Modelling the persistence of Covid-19 positivity rate in Italy

The current Covid-19 pandemic is severely affecting public health and global economies. In this context, accurately predicting its evolution is essential for planning and providing resources effectively. This paper aims at capturing the dynamics of the positivity rate (PPR) of the novel coronavirus using the Heterogeneous Autoregressive (HAR) model. The use of this model is motivated by two main empirical features arising from the analysis of PPR time series: the changing long-run level and the persistent autocorrelation structure. Compared to the most frequently used Autoregressive Integrated Moving Average (ARIMA) models, the HAR is able to reproduce the strong persistence of the data by using components aggregated at different interval sizes, remaining parsimonious and easy to estimate. The relative merits of the proposed approach are assessed by performing a forecasting study on the Italian dataset. As a robustness check, the analysis of the positivity rate is also conducted by considering the case of the United States. The ability of the HAR-type models to predict the PPR at different horizons is evaluated through several loss functions, comparing the results with those generated by ARIMA models. The Model Confidence Set is used to test the significance of differences in the predictive performances of the models under analysis. Our findings suggest that HAR-type models significantly outperform ARIMA specifications in terms of forecasting accuracy. We also find that the PPR could represent an important metric for monitoring the evolution of hospitalizations, as the peak of patients in intensive care units occurs within 12-16 days after the peak in the positivity rate. This can help governments in planning socio-economic and health policies in advance.

Trends of Epidemiological and Demographic Indicators of COVID-19 in India

INTRODUCTION

This study was planned to assess the trends of epidemiological indicators and demographic determinants related to the COVID-19 in India.

METHODOLOGY

This was a descriptive analysis of the COVID-19 cases and their outcomes between 1st March to 31st May 2020 in India. Unpaired t-test and ANOVA were used to determine the statistical differences. Linear regression models were prepared to estimate the effect of testing on the fatalities. The Infection Fatality Rate (IFR)/Case Fatality Rate (CFR), doubling time, and Basic Reproduction Number (R0) per week were calculated.

RESULTS

Two-thirds of the cases were between 21-50 years of age, while three-fourth of deaths were among people above 50-years of age. The mean age of people infected with COVID-19 was declining throughout the study period. The mean age of infected males and females was significantly different. The male-female ratio of both infection and deaths due to COVID-19 was near about 2:1. IFR/CFR was 3.31 (95% CI = 3.13-3.50) in April, which reduced to 2.84 (95% CI = 2.77-2.92) in May. An incremental trend was observed in the recovery rates (9.42% to 48.18%), tests conducted / million population (12 / million to 2708 / million) and doubling time (3.59 to 17.71 days). The number of tests was significantly influencing the fatalities (β = 0.016, 95% CI = 0.012-0.020). The overall R0 was found to be 1.72.

CONCLUSIONS

Public health interventions were likely effective in containing the spread of COVID-19. There is a need to further improve the testing capacity. The high-risk category of individuals being prioritized for hospital admission should be redefined to include individuals older than 50 years.