Accelerated cognitive decline after the COVID-19 pandemic in a community population of older persons with cognitive impairment: A 4-year time series analysis in the Tokyo Metropolis area.

AIM: The coronavirus disease 2019 (COVID-19) pandemic has led to lifestyle restrictions and might be associated with long-term changes in cognitive function. The aim of the present study was to elucidate the overall effect of the COVID-19 pandemic on the cognitive trajectory of a cohort of patients with cognitive impairment. METHODS: We enrolled 160 patients who had been making regular visits to a medical center for dementia. Cognitive function was assessed based on changes in scores on the Mini-Mental State Examination before and during the COVID-19 pandemic throughout a 4-year period. The trajectory of cognitive decline was determined by carrying out a time series analysis using a state-space model. RESULTS: Crude analysis showed that the Mini-Mental State Examination scores decreased from 20.9 ± 4.4 points (mean ± SD) at the time of the initial cognitive assessments to 17.5 ± 5.6 points at the time of the final assessments, and the decline rate was 1.15 ± 1.78 points per year (P < 0.0001). The time series analysis showed an accelerated cognitive trajectory after the COVID-19 outbreak, and the average decline in the Mini-Mental State Examination scores was 0.46 points (95% confidence interval 0.034-0.91) per year before the COVID-19 pandemic, and a steeper decline of 1.87 points (95% confidence interval 1.34-2.67) per year after the outbreak. CONCLUSIONS: The COVID-19 pandemic accelerated the rate of cognitive decline in patients with cognitive impairment fourfold in comparison with before the pandemic. Specific strategies designed for cognitively older people in the "new normal" will reconcile both requirements, reducing the risk of infection, and maintaining their physical and psychological well-being. Geriatr Gerontol Int 2023; 23: 200-204.

Nowcasting India's Quarterly GDP Growth: A Factor-Augmented Time-Varying Coefficient Regression Model (FA-TVCRM).

Governments, central banks, private firms and others need high frequency information on the state of the economy for their decision making. However, a key indicator like GDP is only available quarterly and that too with a lag. Hence decision makers use high frequency daily, weekly or monthly information to project GDP growth in a given quarter. This method, known as nowcasting, started out in advanced country central banks using bridge models. Nowcasting is now based on more advanced techniques, mostly dynamic factor models. In this paper we use a novel approach, a Factor Augmented Time Varying Coefficient Regression (FA-TVCR) model, which allows us to extract information from a large number of high frequency indicators and at the same time inherently addresses the issue of frequent structural breaks encountered in Indian GDP growth. One specification of the FA-TVCR model is estimated using 19 variables available for a long period starting in 2007-08:Q1. Another specification estimates the model using a larger set of 28 indicators available for a shorter period starting in 2015-16:Q1. Comparing our model with two alternative models, we find that the FA-TVCR model outperforms a Dynamic Factor Model (DFM) model and a univariate Autoregressive Integrated Moving Average (ARIMA) model in terms of both in-sample and out-of-sample Root Mean Square Error (RMSE). Further, comparing the predictive power of the three models using the Diebold-Mariano test, we find that FA-TVCR model outperforms DFM consistently. In terms of out-of-sample forecast accuracy both the FA-TVCR model and the ARIMA model have the same predictive accuracy under normal conditions. However, the FA-TVCR model outperforms the ARIMA model when applied for nowcasting in periods of major shocks like the Covid-19 shock of 2020-21.

Single‐month unemployment rate estimates for the Brazilian Labour Force Survey using state‐space models

The Brazilian Labour Force Survey publishes monthly national indicators based on 3-month rolling data. This paper presents state-space models to produce state-level single-month unemployment rate estimates. The models account for sampling errors and the increased dynamics in the labour force series due to the unforeseen SARS-COV-2 pandemic. Bivariate time series models with claimant count auxiliary data and multivariate models combining survey data of several states are investigated. The results demonstrated the benefits of the univariate state-space approach to produce unemployment official statistics for Brazil. Additionally, the regional multivariate model shows promising results but requires further investigation.

Forecasting COVID-19 infections in the Arabian Gulf region.

In this paper, an empirical analysis of linear state space models and long short-term memory neural networks is performed to compare the statistical performance of these models in predicting the spread of COVID-19 infections. Data on the pandemic daily infections from the Arabian Gulf countries from 2020/03/24 to 2021/05/20 are fitted to each model and a statistical analysis is conducted to assess their short-term prediction accuracy. The results show that state space model predictions are more accurate with notably smaller root mean square errors than the deep learning forecasting method. The results also indicate that the poorer forecast performance of long short-term memory neural networks occurs in particular when health surveillance data are characterized by high fluctuations of the daily infection records and frequent occurrences of abrupt changes. One important result of this study is the possible relationship between data complexity and forecast accuracy with different models as suggested in the entropy analysis. It is concluded that state space models perform better than long short-term memory networks with highly irregular and more complex surveillance data.

Differential Equation Model of Staying at Home: Prefectural Data Analysis by Bayesian Statistical Model

In this paper, we propose a mathematical model to explain the sequential change in the number of people who stay at home under the spread of COVID-19. We collected data on the number of people who stay at home for each prefecture based on the location data of about 80 million cell phones. We built a differential equation model to express the characteristics of data that have multiple peaks where the derivatives change depending on the time period. By applying the differential equation model, we found the following implications: in the case where we assumed a quantity of staying at home request as a decreasing function of time, the total number of people who stayed at home was greater than in the case where we assumed an increasing function of time. Additionally, we examined the fit of the theoretical model by applying it to data collected from Tokyo, Osaka, Hokkaido, and Iwate prefectures from February 1 to July 10, 2020. Further, we benchmarked our model against a state-space model. Our model fits the data as well as the benchmark model. © 2021 Japanese Association for Mathematical Sociology. All rights reserved.

A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting COVID-19 cases, hospitalizations and deaths.

Short-term forecasts of the dynamics of coronavirus disease 2019 (COVID-19) in the period up to its decline following mass vaccination was a task that received much attention but proved difficult to do with high accuracy. However, the availability of standardized forecasts and versioned datasets from this period allows for continued work in this area. Here, we introduce the Gaussian infection state space with time dependence (GISST) forecasting model. We evaluate its performance in one to four weeks ahead forecasts of COVID-19 cases, hospital admissions and deaths in the state of California made with official reports of COVID-19, Google's mobility reports and vaccination data available each week. Evaluation of these forecasts with a weighted interval score shows them to consistently outperform a naive baseline forecast and often score closer to or better than a high-performing ensemble forecaster. The GISST model also provides parameter estimates for a compartmental model of COVID-19 dynamics, includes a regression submodel for the transmission rate and allows for parameters to vary over time according to a random walk. GISST provides a novel, balanced combination of computational efficiency, model interpretability and applicability to large multivariate datasets that may prove useful in improving the accuracy of infectious disease forecasts.

Reproducible parallel inference and simulation of stochastic state space models using odin, dust, and mcstate.

State space models, including compartmental models, are used to model physical, biological and social phenomena in a broad range of scientific fields. A common way of representing the underlying processes in these models is as a system of stochastic processes which can be simulated forwards in time. Inference of model parameters based on observed time-series data can then be performed using sequential Monte Carlo techniques. However, using these methods for routine inference problems can be made difficult due to various engineering considerations: allowing model design to change in response to new data and ideas, writing model code which is highly performant, and incorporating all of this with up-to-date statistical techniques. Here, we describe a suite of packages in the R programming language designed to streamline the design and deployment of state space models, targeted at infectious disease modellers but suitable for other domains. Users describe their model in a familiar domain-specific language, which is converted into parallelised C++ code. A fast, parallel, reproducible random number generator is then used to run large numbers of model simulations in an efficient manner. We also provide standard inference and prediction routines, though the model simulator can be used directly if these do not meet the user's needs. These packages provide guarantees on reproducibility and performance, allowing the user to focus on the model itself, rather than the underlying computation. The ability to automatically generate high-performance code that would be tedious and time-consuming to write and verify manually, particularly when adding further structure to compartments, is crucial for infectious disease modellers. Our packages have been critical to the development cycle of our ongoing real-time modelling efforts in the COVID-19 pandemic, and have the potential to do the same for models used in a number of different domains.

A farewell to R: time-series models for tracking and forecasting epidemics.

The time-dependent reproduction number, Rt, is a key metric used by epidemiologists to assess the current state of an outbreak of an infectious disease. This quantity is usually estimated using time-series observations on new infections combined with assumptions about the distribution of the serial interval of transmissions. Bayesian methods are often used with the new cases data smoothed using a simple, but to some extent arbitrary, moving average. This paper describes a new class of time-series models, estimated by classical statistical methods, for tracking and forecasting the growth rate of new cases and deaths. Very few assumptions are needed and those that are made can be tested. Estimates of Rt, together with their standard deviations, are obtained as a by-product.

Market prices, analysts' predictions, and Covid19.

This study employs a relatively new statistical method to analyze the time-series of US market prices. Specifically, it shows, that during Covid19, the strongest structural breaks happened. Moreover, since 1993 analysts were not able to predict market stock prices significantly at the 5% level. The new statistical method allows for a better analysis of market prices and analysts' recommendations.

Assessing the effectiveness of the Italian risk-zones policy during the second wave of COVID-19.

On 4 November 2020 the Italian government introduced a new policy to address the second wave of COVID-19. Based on a battery of indicators, the 21 administrative regions of Italy were assigned a risk level among yellow, orange, red, and, starting on 6 November 2020, different type of restrictions were applied accordingly. This event represents a natural experiment that allows the evaluation of the effects of non-pharmaceutical interventions, free from those nuisance factors affecting cross-national studies. In this work, we extract the daily growth rate of new cases, hospitalizations and patients in ICU from official data using an unobserved components model and assess how the different restrictions had different impacts in reducing the speed of spread of the virus. We find that all the three packages of restrictions have an effect on the speed of spread of the disease, but while the mildest (yellow) policy leads to a constant number of hospitalizations (zero growth rate), the strictest (red) policy is able to halve the number of accesses to regular wards and intensive care units in about one month. The effects of the intermediate (orange) policy are more volatile and seem to be only slightly more effective than the milder (yellow) policy.

Stochastic filtering based transmissibility estimation of novel coronavirus.

In this study, the transmissibility estimation of novel coronavirus (COVID-19) has been presented using the generalized fractional-order calculus (FOC) based extended Kalman filter (EKF) and wavelet transform (WT) methods. Initially, the state-space representation for the bats-hosts-reservoir-people (BHRP) model is obtained using a set of fractional order differential equations for the susceptible-exposed-infectious-recovered (SEIR) model. Afterward, the EKF and Kronecker product based WT methods have been applied to the discrete vector representation of the BHRP model. The main advantage of using EKF in this system is that it considers both the process and the measurement noise, which gives better accuracy and probable states, which are the Markovian (processes). The importance of proposed models lies in the fact that these models can accommodate conventional EKF and WT methods as their special cases. Further, we have compared the estimated number of contagious people and recovered people with the actual number of infectious people and recovered people in India and China.

A Review of Multi-Compartment Infectious Disease Models.

Multi-compartment models have been playing a central role in modelling infectious disease dynamics since the early 20th century. They are a class of mathematical models widely used for describing the mechanism of an evolving epidemic. Integrated with certain sampling schemes, such mechanistic models can be applied to analyse public health surveillance data, such as assessing the effectiveness of preventive measures (e.g. social distancing and quarantine) and forecasting disease spread patterns. This review begins with a nationwide macromechanistic model and related statistical analyses, including model specification, estimation, inference and prediction. Then, it presents a community-level micromodel that enables high-resolution analyses of regional surveillance data to provide current and future risk information useful for local government and residents to make decisions on reopenings of local business and personal travels. r software and scripts are provided whenever appropriate to illustrate the numerical detail of algorithms and calculations. The coronavirus disease 2019 pandemic surveillance data from the state of Michigan are used for the illustration throughout this paper.

Using the kalman filter with Arima for the COVID-19 pandemic dataset of Pakistan.

The current pandemic of the Novel Corona virus (COVID-19) has resulted in multifold challenges related to health, economy, and society, etc. for the entire world. Many mathematical epidemiological models have been tried for the available data of the COVID-19 pandemic with the core objective to observe the trend and trajectories of infected cases, recoveries, and deaths, etc. However, these models have their own assumptions and parameters and vary with regional demography. This article suggests the use of a more pragmatic approach of the Kalman filter with the Autoregressive Integrated Moving Average (ARIMA) models in order to obtain more precise forecasts for the figures of prevalence, active cases, recoveries, and deaths related to the COVID-19 outbreak in Pakistan.