Applied Mixed Generalized Additive Model to Assess the Effect of Temperature on the Incidence of Bacillary Dysentery and Its Forecast

Abstract

BackgroundAssociation between bacillary dysentery (BD) disease and temperature has been reported in some studies applying Poisson regression model, however the effect estimation might be biased due to the data autocorrelation. Furthermore the temperature effect distributed in the time of different lags has not been studied either. The purpose of this work was to obtaining the association between the BD counts and the climatic factors such as temperature in the form of the weighted averages, concerning the autocorrelation pattern of the model residuals, and to make short term predictions using the model. The data was collected in the city of Shanghai from 2004 to 2008.MethodsWe used mixed generalized additive model (MGAM) to analyze data on bacillary dysentery, temperature and other covariates with autoregressive random effect. Short term predictions were made using MGAM with the moving average of the BD counts.Main ResultsOur results showed that temperature was significant linearly associated with the logarithm of BD count for temperature in the range from 12°C to 22°C. Optimal weights in the temperature effect have been obtained, in which the one of 1-day-lag was close to 0, and the one of 2-days-lag was the maximum (p-value of the difference was less than 0.05). The predictive model was showing good fitness on the internal data with R2 value 0.875, and the good short term prediction effect on the external data with correlation coefficient to be 0.859.ConclusionAccording to the model estimation, corresponding Risk Ratio to affect BD was close to 1.1 when temperature effect goes up for 1°C in the range from 12°C to 22°C. And the 1-day incubation period could be inferred from the model estimation. Good prediction has been made using the predictive MGAM.

Climate change impacts the epidemic of dysentery: determining climate risk window, modeling and projection

“…First, we developed a climate-dysentery model to quantify the relationship between climate factors and dysentery cases. Compared with previously developed models including Tem and Pre (Ma et al 2013), ours incorporated more climate factors to provide a clearer explanation of the relationship between climate change and dysentery incidence. In addition, we validated our developed model using the remaining 25% of data, thereby ensuring the model's reliability.…”

“…Previous studies merely identified the minimum thresholds for climate factors favorable for dysentery transmission (relative humid-ity>40% and temperature >12.5°C) (Zhang et al 2007), but the maximum values had not been defined. Additionally, only the temperature range (12°C-22°C) suitable for dysentery had been determined (Ma et al 2013), yet the occurrence of dysentery is affected by multiple climate factors. Our study detected the ranges of three climate factors associated with high incidences of dysentery, so it is more valuable for practical prevention and control.…”

“…Therefore, better models incorporating more comprehensive factors, including socio-economic ones, need to be developed in future studies. Second, this study used monthly data; further study using finer-scale weekly or even daily data could make predictions and estimations more accurate (Ma et al 2013). Although there are some data scale limitations, this study can help us to better understand how climate change might affect the incidence of dysentery, and shed light on the relationship between climate change and dysentery occurrence for improved understanding.…”

“…(Xiang et al 2017), measles(Yang et al 2014), mumps(Hu et al 2018), and childhood bacillary dysentery(Wen et al 2016). Given the nonlinear climate-dysentery relationship and lagged effects of climate factors on dysentery incidence in previous research(Zhang et al 2008, Li et al 2013, Ma et al 2013, DLNM was used as a base to demonstrate the climate-dysentery correlations in this study.…”

The relationship between meteorological factors and the risk of bacillary dysentery in Hunan Province, China

“…Thus, it is mainly influenced by local poverty levels, living conditions, water storage methods, and personal hygiene habits (Kunii et al, ; Kelly‐Hope et al, ). The incidence of bacillary dysentery in the region has changed significantly, even though the social factors mentioned were stable for an extended period, indicating a connection between the disease and the seasonal variation (Zhang et al, ; Ma et al, ). Meteorological factors have been shown to affect the spread of bacillary dysentery, causing changes in the reproductive rate of Shigella dysenteriae by reducing its incubation period (Patz et al, ).…”