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Since the novel coronavirus (COVID-19) outbreak in China, and due to the open accessibility of COVID-19 data, several researchers and modellers revisited the classical epidemiological models to evaluate their practical applicability. While mathematical compartmental models can predict various contagious viruses' dynamics, their efficiency depends on the model parameters. Recently, several parameter estimation methods have been proposed for different models. In this study, we evaluated the Ensemble Kalman filter's performance (EnKF) in the estimation of time-varying model parameters with synthetic data and the real COVID-19 data of Hubei province, China. Contrary to the previous works, in the current study, the effect of damping factors on an augmented EnKF is studied. An augmented EnKF algorithm is provided, and we present how the filter performs in estimating models using uncertain observational (reported) data. Results obtained confirm that the augumented-EnKF approach can provide reliable model parameter estimates. Additionally, there was a good fit of profiles between model simulation and the reported COVID-19 data confirming the possibility of using the augmented-EnKF approach for reliable model parameter estimation. [ABSTRACT FROM AUTHOR]

We examine the different effects of monetary policy actions and central bank communication on China’s stock market bubbles with a Time-varying Parameter SVAR model. We find that with negative responses of fundamental component and positive responses of bubble component of asset prices, contractionary monetary policy induces the observed stock prices to rise during periods of large bubbles. By contrast, central bank communication acts on the market through expectation guidance and has more significant effects on stock prices in the long run, which implies that central bank communication be used as an effective long-term instrument for the central bank’s policymaking. [ABSTRACT FROM AUTHOR]

As a useful tool for geographical cluster detection of events, the spatial scan statistic is widely applied in many fields and plays an increasingly important role. The classic version of the spatial scan statistic for the binary outcome is developed by Kulldorff, based on the Bernoulli or the Poisson probability model. In this paper, we apply the Hypergeometric probability model to construct the likelihood function under the null hypothesis. Compared with existing methods, the likelihood function under the null hypothesis is an alternative and indirect method to identify the potential cluster, and the test statistic is the extreme value of the likelihood function. Similar with Kulldorff’s methods, we adopt Monte Carlo test for the test of significance. Both methods are applied for detecting spatial clusters of Japanese encephalitis in Sichuan province, China, in 2009, and the detected clusters are identical. Through a simulation to independent benchmark data, it is indicated that the test statistic based on the Hypergeometric model outweighs Kulldorff’s statistics for clusters of high population density or large size; otherwise Kulldorff’s statistics are superior. [ABSTRACT FROM AUTHOR]

Turbidity (T) has been widely used to detect the occurrence of pollutants in surface water. Using data collected from January 2013 to June 2014 at eleven sites along two rivers feeding the Taihu Basin, China, the relationship between the concentration of five metals (aluminum (Al), titanium (Ti), nickel (Ni), vanadium (V), lead (Pb)) and turbidity was investigated. Metal concentration was determined using inductively coupled plasma mass spectrometry (ICP-MS). The linear regression of metal concentration and turbidity provided a good fit, with R2 = 0.86–0.93 for 72 data sets collected in the industrial river and R2 = 0.60–0.85 for 60 data sets collected in the cleaner river. All the regression presented good linear relationship, leading to the conclusion that the occurrence of the five metals are directly related to suspended solids, and these metal concentration could be approximated using these regression equations. Thus, the linear regression equations were applied to estimate the metal concentration using online turbidity data from January 1 to June 30 in 2014. In the prediction, the WASP 7.5.2 (Water Quality Analysis Simulation Program) model was introduced to interpret the transport and fates of total suspended solids; in addition, metal concentration downstream of the two rivers was predicted. All the relative errors between the estimated and measured metal concentration were within 30%, and those between the predicted and measured values were within 40%. The estimation and prediction process of metals’ concentration indicated that exploring the relationship between metals and turbidity values might be one effective technique for efficient estimation and prediction of metal concentration to facilitate better long-term monitoring with high temporal and spatial density. [ABSTRACT FROM AUTHOR]

Background: Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS. Methods: Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN) and autoregressive integrated moving average (ARIMA) the other composed of generalized regression neural network (GRNN) and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model. Results: The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve. Conclusion: Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS. [ABSTRACT FROM AUTHOR]

Background: Hypertension is China’s leading cardiovascular disease risk factor. Improved hypertension control in China would result in result in enormous health gains in the world’s largest population. A computer simulation model projected the cost-effectiveness of hypertension treatment in Chinese adults, assuming a range of essential medicines list drug costs. Methods and Findings: The Cardiovascular Disease Policy Model-China, a Markov-style computer simulation model, simulated hypertension screening, essential medicines program implementation, hypertension control program administration, drug treatment and monitoring costs, disease-related costs, and quality-adjusted life years (QALYs) gained by preventing cardiovascular disease or lost because of drug side effects in untreated hypertensive adults aged 35–84 y over 2015–2025. Cost-effectiveness was assessed in cardiovascular disease patients (secondary prevention) and for two blood pressure ranges in primary prevention (stage one, 140–159/90–99 mm Hg; stage two, ≥160/≥100 mm Hg). Treatment of isolated systolic hypertension and combined systolic and diastolic hypertension were modeled as a reduction in systolic blood pressure; treatment of isolated diastolic hypertension was modeled as a reduction in diastolic blood pressure. One-way and probabilistic sensitivity analyses explored ranges of antihypertensive drug effectiveness and costs, monitoring frequency, medication adherence, side effect severity, background hypertension prevalence, antihypertensive medication treatment, case fatality, incidence and prevalence, and cardiovascular disease treatment costs. Median antihypertensive costs from Shanghai and Yunnan province were entered into the model in order to estimate the effects of very low and high drug prices. Incremental cost-effectiveness ratios less than the per capita gross domestic product of China (11,900 international dollars [Int$] in 2015) were considered cost-effective. Treating hypertensive adults with prior cardiovascular disease for secondary prevention was projected to be cost saving in the main simulation and 100% of probabilistic simulation results. Treating all hypertension for primary and secondary prevention would prevent about 800,000 cardiovascular disease events annually (95% uncertainty interval, 0.6 to 1.0 million) and was borderline cost-effective incremental to treating only cardiovascular disease and stage two patients (2015 Int$13,000 per QALY gained [95% uncertainty interval, Int$10,000 to Int$18,000]). Of all one-way sensitivity analyses, assuming adherence to taking medications as low as 25%, high Shanghai drug costs, or low medication efficacy led to the most unfavorable results (treating all hypertension, about Int$47,000, Int$37,000, and Int$27,000 per QALY were gained, respectively). The strengths of this study were the use of a recent Chinese national health survey, vital statistics, health care costs, and cohort study outcomes data as model inputs and reliance on clinical-trial-based estimates of coronary heart disease and stroke risk reduction due to antihypertensive medication treatment. The limitations of the study were the use of several sources of data, limited clinical trial evidence for medication effectiveness and harms in the youngest and oldest age groups, lack of information about geographic and ethnic subgroups, lack of specific information about indirect costs borne by patients, and uncertainty about the future epidemiology of cardiovascular diseases in China. Conclusions: Expanded hypertension treatment has the potential to prevent about 800,000 cardiovascular disease events annually and be borderline cost-effective in China, provided low-cost essential antihypertensive medicines programs can be implemented. [ABSTRACT FROM AUTHOR]

Background: Laiwu District is recognized as a hyper-endemic region for scrub typhus in Shandong Province, but the seriousness of this problem has been neglected in public health circles. Methodology/Principal Findings: A disability-adjusted life years (DALYs) approach was adopted to measure the burden of scrub typhus in Laiwu, China during the period 2006 to 2012. A multiple seasonal autoregressive integrated moving average model (SARIMA) was used to identify the most suitable forecasting model for scrub typhus in Laiwu. Results showed that the disease burden of scrub typhus is increasing yearly in Laiwu, and which is higher in females than males. For both females and males, DALY rates were highest for the 60–69 age group. Of all the SARIMA models tested, the SARIMA(2,1,0)(0,1,0)12 model was the best fit for scrub typhus cases in Laiwu. Human infections occurred mainly in autumn with peaks in October. Conclusions/Significance: Females, especially those of 60 to 69 years of age, were at highest risk of developing scrub typhus in Laiwu, China. The SARIMA (2,1,0)(0,1,0)12 model was the best fit forecasting model for scrub typhus in Laiwu, China. These data are useful for developing public health education and intervention programs to reduce disease. [ABSTRACT FROM AUTHOR]

The impact of regional climate change on net primary productivity (NPP) is an important aspect in the study of ecosystems’ response to global climate change. China’s ecosystems are very sensitive to climate change owing to the influence of the East Asian monsoon. The Lund–Potsdam–Jena Dynamic Global Vegetation Model for China (LPJ-CN), a global dynamical vegetation model developed for China’s terrestrial ecosystems, was applied in this study to simulate the NPP changes affected by future climate change. As the LPJ-CN model is based on natural vegetation, the simulation in this study did not consider the influence of anthropogenic activities. Results suggest that future climate change would have adverse effects on natural ecosystems, with NPP tending to decrease in eastern China, particularly in the temperate and warm temperate regions. NPP would increase in western China, with a concentration in the Tibetan Plateau and the northwest arid regions. The increasing trend in NPP in western China and the decreasing trend in eastern China would be further enhanced by the warming climate. The spatial distribution of NPP, which declines from the southeast coast to the northwest inland, would have minimal variation under scenarios of climate change. [ABSTRACT FROM AUTHOR]

Predicting species range shifts in response to climatic change is a central aspect of global change studies. An ever growing number of species have been modeled using a variety of species distribution models (SDMs). However, quantitative studies of the characteristics of range shifts are rare, predictions of range changes are hard to interpret, analyze and summarize, and comparisons between the various models are difficult to make when the number of species modeled is large. Maxent was used to model the distribution of 12 Abies spp. in China under current and possible future climate conditions. Two fuzzy set defined indices, range increment index (I) and range overlapping index (O), were used to quantify range shifts of the chosen species. Correlation analyses were used to test the relationships between these indices and species distribution characteristics. Our results show that Abies spp. range increments (I) were highly correlated with longitude, latitude, and mean roughness of their current distributions. Species overlapping (O) was moderately, or not, correlated with these parameters. Neither range increments nor overlapping showed any correlation with species prevalence. These fuzzy sets defined indices provide ideal measures of species range shifts because they are stable and threshold-free. They are reliable indices that allow large numbers of species to be described, modeled, and compared on a variety of taxonomic levels. [ABSTRACT FROM AUTHOR]