Model-based single-month unemployment rate estimates for the Brazilian Labour Force Survey1

The Brazilian Labour Force Survey (BLFS) is a quarterly rotating panel survey with 80% sample overlap between two successive quarters. Monthly unemployment rate estimates are regularly produced based on a three-month average of direct estimates. Due to the unforeseen situation of COVID19 pandemic and its effects in the economy and labour market, there was a need to investigate model-based estimation procedures to obtain unemployment rate single-month estimates. We present structural time series models developed to produce model-based single month estimates at national level as well as small area (state-level) estimates at a higher frequency than those currently being published. Using the state-space framework, the models account for the autocorrelation due to sample overlap and the increased dynamics in the labour force series in 2020. In addition, bivariate models that combine claimant count and survey data are investigated. The models not only yield estimates with better precision than direct estimates, since the latter were affected by a rise in non-response, but they can deliver reliable state-level official statistics at a monthly frequency that are presently required. The new improved model-based estimates were proposed as experimental statistics for the Brazilian national statistical office (IBGE).

1603 A Google Trends Analysis of Interest in Ear, Nose and Throat Surgery During The COVID-19 Pandemic

Aim The COVID-19 pandemic has had wide-ranging effects on healthcare, society and everyday life. As the public’s health priorities shift, we sought to investigate the resulting impact of COVID-19 on global interest in Ear, Nose and Throat (ENT) surgery. Method We used Google Trends to examine worldwide search interest in the following core ENT operations following the onset of the COVID-19 pandemic: Tonsillectomy, Adenoidectomy, Thyroidectomy, Rhinoplasty, Septoplasty, Functional Endoscopic Sinus Surgery, Mastoidectomy, and Tympanoplasty. Bayesian structural time-series models were used to generate counterfactual time series, and relative differences between observed and expected search interest were calculated. Causal effects were subsequently determined, along with 95% CIs and posterior probabilities. R version 4.0.3 was used for analyses. Results Search interest in all measured ENT procedures was significantly reduced at the onset of the COVID-19 pandemic. Interest in Rhinoplasty recovered after 8 weeks and continued to rise to a peak of 27% greater than expected, with a cumulative Relative effect of + 11% [95% CI: 3.9%, 20%]. In contrast, significantly reduced search interest was observed for all other procedures analysed (Relative effect, range: -16% to -36%, all p values < 0.05). Conclusions Our findings suggest divergent changes in public interest in common ENT procedures. While all other ENT operations investigated were less frequently searched following the pandemic onset, interest in Rhinoplasty at times increased to over 25% greater than expected. This could represent a shift in patient attitudes to disorders of the Ear, Nose and Throat and warrants further investigation at the individual-level.

Analysis and forecasts for trends of COVID-19 in Pakistan using Bayesian models

Background COVID-19 is currently on full flow in Pakistan. Given the health facilities in the country, there are serious threats in the upcoming months which could be very testing for all the stakeholders. Therefore, there is a need to analyze and forecast the trends of COVID-19 in Pakistan. Methods We have analyzed and forecasted the patterns of this pandemic in the country, for next 30 days, using Bayesian structural time series models. The causal impacts of lifting lockdown have also been investigated using intervention analysis under Bayesian structural time series models. The forecasting accuracy of the proposed models has been compared with frequently used autoregressive integrated moving average models. The validity of the proposed model has been investigated using similar datasets from neighboring countries including Iran and India. Results We observed the improved forecasting accuracy of Bayesian structural time series models as compared to frequently used autoregressive integrated moving average models. As far as the forecasts are concerned, on August 10, 2020, the country is expected to have 333,308 positive cases with 95% prediction interval [275,034–391,077]. Similarly, the number of deaths in the country is expected to reach 7,187 [5,978–8,390] and recoveries may grow to 279,602 [208,420–295,740]. The lifting of lockdown has caused an absolute increase of 98,768 confirmed cases with 95% interval [85,544–111,018], during the post-lockdown period. The positive aspect of the forecasts is that the number of active cases is expected to decrease to 63,706 [18,614–95,337], on August 10, 2020. This is the time for the concerned authorities to further restrict the active cases so that the recession of the outbreak continues in the next month.

Using Absolutist Word Frequency from Online Searches to Measure Population Mental Health Dynamics

The assessment of population mental health relies on survey data from representative samples, which come with considerable costs. Drawing on research which established that absolutist words (e.g. never) are semantic markers for depression, we propose a new measure of population mental health based on the frequency of absolutist words in online search data (Absolute Thinking Index; ATI). Our aims were to first validate the ATI, and to use it to model public mental health dynamics in France and the UK during the current COVID-19 pandemic. To do so, we extracted time series for a validated dictionary of 19 absolutist words, from which the ATI was computed (weekly averages, 2019-2020, n = 208). We then tested the relationship between ATI and longitudinal survey data of population mental health in the UK and France. ATI was linked with survey depression scores in the UK, r = .68, 95%CI[.34,.86], β = .23, 95%CI[.09,.37] in France and displayed similar trends. We finally assessed the pandemic’s impact on ATI using Bayesian structural time-series models. These revealed that the pandemic increased ATI by 3.2%, 95%CI[2.1,4.2] in France and 3.7%, 95%CI[2.9,4.4] in the UK. Mixed-effects models showed that ATI was related to COVID-19 new deaths in both countries β = .14, 95%CI[.14,.21]. Our results demonstrate the validity of the ATI as a measure of population mental health (depression) in France and the UK. We propose that researchers use it as cost-effective public mental health “thermometer” for applied and research purposes.

Development of State Space Models with Weather as Exogenous Input for Sugarcane Yield Prediction in Haryana

Parameter constancy is a fundamental issue for empirical models to be useful for forecasting, analyzing or testing any theory. Unlike classical regression analysis, the state space models (SSM) are time varying parameters models as they allow for known changes in the structure of the system over time and provide a flexible class of dynamic and structural time series models. The work deals with the development of state space models with weather as exogenous input for sugarcane yield prediction in Ambala and Karnal districts of Haryana. The state space models with weather as exogenous input using different types of growth trends viz., polynomial splines; PS(1), PS(2) and PS(3) have been developed however PS(2) with weather input was selected as the best suited model for this empirical study. Timely and effective pre-harvest forecast of crop yield helps in advance planning, formulation and implementation of policies related to the crop procurement, price structure, distribution and import-export decisions etc. These forecasts are also useful to farmers to decide in advance their future prospects and course of action. The sugarcane yield forecasts based on state space models with weather input showed good agreement with state Department of Agriculture and Farmers’ Welfare yield(s) by showing nearly 4 percent average absolute relative deviations in the two districts.

Brexit Announcement: Immediate Impact on British Tourism in Spain

The United Kingdom constitutes the principal tourist source market for Spain. This research note analyzes the immediate impact of the Brexit on British tourism in Spain using the Bayesian structural time series models framework. The results obtained show that between July 2016 and September 2017, Brexit has not produced any initial negative effect on the arrival of British tourists or on their spending in Spain.