Search trends and prediction of human brucellosis using Baidu index data from 2011 to 2018 in China

Zhang

et al. 2021

Transbound Emerg Dis

Brucellosis, caused by infection with Brucella spp., is an important zoonosis worldwide which can significantly affect human health and the profitability of livestock industries (Mcdermott et al., 2013). Although brucellosis in humans has been successfully controlled in several countries, it remains endemic in the Middle East, the Mediterranean region, sub-Saharan Africa, Latin America and parts of Asia, with approximately half a million human cases reported annually (Pappas et al., 2006). Infection of humans is closely associated

Section: Statistical Analysesmentioning

confidence: 99%

An epidemiological study of brucellosis on mainland China during 2004–2018

Zhang

et al. 2021

Transbound Emerg Dis

“…Equation ( 2 ) is the general form of so-called ARIMAX model (ARIMA with eXogeneous inputs), which capture the influence of external factors [ 26 ]. As widely adopted in epidemiological time series studies [ 27 – 29 ], ARIMAX has the capacity to generate predictions while identifying the underlying patterns of changes of both internal and external nature. All analyses were conducted using R version 3.6.1.…”

Section: Methodsmentioning

confidence: 99%

Forecasting disability-adjusted life years for chronic diseases: reference and alternative scenarios of salt intake for 2017–2040 in Japan

Nomura¹,

Yoneoka²,

Tanaka³

et al. 2020

BMC Public Health

Background In Japan, a high-sodium diet is the most important dietary risk factor and is known to cause a range of health problems. This study aimed to forecast Japan’s disability-adjusted life year (DALYs) for chronic diseases that would be associated with high-sodium diet in different future scenarios of salt intake. We modelled DALY forecast and alternative future scenarios of salt intake for cardiovascular diseases (CVDs), chronic kidney diseases (CKDs), and stomach cancer (SC) from 2017 to 2040. Methods We developed a three-component model of disease-specific DALYs: a component on the changes in major behavioural and metabolic risk predictors including salt intake; a component on the income per person, educational attainment, and total fertility rate under 25 years; and an autoregressive integrated moving average model to capture the unexplained component correlated over time. Data on risk predictors were obtained from Japan’s National Health and Nutrition Surveys and from the Global Burden of Disease Study 2017. To generate a reference forecast of disease-specific DALY rates for 2017–2040, we modelled the three diseases using the data for 1990–2016. Additionally, we generated better, moderate, and worse scenarios to evaluate the impact of change in salt intake on the DALY rate for the diseases. Results In our reference forecast, the DALY rates across all ages were predicted to be stable for CVDs, continuously increasing for CKDs, and continuously decreasing for SC. Meanwhile, the age group-specific DALY rates for these three diseases were forecasted to decrease, with some exceptions. Except for the ≥70 age group, there were remarkable differences in DALY rates between scenarios, with the best scenario having the lowest DALY rates in 2040 for SC. This represents a wide scope of future trajectories by 2040 with a potential for tremendous decrease in SC burden. Conclusions The gap between scenarios provides some quantification of the range of policy impacts on future trajectories of salt intake. Even though we do not yet know the policy mix used to achieve these scenarios, the result that there can be differences between scenarios means that policies today can have a significant impact on the future DALYs.

Infectious Disease Modelling

“…The ARIMAX model is an important technique, which is an extension of the ARIMA model. Compared with the ARIMA model, the ARIMAX model was the combination of multiple regression analysis and time series analysis, it can utilize more information and test the relationship between COVID-19 suspected case and multiple keywords Baidu search index ( Granger and Swanson, 1996 ; Zhao et al., 2020 ). In addition, the multivariate models need to examine the stationarity, estimation of coefficients and post-model evaluation, moreover, detected the distribution of model residuals and goodness of fit in the process of the model development.…”

Section: Methodsmentioning

confidence: 99%

Internet search data could Be used as novel indicator for assessing COVID-19 epidemic

Liang

et al. 2020

The pandemic of the coronavirus disease (COVID-19) poses a huge challenge all countries, since no one is well prepared for it. To be better prepared for future pandemics, we evaluated association between the internet search data with reported COVID-19 cases to verify whether it could become an early indicator for emerging epidemic. After the keyword filtering and Index composition, we found that there were close correlations between Composite Index and suspected cases for COVID-19 (r = 0.921, P < 0.05). The Search Index was applied for the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model to quantify the relationship. Compared with the model based on surveillance data only, the ARIMAX model had smaller Akaike Information Criterion (AIC = 403.51) and the most accurate predictive values. Overall, the Internet search data could serve as a convenient indicator for predicting the epidemic and to monitor its trends.