Using GAM functions and Markov-Switching models in an evaluation framework to assess countries’ performance in controlling the COVID-19 pandemic

Abstract

Background The COVID-19 pandemic has initiated several initiatives to better understand its behavior, and some projects are monitoring its evolution across countries, which naturally leads to comparisons made by those using the data. However, most “at a glance” comparisons may be misleading because the curve that should explain the evolution of COVID-19 is different across countries, as a result of the underlying geopolitical or socio-economic characteristics. Therefore, this paper contributes to the scientific endeavour by creating a new evaluation framework to help stakeholders adequately monitor and assess the evolution of COVID-19 in countries, considering the occurrence of spikes, "secondary waves" and structural breaks in the time series. Methods Generalized Additive Models were used to model cumulative and daily curves for confirmed cases and deaths. The Root Relative Squared Error and the Percentage Deviance Explained measured how well the models fit the data. A local min-max function was used to identify all local maxima in the fitted values. The pure Markov-Switching and the family of Markov-Switching GARCH models were used to identify structural breaks in the COVID-19 time series. Finally, a quadrants system to identify countries that are more/less efficient in the short/long term in controlling the spread of the virus and the number of deaths was developed. Such methods were applied in the time series of 189 countries, collected from the Centre for Systems Science and Engineering at Johns Hopkins University. Results Our methodology proves more effective in explaining the evolution of COVID-19 than growth functions worldwide, in addition to standardizing the entire estimation process in a single type of function. Besides, it highlights several inflection points and regime-switching moments, as a consequence of people’s diminished commitment to fighting the pandemic. Although Europe is the most developed continent in the world, it is home to most countries with an upward trend and considered inefficient, for confirmed cases and deaths. Conclusions The new outcomes presented in this research will allow key stakeholders to check whether or not public policies and interventions in the fight against COVID-19 are having an effect, easily identifying examples of best practices and promote such policies more widely around the world.

Impact of COVID-19 on Stock Indices Volatility: Long-Memory Persistence, Structural Breaks, or Both?

“…4.2 and 4.3 ; The null ( ) for all VaR and ES tests should not be rejected using in-sample data; The annual volatilities for both regimes must not be 0, and the annual volatility of the first regime (“low”) must be less than that of the second regime (“high”); The probabilities of staying in the original regime, and , must be higher than 85%; The half-life of volatility persistence should be less than 100 days, and the volatility half-life of the first regime must be less than that of the second regime; The model with the highest mean ( ) should be selected because the higher this value, the more persistent the regime. Otherwise, regime-switching might occur too often, which would not be plausible for MS-GARCH models, as even a single-regime GARCH model could perform better to explain periods of high and low volatility in stock indices [ 67 ]; Figure 4 shows the best-fitting models that meet the filtering protocol for each of the 16 stock indices. The purple dashed line is the 5% conditional VaR.…”

“…The model with the highest mean ( ) should be selected because the higher this value, the more persistent the regime. Otherwise, regime-switching might occur too often, which would not be plausible for MS-GARCH models, as even a single-regime GARCH model could perform better to explain periods of high and low volatility in stock indices [ 67 ];…”

Inference and forecasting phase shift regime of COVID-19 sub-lineages with a Markov-switching model

“…While molecular biology has attempted to predict new mutations, no successful method has been developed thus far. Therefore, we still needed a developed way of forecasting outbreaks of the new coronavirus variants because the evolution of COVID-19 variants differed across countries depending on socio-economic characteristics ( 10 ).…”