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Sandie AB, Tejiokem MC, Faye CM, Hamadou A, Abah AA, Mbah SS, Tagnouokam-Ngoupo PA, Njouom R, Eyangoh S, Abanda NK, Diarra M, Ben Miled S, Tchuente M, Tchatchueng-Mbougua JB, Tchatchueng-Mbougua JB. Observed versus estimated actual trend of COVID-19 case numbers in Cameroon: A data-driven modelling. Infect Dis Model 2023; 8:228-239. [PMID: 36776734 PMCID: PMC9905042 DOI: 10.1016/j.idm.2023.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 02/02/2023] [Accepted: 02/02/2023] [Indexed: 02/10/2023] Open
Abstract
Controlling the COVID-19 outbreak remains a challenge for Cameroon, as it is for many other countries worldwide. The number of confirmed cases reported by health authorities in Cameroon is based on observational data, which is not nationally representative. The actual extent of the outbreak from the time when the first case was reported in the country to now remains unclear. This study aimed to estimate and model the actual trend in the number of COVID -19 new infections in Cameroon from March 05, 2020 to May 31, 2021 based on an observed disaggregated dataset. We used a large disaggregated dataset, and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05, 2020 to May 31, 2021. Subsequently, seasonal autoregressive integrated moving average (SARIMA) modeling was used for forecasting purposes. Based on the prospective MRP modeling findings, a total of about 7450935 (30%) of COVID-19 cases was estimated from March 05, 2020 to May 31, 2021 in Cameroon. Generally, the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 times. The forecasting indicated a succession of two waves of the outbreak in the next two years following May 31, 2021. If no action is taken, there could be many waves of the outbreak in the future. To avoid such situations which could be a threat to global health, public health authorities should effectively monitor compliance with preventive measures in the population and implement strategies to increase vaccination coverage in the population.
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Key Words
- ACF, Autocorrelation Function
- AIC, Akaike information criterion
- COVID-19
- COVID-19, Coronavirus Disease 2019
- Cameroon
- Forecasting
- MAE, Mean Absolute Error
- MAPE, Mean Absolute Percentage Error
- MASE, Mean Absolute Scaled Error
- ME, Mean Error
- MPE, Mean Percentage Error
- MRP, Multilevel Regression and Post-stratification
- Observed
- PACF, Partial Autocorrelation Function
- PLACARD, Platform for Collecting, Analyzing and Reporting Data
- Post-stratification
- SARIMA, Seasonal Autoregressive integrated moving average
- SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2
- Underestimated
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Affiliation(s)
- Arsène Brunelle Sandie
- African Population and Health Research Center, West Africa Regional Office, Dakar, Senegal,Centre Pasteur du Cameroon, membre du Réseau International des Instituts Pasteur, Cameroon,Corresponding author. African Population and Health Research Center, West Africa Regional Office, Dakar, Senegal.
| | | | - Cheikh Mbacké Faye
- African Population and Health Research Center, West Africa Regional Office, Dakar, Senegal
| | - Achta Hamadou
- Centre Pasteur du Cameroon, membre du Réseau International des Instituts Pasteur, Cameroon
| | - Aristide Abah Abah
- Direction de la lutte contre les Maladies épidémiques et les pandémies, Ministère de la santé publique, Cameroon
| | - Serge Sadeuh Mbah
- Centre Pasteur du Cameroon, membre du Réseau International des Instituts Pasteur, Cameroon
| | | | - Richard Njouom
- Centre Pasteur du Cameroon, membre du Réseau International des Instituts Pasteur, Cameroon
| | - Sara Eyangoh
- Centre Pasteur du Cameroon, membre du Réseau International des Instituts Pasteur, Cameroon
| | - Ngu Karl Abanda
- Centre Pasteur du Cameroon, membre du Réseau International des Instituts Pasteur, Cameroon
| | | | | | - Maurice Tchuente
- Fondation pour la recherche l'ingénierie et l'innovation, Cameroon,IRD UMI 209 UMMISCO, University of Yaounde I, P.O. Box 337, Yaounde, Cameroon
| | - Jules Brice Tchatchueng-Mbougua
- Centre Pasteur du Cameroon, membre du Réseau International des Instituts Pasteur, Cameroon,IRD UMI 209 UMMISCO, University of Yaounde I, P.O. Box 337, Yaounde, Cameroon
| | - Jules Brice Tchatchueng-Mbougua
- Centre Pasteur du Cameroon, membre du Réseau International des Instituts Pasteur, Cameroon,IRD UMI 209 UMMISCO, University of Yaounde I, P.O. Box 337, Yaounde, Cameroon
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Ayoobi N, Sharifrazi D, Alizadehsani R, Shoeibi A, Gorriz JM, Moosaei H, Khosravi A, Nahavandi S, Gholamzadeh Chofreh A, Goni FA, Klemeš JJ, Mosavi A. Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. Results Phys 2021; 27:104495. [PMID: 34221854 PMCID: PMC8233414 DOI: 10.1016/j.rinp.2021.104495] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 06/19/2021] [Accepted: 06/22/2021] [Indexed: 05/17/2023]
Abstract
The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.
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Key Words
- ANFIS, Adaptive Network-based Fuzzy Inference System
- ANN, Artificial Neural Network
- AU, Australia
- Bi-Conv-LSTM, Bidirectional Convolutional Long Short Term Memory
- Bi-GRU, Bidirectional Gated Recurrent Unit
- Bi-LSTM, Bidirectional Long Short-Term Memory
- Bidirectional
- COVID-19 Prediction
- COVID-19, Coronavirus Disease 2019
- Conv-LSTM, Convolutional Long Short Term Memory
- Convolutional Long Short Term Memory (Conv-LSTM)
- DL, Deep Learning
- DLSTM, Delayed Long Short-Term Memory
- Deep learning
- EMRO, Eastern Mediterranean Regional Office
- ES, Exponential Smoothing
- EV, Explained Variance
- GRU, Gated Recurrent Unit
- Gated Recurrent Unit (GRU)
- IR, Iran
- LR, Linear Regression
- LSTM, Long Short-Term Memory
- Lasso, Least Absolute Shrinkage and Selection Operator
- Long Short Term Memory (LSTM)
- MAE, Mean Absolute Error
- MAPE, Mean Absolute Percentage Error
- MERS, Middle East Respiratory Syndrome
- ML, Machine Learning
- MLP-ICA, Multi-layered Perceptron-Imperialist Competitive Calculation
- MSE, Mean Square Error
- MSLE, Mean Squared Log Error
- Machine learning
- New Cases of COVID-19
- New Deaths of COVID-19
- PRISMA, Preferred Reporting Items for Precise Surveys and Meta-Analyses
- RMSE, Root Mean Square Error
- RMSLE, Root Mean Squared Log Error
- RNN, Repetitive Neural Network
- ReLU, Rectified Linear Unit
- SARS, Serious Intense Respiratory Disorder
- SARS-COV, SARS coronavirus
- SARS-COV-2, Serious Intense Respiratory Disorder Coronavirus 2
- SVM, Support Vector Machine
- VAE, Variational Auto Encoder
- WHO, World Health Organization
- WPRO, Western Pacific Regional Office
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Affiliation(s)
- Nooshin Ayoobi
- Department of Mathematics, Savitribai Phule Pune University, Pune 411007, India
| | - Danial Sharifrazi
- Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia
| | - Afshin Shoeibi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
- Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran
| | - Juan M Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Spain
| | - Hossein Moosaei
- Department of Mathematics, Faculty of Science, University of Bojnord, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia
| | - Abdoulmohammad Gholamzadeh Chofreh
- Sustainable Process Integration Laboratory - SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69 Brno, Czech Republic
| | - Feybi Ariani Goni
- Department of Management, Faculty of Business and Management, Brno University of Technology - VUT Brno, Kolejní 2906/4, 612 00 Brno, Czech Republic
| | - Jiří Jaromír Klemeš
- Sustainable Process Integration Laboratory - SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69 Brno, Czech Republic
| | - Amir Mosavi
- John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
- School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway
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Kınacı H, Ünsal MG, Kasap R. A close look at 2019 novel coronavirus (COVID 19) infections in Turkey using time series analysis & efficiency analysis. Chaos Solitons Fractals 2021; 143:110583. [PMID: 33519117 PMCID: PMC7836434 DOI: 10.1016/j.chaos.2020.110583] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 11/17/2020] [Accepted: 12/10/2020] [Indexed: 05/24/2023]
Abstract
2019 novel coronavirus (COVID 19) infections detected as the first official records of the disease in Wuhan, China, affected almost all countries worldwide, including Turkey. Due to the number of infected cases, Turkey is one of the most affected countries in the world. Thus, an examination of the pandemic data of Turkey is a critical issue to understand the shape of the spread of the virus and its effects. In this study, we have a close look at the data of Turkey in terms of the variables commonly used during the pandemic to set an example for possible future pandemics. Both time series modeling and popular efficiency measurement methods are used to evaluate the data and enrich the results. It is believed that the results and discussions are useful and can contribute to the language of numbers for pandemic researchers working on the elimination of possible future pandemics.
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Key Words
- AICA, Akaike Information Criterion
- ARIMA, Autoregressive Integrated Moving Average
- CE, Cross Efficiency Evaluation
- COVID 19
- DEA, Data Envelopment Analysis
- DNOC, Daily Number of Cases
- DNOD, Daily Number of Deaths
- DNOR, Daily Number of Recovered Cases
- DNOT, Daily Number of Tests
- DROC, Daily Rate of Cases
- Efficiency analysis
- MAE, Mean Absolute Error
- Pandemic
- RC, Rate of Cases
- RD, Rate of Deaths
- SC, Speed of Cases
- SD, Speed of Deaths
- SFA, Stochastic Frontier Analysis
- TNOC, Total Number of Cases
- TNOD, Total Number of Deaths
- TNOE, Total Number of İntubated Cases
- TNOIC, Total Number of The Cases İn İntensive Care Unit
- TNOR, Total Number of Recovered Cases
- TNORC, Total Number of Remaining Cases
- Time series analysis
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Affiliation(s)
- Harun Kınacı
- Erciyes University, Faculty of Economics and Administrative Sciences Department of Business, 38225, Kayseri, Turkey
| | - Mehmet Güray Ünsal
- Usak University, Faculty of Art and Science, Department of Statistics, Usak, Turkey
| | - Reşat Kasap
- Gazi University, Faculty of Science, Department of Statistics, Ankara, Turkey
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