1,000,000 cases of COVID-19 outside of China: The date predicted by a simple heuristic

Koczkodaj, Waldemar W.; Mansournia, Mohammad Alì; Pedrycz, Witold; Wolny-Dominiak, Alicja; Zabrodskii, P.F.; Strzałka, Dominik; Armstrong, Terri S.; Zolfaghari, Amir Hussain; Dębski, Maciej; Mazurek, Jiří

doi:10.1016/j.gloepi.2020.100023

Cited by 42 publications

(33 citation statements)

References 6 publications

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“…In what follows, we consider a collection of works which focused on the spread of the COVID-19 pandemic outside of China. In [9], a simple heuristic was proposed in order to identify the date at which the number of confirmed cases outside China will reach one million. The proposed heuristic consists of approximating the number of cases using an exponential curve.…”

Section: A Studies About Covid-19 Spread Predictionmentioning

confidence: 99%

Predicting COVID-19 Spread Level using Socio-Economic Indicators and Machine Learning Techniques

Mihoub¹,

Snoun²,

Krichen³

et al. 2020

Preprint

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<p>The new so-called COVID-19 virus is unfortunately founded to be highly transmissible across the globe. In this study, we propose a novel approach for estimating the spread level of the virus for each country for three different dates between April and May 2020. Unlike previous studies, this investigation does not process any historical data of spread but rather relies on the socio-economic indicators of each country. Actually, more than 1000 socio-economic indicators and more than 190 countries were processed in this study. Concretely, data preprocessing techniques and feature selection approaches were applied to extract relevant indicators for the classification process. Countries around the globe were assigned to 4 classes of spread. To find the class level of each country, many classifiers were proposed based especially on Support Vectors Machines (SVM), Multi-Layer Perceptrons (MLP) and Random Forests (RF). Obtained results show the relevance of our approach since many classifiers succeeded in capturing the spread level, especially the RF classifier, with an F-measure equal to 93.85% for April 15th, 2020. Moreover, a feature importance study is conducted to deduce the best indicators to build robust spread level classifiers. However, as pointed out in the discussion, classifiers may face some difficulties for future dates since the huge increase of cases and the lack of other relevant factors affecting this widespread.<i></i></p>

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Section: A Studies About Covid-19 Spread Predictionmentioning

confidence: 99%

Predicting COVID-19 Spread Level using Socio-Economic Indicators and Machine Learning Techniques

Mihoub¹,

Snoun²,

Krichen³

et al. 2020

Preprint

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show abstract

“…In this present study, four phenomenological models (Generalized Growth, Generalized Logistic Growth, Exponential Growth, and Richard Growth), been validated in previous outbreaks [17,[19][20][21][22][23] (Ebola virus, influenza, smallpox, plague, measles, HIV/AIDS, severe acute respiratory syndrome, Zika virus, COVID-19, etc. ), were applied to the data to identify the best model to adequately fit the data, which was then used to characterize the early ascending phase of the outbreak and to assess short-term forecasts in Kadiogo province, the epicenter of the outbreak in Burkina Faso.…”

Section: Introductionmentioning

confidence: 99%

What Can We Learn from Burkina Faso COVID-19 Data? Using Phenomenological Models to Characterize the Initial Growth Dynamic of the Outbreak and to Generate Short-Term Forecasts

Rouamba¹,

Samadoulougou²,

Bonnechère³

et al. 2020

Preprint

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On 9 March 2020, two cases of COVID-19 were reported in Burkina Faso. As of 10 April 2020, a total number of 484 cases (404 cases in the Kadiogo province) were reported nationwide. Real-time forecasts of COVID-19 are important to inform decision-making in the country. Here, we propose an approach that tests the performance of four models (Exponential Growth model, the Generalized Growth model (GGM), the Generalized Logistic Growth, and Richards Growth model) to select the model that best fit data and to generate short-term forecasting (5-, 10-, and 15-day forecasts from 11 to 25 April 2020) in Kadiogo, the epicenter of the outbreak. Using daily number of confirmed COVID-19 cases, the results suggests that GGM performed the best out of the 4 models. Overall, our GGM predictions suggested an average total number of cumulative cases of 514 (95% CI, 464–559), 629 (95% CI, 559–691), and 750 (95% CI, 661–840) between 11 to 15 April, 16 to 20 April, and 20 to 25 April 2020, respectively. COVID-19 in this province was best approximated by sub exponential growth rather than exponential or logistic growth. Current data suggest that COVID-19 cases would continue to increase over the next 15-days.

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“…studied about the association of comorbidities with pneumonia and death on COVID-19 patients in Mexico ( Hernández-V et al, 2020 ); and there are some Forecasting or predictions related to Covid-19 have been studied by various researchers: Koczkodaj, et. al predicts Covid-19 outside of China by using a simple heuristic (exponential curve) ( Koczkodaj et al, 2020 ); and Roosa, et. al studying about Covid-19 real-time forecast in China with generalized logistic growth model (GLM) ( Roosa et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Will COVID-19 confirmed cases in the USA reach 3 million? A forecasting approach by using SutteARIMA Method

Ahmar

Val

2020

Current Research in Behavioral Sciences

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1,000,000 cases of COVID-19 outside of China: The date predicted by a simple heuristic

Cited by 42 publications

References 6 publications

Predicting COVID-19 Spread Level using Socio-Economic Indicators and Machine Learning Techniques

Predicting COVID-19 Spread Level using Socio-Economic Indicators and Machine Learning Techniques

What Can We Learn from Burkina Faso COVID-19 Data? Using Phenomenological Models to Characterize the Initial Growth Dynamic of the Outbreak and to Generate Short-Term Forecasts

Will COVID-19 confirmed cases in the USA reach 3 million? A forecasting approach by using SutteARIMA Method

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