Abstract:A proposal for monitoring and evaluating the performance of Brazilian states against COVID-19 is presented, employing a set of non-linear growth functions. This is necessary given its unprecedented nature in the face of public policies known to Brazilian society in the 21st century. The daily data on the accumulated cases of the 27 Brazilian states were collected on the Ministry of Health of Brazil's website. As of 14-November-2020, the Richards, Weibull, Gompertz and logistic functions, respectively in order,… Show more
“…Next, we computed their RRSE. To estimate the growth functions parameters, we based our estimation on the previous study by [ 18 ], choosing the function that best fits each country using RRSE.…”
Section: Resultsmentioning
confidence: 99%
“…Figure 3 shows the RRSE for each of the five growth functions considered, for the confirmed cases, given that [ 18 ] analyzed only this time series. Our results clearly show that the GAM functions outperform those of [ 18 ] for the fitting of the COVID-19 time series, in addition to standardizing the entire estimation process in a single type of function. Fig.…”
Section: Resultsmentioning
confidence: 99%
“…However, we recognize that for monitoring pandemics and epidemic outbreaks in the early stages, growth functions are still important for this purpose, as demonstrated by [ 18 ]. This brings us to the main limitation of our evaluation framework: the size of the epidemiological time series.…”
Section: Discussionmentioning
confidence: 99%
“…These equations do not directly calculate the occurrence of the inflection points, however: these are the moments when the growth rate is going to decrease. This is common when using growth functions [ 24 ] to investigate outbreaks and epidemics [ 2 , 18 , 25 ]. To counter this limitation, Eqs.…”
Section: Methodsmentioning
confidence: 99%
“…To deal with this situation, [ 18 ] proposed a framework to monitor and evaluate the performance of public policies in confronting COVID-19 that are more/less efficient in the short/long term, employing a set of non-linear growth functions (exponential, logistic, Gompertz, Weibull, Richards) through a quadrants system. These functions are not highly accurate in the long run owing to spikes and “second waves” now evident in the COVID-19 data worldwide [ 19 ].…”
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.
“…Next, we computed their RRSE. To estimate the growth functions parameters, we based our estimation on the previous study by [ 18 ], choosing the function that best fits each country using RRSE.…”
Section: Resultsmentioning
confidence: 99%
“…Figure 3 shows the RRSE for each of the five growth functions considered, for the confirmed cases, given that [ 18 ] analyzed only this time series. Our results clearly show that the GAM functions outperform those of [ 18 ] for the fitting of the COVID-19 time series, in addition to standardizing the entire estimation process in a single type of function. Fig.…”
Section: Resultsmentioning
confidence: 99%
“…However, we recognize that for monitoring pandemics and epidemic outbreaks in the early stages, growth functions are still important for this purpose, as demonstrated by [ 18 ]. This brings us to the main limitation of our evaluation framework: the size of the epidemiological time series.…”
Section: Discussionmentioning
confidence: 99%
“…These equations do not directly calculate the occurrence of the inflection points, however: these are the moments when the growth rate is going to decrease. This is common when using growth functions [ 24 ] to investigate outbreaks and epidemics [ 2 , 18 , 25 ]. To counter this limitation, Eqs.…”
Section: Methodsmentioning
confidence: 99%
“…To deal with this situation, [ 18 ] proposed a framework to monitor and evaluate the performance of public policies in confronting COVID-19 that are more/less efficient in the short/long term, employing a set of non-linear growth functions (exponential, logistic, Gompertz, Weibull, Richards) through a quadrants system. These functions are not highly accurate in the long run owing to spikes and “second waves” now evident in the COVID-19 data worldwide [ 19 ].…”
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.
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