Forecasting COVID-19 cases at the Amazon region: a comparison of classical and machine learning models

Abstract

BACKGROUNDSince the first reports of COVID-19, decision-makers have been using traditional epidemiological models to predict the days to come. However, the enhancement of computational power, the demand for adaptable predictive frameworks, the short past of the disease, and uncertainties related to input data and prediction rules, also make other classical and machine learning techniques viable options.OBJECTIVEThis study investigates the efficiency of six models in forecasting COVID-19 confirmed cases with 17 days ahead. We compare the models autoregressive integrated moving average (ARIMA), Holt-Winters, support vector regression (SVR), k-nearest neighbors regressor (KNN), random trees regressor (RTR), seasonal linear regression with change-points (Prophet), and simple logistic regression (SLR).MATERIAL AND METHODSWe implement the models to data provided by the health surveillance secretary of Amapáa, a Brazilian state fully carved in the Amazon rainforest, which has been experiencing high infection rates. We evaluate the models according to their capacity to forecast in different historical scenarios of the COVID-19 progression, such as exponential increases, sudden decreases, and stability periods of daily cases. To do so, we use a rolling forward splitting approach for out-of-sample validation. We employ the metrics RMSE, R-squared, and sMAPE in evaluating the model in different cross-validation sections.FINDINGSAll models outperform SLG, especially Holt-Winters, that performs satisfactorily in all scenarios. SVR and ARIMA have better performances in isolated scenarios. To implement the comparisons, we have created a web application, which is available online.CONCLUSIONThis work represents an effort to assist the decision-makers of Amapá in future decisions to come, especially under scenarios of sudden variations in the number of confirmed cases of Amapá, which would be caused, for instance, by new contamination waves or vaccination. It is also an attempt to highlight alternative models that could be used in future epidemics.

Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review

“…2021 Bangladesh Worldometer website Daily confirmed cases 10 April to 30 June 2020 LSTM 6.55 - - 4.51 [ 37 ] da Silva, C. C., et al. 2021 Brazil Brasil.io portal Daily confirmed cases till 6 June 2020 Linear Regression 11.42% - - - [ 38 ] Pernambuco (A state in brazil) 1.92% de Souza, D. G. S. et al., 2020 Amapa (A state in Brazil) Health surveillance secretary of Amapa Cumulative confirmed cases from 20 March to 31 August, 2020 Holt-Winters 162 - 0.98 0.34 [ 39 ] Dharani, N. P., et al. 2021 India Kaggle website Daily confirmed cases 30 January to 21 May 2020 Linear Regression 223.89 157.78 1.0 - [ 40 ] Doe, S. W., et al., 2020 USA Johns Hopkins University confirmed cases data for US counties Daily confirmed cases from 22 January to 31 May, 2020 and latitude, and longitude of each county CLEIR-Net 264.33 - - - [ 41 ] Fokas, A.…”

COVID-19 ICU demand forecasting: A two-stage Prophet-LSTM approach

“…During the COVID-19 pandemic, forecasting models that once were kept in vaults as strategic secrets became available to society, as part of a worldwide effort to suppress this crisis through knowledge sharing and science. Recently, many researchers have investigated and created forecasting approaches to predict the numbers related to COVID-19, such as: when the pandemic will peak, how long it will last, how many will be infected or die, and how big will be the demand for hospital beds, ventilators, and PPE (Personal Protective Equipment) [4] , [15] , [16] , [17] , [18] , [19] .…”

On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study

“…On the other hand, it relies on the Fourier series to incorporate daily, weekly, and annual seasonalities. In the case of COVID-19, we are more concerned about weekly seasonality [8].…”

“…The Amapá state has just 877,613 residents who live in an area larger than England, but it is 67-times denser in England. As a result, Amapá has already experienced an overload of mortality from transmissible infections, predominantly amidst indigenous groups, such as other parts of the Brazilian Amazon [8]. In addition, despite previous government efforts, several social and health challenges remain for the many people residing in the state, such as public sanitation and minimal access to clean water [9].…”