Machine Learning–Based Prediction of Growth in Confirmed COVID-19 Infection Cases in 114 Countries Using Metrics of Nonpharmaceutical Interventions and Cultural Dimensions: Model Development and Validation

Yeung, Arnold; Roewer-Després, François; Rosella, Laura; Rudzicz, Frank

doi:10.2196/26628

Cited by 23 publications

(20 citation statements)

References 74 publications

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“…Ayyoubzadeh et al analyzed the rate of COVID-19 incidence in Iran using Google Trends data and deep learning methods [ 14 ]. Yeung et al combined several online COVID-19 data to train and evaluate five nontime series machine learning models in predicting confirmed infection growth [ 15 ]. These studies have shown that AI is suitable for evaluating disease trends and can provide governments with information that can be used to prevent outspread.…”

Section: Introductionmentioning

confidence: 99%

A COVID-19 Pandemic Artificial Intelligence–Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study

Yu¹,

Chang²,

Chang³

et al. 2021

J Med Internet Res

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Background More than 79.2 million confirmed COVID-19 cases and 1.7 million deaths were caused by SARS-CoV-2; the disease was named COVID-19 by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of the COVID-19 pandemic together with each country’s policy measures. Objective We aimed to develop an online artificial intelligence (AI) system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heat map visualization of policy measures in 171 countries. Methods The COVID-19 Pandemic AI System (CPAIS) integrated two data sets: the data set from the Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government, which is maintained by the University of Oxford, and the data set from the COVID-19 Data Repository, which was established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting: autoregressive integrated moving average (ARIMA), feedforward neural network (FNN), multilayer perceptron (MLP) neural network, and long short-term memory (LSTM). With regard to 1-year records (ie, whole time series data), records from the last 14 days served as the validation set to evaluate the performance of the forecast, whereas earlier records served as the training set. Results A total of 171 countries that featured in both databases were included in the online system. The CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July 2020 and another peak of 6,368,591 in December 2020. A dynamic heat map with policy measures depicts changes in COVID-19 measures for each country. A total of 19 measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting; the performances of ARIMA, FNN, and the MLP neural network were not stable because their forecast accuracy was only better than LSTM for a few countries. LSTM demonstrated the best forecast accuracy for Canada, as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA (RMSE=317.53169; MAPE=0.4641688) and FNN (RMSE=181.29894; MAPE=0.2708482) demonstrated better performance for South Korea. Conclusions The CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning–based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic.

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Section: Introductionmentioning

confidence: 99%

A COVID-19 Pandemic Artificial Intelligence–Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study

Yu¹,

Chang²,

Chang³

et al. 2021

J Med Internet Res

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“…A different line of work replaces epidemiological models with machine learning methods to directly predict the number of new infections [22][23][24][25]. Importantly, Yeung et al [26] added non-pharmaceutical interventions (policies) as features in their models; however, their approach is limited to make predictions up to two weeks in advance, since information about the policies that will be implemented in the future is not available at inference time. Our SIMLR approach differs by being interpretable and also by forecasting policy changes, which allows it to extend the horizon of the ∆I predictions.…”

Section: Related Workmentioning

confidence: 99%

SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting

Vega

Flores²,

Greiner

2022

Forecasting

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Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which incorporates machine learning (ML) into the epidemiological SIR model. For each region, SIMLR tracks the changes in the policies implemented at the government level, which it uses to estimate the time-varying parameters of an SIR model for forecasting the number of new infections one to four weeks in advance. It also forecasts the probability of changes in those government policies at each of these future times, which is essential for the longer-range forecasts. We applied SIMLR to data from in Canada and the United States, and show that its mean average percentage error is as good as state-of-the-art forecasting models, with the added advantage of being an interpretable model. We expect that this approach will be useful not only for forecasting COVID-19 infections, but also in predicting the evolution of other infectious diseases.

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“…Namun, model tersebut biasanya tidak menggambarkan interaksi dan hubungan non-linear [24]. Seiring perkembangan bidang Artificial Intelligence terus meningkat seperti penggunaan machine learning techniques untuk membantu pekerjaan manusia [25] serta permasalahan pada model statistik linear. Machine learning techniques dapat menjadi salah satu pilihan dalam prakiraan kualitas udara seperti yang telah dilakukan Mahajan dkk [26] menggunakan model Exponential Smoothing (ES) untuk melakukan prakiraan terhadap PM, serta membandingkannya dengan beberapa model seperti ARIMA, NNAR, dan ANN (Hybrid).…”

Section: Pendahuluanunclassified

Perbandingan Additive dan Multiplicative Exponential Smoothing Terhadap Prakiraan Kualitas Udara di Banjarmasin

Yusuf

Kusrini

Muhammad

2022

eltikom

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Kekhawatiran kesehatan manusia adalah salah satu konsekuensi penting dari rendahnya kualitas udara. Kondisi rendahnya kualitas udara setiap kota akan memberikan dampak jangka panjang seperti terjadinya pemanasan global serta efek rumah kaca antropogenik. Masalah kualitas udara biasanya terjadi pada daerah yang berada beberapa bagian negara seperti Pulau Kalimantan. Sebagai pulau terbesar ketiga di dunia, Kalimantan dapat dikatakan sebagai paru-paru dunia seperti permasalahan kabut asap yang menyelimuti Kota Banjarmasin pada 2019. Kondisi tersebut dapat mengakibatkan tingginya penderita Infeksi Saluran Pernafasan Akut (ISPA). Pengambilan keputusan oleh pemangku kepentingan perlu dikaji secara mendalam untuk mencegah hal tersebut. Salah satu upaya yang dapat dilakukan adalah prakiraan kualitas udara yang akan terjadi. Data yang didapatkan dari BMKG Kota Banjarmasin merupakan bahan awal untuk prakiraan tersebut. Prakiraan kualitas udara akan menggunakan Triple Exponential Smoothing dengan 2 jenis pemodelan yaitu additive dan multiplicative, sehingga penelitian ini bertujuan untuk melakukan prakiraan kualitas udara di Kota Banjarmasin pada tahun 2021 dan 2022 menggunakan Additive dan Multiplicative Triple Exponential Smoothing. Pada prakiraan menggunakan metode tersebut, pembobotan pada nilai konstanta α, β, γ dapat menghasilkan nilai error yang kecil. Untuk menentukan perbandingan akurasi kedua pemodelan dilakukan dengan nilai RMSE. Hasil penelitian menunjukkan bahwa kondisi kualitas udara di Banjarmasin selama 2021 dan 2022 untuk polutan CO, O3, dan PM berada pada kategori aman untuk kesehatan manusia, sedangkan untuk polutan NO2 dan SO2 dinyatakan memiliki indeks yang tinggi sehingga kualitas udara dapat membahayakan kesehatan makhluk hidup. Secara perbandingan, pemodelan multiplicative pada prakiraan CO (α = 0.5, β = 0.001, dan γ = 0.149), NO2 (α = 0.5, β = 0.024, dan γ = 0.022), dan SO2 (α = 0.5, β = 0.001, dan γ = 0.037) memiliki akurasi tinggi dan nilai error yang kecil dibandingkan dengan pemodelan additive. Sebaliknya, pemodelan additive pada O3 (α = 0.5, β = 0.001, dan γ = 0.06) dan PM (α = 0.434, β = 0.001, dan γ = 0.213) memilik akurasi tinggi dan nilai error yang rendah dibandingkan pemodelan multiplicative. Kesimpulan yang didapatkan adalah perbedaan hasil prakiraan antara pemodelan additive dan multiplicative pada prakiraan kualitas udara di Banjarmasin karena pemodelan multiplicative digunakan apabila terdapat kecenderungan atau tanda bahwa pola musiman bergantung pada ukuran data. Dengan kata lain, pola musiman membesar seiring meningkatnya ukuran data. Sedangkan model additive digunakan jika kecenderungan tersebut tidak terjadi.

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Machine Learning–Based Prediction of Growth in Confirmed COVID-19 Infection Cases in 114 Countries Using Metrics of Nonpharmaceutical Interventions and Cultural Dimensions: Model Development and Validation

Cited by 23 publications

References 74 publications

A COVID-19 Pandemic Artificial Intelligence–Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study

A COVID-19 Pandemic Artificial Intelligence–Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study

SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting

Perbandingan Additive dan Multiplicative Exponential Smoothing Terhadap Prakiraan Kualitas Udara di Banjarmasin

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