Influence and prediction of meteorological factors on brucellosis in a northwest region of China

Risk effects of environmental factors on human brucellosis in Aksu Prefecture, Xinjiang, China, 2014-2023

The context of rapid global environmental change underscores the pressing necessity to investigate the environmental factors and high-risk areas that contribute to the occurrence of brucellosis. In this study, a maximum entropy (MaxEnt) model was employed to analyze the factors influencing brucellosis in the Aksu Prefecture from 2014 to 2023. A distributed lag nonlinear model (DLNM) was employed to investigate the lagged effect of meteorological factors on the occurrence of brucellosis. A total of 17 environmental factors were identified as affecting the distribution of brucellosis to varying degrees. The largest contributing was the normalized difference vegetation index (NDVI), followed by gross domestic product (GDP), and then meteorological factors such as average temperature, average relative humidity, and average wind speed. The receiver operating characteristic (ROC) curve demonstrated that the MaxEnt model exhibited a high degree of predictive efficacy, with an area under the curve (AUC) value of 0.921. The impact of high temperature (25℃ with a 2-month lag, RR = 3.130, 95% CI 1.642 ~ 5.965), low relative humidity (28% with a 2.5-month lag, RR = 1.795, 95% CI 1.298 ~ 2.483), and low wind speed (1.9 m/s with a 0-month lag, RR = 2.408, 95% CI 1.360 ~ 4.264) are the most significant meteorological factors associated with the incidence of brucellosis. The trends in the impact of extreme meteorological conditions on the spread of brucellosis were found to be generally consistent. Stratified analyses indicated that males were more affected by meteorological factors than females. The prevalence of brucellosis is influenced by a range of socio-economic and meteorological factors, and a multifaceted approach is necessary to prevent and control brucellosis.

Effective intervention of brucellosis prevention in developing countries: A dynamic modelling study

Objective

Brucellosis has a considerable impact on human health and the economy in developing countries. In China, the biggest developing country, brucellosis shifted spread of the epidemic from northern to southern regions. Understanding the transmission characteristic of brucellosis on Hunan province, located in central China, is of great significance for successful control.

Methods

We developed a multi-population and multi-route dynamic model (MPMRDM), which is an animal-human-environment coupled model. The model is an extension of the SEIR model, taking into account direct transmission and indirect transmission. We used the model to explore the spread of brucellosis and evaluate the effectiveness of various intervention strategies.

Results

The animal-to-animal transmission rate was the highest at 5.14 × 10-8, while the environment-to-person transmission rate was the lowest at 9.49 × 10-12. The mean R0 was 1.51. The most effective intervention was taking personal protection, followed by shortening the infection period. Shortening the infection period combined with personal protection is the most effective two-combined intervention strategy. After any comprehensive intervention strategy was implemented, TAR dropped by 90% or more.

Conclusion

The results demonstrate that animal transmission route is essential for controlling human brucellosis. Strengthening personal protection, early detection, and early treatment can effectively control the trend of brucellosis. These results can provide an important reference for optimizing brucellosis intervention plans.

Spatiotemporal risk of human brucellosis under intensification of livestock keeping based on machine learning techniques in Shaanxi, China

As one of the most neglected zoonotic diseases, brucellosis has posed a serious threat to public health worldwide. This study is purposed to apply different machine learning models to improve the prediction accuracy of human brucellosis (HB) in Shaanxi, China from 2008 to 2020, under livestock husbandry intensification from a spatiotemporal perspective. We quantitatively evaluated the performance and suitability of ConvLSTM, RF, and LSTM models in epidemic forecasting, and investigated the spatial heterogeneity of how different factors drive the occurrence and transmission of HB in distinct sub-regions by using Kernel Density Analysis and Shapley Additional Explanations. Our findings demonstrated that ConvLSTM network yielded the best predictive performance with the lowest average RMSE of 13.875 and MAE values of 18.393. RF model generated an underestimated outcome while LSTM model had an overestimated one. In addition, climatic conditions, intensification of livestock keeping and socioeconomic status were identified as the dominant factors that drive the occurrence of HB in Shaanbei Plateau, Guanzhong Plain, and Shaannan Region, respectively. This work provided a comprehensive understanding of the potential risk of HB epidemics in Northwest China driven by both anthropogenic activities and natural environment, which can support further practice in disease control and prevention.

Risk effects of meteorological factors on human brucellosis in Jilin province, China, 2005-2019

Background

The impact of climate on zoonotic infectious diseases (or can be referred to as climate-sensitive zoonotic diseases) is confirmed. Yet, research on the association between brucellosis and climate is limited. We aim to understand the impact of meteorological factors on the risk of brucellosis, especially in northeastern China.

Methods

Monthly incidence data for brucellosis from 2005 to 2019 in Jilin province was obtained from the China Information System for Disease Control and Prevention (CDC). Monthly meteorological data (average temperature (°C), wind velocity (m/s), relative humidity (%), sunshine hours (h), air pressure (hPa), and rainfall (mm)) in Jilin province, China, from 2005 to 2019 were collected from the China Meteorological Information Center (http://data.cma.cn/). The Spearman's correlation was used to choose among the several meteorological variables. A distributed lag non-linear model (DLNM) was used to estimate the lag and non-linearity effect of meteorological factors on the risk of brucellosis.

Results

A total of 24,921 cases of human brucellosis were reported in Jilin province from 2005 to 2019, with the peak epidemic period from April to June. Low temperature and low sunshine hours were protective factors for the brucellosis, where the minimum RR values were 0.50 (95 % CI = 0.31-0.82) for -13.7 °C with 1 month lag and 0.61 (95 % CI = 0.41-0.91) for 110.5h with 2 months lag, respectively. High temperature, high sunshine hours, and low wind velocity were risk factors for brucellosis. The maximum RR values were 2.91 (95 % CI = 1.43-5.92, lag = 1, 25.7 °C), 1.85 (95 % CI = 1.23-2.80, lag = 2, 332.6h), and 1.68 (95 % CI = 1.25-2.26, lag = 2, 1.4 m/s). The trends in the impact of extreme temperature and extreme sunshine hours on the transmission of brucellosis were generally consistent.

Conclusion

High temperature, high sunshine hours, and low wind velocity are more conducive to the transmission of brucellosis with an obvious lag effect. The results will deepen the understanding of the relationship between climate and brucellosis and provide a reference for formulating relevant public health policies.

Asymmetric Effects of Weather-Integrated Human Brucellosis Forecasting System Using a New Nonlinear Autoregressive Distributed Lag Model

Human brucellosis (HB) remains a significant public health concern in China. This study aimed to investigate the long- and short-term asymmetric impacts of meteorological variables on HB and develop an early prediction system. Monthly data on HB incidence and meteorological variables were collected from 2005 to 2020. The study employed the autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) to analyze the long- and short-term effects of climate variables on HB. Subsequently, the data were split into training (from January 2005 to December 2019) and testing parts (from January to December 2020) to develop and validate the forecasting accuracy of both models. During 2005-2020, there were 34,993 HB cases (2.03 per 100,000 persons) and there was an overall rising trend (average annual percentage change = 21.18%, 95%CI 18.36%-26.01%) in HB incidence, peaked in May and troughed in December per year. A 1 m/s increment and decrement in differenced (Δ) average wind velocity (AWV) contributed to 73.8% and 87.5% increases in ΔHB incidence, respectively (Wald long-run asymmetry test (WLR) = 1.17, P=0.25). A 1 hr increment and decrement in Δ(average relative humidity) contributed to both 3.1% increases in ΔHB incidence (Wald short-run asymmetry test = 3.01, P=0.003). Average temperature (AT) (P < 0.001) and average air pressure (P=0.012) played a long-run linear impact on HB. Δ(aggregate precipitation) (WLR = 1.76, P=0.08) and Δ(aggregate sunshine hours) (WLR = 0.07, P=0.94) did not have a significant long-term asymmetric impact on Δlog(HB). ΔΔAT(+) and ΔΔAWV(-) at a 1-month lag had a meaningful short-run effect on Δlog(HB). In the forecasting aspect, the NARDL produced significantly smaller error rates compared to the ARDL. Weather variability played significant long- and short-run asymmetric roles in HB incidence. The NARDL by integrating climatic variables could accurately capture the dynamic structure of HB epidemic, meaning that meteorological variables should be integrated into the public health intervention plan for HB.

Detrended seasonal relationships and impact of climatic factors combined with spatiotemporal effect on the prevalence of human brucellosis

Human brucellosis (HB) is a seasonal and climate-affected infectious disease that is posing an increasing threat to public health and economy. However, most of the research on the seasonal relationships and impact of climatic factors on HB did not consider the secular trend and spatiotemporal effect related to the disease. We herein utilized long-term surveillance data on HB from 2008 to 2020 using sinusoidal models to explore detrended relationships between climatic factors and HB. In addition, we assessed the impact of such climatic factors on HB using a spatial panel data model combined with the spatiotemporal effect. HB peaked around mid-May. HB was significantly correlated with climatic factors with 1-5-month lag when the respective correlations reached the maximum across the different lag periods. Each 0.1 °C increase in temperature led to 0.5% decrease in the 5-month lag incidence of HB. We also observed a positive spatiotemporal effect on the disease. Our study provides a detailed and in-depth overview of seasonal relationships and impact of climatic factors on HB. In addition, it proposes a novel approach for exploring the seasonal relationships and quantifying the impacts of climatic factors on various infectious diseases.