Liaoning Province, China

Cited by 8 publications

References 11 publications

Heat health risk assessment analysing heatstroke patients in Fukuoka City, Japan

Heat health risk assessment analysing heatstroke patients in Fukuoka City, Japan

Using a Bayesian belief network model for early warning of death and severe risk of HFMD in Hunan province, China

Mid-Epidemic Forecasts of COVID-19 Cases and Deaths: A Bivariate Model Applied to the UK

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