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Open Access 01-12-2022 | COVID-19 | Research

Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models

Authors: Yanding Wang, Zehui Yan, Ding Wang, Meitao Yang, Zhiqiang Li, Xinran Gong, Di Wu, Lingling Zhai, Wenyi Zhang, Yong Wang

Published in: BMC Infectious Diseases | Issue 1/2022

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Abstract

Background

COVID-19 poses a severe threat to global human health, especially the USA, Brazil, and India cases continue to increase dynamically, which has a far-reaching impact on people's health, social activities, and the local economic situation.

Methods

The study proposed the ARIMA, SARIMA and Prophet models to predict daily new cases and cumulative confirmed cases in the USA, Brazil and India over the next 30 days based on the COVID-19 new confirmed cases and cumulative confirmed cases data set(May 1, 2020, and November 30, 2021) published by the official WHO, Three models were implemented in the R 4.1.1 software with forecast and prophet package. The performance of different models was evaluated by using root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

Results

Through the fitting and prediction of daily new case data, we reveal that the Prophet model has more advantages in the prediction of the COVID-19 of the USA, which could compose data components and capture periodic characteristics when the data changes significantly, while SARIMA is more likely to appear over-fitting in the USA. And the SARIMA model captured a seven-day period hidden in daily COVID-19 new cases from 3 countries. While in the prediction of new cumulative cases, the ARIMA model has a better ability to fit and predict the data with a positive growth trend in different countries(Brazil and India).

Conclusions

This study can shed light on understanding the outbreak trends and give an insight into the epidemiological control of these regions. Further, the prediction of the Prophet model showed sufficient accuracy in the daily COVID-19 new cases of the USA. The ARIMA model is suitable for predicting Brazil and India, which can help take precautions and policy formulation for this epidemic in other countries.
Appendix
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Literature
1.
go back to reference Wang L, Wang Y, Ye D, Liu Q. Erratum to “A review of the 2019 Novel Coronavirus (COVID-19) based on current evidence” [International Journal of Antimicrobial Agents 55/6 (2020) 105948]. Int J Antimicrob Agents. 2020;56(3): 106137.PubMedPubMedCentralCrossRef Wang L, Wang Y, Ye D, Liu Q. Erratum to “A review of the 2019 Novel Coronavirus (COVID-19) based on current evidence” [International Journal of Antimicrobial Agents 55/6 (2020) 105948]. Int J Antimicrob Agents. 2020;56(3): 106137.PubMedPubMedCentralCrossRef
2.
go back to reference Fauci AS, Lane HC, Redfield RR. Covid-19—navigating the uncharted. Mass Medical Soc. 2020;382:1268–9. Fauci AS, Lane HC, Redfield RR. Covid-19—navigating the uncharted. Mass Medical Soc. 2020;382:1268–9.
3.
go back to reference Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA. 2020;323(13):1239–42.PubMedCrossRef Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA. 2020;323(13):1239–42.PubMedCrossRef
4.
go back to reference Bavel JJV, Baicker K, Boggio PS, Capraro V, Cichocka A, Cikara M, Crockett MJ, Crum AJ, Douglas KM, Druckman JN. Using social and behavioural science to support COVID-19 pandemic response. Nat Hum Behav. 2020;4(5):460–71.PubMedCrossRef Bavel JJV, Baicker K, Boggio PS, Capraro V, Cichocka A, Cikara M, Crockett MJ, Crum AJ, Douglas KM, Druckman JN. Using social and behavioural science to support COVID-19 pandemic response. Nat Hum Behav. 2020;4(5):460–71.PubMedCrossRef
5.
go back to reference Peeling RW, Olliaro PL, Boeras DI, Fongwen N. Scaling up COVID-19 rapid antigen tests: promises and challenges. Lancet Infect Dis. 2021;21(9):e290–5.PubMedPubMedCentralCrossRef Peeling RW, Olliaro PL, Boeras DI, Fongwen N. Scaling up COVID-19 rapid antigen tests: promises and challenges. Lancet Infect Dis. 2021;21(9):e290–5.PubMedPubMedCentralCrossRef
6.
go back to reference Cheng LL, Guan WJ, Duan CY, Zhang NF, Lei CL, Hu Y, Chen AL, Li SY, Zhuo C, Deng XL, et al. Effect of recombinant human granulocyte colony-stimulating factor for patients with coronavirus disease 2019 (COVID-19) and lymphopenia: a randomized clinical trial. JAMA Intern Med. 2021;181(1):71–8.PubMedCrossRef Cheng LL, Guan WJ, Duan CY, Zhang NF, Lei CL, Hu Y, Chen AL, Li SY, Zhuo C, Deng XL, et al. Effect of recombinant human granulocyte colony-stimulating factor for patients with coronavirus disease 2019 (COVID-19) and lymphopenia: a randomized clinical trial. JAMA Intern Med. 2021;181(1):71–8.PubMedCrossRef
7.
go back to reference Covid C, Team R, Covid C, Team R, Bialek S, Bowen V, Chow N, Curns A, Gierke R, Hall AJM et al: Geographic differences in COVID-19 cases, deaths, and incidence—United States. Mmwr 2020, 69(15):465. Covid C, Team R, Covid C, Team R, Bialek S, Bowen V, Chow N, Curns A, Gierke R, Hall AJM et al: Geographic differences in COVID-19 cases, deaths, and incidence—United States. Mmwr 2020, 69(15):465.
8.
go back to reference Labrague LJ, De Los Santos JAA. COVID-19 anxiety among front-line nurses: predictive role of organisational support, personal resilience and social support. J Nurs Manag. 2020;28(7):1653–61.PubMedCrossRef Labrague LJ, De Los Santos JAA. COVID-19 anxiety among front-line nurses: predictive role of organisational support, personal resilience and social support. J Nurs Manag. 2020;28(7):1653–61.PubMedCrossRef
9.
go back to reference Roustaei N, Allahyari E. COVID-19 screening: use of an artificial neural network. BMJ Support Palliat Care. 2021;31(12):003391. Roustaei N, Allahyari E. COVID-19 screening: use of an artificial neural network. BMJ Support Palliat Care. 2021;31(12):003391.
10.
go back to reference Oshinubi K, Amakor A, Peter OJ, Rachdi M, Demongeot J. Approach to COVID-19 time series data using deep learning and spectral analysis methods. Aims Bioeng. 2022;9(1):1–21.CrossRef Oshinubi K, Amakor A, Peter OJ, Rachdi M, Demongeot J. Approach to COVID-19 time series data using deep learning and spectral analysis methods. Aims Bioeng. 2022;9(1):1–21.CrossRef
11.
go back to reference Zhang J, Jiang Z. A new grey quadratic polynomial model and its application in the COVID-19 in China. Sci Rep. 2021;11(1):1–27. Zhang J, Jiang Z. A new grey quadratic polynomial model and its application in the COVID-19 in China. Sci Rep. 2021;11(1):1–27.
13.
go back to reference Martinez EZ, Aragon DC, Nunes AA. Short-term forecasting of daily COVID-19 cases in Brazil by using the Holt’s model. Rev Soc Bras Med Trop. 2020;53: e20200283.PubMedPubMedCentralCrossRef Martinez EZ, Aragon DC, Nunes AA. Short-term forecasting of daily COVID-19 cases in Brazil by using the Holt’s model. Rev Soc Bras Med Trop. 2020;53: e20200283.PubMedPubMedCentralCrossRef
14.
go back to reference Bhimala KR, Patra GK, Mopuri R, Mutheneni SR. Prediction of COVID-19 cases using the weather integrated deep learning approach for India. Transbound Emerg Dis. 2022;69(3):1349–63.PubMedCrossRef Bhimala KR, Patra GK, Mopuri R, Mutheneni SR. Prediction of COVID-19 cases using the weather integrated deep learning approach for India. Transbound Emerg Dis. 2022;69(3):1349–63.PubMedCrossRef
15.
go back to reference Hazarika BB, Gupta D. Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks. Appl Soft Comput. 2020;96:PubMedPubMedCentralCrossRef Hazarika BB, Gupta D. Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks. Appl Soft Comput. 2020;96:PubMedPubMedCentralCrossRef
16.
go back to reference Gupta AK, Singh V, Mathur P, Travieso-Gonzalez CM. Prediction of COVID-19 pandemic measuring criteria using support vector machine, prophet and linear regression models in Indian scenario. J Interdiscip Math. 2021;24(1):89–108.CrossRef Gupta AK, Singh V, Mathur P, Travieso-Gonzalez CM. Prediction of COVID-19 pandemic measuring criteria using support vector machine, prophet and linear regression models in Indian scenario. J Interdiscip Math. 2021;24(1):89–108.CrossRef
17.
go back to reference Oshinubi K, Rachdi M, Demongeot J. Analysis of reproduction number R(0) of COVID-19 using current health expenditure as gross domestic product percentage (CHE/GDP) across countries. Healthcare (Basel, Switzerland). 2021;9(10):1247. Oshinubi K, Rachdi M, Demongeot J. Analysis of reproduction number R(0) of COVID-19 using current health expenditure as gross domestic product percentage (CHE/GDP) across countries. Healthcare (Basel, Switzerland). 2021;9(10):1247.
18.
go back to reference Dorantes-Acosta E, Avila-Montiel D, Klunder-Klunder M, Juarez-Villegas L, Marquez-Gonzalez H. Survival and complications in pediatric patients with cancer and COVID-19: a meta-analysis. Front Oncol. 2020;10: 608282.PubMedCrossRef Dorantes-Acosta E, Avila-Montiel D, Klunder-Klunder M, Juarez-Villegas L, Marquez-Gonzalez H. Survival and complications in pediatric patients with cancer and COVID-19: a meta-analysis. Front Oncol. 2020;10: 608282.PubMedCrossRef
19.
go back to reference Kupferschmidt K. After Omicron, some scientists foresee “a period of quiet.” Science. 2022;375(6579):366–7.PubMedCrossRef Kupferschmidt K. After Omicron, some scientists foresee “a period of quiet.” Science. 2022;375(6579):366–7.PubMedCrossRef
20.
21.
go back to reference Oshinubi K, Rachdi M, Demongeot J. Analysis of reproduction number R0 of COVID-19 using current health expenditure as gross domestic product percentage (CHE/GDP) across countries. Healthcare (Basel). 2021;9(10):1247.PubMedPubMedCentralCrossRef Oshinubi K, Rachdi M, Demongeot J. Analysis of reproduction number R0 of COVID-19 using current health expenditure as gross domestic product percentage (CHE/GDP) across countries. Healthcare (Basel). 2021;9(10):1247.PubMedPubMedCentralCrossRef
22.
go back to reference Tan AX, Hinman JA, Abdel Magid HS, Nelson LM, Odden MC. Association between income inequality and county-level COVID-19 cases and deaths in the US. JAMA Netw Open. 2021;4(5): e218799.PubMedPubMedCentralCrossRef Tan AX, Hinman JA, Abdel Magid HS, Nelson LM, Odden MC. Association between income inequality and county-level COVID-19 cases and deaths in the US. JAMA Netw Open. 2021;4(5): e218799.PubMedPubMedCentralCrossRef
23.
go back to reference Zheng Z, Peng F, Xu B, Zhao J, Liu H, Peng J, Li Q, Jiang C, Zhou Y, Liu SJ. Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. J Infect. 2020;81(2):e16–25.PubMedPubMedCentralCrossRef Zheng Z, Peng F, Xu B, Zhao J, Liu H, Peng J, Li Q, Jiang C, Zhou Y, Liu SJ. Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. J Infect. 2020;81(2):e16–25.PubMedPubMedCentralCrossRef
24.
go back to reference Zhang X, Liu Y, Yang M, Zhang T, Young AA, Li X. Comparative study of four time series methods in forecasting typhoid fever incidence in China. PLoS ONE. 2013;8(5): e63116.PubMedPubMedCentralCrossRef Zhang X, Liu Y, Yang M, Zhang T, Young AA, Li X. Comparative study of four time series methods in forecasting typhoid fever incidence in China. PLoS ONE. 2013;8(5): e63116.PubMedPubMedCentralCrossRef
25.
go back to reference Zheng YL, Zhang LP, Zhang XL, Wang K, Zheng YJ. Forecast model analysis for the morbidity of tuberculosis in Xinjiang, China. PLoS ONE. 2015;10(3): e0116832.PubMedPubMedCentralCrossRef Zheng YL, Zhang LP, Zhang XL, Wang K, Zheng YJ. Forecast model analysis for the morbidity of tuberculosis in Xinjiang, China. PLoS ONE. 2015;10(3): e0116832.PubMedPubMedCentralCrossRef
26.
go back to reference He Z, Tao H. Epidemiology and ARIMA model of positive-rate of influenza viruses among children in Wuhan, China: a nine-year retrospective study. Int J Infect Dis. 2018;74:61–70.PubMed He Z, Tao H. Epidemiology and ARIMA model of positive-rate of influenza viruses among children in Wuhan, China: a nine-year retrospective study. Int J Infect Dis. 2018;74:61–70.PubMed
28.
go back to reference Demongeot J, Oshinubi K, Rachdi M, Hobbad L, Alahiane M, Iggui S, Gaudart J. Ouassou IJJMCS: The application of ARIMA model to analyze COVID-19 incidence pattern in several countries. J Math Comput Sci. 2021;12:Article ID 10. Demongeot J, Oshinubi K, Rachdi M, Hobbad L, Alahiane M, Iggui S, Gaudart J. Ouassou IJJMCS: The application of ARIMA model to analyze COVID-19 incidence pattern in several countries. J Math Comput Sci. 2021;12:Article ID 10.
29.
go back to reference Anastassopoulou C, Russo L, Tsakris A, Siettos C. Data-based analysis, modelling and forecasting of the COVID-19 outbreak. PLoS ONE. 2020;15(3): e0230405.PubMedPubMedCentralCrossRef Anastassopoulou C, Russo L, Tsakris A, Siettos C. Data-based analysis, modelling and forecasting of the COVID-19 outbreak. PLoS ONE. 2020;15(3): e0230405.PubMedPubMedCentralCrossRef
30.
go back to reference Peter OJ, Shaikh AS, Ibrahim MO, Nisar KS, Baleanu D, Khan I, Abioye AI. Analysis and dynamics of fractional order mathematical model of COVID-19 in Nigeria Using Atangana-Baleanu Operator. Comput Mater Continua. 2021;2:1823–48.CrossRef Peter OJ, Shaikh AS, Ibrahim MO, Nisar KS, Baleanu D, Khan I, Abioye AI. Analysis and dynamics of fractional order mathematical model of COVID-19 in Nigeria Using Atangana-Baleanu Operator. Comput Mater Continua. 2021;2:1823–48.CrossRef
31.
go back to reference Abioye AI, Umoh MD, Peter OJ, Edogbanya HO, Oguntolu FA, Kayode O, Amadiegwu SJ. Forecasting of COVID-19 pandemic in Nigeria using real statistical data. Commun Math Biol Neurosci. 2021;2021:Article ID 2. Abioye AI, Umoh MD, Peter OJ, Edogbanya HO, Oguntolu FA, Kayode O, Amadiegwu SJ. Forecasting of COVID-19 pandemic in Nigeria using real statistical data. Commun Math Biol Neurosci. 2021;2021:Article ID 2.
32.
go back to reference Bartholomew DJ, Box GEP, Jenkins GM. Time Series Analysis Forecasting and Control. Operat Res Quarter. 1971;22(2):199.CrossRef Bartholomew DJ, Box GEP, Jenkins GM. Time Series Analysis Forecasting and Control. Operat Res Quarter. 1971;22(2):199.CrossRef
33.
go back to reference Demongeot J, Oshinubi K, Rachdi M, Hobbad L, Alahiane M, Iggui S, Gaudart J, Ouassou I. The application of ARIMA model to analyze COVID-19 incidence pattern in several countries. J Math Comput Sci. 2021;12:Article ID. Demongeot J, Oshinubi K, Rachdi M, Hobbad L, Alahiane M, Iggui S, Gaudart J, Ouassou I. The application of ARIMA model to analyze COVID-19 incidence pattern in several countries. J Math Comput Sci. 2021;12:Article ID.
34.
go back to reference Schaffer AL, Dobbins TA, Pearson SA. Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC Med Res Methodol. 2021;21(1):58.PubMedPubMedCentralCrossRef Schaffer AL, Dobbins TA, Pearson SA. Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC Med Res Methodol. 2021;21(1):58.PubMedPubMedCentralCrossRef
35.
36.
go back to reference Fang WX, Lan PC, Lin WR, Chang HC, Chang HY, Wang YH. Combine facebook prophet and LSTM with BPNN forecasting financial markets: the morgan Taiwan index. 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). 2019: 1-2. Fang WX, Lan PC, Lin WR, Chang HC, Chang HY, Wang YH. Combine facebook prophet and LSTM with BPNN forecasting financial markets: the morgan Taiwan index. 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). 2019: 1-2.
37.
go back to reference Hasan MN, Haider N, Stigler FL, Khan RA, McCoy D, Zumla A, Kock RA, Uddin MJ. The global case-fatality rate of COVID-19 has been declining since May 2020. Am J Trop Med Hyg. 2021;104(6):2176–84.PubMedPubMedCentralCrossRef Hasan MN, Haider N, Stigler FL, Khan RA, McCoy D, Zumla A, Kock RA, Uddin MJ. The global case-fatality rate of COVID-19 has been declining since May 2020. Am J Trop Med Hyg. 2021;104(6):2176–84.PubMedPubMedCentralCrossRef
38.
go back to reference Zhao N, Liu Y, Vanos JK, Cao G. Day-of-week and seasonal patterns of PM2 5. concentrations over the United States: time-series analyses using the Prophet procedure. Atmos Environ. 2018;192:116–27.CrossRef Zhao N, Liu Y, Vanos JK, Cao G. Day-of-week and seasonal patterns of PM2 5. concentrations over the United States: time-series analyses using the Prophet procedure. Atmos Environ. 2018;192:116–27.CrossRef
39.
go back to reference Papacharalampous GA, Tyralis H. Evaluation of random forests and Prophet for daily streamflow forecasting. Adv Geosci. 2018;45:201–8.CrossRef Papacharalampous GA, Tyralis H. Evaluation of random forests and Prophet for daily streamflow forecasting. Adv Geosci. 2018;45:201–8.CrossRef
40.
go back to reference Becerra M, Jerez A, Aballay B, et al. Forecasting emergency admissions due to respiratory diseases in high variability scenarios using time series: a case study in Chile. Sci Total Environ. 2020;706: 134978.PubMedCrossRef Becerra M, Jerez A, Aballay B, et al. Forecasting emergency admissions due to respiratory diseases in high variability scenarios using time series: a case study in Chile. Sci Total Environ. 2020;706: 134978.PubMedCrossRef
41.
go back to reference Dhungana HN, Ghimire S. Commentary: statistical modeling for the prediction of infectious disease dissemination with special reference to COVID-19 spread. Front Public Health. 2021;9: 735857.PubMedPubMedCentralCrossRef Dhungana HN, Ghimire S. Commentary: statistical modeling for the prediction of infectious disease dissemination with special reference to COVID-19 spread. Front Public Health. 2021;9: 735857.PubMedPubMedCentralCrossRef
42.
go back to reference Benz LS, Lopez MJ. Estimating the change in soccer’s home advantage during the Covid-19 pandemic using bivariate Poisson regression. Adv Stat Anal. 2021;27(7):1–28. Benz LS, Lopez MJ. Estimating the change in soccer’s home advantage during the Covid-19 pandemic using bivariate Poisson regression. Adv Stat Anal. 2021;27(7):1–28.
43.
go back to reference Murakami D, Matsui T. Improved log-Gaussian approximation for over-dispersed Poisson regression: application to spatial analysis of COVID-19. PLoS ONE. 2022;17(1): e0260836.PubMedPubMedCentralCrossRef Murakami D, Matsui T. Improved log-Gaussian approximation for over-dispersed Poisson regression: application to spatial analysis of COVID-19. PLoS ONE. 2022;17(1): e0260836.PubMedPubMedCentralCrossRef
44.
go back to reference Lu JQ, Lu JY, Wang W, Liu Y, Buczek A, Fleysher R, Hoogenboom WS, Zhu W, Hou W, Rodriguez CJ, et al. Clinical predictors of acute cardiac injury and normalization of troponin after hospital discharge from COVID-19. EBioMedicine. 2022;76: 103821.PubMedPubMedCentralCrossRef Lu JQ, Lu JY, Wang W, Liu Y, Buczek A, Fleysher R, Hoogenboom WS, Zhu W, Hou W, Rodriguez CJ, et al. Clinical predictors of acute cardiac injury and normalization of troponin after hospital discharge from COVID-19. EBioMedicine. 2022;76: 103821.PubMedPubMedCentralCrossRef
45.
go back to reference Melin P, Sánchez D, Monica JC, Castillo O. Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction. Soft comput. 2021;13(1):1-38. Melin P, Sánchez D, Monica JC, Castillo O. Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction. Soft comput. 2021;13(1):1-38.
46.
go back to reference Fernandes FT, de Oliveira TA, Teixeira CE, Batista AFM, Dalla Costa G, Chiavegatto Filho ADP. A multipurpose machine learning approach to predict COVID-19 negative prognosis in Sao Paulo, Brazil. Sci Rep. 2021;11(1):3343.PubMedPubMedCentralCrossRef Fernandes FT, de Oliveira TA, Teixeira CE, Batista AFM, Dalla Costa G, Chiavegatto Filho ADP. A multipurpose machine learning approach to predict COVID-19 negative prognosis in Sao Paulo, Brazil. Sci Rep. 2021;11(1):3343.PubMedPubMedCentralCrossRef
Metadata
Title
Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models
Authors
Yanding Wang
Zehui Yan
Ding Wang
Meitao Yang
Zhiqiang Li
Xinran Gong
Di Wu
Lingling Zhai
Wenyi Zhang
Yong Wang
Publication date
01-12-2022
Publisher
BioMed Central
Keyword
COVID-19
Published in
BMC Infectious Diseases / Issue 1/2022
Electronic ISSN: 1471-2334
DOI
https://doi.org/10.1186/s12879-022-07472-6

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