 Research
 Open access
 Published:
Global prediction model for COVID19 pandemic with the characteristics of the multiple peaks and local fluctuations
BMC Medical Research Methodology volumeÂ 22, ArticleÂ number:Â 137 (2022)
Abstract
Background
With the spread of COVID19, the timeseries prediction of COVID19 has become a research hotspot. Unlike previous epidemics, COVID19 has a new pattern of longtime series, large fluctuations, and multiple peaks. Traditional dynamical models are limited to curves with shorttime series, single peak, smoothness, and symmetry. Secondly, most of these models have unknown parameters, which bring greater ambiguity and uncertainty. There are still major shortcomings in the integration of multiple factors, such as human interventions, environmental factors, and transmission mechanisms.
Methods
A dynamical model with only infected humans and removed humans was established. Then the process of COVID19 spread was segmented using a local smoother. The change of infection rate at different stages was quantified using the continuous and periodic Logistic growth function to quantitatively describe the comprehensive effects of natural and human factors. Then, a nonlinear variable and NO_{2} concentrations were introduced to qualify the number of people who have been prevented from infection through human interventions.
Results
The experiments and analysis showed the R^{2} of fitting for the US, UK, India, Brazil, Russia, and Germany was 0.841, 0.977, 0.974, 0.659, 0.992, and 0.753, respectively. The prediction accuracy of the US, UK, India, Brazil, Russia, and Germany in October was 0.331, 0.127, 0.112, 0.376, 0.043, and 0.445, respectively.
Conclusion
The model can not only better describe the effects of human interventions but also better simulate the temporal evolution of COVID19 with local fluctuations and multiple peaks, which can provide valuable assistant decisionmaking information.
Background
The rapid spread of COVID19 brought unprecedented harm to human life, economic development, and social stability. How to control the spread of COVID19 in a way that minimizes the risk and the cost has become the focus of research. A timely grasp of the characteristics of spread and future development of COVID19 can turn passive prevention and control into the initiative. However, the prevalence of COVID19 is influenced by a combination of subjective factors (such as population activity and human control) and objective factors (such as temperature, humidity, and social economy) [1,2,3], which leads to a new curve form of longtime series, large fluctuations, and multiple peaks. As shown in Fig. 1, the epidemic duration is long (more than 2â€‰years), and the trends of the rising and falling curves are asymmetrical and not smooth.
The researches on the prediction of COVID19 can be mainly divided into three parts: classical dynamical models of infectious diseases, timeseries prediction models, and multivariate prediction models. The timeseries prediction models [4,5,6] use timeseries forecasting methods to solve the problem of prediction, such as long shortterm memory (LSTM), sliding window averaging, and Autoregressive Integrated Moving Average model (ARIMA). However, these models only mine the laws of changes in the time series of the epidemic curves and still lack the consideration of influencing factors, only for shortterm predictions. The multivariate prediction models [7,8,9] use regression methods to establish the relationship between the number of confirmed cases and correlated factors. However, the effects of these factors are not combined with the transmission chain and cannot explain how they affect the spread of COVID19. The dynamical models of infectious diseases divide the population into different groups based on the epidemiological characteristics of individuals and use differential equations to express the process of contact infection between populations. It has two advantages over the above two approaches: it can represent the dynamical process of infectious diseases (susceptible  infected  recovered); its epidemiological parameters are important for the prevention and control of epidemics and pathological studies. Therefore, dynamical models are gradually becoming the mainstream mathematical approaches for the researches of infectious diseases. For example, SEIQR [10], SIRX [11], and SIQR [12], SEIRD [13], SEIRS [14], eISHR [15], exponential and nonlinear growth model [16] and new infectious disease models by adding asymptomatic infectors [17,18,19] and environmental infection [20, 21]. However, they are usually only suitable for singlepeaked and shortterm prediction, and lack consideration of multiple human or natural factors leading to the predicted curves presenting a smooth and symmetric form.
Huang firstly proposed the Global Prediction System for COVID19 Pandemic (GPCP) by combining dynamical models with meteorological factors [8]. Among them, the daily number of confirmed cases (dI) was quantified by introducing infection rate (Î²) and adjustment parameters (Îµ), i.e., dIâ€‰=â€‰Î²IÎµI^{2}. The item (ÎµI^{2}) expresses the number of infected people reduced due to human interventions. But when the number of confirmed cases (I) is large, the value of this item (ÎµI^{2}) is much greater than the number of new infections per day (Î²I), which will lead to the daily number of confirmed cases (dI) being negative numbers. Therefore, this method is more suitable for early short epidemic prediction (I is relatively small). Besides, this system did not predict epidemics with multiple peaks. Subsequently, a secondgeneration global prediction system was proposed [22] and simulated the second wave of the outbreak. Because the dynamical models are a forward flow model, the number of susceptible individuals keeps decreasing and there will be no one infected at the end, which makes it impossible for the infection curve to continue to rise. Thus, these methods are not suitable to describe the characteristics of the development of a prolonged outbreak.
In summary, the current dynamical prediction models have the following shortcomings:â‘ most models are only for smooth and symmetric curves with the short time and single peak; â‘¡most models lack integrated consideration of human prevention and control, environmental factors, and transmission mechanisms of infectious disease; â‘¢a large amount of unknown data is included in the models, which will lead to difficulty in verifying the results and bring more ambiguity and uncertainty. This study proposed novel prediction models for COVID19, aiming to better describe epidemic curves with the long time, multiple peaks, and high fluctuations and provide valuable auxiliary decisionmaking information. Firstly, considering the virus mutations and the effectiveness of the vaccine, the circular SEAICR_{loop} model was proposed based on the SEIR model, then the IR_{loop} model was established only by retaining infected humans and removed humans. Secondly, the logistic growth function was used to describe the change laws of infection rate caused by natural factors and human interventions in each stage. Finally, anomalous values of NO_{2} concentrations and nonlinear function were introduced to quantify the number of infected people reduced due to human interventions, which solved the problem of local and large fluctuations in the epidemic curve. The main contributions of this study are as follows:

(1)
A theoretical prediction model was proposed to describe the epidemic curve with the characteristics of long time series, multi peaks, asymmetry, and local fluctuations. The model is simple and retains the epidemiological significance of model parameters. The parameters can be completely verified by the actual data, which greatly reduces the uncertainty and fuzziness of results.

(2)
The model uses the logistic growth function to describe the change of infection rate in different stages, which can measure the impact of natural and human factors. At the same time, the NO_{2} concentrations were introduced to quantify the number of infected people reduced due to human interventions, which effectively integrates the characteristics of local fluctuations for epidemic curves.
Methods
The spread of COVID19 is influenced synthetically by both natural and human factors. To ensure the accuracy and scientificity of the timeseries prediction model, it is necessary to integrate the influence of these factors. Therefore, the SEAICR_{loop} and the IR_{loop} model were proposed based on the SEIR model, and the Logistic function was used to quantify the infection rate under the influences of multiple factors. Meanwhile, the impacts on human intervention were modeled using NO_{2} concentrations outliers.
Infectious disease model based on characteristics of longtime series
SEAICR_{loop} model
The classical dynamical models translate the problem of the change in the number of infected people into mathematical differential equations. Among them, the SEIR and SIR model is the most classic. However, the premise for the use of dynamic models is that population movements in and out are not taken into account. In addition, the dynamical models are a positive oneway population transformation and usually do not return from removed humans (R) to susceptible humans (S), because the model assumes that people who die or acquire antibodies will not be infected again. However, this is the opposite of COVID19 infection, and the traditional dynamical models are inoperative for COVID19 with the characteristics of longtime series and multiple peaks.
The SEAICR_{loop} model was proposed by improving the classic model, which has a more complete dynamical mechanism. It divides the population into susceptible (S), exposed (E), asymptomatic (A), infected (I), detected (C), and removed (R) humans. At the beginning of the outbreak, all but one or a few migratory infected persons are susceptible (S). When they contact effectively with infected cases, they, called the exposed humans (E), do not show symptoms immediately. After a period of incubation, a part of exposed humans show clinical symptoms and then become symptomatic infected humans (I) and the rest of them still show no symptoms but are infectious, and are called asymptomatic infected humans (A). Subsequently, there are two ways for infected humans (A and I) to exit the transmission system. One way is isolation through testing and they are called the detected infected humans (C). The other way is through immunization, treatment, and death and they are called removed humans (R). Finally, people who recover will be infected again due to the effectiveness of vaccines and viral mutations, which can achieve a closedloop transmission mechanism of infectious diseases to adapt to the COVID19 pandemic. As shown in the following Eqs. (1, 2, 3, 4, 5, 6) and Fig. 2.
Where Î² is the effective transmission rate; Ïƒ is the progression rate from exposed state to infectious state; Î³ is the recovery rate; d is the mortality rate; Î¸ and Ï† are the detection rate of asymptomatic and symptomatic infected cases, respectively; Î½ is the fraction of new infectious humans that are asymptomatic; Î· is the proportion of recovered humans who are likely to be infected again.
IR_{loop} model
There are a lot of unknowns in the epidemiology of infectious diseases, especially in the face of the sudden outbreak of new infectious diseases, such as the number of exposed humans, the number of asymptomatic infected humans, and the infectivity of the latent period. The actual reported data are only the number of confirmed people, dead people, and recovered people. However, the number of latent people is not known. In this case, it is inaccurate that the dynamical model parameters are estimated using only the numbers of confirmed people, dead people, and recovered people as validation data, which will bring some ambiguity and uncertainty to the prediction results. Okuonghae [17], Alberti [23] and Cao [24] also pointed out that there is great uncertainty in using early sample data to predict the unknown parameters. Therefore, the population is only divided into infected humans and removed humans. Removed humans contain the dead people and recovered people. As shown in the following Eqs. (7 and 8). While the model is simple, it retains the dynamic mechanism of infectious diseases and the significance of epidemiological parameters of model parameters.
Improvement of dynamical model parameters under the influences of multiple factors
Classical dynamical models are the ideal transmission of infectious diseases, and their predicted outcomes usually present smooth and standard normal curves. However, the spread of infectious diseases is influenced by a variety of factors, and the infection curve is irregular and has large fluctuations, asymmetry, and multiple peaks. Therefore, dynamical models need to consider the influence of multiple factors.
Model of infection rate based on the periodic logistic function
Although many factors have different effects on infectious diseases, they can be attributed to the change in infection rate in the dynamic models. For example, human interventions are to reduce the infection rate, and the infection rate of the influenza virus shows seasonal characteristics. In the classical dynamical model, Î² is considered as a constant, which can only be applied when the infectious disease is in the ideal spreading state. At the beginning of an outbreak, COVID19 is in a state of free transmission and the infection rate is relatively high. As the number of infected people continues to increase, the interventions will start to perform; the infection rate will continue to decrease after some time. When the intensity of interventions is alleviated, COVID19 may spread again and the infection rate will continue to increase again. This process is very similar to the Logistic growth function in mathematics, as shown in Fig. 3, and its form is shown in Eq. 9 below. The parameters of the Logistic function are estimated by using genetic arithmetic for approximate parameters solution from the corresponding epidemic data.
Where t is days. During a declining period of infection rates, p_{1}â€‰+â€‰p_{2} is the initial infection rate; p_{1} is the eventual infection rate after human prevention and control; p_{3} is the hysteresis of human interventions, and the larger its value indicates that the intensity of human interventions is high and the infection rate decreases rapidly. On the contrary, the smaller its value, the intensity of human interventions is low and the infection rate decreases slowly. During a rising period of infection rates, p_{1}â€‰+â€‰p_{5} is the eventual infection rate after relaxing the prevention and control; p_{6} is the hysteresis of relaxation of human interventions, and the larger its value indicates that the faster the interventions are relaxed, the faster the infection rate will rise. On the contrary, it indicates that the human interventions are relaxed slowly and the infection rate rises slowly. p_{4} and p_{6} are the inflection point of changes in infection rate. When the virus mutates, the p_{1} will not be the same in both periods because it does not belong to the same nature of the virus, and the curve of the rising period changes as the dark purple dashed line in Fig. 3.
Model of nonpharmaceutical interventions based on NO_{2} concentration
Dynamical models of infectious diseases usually are smooth curves. However, the actual curve with large fluctuations may be due to the inaccuracy of the human detection on the one hand, and the human interventions on the other hand. Researchers [25,26,27] worldwide observed reductions in NO_{2} concentrations due to lockdown and related diminished human activities, notably the reduced industrial and vehicular use. In addition, there are also many studies [28, 29] show that a strong correlation between changes in NO_{2} concentrations and COVID19. NO_{2} concentrations, as the exhaust gases of vehicle emissions and industrial production, can reflect indirectly the human interventions to restrict the work, travel, and activities of people [8, 22]. The impact of human interventions is mainly reflected in the reduction of the number of infected people. Therefore, the parameter Îµ is introduced to express the proportion of the reduction and added to the dynamics model of infectious diseases. The specific improvements are divided into the improvement of the SEAICR_{loop} model and IR_{loop} model, as the following eqs. (11, 12, 13 and 14). The parameter Îµ is linearized by the difference between the NO_{2} concentrations and the concentrations without the human interventions, as in Eq. 10.
â€¢ SEAICR_{loop} Model
â€¢ IR_{loop} Model
where Îµ is the moderating parameter of the epidemic curve, which mainly corresponds to the uninfected people protected due to human prevention and control. \(\overline{C}\) is the average NO_{2 }concentration without human interventions, and C is the daily NO_{2} concentration in Î¼g/m^{3}.
Results
To validate the correctness and rationality of the improved model, global epidemic data were collected from the COVID19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins [30]. The data period is from January 22, 2020 to November 30, 2021. Among them, the data from January 22, 2020 to September 30, 2021, will be used as the historical epidemic fitting, and the data from October 1, 2021 to November 30, 2021, will be used to verify the future prediction results. Global climate and air quality data were collected from a dedicated dataset provided by the Air Quality Open Data Platform Worldwide COVID19 dataset (WAQI Project, https://aqicn.org/dataplatform/covid19/cn/). The values of relevant parameters are shown in Table 1.
Timeseries fitting of historical epidemic data
The parameters of the dynamical model express the epidemic trends under the countriesâ€™ state at that time, such as the intensity of interventions, economic development, and population activity. The fitting of historical data has two purposes. One is to obtain the number of different groups as an initial input parameter for the new prediction at the next moment, and the other is to evaluate the model parameters at different stages to select them to predicting future epidemics. The transmission stages of COVID19 were divided using the methods of curve smooth and firstorder difference. In the initial phase, there are no human interventions, so NO_{2} will not be considered (Îµâ€‰=â€‰0). The genetic algorithm was used to calculate the optimal parameters with 100â€‰cycles to avoid local optima. The parameters with the best fitgoodness (R^{2}) as shown in Fig. 4 and Tables 2 and 3, and the time is from January 22, 2020 to September 30, 2021.
Figure 4 shows: the IR_{loop} and the SEAICR_{loop} models can achieve the epidemic prediction, which is reflected in the fitting characteristics with large fluctuations and multiple peaks. Among them, the IR_{loop} model can achieve better results overall than the SEAIR_{loop} model, especially for epidemic curves with large fluctuations. This is mainly because the parameters of the IR_{loop} model have better actual validation and the SEAIR_{loop} model contains a large number of parameters that cannot be validated (the actual data contains only the number of confirmed people and recovered people and dead people), which also makes the model more ambiguous and uncertain. However, in the second phase in India and the US, the SEAIR_{loop} model achieved better results. This is mainly because the number of infected cases is small at the beginning of COVID19 and the ambiguity and uncertainty of the model are relatively small. The SEAIR_{loop} model has a more complete dynamical mechanism, which can better describe the transmission process of COVID19. However, this ambiguity and uncertainty will continue to be superimposed as the epidemic continues, eventually leading to the prediction of the SEAIR_{loop} model does not achieve better results. In addition, the SEAIR_{loop} model can obtain better prediction results mostly for curves with smooth and small volatility, which also reflects that the IR_{loop} model has an advantage in terms of volatility.
Timeseries predictions of NO_{2} concentrations
To predict the development of COVID19 in the future, the intensity of human interventions is a key factor. NO_{2} concentrations can reflect indirectly the intensity of interventions. The changes of NO_{2} have stability in a short period and have the timing characteristics of seasonality, longterm trends, stochastic fluctuations, and cyclic changes in time series. Therefore, the ARIMA model was used to predict the NO_{2} concentrations from October 1, 2021 to November 30, 2021, which provides a data basis for the later epidemic prediction in the future. The results are shown in Fig. 5.
As can be seen in Fig. 5, the predicted NO_{2} concentrations remain relatively stable, and the main trends maintain the upward or downward of the previous phase. This is mainly because NO_{2} concentrations are meteorological factors that vary continuously over time and space. It is difficult to have large changes aggregated nationwide in shortterm time. The predictions of the ARIMA model are less volatile compared to the actual NO_{2} concentrations because multiple predictions are averaged. This treatment ensures the stability of the NO_{2} concentrations trends. Among them, the prediction results for India differed from the actual one, mainly due to the sudden relaxation of the policy that led to a sharp increase in NO_{2}. However, such sudden events are difficult to predict.
Prediction of the development trends of COVID19 in the future
The epidemic prediction is of great importance for epidemic prevention and control. The trends of the infection curve are not the same due to the unknown nature of the future and the variability of viruses. Meanwhile, sample data for the last phase of the epidemic curves are usually too small, so it is unreasonable to estimate the model parameters for that phase using only these data. How to determine the model parameters for future curves will be a key issue for epidemic prediction.
The parameters of dynamics models can control the rise or fall of the overall trend of the epidemic curve. However, the changes in the overall trend of the epidemics are due to the occurrence of unpredictable events, such as secondary outbreaks caused by virus mutations and rapid declines in the epidemic caused by the intensification of prevention and control measures. Thus, the prediction of the epidemic inflection point is very difficult. The significance of prediction is to let people know how the epidemic will develop in the future by continuing the current epidemic status, national interventions, and economic status. Then, this can provide decision support information to regulate the current epidemic prevention and control status in response to future changes. The parameters of historical epidemic curves contain the development trends of COVID19 under the influence of the current epidemic condition, human intervention status, and national economic condition. Therefore, the approximate reflection of the future epidemic trends can be expressed by these parameters. The model parameters with the same trends in the historical stages were selected to predict future epidemic development. Meanwhile, the small amount of sample data in the last stage could be used as validation to select the results. Results were shown in Fig. 6 and Table 1 (time from October 1, 2021, to November 30, 2021). Mean Absolute Percentage Error (\(MAPE=\frac{1}{n}\sum \limits_{i=1}^n\mid \left({y}_i{\hat{y}}_i\right)\mid\)) was used to measure the accuracy of results. As the effects of temperature and humidity on COVID19 are still controversial, the influence of this factor on the results is excluded from the experiments [22].
As can be seen from Fig. 6, the prediction results have good stability in the overall trend. The prediction accuracy (MAPE) of the US, UK, India, Brazil, Russia, and Germany in October is 0.331, 0.127, 0.112, 0.376, 0.043, and 0.445, respectively. There are two main problems: firstly, the local fluctuations of the predicted results are low, mainly because the NO_{2} concentrations used were the mean of ARIMA model prediction results, and they were relatively stable. This treatment can better ensure that the general trend of NO_{2} concentrations change is correct because the longterm prediction of future NO_{2} concentrations is difficult to achieve. Secondly, in Russia, the United Kingdom, and the United States, the predicted trends exit opposite to the actual situation. After introducing the actual NO_{2} concentrations, the prediction curve has a great improvement, but the overall trend remains unchanged, which is verified obviously in the UK. This is mainly because NO_{2} concentrations are more the adjustment parameter of curve volatility, and the parameters of the dynamical model are the main factor controlling the overall trend change. However, these changes in overall trend in epidemic trends are due to abrupt and unpredictable events, for example, omicron infection was found in the United States and the United Kingdom in late November 2021. The significance of epidemic prediction is to let people know how the epidemic will develop in the future by continuing the current epidemic status, national prevention, and control, and economic status. Therefore, the prediction model in this study pays more attention to the epidemic situation under the continuation of the current prevention and control status.
Discussion
COVID19 is still in a very serious state and is also showing multiple differentscale outbreaks. All countries need to prepare for proactive prevention and control rather than passive defense. The prediction of the epidemic is important assistant information. This study proposed a novel model to predict the COVID19 pandemic with the characteristics of longtime series, multiple peaks, and large fluctuations.
There are several points worth explaining here. Firstly, the problem of longtime series refers to the historical duration of the epidemic, such as foreign epidemics that have lasted more than 2 years. Our model can well describe the transmission process of such a longtime series of infectious diseases. The prediction of longtime series refers to the forecasting for almost 1â€“3 months under the continuation of the current state of prevention and control, which is also more suitable for the asymmetric and nonsmooth epidemic curves with large fluctuations. Secondly, the fitting of historical epidemics is also very good using our model. However, there are still two limitations to the prediction of future epidemics. First, the model is difficult to predict the outbreak of new epidemics and the inflection point of epidemics. The introduction of NO_{2} concentrations is only a parameter to regulate the fluctuation feature of the epidemic curve and the parameters of the dynamic models of infectious diseases are a major factor in regulating the upward or downward trend of the epidemic curve. However, the changes in the overall trend of the epidemics are due to the occurrence of unpredictable events, such as secondary outbreaks caused by virus mutations and rapid declines caused by the intervention of prevention and control measures. Authors think that the significance of epidemic prediction should be to let people know how the epidemic will develop in the future by continuing the current epidemic status, national prevention, and control, and economic status. Thus, the future development of COVID19 can be predicted by selecting epidemic parameters with similar trends in past periods. Because these historical parameters contain the development trends of COVID19 under the current conditions of epidemic conditions, human prevention and control, and the national economy. This study provides an idea to predict the trend of future epidemics if the current prevention and control situation continues. This prediction is not focused on the specific daily changes in the number of infected cases and more reflects the longterm trends of the future epidemics. Second, there is still room to improve the volatility of the future prediction curve. This fluctuation is mainly caused by the restriction on peopleâ€™s activities of human intervention measures, which is reflected by NO_{2} concentrations. When we bring in the real NO_{2} data, the prediction curve has better improved. But it is difficult to predict well the NO_{2} concentrations for a longer period in the future. The predictions of the ARIMA model are very different in the multiple simulations. Therefore, we choose the mean value of multiple simulations so that prediction curves are relatively smooth. This way can avoid subjective errors but also lead to the unobvious fluctuations. Finally, the epidemiological mechanisms of the SEAICR_{loop} model are more complex. The more complex mechanism also leads to the fact that a large number of parameters cannot be verified by actual report data, which will increase the fuzziness and uncertainty of the model. Therefore, the prediction result of the SEAICR_{loop} model is better than that of the IR_{loop} model only in the early stage of the epidemic because the number of infected people is relatively small in this term. However, as the number of infected people increases, the fuzziness of the model will increase, and the advantages of the IR_{loop} model will be more reflected.
Conclusion
This study proposed a new dynamical model of infectious diseases, which aims to quantify the COVID19 pandemic with the characteristics of longtime series, multiple peaks, and large fluctuations and predict the development trend of the epidemic. Through experimental results, the model could realize the epidemic prediction with high accuracy and reasonableness, especially for the epidemic curve with a large fluctuation. The method also breaks the limitation of the traditional epidemic curve with smooth and symmetrical characteristics. The goodnessoffit R^{2} of the prediction for the US, UK, India, Brazil, Russia, and Germany were 0.841, 0.977, 0.974, 0.659, 0.992, and 0.753, respectively. The model used the trends of epidemic changes in the historical stages as the empirical parameters and the predictions have high consistency in the overall trend. The prediction accuracy (MAPE) of the US, UK, India, Brazil, Russia, and Germany in October is 0.331, 0.127, 0.112, 0.376, 0.043, and 0.445, respectively.
The model is still in an early stage of research and still lacks the incorporation of a large amount of data, such as local medical conditions, the degree of population aging, and the travel characteristics of the population, which would greatly reduce the uncertainty of the epidemic prediction. The strength of the model lies more in describing the characteristics of long series, multiple peaks, and large fluctuations for COVID19, which will also help to understand and mitigate the impact of the epidemic and provide a valuable reference for decisionmakers.
Availability of data and materials
The datasets generated during the current study are available in the zenodo repository, [https://doi.org/10.5281/zenodo.6220224]. The code and original data have been uploaded to GitHub, [https://github.com/daihaoran1/GlobalPredictionModelforCOVID19PandemicwiththeCharacteristcsoftheMultiplePeaksandLoca].
References
Han WG. Datadrive and modeldriven Spatiotemporal data Ming â€“ respective case study in traffic flow data and epidemic data. School Chin Acad Sci. 2005;119â€“39.Â http://ir.igsnrr.ac.cn/handle/311030/106.
FranchPardo I, Napoletano BM, RoseteVerges F, et al. Spatial analysis and GIS in the study of COVID19. A review. Sci Total Environ. 2020;739:140033.
Viezzer J, Biondi D. The influence of urban, socioeconomic, and ecoenvironmental aspects on COVID19 cases, deaths and mortality: a multicity case in the Atlantic Forest, Brazil. Sustain Cities Soc. 2021;69:102859.
Ronaghi F, Salimibeni M, Naderkhani F, et al. COVID19HPSMP: COVID19 adopted hybrid and parallel deep information fusion framework for stock price movement prediction. Expert Syst Appl. 2022;187:115879.
Satish C, Vijaya P, Joseph M. Jaya spider monkey optimizationdriven deep convolutional LSTM for the prediction of COVIDâ€™19. Bio Algorithms Med Syst. 2020;16(4):20200030.
Quintero Y, Ardila D, Camargo E, et al. Machine learning models for the prediction of the SEIRD variables for the COVID19 pandemic based on a deep dependence analysis of variables. Comput Biol Med. 2021;134:104500.
Wang RX, Ji CJ, Jiang ZM, et al. A shortterm prediction model at the early stage of the COVID19 pandemic based on multisource urban data. IEEE Trans Comput Soc Syst. 2021;8(4):1021â€“8.
Huang JP, Zhang L, Liu XY, et al. Global prediction system for COVID19 pandemic. Science Bull. 2020;65:884â€“1887.
Dansana D, Kumar R, Bhattacharjee A, Mahanty C. COVID19 outbreak prediction and analysis of Ehealthcare data using random Forest Algorithms. Int J Reliab Qual E Healthc (IJRQEH). 2022;11(1):1â€“13.
Jumpen W, Wiwatanapataphee B, Wu YH, et al. A SEIQR model for pandemic influenza and its parameter identification. Int J Pure Appl Math. 2020;52(2):247â€“65.
Maier BF, Brockmann D. Effective containment explains subexponential growth in confirmed cases of recent COVID19 outbreak in mainland China. Science. 2020;368(6492):742â€“6.
Crokidakis N. COVID19 spreading in Rio de Janeiro, Brazil: do the policies of social isolateon really work? Chaos, Solitons Fractals. 2020;136:109930.
Viguerie A, Lorenzo G, Auricchio F, et al. Simulating the spread of COVID19 via a spatiallyresolved susceptible  exposed  infected  recovered  deceased (SEIRD) model with heterogeneous diffusion. Appl Math Lett. 2020;111:106617.
Cooke KL, Driessche PVD. Analysis of an SEIRS epidemic model with two delays. J Math Biol. 1996;35(2):240â€“60.
Li SJ, Song K, Yang BR, et al. Preliminary assessment of the COVID19 outbreak using 3staged model eISHR. J Shanghai Jiaotong Univ (Sci). 2020;25(2):157â€“64.
Mahanty C, Kumar R, Mishra BK, Hemanth DJ, Gupta D, Khanna A. Prediction of COVID19 active cases using exponential and nonlinear growth models. Expert Syst. 2022;39(3):e12648.
Okuonghae D, Omame A. Analysis of a mathematical model COVID19 crowds dynamics in Lagos, Nigeria. Chaos, Solitons Fractals. 2020;139:110032.
Davies N, Klepac P, Liu Y, et al. Agedependent effects in the spread and control of COVID19 epidemics. Nat Med. 2020;26:1205â€“11.
Davies N, Kucharski A, Eggo R, et al. Effects of nonpharmaceutical interventions on COVID19 cases, deaths, and demand for hospital services in the UK: a modelling study. Lancet. 2020;7(5):e375â€“85.
Poleneni V, Rao JK, Hidayathulla SA. COVID19 prediction using ARIMA model. In: Proceedings of the Confluence 2021: 11th international conference on cloud computing, Data Science and Engineering, vol. 9377038; 2020. p. 860â€“5.
MHDM R, RGD S, Mariani VC, et al. Shortterm forecasting COVID19 cumulative confirmed cases: perspectives for Brazil. Chaos Solit Fractals. 2020;135:109853.
Huang J, Liu X, Zhang L, et al. The amplified second outbreaks of global COVID19 pandemic. medRxiv. 2020. https://doi.org/10.1101/2020.07.15.20154161.
Alberti T, Faranda D. On the uncertainty of realtime predictions of epidemic growths: a COVID19 case study for China and Italy. Commun Nonlinear Sci Numer Simul. 2020;90:105372.
Cao W, Dai HR, Zhu JW, et al. Evaluation of nonpharmaceutical interventions on prevention and control of COVID19: a case study of Wuhan City. ISPRS Int J Geo Inf. 2021;48(10):1â€“27.
Cooper MJ, Martin R, Hanmmer M. Global finescale changes in ambient NO2 during COVID19 lockdowns. Nature. 2022;601:380â€“7. https://doi.org/10.1038/s41586021042290.
Abdelsattar A, Nadhairi R, Hassan A. Spacebased monitoring of NO2 levels during COVID19 lockdown in Cairo, Egypt and Riyadh, Saudi Arabia. Egypt J Remote Sens Space Sci. 2021;24(3):659â€“64. https://doi.org/10.1016/j.ejrs.2021.03.004.
Ropkins K, Tate JE. Early observations on the impact of the COVID19 lockdown on air quality trends across the UK. Sci Total Environ. 2021;754:142374. https://doi.org/10.1016/j.scitotenv.2020.142374.
Lian X, Huang J, Zhang L, Liu C, Liu X, Wang L. Environmental indicator for COVID19 nonpharmaceutical interventions. Geophys Res Lett. 2021;48:e2020GL090344. https://doi.org/10.1029/2020GL090344.
Cao H, Han L. The shortterm impact of the COVID19 epidemic on socioeconomic activities in China based on the OMINO2data. Environ Sci Pollut Res. 2021;29(15):21682â€“91.
Dong E, Du H, Gardner L. An interactive webbased dashboard to track COVID19 in real time. Lancet Infect Dis. 2020;20(5):422â€“534.
Acknowledgments
Not applicable.
Funding
This study was funded by the National Key Research and Development Program of China (2018YFB0505304) and the National Natural Science Foundation of China (Grant No.41671409).
Author information
Authors and Affiliations
Contributions
Haoran Dai: Data curation, Methodology, Visualization, Software, Writing  original draft, Writing  review & editing. Wen Cao: Conceptualization, Methodology, Validation, Funding acquisition, Writing  review & editing. Xiaochong Tong: Funding acquisition, Validation. Yunxing Yao: Software, Review, and editing. Jingwen Zhu: Data curation, Review, and editing. Yuzhen Tian: Software, Validation. Feilin Peng: Visualization, Validation. All authors were involved in the manuscript preparation. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Additional information
Publisherâ€™s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Dai, H., Cao, W., Tong, X. et al. Global prediction model for COVID19 pandemic with the characteristics of the multiple peaks and local fluctuations. BMC Med Res Methodol 22, 137 (2022). https://doi.org/10.1186/s1287402201604x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s1287402201604x