Stochastic mortality models are important for a variety of actuarial tasks, from best-estimate forecasting to assessment of risk capital requirements. However, the mortality shock associated with the Covid-19 pandemic of 2020 distorts forecasts by (i) biasing parameter estimates, (ii) biasing starting points, and (iii) inflating variance. Stochastic mortality models therefore require outlier-robust methods for forecasting. Objective methods are required, as outliers are not always obvious on visual inspection. In this paper we look at the robustification of three broad classes of forecast: univariate time indices (such as in the Lee-Carter and APC models); multivariate time indices (such as in the Cairns-Blake-Dowd and newer Tang-Li-Tickle model families); and penalty projections (such as with the 2D P-spline model). In each case we identify outliers using quantitative methods, then co-estimate outlier effects along with other parameters. Doing so removes the bias and distortion to the forecast caused by a mortality shock, while providing a robust starting point for projections. Illustrations are given for various models in common use.

As acute infectious pneumonia, the coronavirus disease-2019 (COVID-19) has created unique challenges for each nation and region. Both India and the United States (US) have experienced a second outbreak, resulting in a severe disease burden. The study aimed to develop optimal models to predict the daily new cases, in order to help to develop public health strategies. The autoregressive integrated moving average (ARIMA) models, generalised regression neural network (GRNN) models, ARIMA–GRNN hybrid model and exponential smoothing (ES) model were used to fit the daily new cases. The performances were evaluated by minimum mean absolute per cent error (MAPE). The predictive value with ARIMA (3, 1, 3) (1, 1, 1)14 model was closest to the actual value in India, while the ARIMA–GRNN presented a better performance in the US. According to the models, the number of daily new COVID-19 cases in India continued to decrease after 27 May 2021. In conclusion, the ARIMA model presented to be the best-fit model in forecasting daily COVID-19 new cases in India, and the ARIMA–GRNN hybrid model had the best prediction performance in the US. The appropriate model should be selected for different regions in predicting daily new cases. The results can shed light on understanding the trends of the outbreak and giving ideas of the epidemiological stage of these regions.

This work aims to evaluate the energy savings that can be achieved in domestic hot water (DHW) production using consumption forecasting through statistical modeling. It uses our forecast algorithm and aims at investigating how it can improve energy efficiency depending on the system configuration. Especially, the influence of the DHW production type used is evaluated as well as the water tank insulation. To that end, real consumption measurements are used for model training. Then simulations are run on using TRNSYS software to compute the total energy consumption of DHW production systems over 1 year. Simulations are also based on real consumption measurements for realistic results. To appraise the energy savings, we compared simulations that consider either no forecast (reactive control), perfect forecast (to estimate the control ability to consider forecast), or the forecast provided by our algorithm. The measurements and simulations are run on 26 different but real dwellings to assess results variability. Several system configurations are also compared with varying thermal insulation indices for a complete benchmark of the approach so that an overall performance of the system and the anticipation strategy could be evaluated.

Tuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

Traditional regression models have reported conflicting results on the effectiveness of tax incentives in stimulating business investment. This study investigates the effects of the Bush tax cuts on U.S. investment using intervention analysis in conjunction with regression analysis that controls for relevant variables. Although intervention analysis has the advantage of allowing the behavior of investment to be influenced only by the time path of exogenous shocks such as tax reforms, control variables can test for the robustness of the results. We have found that the two tax reforms enacted in 2001 and 2003 had little impact on marginal investment incentives. The intervention analysis results are further reinforced by the evidence provided by alternative regression models that control for a host of variables. The failure of the tax reforms to stimulate investment spending may be attributable to several factors, such as a global savings glut, cheap global money, inappropriate designs for tax incentives, and budget deficits.

Dengue activity depends on fluctuations in Aedes populations which in turn are known to be influenced by climate factors including temperature, humidity and rainfall. It has been hypothesized that haze may reduce dengue transmission. Due to its geographical location Singapore suffers almost every year from hazes caused by wildfires from Indonesia. Such hazes have a significant impact on pollution indexes in Singapore. We set out to study the relationship of dengue activity and haze (measured as pollution standard index) in Singapore, using ARIMA models. We ran different univariate models, each encompassing a different lag period for the effects of haze and temperature (from lag 0 to lag 12 weeks). We analysed the data on a natural logarithmic scale to stabilize the variance and improve the estimation. No association between dengue activity and haze was found. Our findings do not lend support to the hypothesis that haze is associated with reduced dengue activity in Singapore.