Analysis of meteorological factors influencing the incidence of influenza in Fujian Province based on a neural network model

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Methods : From 2016 to 2020,weekly meteorological and influenza surveillance data in Fujian Province were collected. Using four different deep learning network models, including ordinary neural network (ANN), deep neural network (DNN), recurrent neural network (RNN), and gated recurrent unit (GRU), the prediction model of the weekly average temperature, influenza lag and influenza incidence were determined, and the predictive effects from each different models were compared. Results : The incidence of influenza in Fujian Province showed obvious seasonality, with a high incidence in winter, especially from November to March, during which influenza incidence reached the highest value each year. A non-linear negative correlation between temperature and incidence of influenza was obtained. Compared with the prediction model that only considers “temperature” as a factor, the model that includes both temperature and lag had a better predictive effect. Overall, the GRU model, with three hidden layers (constructed from temperature, influenza lag of one week and two weeks), had the best prediction ability, followed by RNN, DNN, and ANN, respectively. Conclusion : Temperature and influenza incidence showed a non-linear negative correlation. Furthermore, the GRU model provides a better prediction of the influenza incidence and, therefore, can be used to develop an influenza risk early warning system based on temperature and influenza lag, to prevent the incidence and spread of influenza. Influenza neural network model weather temperature lag Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Influenza is a respiratory infectious disease caused by the influenza virus. Hundreds of millions of people worldwide are infected with influenza every year, and the number of death is as high as 500,000 per year. Although immunity to reinfection with the same strain persists for many years, the rapid antigenic changes of influenza strains allow the virus to escape from the herd immunity pre-established after a first infection [ 1 , 2 ]. Influenza pandemics occur when a new virus strain spreads across species [ 3 ]. The elderly, immunosuppressant and people with underlying medical conditions, newborns, and infants are more likely to get the flu and develop severe illness and even death. Influenza epidemics and pandemics have a large impact on morbidity and mortality. For instance, the H1N1 influenza pandemic in 1918/1919 was characterized by three independent peaks, causing around 80 million deaths [ 4 , 5 ]. Besides, the two influenza pandemics in 1957/1958 and 1968/1969 also caused about 10 000 deaths [ 3 , 6 ]. Moreover, in April 2009, influenza H1N1pdm09,identified in Mexico and the United States, caused an estimated 123 000 to 203 000 deaths during its first year of transmission [ 7 ]. Recent prediction and early warning models,including Susceptible- Infectious(SI), Susceptible- Infectious- Susceptible(SIS), Susceptible- Infectious-Recovered(SIR), Susceptible- Infectious- Recovered - Susceptible (SIRS), Susceptible- Exposed-Infectious-Recovered(SEIR) model, Logistics, neural network model, and ARIMA model, have been given special attention because of their ability to predict a sanitary event, including disease incidence [ 8 ]. The neural network is a kind of machine learning that first started in the 1990s as an artificial neural network (ANN), a computational model, which imitates the structure and function of a biological neural network. From the improved optimization of algorithms and the increase in the number of layers of neural networks, Deep Neural Networks (DNNs) emerged. DNN can significantly enhance the prediction ability of the network compared to ANN. To meet the needs of time-series data analysis, the Recurrent Neural Network (RNN) was developed and in need to address the insensitivity of RNNs to long-term dependencies, Hochreiter S et al. proposed and developed a long short-term memory (LSTM) unit [ 9 ]. Gated Recurrent Unit (GRU) is another variant of RNN [ 10 ], which uses a gate mechanism with LSTM to solve the above problems. Specifically, GRU can be regarded as a simplified version of LSTM. Since GRU has fewer parameters, it converges faster and can speed up the iterative process. In the context of the global COVID-19 epidemic, artificial intelligence (AI) research has been widely used for prediction [ 11 ]. Common predictions include cumulative cases,recurrence of infection (waves), and time to peak [ 12 ]. The main AI research models used to predict the occurrence of diseases and the infectious disease-related transmission patterns include optimized SIR models [ 13 ], mixed partial differential equation-based ABM models [ 14 ], and multi-agent systems [ 15 , 16 ]. Many studies used DNN models to predict the incidence of COVID-19 [ 17 ], classify COVID-19 and influenza patients [ 18 , 19 ], distinguish influenza A virus from rabies virus based on viral genome sequences, and also for the taxonomy of coronaviruses [ 20 ]. The DNN model has shown good prediction and classification performance. However, many other models present different predictions. In this study, we assessed different deep learning network models,including ANN, DNN, RNN and GRU,to predict the incidence of influenza in a local area (Fujian Province, China) and compared the predictionabilities of the four different models used. Materials And Methods Data collection The present study assessing the effects of meteorological factors on the incidence of influenza was carried out in Fujian Province, China. Data on influenza incidence was obtained from the information system of Chinese Disease Control and Prevention. These data only included confirmed cases reported by medical and health institutions in Fujian Province, excluding overseas or Hong Kong, Macao and Taiwan regions. The obtained and confirmed but not suspected cases were classified based on laboratory or clinical diagnoses. Overall, from 2016 to 2020, 95 025 influenza cases in 265 weeks were obtained in Fujian Province. The meteorological data of the same period came from the National Meteorological Information Center (http://cdc.cma.gov.cn/). Model building The all obtained data contains 265 values, which were classified into 10 groups. The first group contains the first 175 data, and the second group contains the first 185 data, and then the third group contains the first 195, and so on. Finally, the last group of data contains all 265 values. The proportions of model training, validation, and prediction for each data set are the first 70%, the middle 15%, and the last 15%, respectively. The purpose of model validation is to prevent over-fitting in network training. The main factor considered in the model prediction is the temperature (x), and the lagged effect of influenza incidence y is analyzed. The ordinary neural network (ANN) with one hidden layer was first considered. The number of neurons in the hidden layer is 5 to 80 for network training, and the appropriate number of neurons in the hidden layer is selected according to the final result. The training, validation and prediction results of ANN under different input factors and numbers of neurons in the hidden layer were compared using the mean square error (MSE). From the comparison results, we concluded that when the input variable is “temperature”(x) and the lag term of influenza incidence ), the effect prediction of ANN is the best. The hidden layer contains 10 neurons. Based on the comparison results of the ANN models, the subsequent models only consider the three input variables of temperature (x) and the lag term of influenza incidence). In order to fully discuss the influence of the number of hidden layers and the number of neurons contained in each hidden layer on the effect prediction, DNN, RNN, and GRU models were used, with 3 to 11 hidden layers, and each hidden layer contains 5 to 11 layers and 80 neurons. Regularization Methods and Model Training To avoid over-fitting, the dropout regularization method was adopted. For comparison, dropout rates of 0, 0.1, and 0.2 were used in each hidden layer, where 0 represents no regularization. The training adopted a limited maximum training number of 1000 to prevent the network optimization from being in an infinite loop state. All data analysis was performed using R 4.1.2 (https://www.r-project.org/) and Python 3.9.7 (https://www.python.org/) software. Results 1. Epidemiological characteristics of influenza incidence From January 1, 2016, to December 31, 2020, medical and health institutions reported 95 025 confirmed cases of influenza in Fujian Province. In the 2019 influenza outbreak, the average weekly incidence rate for the whole year was 1.8/100 000.In 2020, there was a significant reduction in the incidence of influenza compared with 2019. Except for 2019, the weekly average rate from 2016 to 2020 was between 0.50and 1.01/100 000, and the median incidence rate in each year was less than 0.42/100 000. The difference in the distribution of the incidence rate in each year was statistically significant (rank-sum test, p <0.001). The overall statistics of influenza morbidity in Fujian Province each year are presented in Table 1. The trend of influenza incidence in Fujian Province shows obvious seasonality, with high incidence in winter. Specifically, most cases were observed from November to March each year, and the remaining months were fewer. The five-year incidence rate ranged from 0.086/100 000to 8.53/100 000, and the 95% incidence ranged from 0.17/100 000 to 5.40/100 000, with a median of 0.45/100 000 (Fig. 1 and 2).During the 2016 and 2017 winters, the influenza incidence was around 0.45/100 000. During the winter of 2018,the incidence of influenza was around 2.06/100 000, while it was above 3.27/100 000 during the 2019 and 2020 winters. The incidence of influenza in summer was found to be extremely low. Except in 2019, when the influenza outbreak, the incidence of influenza in summer generally fluctuated between 0.20/100 000to 0.30/100 000. 2. Distribution of meteorological factors From 2016 to 2020, the temperature in Fujian Province changed significantly according to the seasons, which is characteristic of a typical subtropical climate, with a low temperature in winter and a high temperature in summer (Figure 3). Interestingly, the air temperature negatively correlated with the influenza incidence (Figure 1). In summers, temperatures were the highest, but fewer new influenza cases were observed, whereas in winters, temperatures were the lowest, but the incidence of influenza cases was the highest. The distribution range of the weekly temperature average was between 6 and 30 °C, and the median temperature was about 19.9 °C. The shape of the violin diagram of the temperature distribution was opposite to that of the influenza incidence diagram. When the value was larger, the phenomenon of data distribution concentration occurred (Figure 4). 3. The effect of temperature on the incidence of influenza For a better picture of the changing trend between temperature and influenza incidence, we drew a scatter plot graph (Figure 5). As presented in Figure 5 there is a clear negative correlation between temperature and influenza incidence. Specifically, when the temperatures are low, the scatter points are mainly distributed, and the influenza incidence is higher. However, when temperatures are higher, influenza incidence is relatively low:at around 30°C, the incidence rate was around 0.2/100 000. The Spearman correlation between air temperature and influenza incidence is shown in Table 2. 4. Neural Network Prediction of Temperature and Influenza Lag effect on Influenza Incidence This retrospective study included weekly average temperatures of 265-weeks(hereinafter referred to as temperature) and influenza incidence data from 2016 to 2020. The main factor considered in the model prediction was “temperature”, and the lagged effect of the incidence of influenza was also analyzed. Different models were used for neural network training. The features of the specific models used areas follows: 4.1 Ordinary Neural Network Training (ANN) In general, in our analyses, the closer the mean square error (MSE)value is to 0, the better the prediction ability of the model.As presented in Table 3,with the unique use of “temperature”as a factor by ANN modeling, the predicted MSE results were poor, and the number of neurons in the hidden layer had a low effect on the results. When the first-order lag of influenza incidence was considered in the analysis together with “temperature”, the prediction by ANN was significantly improved, and the number of neurons in the hidden layer had little effect on the results. Based on what precedes, when the second-order lag of influenza incidence was added, the predictive effect by ANN was further improved. When the number of neurons in the hidden layer was 5 and 10, the predictive effect was practically the best. 4.2 Deep Neural Network Training (DNN) The results of the Deep Neural Network assay are presented in Table 4. Without regularization, the prediction ability of the optimal network by ANN was generally better than that of the DNN network. However, after regularization was applied,optimal network results by DNN and a better prediction ability were obtained. Interestingly, the best DNN network contained 11 hidden layers, with a number of 80 neurons in each hidden layer and a drop out rate of 0.1. 4.3 Recurrent Neural Network Training (RNN) The results of the Recurrent Neural Network Training assay are presented in Table 5. The optimal network of RNN was obtained using three hidden layers, with the number of neurons in each layer of 30 and a drop out rate of 0.1. These characteristics could achieve better predictive effects than the optimal DNN network.When the number of hidden layers increased, the predictive ability of RNN decreased because the RNN network itself is relatively complex, and the number of hidden layers is prone to over-fitting. Therefore, the relatively simple 3-layer hidden layer RNN had the best predictive effect. 4.4 Gated Recurrent Unit (GRU) Similar to the DNN network, the input variable was “temperature”, and the lag term of influenza incidence was. For this end, the dropout regularization method was used. Dropout rates of 0, 0.1, and 0.2 were respectively used in each hidden layer. Then, we considered GRUs with 3 to 11 layers, with each hidden layer containing 5 to 80 neurons. The training used a limited maximum number of training times of 1000. Overall, the network operation results are shown in Table 6. The optimal network of GRU contained three hidden layers, with 30 neurons in each layer and a drop out rate of 0.2. Under the same network structure, GRU had a stronger prediction ability than RNN. With an increase in the number of hidden layers, the prediction ability of the GRU network decreased, and the simpler GRU with three hidden layers had the best predictive effect. In the neural network, the predictive effect when considering only “temperature” as a factor was not good with its lag because influenza is contagious, and the autocor- relation between the incidence of influenza and the incidence of the previous period is higher. Considering its infectivity and the MSE results of the neural network simulation, the temperature and influenza lag of one week and two weeks were finally selected as the input layer for the 3-layer GRU training simulation. The simulation results are shown in Figure 6. The results show that GRU has better prediction capabilities, followed by RNN, DNN, and ANN, from the utmost to the lowest, respectively. Discussion Influenza incidence is time-series data, and the commonly used prediction method is traditional time series models such as ARIMA. However, its linear structure cannot describe the non-linear relationship that is more common in reality. The neural network model used in this study can better model and fit the non-linear functional relationship. The ANN model is the simplest ordinary neural network model, which only contains one hidden layer and can construct non-linear relationships between variables. In theory, as long as there are enough neurons, an ANN with only one hidden layer can model the most complex functional relationships. But for complex problems, the parameter efficiency of deep network DNN is much higher than that of the external network. However, DNN involves more hidden layers, and its model parameters increase significantly, which needs to be regularized to avoid over-fitting [ 21 ]. In ordinary fully connected networks, such as ANN and DNN, the signal of each layer of neurons can only be propagated to the upper layer, so it is also called a forward neural network. In RNN, the neuron’s output can directly act on itself in the next time period to process time-series data more efficiently. There are also some problems with RNN itself: some information in the long-term memory of the RNN network will be covered up by the short-term memory. Gated Recurrent Unit (GRU) is a variant of RNN [ 10 ], which, together with LSTM, adopts the gate mechanism to solve the above problems, and GRU can be regarded as a simplified version of LSTM. Since GRU has fewer parameters, the convergence speed is faster, and the actual time is much less, which can greatly speed up the iterative process [ 21 ]. This study found that there was a significant negative correlation between temperature and the incidence of influenza, which was consistent with previous research results [ 22 ]. For instance, Chen et al. [ 23 ] used GAMs analysis and demonstrated that the average temperature (AT) was roughly negatively related to the incidence of influenza. Specifically, AT rangingfrom-5.35°C to 18.31°C had a significant impact on influenza incidence. More interestingly, at an AT of-5.35°C, the risk of influenza incidence was highest, and AT was negatively correlated with influenza incidence in all age groups. Several studies have shown that temperature changes significantly impact influenza transmission and that the relative risks (RRs) of influenza activity increase with the decrease in weekly AT, and the influenza infection rate decreases by 1.1% for every 1°C increase [ 24 , 25 ]. Another study has shown that for influenza A or B, the temperature and the incidence of influenza have a non-linear negative correlation. When the temperature is higher than 0°C, the incidence of influenza A decreases rapidly; when the temperature exceeds 15°C, the number of cases of influenza B decreases rapidly [ 26 ]. Related experimental studies have shown that long-term high-temperature exposure may reduce influenza RNA virus replication by affecting the function of acidic endosomes and inhibiting IL-6-mediated processes [ 27 ]. The possible reason for the significantly increased risk of influenza at low temperature is that low temperature can promote the spread of influenza by prolonging the survival time of the influenza virus, enhancing the transmissibility of influenza virus, and increasing the host susceptibility [ 28 ]. In addition, it was found that indoor environmental cold conditions (like when using air conditioners) favor virus-host interactions and a long time staying in cold conditions highly increases the chance of influenza transmission [ 29 , 30 ]. Furthermore, this study found that the predictive effect obtained by the neural network model established by incorporating the parameters into the influenza lag of 1 week and 2 weeks was significantly better than that without considering the lag effect. A study that built a distributed lag non-linear model to analyze the relationship of influenza-like illness to each meteorological variable showed that average weekly temperature was the most important risk factor with an exponential relationship to the incidence of influenza-like illness [ 31 ]. A study in South Korea showed that temperatures below 10°C were associated with an increased incidence of influenza with a lag of 0–2 weeks. The day-night temperature difference showed a significant positive correlation with the influenza incidence with a lag of 1 and 2 weeks [ 22 ]. Tsuchihashi et al. [ 32 ] reported that hypothermia 8 days before presentation affected influenza infection. Bai et al . [ 33 ] reported that the lag time of average temperature, maximum temperature, and minimum temperature to the onset of influenza-like disease was 2 weeks, 2 weeks, and 1 week, respectively, and proposed that changes in climate variables could predict the trend of influenza-like disease prevalence. In summary, the temperature should be incorporated into the current influenza surveillance system and, together with the lag effect of influenza, these parameters can help develop an early warning system to better predict and prepare for the risk of influenza. In this study, both the flu and temperature data of the whole province were used to analyze the influence of temperature on the incidence and the hysteresis effect of influenza in detail. Predictive value of influenza morbidity factors [ 34 , 35 ]. This result will help develop an influenza epidemic prediction and early warning model based on temperature factors and provide guidance for early intervention and long-term control strategies for future influenza outbreaks. This study also has certain limitations. First, only one association is reported, and causality cannot be established. Second, only the single meteorological factor of temperature was analyzed, and the influence of other meteorological factors such as humidity, temperature difference between day and night, and rainfall on the incidence of influenza was not analyzed. Third, the age-stratified analysis was not carried out, and it was impossible to explore the influence of temperature on the incidence of influenza in different age groups. Fourth, this study does not have data on the living behavior of the population under different temperature conditions and cannot evaluate the effect of changes in living behavior under different temperature conditions on influenza transmission, so it is impossible to explore the potential relationship between them. Conclusion There is a non-linear negative correlation between air temperature and influenza incidence. GRU has the best ability to predict the incidence of influenza and can be used to develop an influenza risk early warning system based on factors such as temperature and influenza lag to predict and prevent better the influenza incidence and spread. Declarations Ethics approval and consent to participate The study has been approved by the Medical Ethics Committee of the Fujian Provincial Center for Disease Control and Prevention ([2020] Fujian CDC Ethics Review (No. 001)). All participants signed informed consent forms. All methods were carried out in accordance with relevant guidelines and regulations. Consent to publish Not applicable. Availability of data and materials The datasets that support the findings of this study are available from Fujian Provincial Centre of Disease Control and Prevention, Fujian Climate Center, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of these two institutions (E-mail: [email protected] ). Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by Natural Science Foundation of Fujian Province [grant number 2021R0111]; Fujian Provincial Health Commission Middle-aged and Young Backbone Talent Training Project [grant number 2020GGB019]; and Fujian Province Science and Technology Innovation Platform Construction Project [grant number 2019Y2001]; Joint Funds for the Innovation of Science and Technology, Fujian Province [grant number 2019Y9022]; The Major Health Research Project of Fujian Province [grant number 2021ZD01001]. Acknowledgement The authors thank all the medical staff who contributed to the maintenance of the medical record database. Authors Contribution Guangmin Chen designed the study. Yuze Yuan performed the analysis. Meifang Lan, Jing Guo, Fanglin Yu, Fei He, Yixian Jiang contributed to interpretation of the results. Yuze Yuan, Meifang Lan, Jing Guo, Fanglin Yu collected the data. Yuze Yuan, Xinying Xu, Yixian Jiang, Jing Guo and Kuicheng Zheng drafted the manuscript. 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Tables Table 1 Statistics of influenza incidence in different years in Fujian Province Year Week Min Max M P 25 P 75 Wilcox 2016 53 0.09 2.74 0.53 0.40 0.28 0.57 P <0.001 2017 53 0.18 2.26 0.50 0.41 0.34 0.56 2018 53 0.17 5.62 1.01 0.40 0.26 1.09 2019 53 0.56 8.53 1.80 1.28 0.83 1.80 2020 53 0.14 7.13 0.73 0.25 0.20 0.38 Table 2 Correlation coefficient between temperature and influenza incidence rate Incidence rate Temperature Incidence rate 1.00 -0.45 ** Temperature -0.45 ** 1.00 *：P<0.05；**：P<0.01 Table 3 ANN mean square error MSE with different variables and different numbers of neurons in the hidden layer Var number of neurons 5 10 20 30 50 60 80 1.975 1.973 1.976 1.982 1.980 1.981 1.989 0.540 0.538 0.538 0.535 0.533 0.529 0.540 0.496 0.495 0.523 0.544 0.512 0.547 0.539 Table 4 DNN mean square error MSE 3-layer dropout rate number of neurons 5 10 20 30 50 60 80 0 0.540 0.503 0.588 0.551 0.522 0.560 0.588 0.1 0.504 0.497 0.456 0.470 0.483 0.484 0.483 0.2 0.603 0.516 0.458 0.479 0.484 0.467 0.474 5-layer dropout rate number of neurons 5 10 20 30 50 60 80 0 0.628 0.648 0.589 0.528 0.569 0.562 0.579 0.1 0.552 0.491 0.475 0.468 0.461 0.483 0.455 0.2 0.686 0.574 0.496 0.488 0.474 0.446 0.465 8-layer dropout rate number of neurons 5 10 20 30 50 60 80 0 0.501 0.614 0.584 0.592 0.509 0.518 0.535 0.1 0.630 0.526 0.493 0.476 0.450 0.451 0.438 0.2 0.863 0.673 0.539 0.529 0.516 0.484 0.459 11-layer dropout rate number of neurons 5 10 20 30 50 60 80 0 0.573 0.503 0.550 0.523 0.483 0.445 0.498 0.1 0.746 0.531 0.488 0.467 0.443 0.473 0.424 0.2 0.970 0.732 0.651 0.545 0.527 0.543 0.481 Table 5 RNN mean square error MSE 3-layer dropout rate number of neurons 5 10 20 30 50 60 80 0 0.493 0.528 0.480 0.467 0.465 0.483 0.471 0.1 0.527 0.494 0.431 0.376 0.374 0.374 0.373 0.2 0.600 0.488 0.462 0.449 0.464 0.428 0.426 5-layer dropout rate number of neurons 5 10 20 30 50 60 80 0 0.662 0.651 0.485 0.546 0.551 0.524 0.559 0.1 0.635 0.495 0.441 0.439 0.499 0.509 0.523 0.2 0.711 0.603 0.481 0.481 0.459 0.468 0.506 8-layer dropout rate number of neurons 5 10 20 30 50 60 80 0 0.673 0.596 0.619 0.542 0.526 0.563 0.593 0.1 0.758 0.591 0.504 0.477 0.564 0.555 0.522 0.2 0.928 0.657 0.618 0.591 0.546 0.531 0.576 11-layer dropout rate number of neurons 5 10 20 30 50 60 80 0 0.649 0.580 0.581 0.534 0.681 0.573 0.750 0.1 0.816 0.609 0.582 0.608 0.558 0.622 0.654 0.2 1.067 0.784 0.629 0.627 0.566 0.607 0.597 Table 6 GRU mean square error MSE 3-layer dropout rate number of neurons 5 10 20 30 50 60 80 0 0.515 0.502 0.391 0.409 0.416 0.400 0.403 0.1 0.518 0.454 0.378 0.364 0.405 0.396 0.431 0.2 0.545 0.479 0.399 0.333 0.424 0.402 0.409 5-layer dropout rate number of neurons 5 10 20 30 50 60 80 0 0.528 0.507 0.500 0.430 0.473 0.447 0.484 0.1 0.506 0.458 0.385 0.384 0.410 0.459 0.464 0.2 0.562 0.501 0.487 0.411 0.374 0.429 0.421 8-layer dropout rate number of neurons 5 10 20 30 50 60 80 0 0.481 0.547 0.533 0.535 0.535 0.525 0.543 0.1 0.620 0.484 0.474 0.504 0.482 0.567 0.549 0.2 0.829 0.521 0.446 0.509 0.498 0.520 0.503 11-layer dropout rate number of neurons 5 10 20 30 50 60 80 0 0.738 0.839 0.614 0.578 0.649 0.588 0.576 0.1 1.209 0.722 0.589 0.583 0.553 0.551 0.576 0.2 1.709 1.031 0.609 0.550 0.539 0.629 0.580 Additional Declarations No competing interests reported. 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and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Xinying","middleName":"","lastName":"Xu","suffix":""},{"id":126574930,"identity":"ddd48fd7-13f9-43d3-a56a-2442cfb01259","order_by":2,"name":"Meifang Lan","email":"","orcid":"","institution":"Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Meifang","middleName":"","lastName":"Lan","suffix":""},{"id":126574931,"identity":"d2d38a65-fd53-4e47-a5fb-ec65c772ccb9","order_by":3,"name":"Jing Guo","email":"","orcid":"","institution":"Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 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School of Public Health, Fujian Medical University, Fuzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Kuicheng","middleName":"","lastName":"Zheng","suffix":""},{"id":126574935,"identity":"b8cda858-4728-4955-aebe-336b7f211774","order_by":7,"name":"Fei He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIie3QPQrCMByH4V/5Q1yCXeugXiFS0EU8S0ToGdwUhLo5K3iIguAcCNQrKAp2sZOLm5u2cXJpMwrmHUIIecgH4HL9YAxeBgkFn5QsF7x5PSFhSCuWlgQlQUFEaoQFaYK8LMOlE6a47TiG7URRntVcjIREHvZTRGeOKEwUG4hq4qeBhB7vr/OS6HGiOAtqTmk8CzLbxeaUlxVhxY9pKZghyoIQUSCF7q2Lt5y2YhJuNOtXku5y4T2eU931Yx4d79NRe3VY5JUEVA7mh7j8TKhy/1cNZb/X5XK5/qo3S0hDkrWmFLEAAAAASUVORK5CYII=","orcid":"","institution":"Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China","correspondingAuthor":true,"prefix":"","firstName":"Fei","middleName":"","lastName":"He","suffix":""},{"id":126574936,"identity":"0bf8922a-1382-4230-8837-702a51537bb8","order_by":8,"name":"Guangmin Chen","email":"","orcid":"","institution":"The practice base on the School of Public Health, Fujian Medical University, Fuzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Guangmin","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2022-07-25 02:14:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1891828/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1891828/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24989163,"identity":"97db01c1-dd8e-48a2-af77-b09226bcd6ed","added_by":"auto","created_at":"2022-08-09 16:07:30","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71046,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of weekly influenza incidence in Fujian Province from 2016 to 2020\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1891828/v1/6fd8638b637c1dd9aa08dbbb.jpeg"},{"id":24988181,"identity":"d266c388-d957-4497-bb38-64c002c7fef6","added_by":"auto","created_at":"2022-08-09 16:02:30","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41513,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plot of the overall distribution of weekly influenza incidence in Fujian Province from 2016 to 2020\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1891828/v1/753812fcfde1d9e6b2aa3879.jpeg"},{"id":24988185,"identity":"de15427c-8636-4eb8-91aa-f7d1a97614cc","added_by":"auto","created_at":"2022-08-09 16:02:30","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96661,"visible":true,"origin":"","legend":"\u003cp\u003eWeekly time series of air temperature in Fujian Province\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1891828/v1/a55b1c99e5896212b553449a.jpeg"},{"id":24988182,"identity":"ca860c77-4918-48a8-a701-5a950792a6ce","added_by":"auto","created_at":"2022-08-09 16:02:30","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56850,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plot of weekly mean temperature distribution\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1891828/v1/3a44cc2f2e8012e53c5f898a.jpeg"},{"id":24988184,"identity":"7e78798d-c844-4f88-ac6b-552789955275","added_by":"auto","created_at":"2022-08-09 16:02:30","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":70297,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot between air temperature and influenza incidence\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1891828/v1/4bf82d36695a7b30fbec40c8.jpeg"},{"id":24988186,"identity":"12dd88fb-fa7d-4265-85c0-4dbed50c48aa","added_by":"auto","created_at":"2022-08-09 16:02:30","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":115533,"visible":true,"origin":"","legend":"\u003cp\u003eGRU prediction results of temperature and influenza lag of 1 week and 2 weeks\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1891828/v1/7a5a42bd02b4c681b3b349b2.jpeg"},{"id":32307694,"identity":"f9d6fd67-de13-48ed-9f1d-f6a191d3a095","added_by":"auto","created_at":"2023-02-01 05:59:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1176236,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1891828/v1/eb2e88a3-8859-44fd-8bf6-79297b1243f0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of meteorological factors influencing the incidence of influenza in Fujian Province based on a neural network model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInfluenza is a respiratory infectious disease caused by the influenza virus. Hundreds of millions of people worldwide are infected with influenza every year, and the number of death is as high as 500,000 per year. Although immunity to reinfection with the same strain persists for many years, the rapid antigenic changes of influenza strains allow the virus to escape from the herd immunity pre-established after a first infection [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Influenza pandemics occur when a new virus strain spreads across species [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The elderly, immunosuppressant and people with underlying medical conditions, newborns, and infants are more likely to get the flu and develop severe illness and even death. Influenza epidemics and pandemics have a large impact on morbidity and mortality. For instance, the H1N1 influenza pandemic in 1918/1919 was characterized by three independent peaks, causing around 80\u0026nbsp;million deaths [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Besides, the two influenza pandemics in 1957/1958 and 1968/1969 also caused about 10 000 deaths [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, in April 2009, influenza H1N1pdm09,identified in Mexico and the United States, caused an estimated 123 000 to 203 000 deaths during its first year of transmission [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent prediction and early warning models,including Susceptible- Infectious(SI), Susceptible- Infectious- Susceptible(SIS), Susceptible- Infectious-Recovered(SIR), Susceptible- Infectious- Recovered - Susceptible (SIRS), Susceptible- Exposed-Infectious-Recovered(SEIR) model, Logistics, neural network model, and ARIMA model, have been given special attention because of their ability to predict a sanitary event, including disease incidence [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The neural network is a kind of machine learning that first started in the 1990s as an artificial neural network (ANN), a computational model, which imitates the structure and function of a biological neural network. From the improved optimization of algorithms and the increase in the number of layers of neural networks, Deep Neural Networks (DNNs) emerged. DNN can significantly enhance the prediction ability of the network compared to ANN. To meet the needs of time-series data analysis, the Recurrent Neural Network (RNN) was developed and in need to address the insensitivity of RNNs to long-term dependencies, Hochreiter S \u003cem\u003eet al.\u003c/em\u003e proposed and developed a long short-term memory (LSTM) unit [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Gated Recurrent Unit (GRU) is another variant of RNN [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which uses a gate mechanism with LSTM to solve the above problems. Specifically, GRU can be regarded as a simplified version of LSTM. Since GRU has fewer parameters, it converges faster and can speed up the iterative process.\u003c/p\u003e \u003cp\u003eIn the context of the global COVID-19 epidemic, artificial intelligence (AI) research has been widely used for prediction [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Common predictions include cumulative cases,recurrence of infection (waves), and time to peak [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The main AI research models used to predict the occurrence of diseases and the infectious disease-related transmission patterns include optimized SIR models [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], mixed partial differential equation-based ABM models [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and multi-agent systems [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Many studies used DNN models to predict the incidence of COVID-19 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], classify COVID-19 and influenza patients [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], distinguish influenza A virus from rabies virus based on viral genome sequences, and also for the taxonomy of coronaviruses [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The DNN model has shown good prediction and classification performance. However, many other models present different predictions. In this study, we assessed different deep learning network models,including ANN, DNN, RNN and GRU,to predict the incidence of influenza in a local area (Fujian Province, China) and compared the predictionabilities of the four different models used.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study assessing the effects of meteorological factors on the incidence of influenza was carried out in Fujian Province, China. Data on influenza incidence was obtained from the information system of Chinese Disease Control and Prevention. These data only included confirmed cases reported by medical and health institutions in Fujian Province, excluding overseas or Hong Kong, Macao and Taiwan regions. The obtained and confirmed but not suspected cases were classified based on laboratory or clinical diagnoses.\u003c/p\u003e\n\u003cp\u003eOverall, from 2016 to 2020, 95 025 influenza cases in 265 weeks were obtained in Fujian Province. The meteorological data of the same period came from the National Meteorological Information Center (http://cdc.cma.gov.cn/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel building\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe all obtained data contains 265 values, which were classified into 10 groups. The first group contains the first 175 data, and the second group contains the first 185 data, and then the third group contains the first 195, and so on. Finally, the last group of data contains all 265 values. The proportions of model training, validation, and prediction for each data set are the first 70%, the middle 15%, and the last 15%, respectively. The purpose of model validation is to prevent over-fitting in network training. The main factor considered in the model prediction is the temperature (x), and the lagged effect of influenza incidence y is analyzed.\u003c/p\u003e\n\u003cp\u003eThe ordinary neural network (ANN) with one hidden layer was first considered. The number of neurons in the hidden layer is 5 to 80 for network training, and the appropriate number of neurons in the hidden layer is selected according to the final result.\u003c/p\u003e\n\u003cp\u003eThe training, validation and prediction results of ANN under different input factors and numbers of neurons in the hidden layer were compared using the mean square error (MSE). From the comparison results, we concluded that when the input variable is \u0026ldquo;temperature\u0026rdquo;(x) and the lag term of influenza incidence ), the effect prediction of ANN is the best. The hidden layer contains 10 neurons. Based on the comparison results of the ANN models, the subsequent models only consider the three input variables of temperature (x) and the lag term of influenza incidence). In order to fully discuss the influence of the number of hidden layers and the number of neurons contained in each hidden layer on the effect prediction, DNN, RNN, and GRU models were used, with 3 to 11 hidden layers, and each hidden layer contains 5 to 11 layers and 80 neurons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegularization Methods and Model Training\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo avoid over-fitting, the dropout regularization method was adopted. For comparison, dropout rates of 0, 0.1, and 0.2 were used in each hidden layer, where 0 represents no regularization. The training adopted a limited maximum training number of 1000 to prevent the network optimization from being in an infinite loop state. All data analysis was performed using R 4.1.2 (https://www.r-project.org/) and Python 3.9.7 (https://www.python.org/) software.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1. Epidemiological characteristics of influenza incidence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom January 1, 2016, to December 31, 2020, medical and health institutions reported 95 025 confirmed cases of influenza in Fujian Province. In the 2019 influenza outbreak, the average weekly incidence rate for the whole year was 1.8/100 000.In 2020, there was a significant reduction in the incidence of influenza compared with 2019. Except for 2019, the weekly average rate from 2016 to 2020 was between 0.50and 1.01/100 000, and the median incidence rate in each year was less than 0.42/100 000. The difference in the distribution of the incidence rate in each year was statistically significant (rank-sum test, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001).\u0026nbsp;The\u0026nbsp;overall statistics of\u0026nbsp;influenza\u0026nbsp;morbidity in Fujian Province each year are presented in Table 1.\u003c/p\u003e\n\u003cp\u003eThe trend of influenza incidence in Fujian Province shows obvious seasonality,\u0026nbsp;with high\u0026nbsp;incidence in winter. Specifically, most\u0026nbsp;cases were observed from November to March each year, and the remaining months were fewer. The five-year incidence rate ranged from 0.086/100\u0026nbsp;000to 8.53/100\u0026nbsp;000, and the 95% incidence ranged from 0.17/100\u0026nbsp;000 to 5.40/100\u0026nbsp;000, with a median of 0.45/100\u0026nbsp;000 (Fig. 1 and 2).During the 2016 and 2017 winters, the influenza incidence was around 0.45/100 000.\u0026nbsp;During the winter of 2018,the incidence of influenza was around 2.06/100\u0026nbsp;000, while it was above 3.27/100 000 during\u0026nbsp;the 2019 and 2020 winters. The incidence of influenza in summer was found to be extremely low. Except in 2019, when the influenza outbreak, the incidence of influenza in summer generally fluctuated between 0.20/100\u0026nbsp;000to 0.30/100\u0026nbsp;000.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Distribution of meteorological factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 2016 to 2020, the\u0026nbsp;temperature in Fujian Province\u0026nbsp;changed significantly\u0026nbsp;according to the seasons, which is characteristic of\u0026nbsp;a typical subtropical climate, with a low temperature in winter and a high temperature in summer (Figure 3). Interestingly, the air temperature negatively correlated with the influenza incidence (Figure 1). In summers, temperatures were the highest, but fewer new influenza cases were observed, whereas in winters, temperatures were the lowest, but the incidence of influenza cases was the highest.\u003c/p\u003e\n\u003cp\u003eThe distribution range of the\u0026nbsp;weekly\u0026nbsp;temperature average was between 6 and 30 \u0026deg;C, and the median temperature was about 19.9 \u0026deg;C. The shape of the\u0026nbsp;violin diagram of the temperature distribution\u0026nbsp;was opposite to that of the influenza incidence\u0026nbsp;diagram. When the value was\u0026nbsp;larger, the phenomenon of data distribution concentration occurred (Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. The effect of temperature on the incidence of influenza\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor a better picture of the changing trend between temperature and influenza incidence, we drew a scatter plot graph (Figure 5). As presented in Figure 5 there is a clear negative correlation between temperature and influenza incidence. Specifically, when the temperatures are low, the scatter points are mainly distributed, and the influenza incidence is higher. However, when temperatures are higher, influenza incidence is relatively low:at around 30\u0026deg;C, the incidence rate was around 0.2/100\u0026nbsp;000. The Spearman correlation between air temperature and influenza incidence is shown in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Neural Network Prediction of Temperature and Influenza Lag effect on Influenza Incidence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis\u0026nbsp;retrospective study\u0026nbsp;included\u0026nbsp;weekly average temperatures of\u0026nbsp;265-weeks(hereinafter referred to as temperature) and influenza incidence data from\u0026nbsp;2016 to 2020.\u0026nbsp;The main factor considered in the model prediction\u0026nbsp;was\u0026nbsp;\u0026ldquo;temperature\u0026rdquo;, and the lagged effect of\u0026nbsp;the incidence of\u0026nbsp;influenza\u0026nbsp;was also analyzed. Different\u0026nbsp;models were used for neural network training. The features of the specific models used areas follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 Ordinary Neural Network Training (ANN)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn general, in our analyses, the closer the mean square error (MSE)value is to 0, the better the prediction ability of the model.As presented in Table 3,with the unique use of \u0026ldquo;temperature\u0026rdquo;as a factor by ANN modeling, the predicted MSE results were poor, and the number of neurons in the hidden layer had a low effect on the results. When the first-order lag of influenza incidence was considered in the analysis together with \u0026ldquo;temperature\u0026rdquo;, the prediction by ANN was significantly improved, and the number of neurons in the hidden layer had little effect on the\u0026nbsp;results. Based on what precedes, when the second-order lag of influenza incidence was added, the predictive effect by ANN was further improved.\u0026nbsp;When\u0026nbsp;the number of neurons in the hidden layer was 5 and 10, the predictive effect was practically the best.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Deep Neural Network Training (DNN)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the Deep Neural Network assay are presented in Table 4. Without regularization, the prediction ability of the optimal network by ANN was generally better than that of the DNN network. However, after regularization was applied,optimal network results by DNN and a better prediction ability were obtained. Interestingly, the best DNN network contained \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 11 hidden layers, with a number of 80 neurons in each hidden layer and a drop out rate of 0.1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Recurrent Neural Network Training (RNN)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results\u0026nbsp;of the Recurrent Neural Network Training assay are presented\u0026nbsp;in Table 5.\u0026nbsp;The\u0026nbsp;optimal network of RNN was obtained using three hidden layers, with the number of neurons in each layer of 30 and a drop out rate of 0.1.\u0026nbsp;These characteristics could achieve\u0026nbsp;better predictive effects\u0026nbsp;than the\u0026nbsp;optimal DNN\u0026nbsp;network.When the number of\u0026nbsp;hidden layers\u0026nbsp;increased,\u0026nbsp;the\u0026nbsp;predictive\u0026nbsp;ability of RNN decreased\u0026nbsp;because\u0026nbsp;the RNN network itself is relatively complex,\u0026nbsp;and the number of\u0026nbsp;hidden layers\u0026nbsp;is\u0026nbsp;prone to\u0026nbsp;over-fitting. Therefore, the relatively simple 3-layer hidden layer RNN had the best predictive effect.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Gated Recurrent Unit (GRU)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilar to the DNN network, the input variable was \u0026ldquo;temperature\u0026rdquo;, and the lag term of influenza incidence was. For this end, the dropout regularization method was used.\u0026nbsp;Dropout rates of\u0026nbsp;0, 0.1,\u0026nbsp;and 0.2\u0026nbsp;were respectively used\u0026nbsp;in each hidden layer. Then, we\u0026nbsp;considered GRUs with 3 to 11 layers, with each hidden layer containing 5 to 80 neurons. The training\u0026nbsp;used\u0026nbsp;a limited maximum\u0026nbsp;number of\u0026nbsp;training\u0026nbsp;times\u0026nbsp;of 1000. Overall, the network operation results are shown in Table 6.\u0026nbsp;The\u0026nbsp;optimal network of GRU contained three hidden layers, with 30 neurons in each layer and a drop out rate of 0.2.\u0026nbsp;Under\u0026nbsp;the same network structure,\u0026nbsp;GRU had a stronger\u0026nbsp;prediction ability\u0026nbsp;than RNN. With\u0026nbsp;an increase in the number of hidden layers,\u0026nbsp;the prediction ability of the GRU network decreased, and the simpler GRU with three\u0026nbsp;hidden\u0026nbsp;layers\u0026nbsp;had the best predictive effect.\u003c/p\u003e\n\u003cp\u003eIn the neural network, the predictive effect when considering only \u0026ldquo;temperature\u0026rdquo; as a factor was not good with its lag because influenza is contagious, and the autocor- relation between the incidence of influenza and the incidence of the previous period is higher. Considering its infectivity and the MSE results of the neural network simulation, the temperature and influenza lag of one week and two weeks were finally selected as the input layer for the 3-layer GRU training simulation. The simulation results are shown in Figure 6. The results show that GRU has better prediction capabilities, followed by RNN, DNN, and ANN, from the utmost to the lowest, respectively.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eInfluenza incidence is time-series data, and the commonly used prediction method is traditional time series models such as ARIMA. However, its linear structure cannot describe the non-linear relationship that is more common in reality. The neural network model used in this study can better model and fit the non-linear functional relationship. The ANN model is the simplest ordinary neural network model, which only contains one hidden layer and can construct non-linear relationships between variables. In theory, as long as there are enough neurons, an ANN with only one hidden layer can model the most complex functional relationships. But for complex problems, the parameter efficiency of deep network DNN is much higher than that of the external network. However, DNN involves more hidden layers, and its model parameters increase significantly, which needs to be regularized to avoid over-fitting [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In ordinary fully connected networks, such as ANN and DNN, the signal of each layer of neurons can only be propagated to the upper layer, so it is also called a forward neural network. In RNN, the neuron\u0026rsquo;s output can directly act on itself in the next time period to process time-series data more efficiently. There are also some problems with RNN itself: some information in the long-term memory of the RNN network will be covered up by the short-term memory. Gated Recurrent Unit (GRU) is a variant of RNN [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which, together with LSTM, adopts the gate mechanism to solve the above problems, and GRU can be regarded as a simplified version of LSTM. Since GRU has fewer parameters, the convergence speed is faster, and the actual time is much less, which can greatly speed up the iterative process [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study found that there was a significant negative correlation between temperature and the incidence of influenza, which was consistent with previous research results [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For instance, Chen \u003cem\u003eet al.\u003c/em\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] used GAMs analysis and demonstrated that the average temperature (AT) was roughly negatively related to the incidence of influenza. Specifically, AT rangingfrom-5.35\u0026deg;C to 18.31\u0026deg;C had a significant impact on influenza incidence. More interestingly, at an AT of-5.35\u0026deg;C, the risk of influenza incidence was highest, and AT was negatively correlated with influenza incidence in all age groups. Several studies have shown that temperature changes significantly impact influenza transmission and that the relative risks (RRs) of influenza activity increase with the decrease in weekly AT, and the influenza infection rate decreases by 1.1% for every 1\u0026deg;C increase [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Another study has shown that for influenza A or B, the temperature and the incidence of influenza have a non-linear negative correlation. When the temperature is higher than 0\u0026deg;C, the incidence of influenza A decreases rapidly; when the temperature exceeds 15\u0026deg;C, the number of cases of influenza B decreases rapidly [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Related experimental studies have shown that long-term high-temperature exposure may reduce influenza RNA virus replication by affecting the function of acidic endosomes and inhibiting IL-6-mediated processes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The possible reason for the significantly increased risk of influenza at low temperature is that low temperature can promote the spread of influenza by prolonging the survival time of the influenza virus, enhancing the transmissibility of influenza virus, and increasing the host susceptibility [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In addition, it was found that indoor environmental cold conditions (like when using air conditioners) favor virus-host interactions and a long time staying in cold conditions highly increases the chance of influenza transmission [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, this study found that the predictive effect obtained by the neural network model established by incorporating the parameters into the influenza lag of 1 week and 2 weeks was significantly better than that without considering the lag effect. A study that built a distributed lag non-linear model to analyze the relationship of influenza-like illness to each meteorological variable showed that average weekly temperature was the most important risk factor with an exponential relationship to the incidence of influenza-like illness [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A study in South Korea showed that temperatures below 10\u0026deg;C were associated with an increased incidence of influenza with a lag of 0\u0026ndash;2 weeks. The day-night temperature difference showed a significant positive correlation with the influenza incidence with a lag of 1 and 2 weeks [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Tsuchihashi\u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] reported that hypothermia 8 days before presentation affected influenza infection. Bai \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] reported that the lag time of average temperature, maximum temperature, and minimum temperature to the onset of influenza-like disease was 2 weeks, 2 weeks, and 1 week, respectively, and proposed that changes in climate variables could predict the trend of influenza-like disease prevalence. In summary, the temperature should be incorporated into the current influenza surveillance system and, together with the lag effect of influenza, these parameters can help develop an early warning system to better predict and prepare for the risk of influenza.\u003c/p\u003e \u003cp\u003eIn this study, both the flu and temperature data of the whole province were used to analyze the influence of temperature on the incidence and the hysteresis effect of influenza in detail. Predictive value of influenza morbidity factors [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This result will help develop an influenza epidemic prediction and early warning model based on temperature factors and provide guidance for early intervention and long-term control strategies for future influenza outbreaks. This study also has certain limitations. First, only one association is reported, and causality cannot be established. Second, only the single meteorological factor of temperature was analyzed, and the influence of other meteorological factors such as humidity, temperature difference between day and night, and rainfall on the incidence of influenza was not analyzed. Third, the age-stratified analysis was not carried out, and it was impossible to explore the influence of temperature on the incidence of influenza in different age groups. Fourth, this study does not have data on the living behavior of the population under different temperature conditions and cannot evaluate the effect of changes in living behavior under different temperature conditions on influenza transmission, so it is impossible to explore the potential relationship between them.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThere is a non-linear negative correlation between air temperature and influenza incidence. GRU has the best ability to predict the incidence of influenza and can be used to develop an influenza risk early warning system based on factors such as temperature and influenza lag to predict and prevent better the influenza incidence and spread.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study has been approved by the Medical Ethics Committee of the Fujian Provincial Center for Disease Control and Prevention ([2020] Fujian CDC Ethics Review (No. 001)). All participants signed informed consent forms. All methods were carried out in accordance with relevant guidelines and regulations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets that support the findings of this study are available from Fujian Provincial Centre of Disease Control and Prevention, Fujian Climate Center, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of these two institutions (E-mail: [email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Natural Science Foundation of Fujian Province [grant number 2021R0111]; Fujian Provincial Health Commission Middle-aged and Young Backbone Talent Training Project [grant number 2020GGB019]; and Fujian Province Science and Technology Innovation Platform Construction Project [grant number 2019Y2001]; Joint Funds for the Innovation of Science and Technology, Fujian Province [grant number 2019Y9022]; The Major Health Research Project of Fujian Province [grant number 2021ZD01001].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the medical staff who contributed to the maintenance of the medical record database.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuangmin Chen designed the study. Yuze Yuan performed the analysis. Meifang Lan, Jing Guo, Fanglin Yu, Fei He, Yixian Jiang contributed to interpretation of the results. Yuze Yuan, Meifang Lan, Jing Guo, Fanglin Yu collected the data. Yuze Yuan, Xinying Xu, Yixian Jiang, Jing Guo and Kuicheng Zheng drafted the manuscript. Fei He and Guangmin Chen contributed to critical revision of the manuscript for important intellectual content. Fei He and Xinying Xu approved the final version of the manuscript. All authors approved the submitted version.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSaad-Roy CM, McDermott AB, Grenfell BT: \u003cstrong\u003eDynamic Perspectives on the Search for a Universal Influenza Vaccine\u003c/strong\u003e. \u003cem\u003eThe Journal of infectious diseases \u003c/em\u003e2019, \u003cstrong\u003e219\u003c/strong\u003e(Suppl_1):S46-s56.\u003c/li\u003e\n\u003cli\u003eLam EKS, Morris DH, Hurt AC, Barr IG, Russell CA: \u003cstrong\u003eThe impact of climate and antigenic evolution on seasonal influenza virus epidemics in Australia\u003c/strong\u003e. \u003cem\u003eNature communications \u003c/em\u003e2020, \u003cstrong\u003e11\u003c/strong\u003e(1):2741.\u003c/li\u003e\n\u003cli\u003eSaunders-Hastings PR, Krewski D: \u003cstrong\u003eReviewing the History of Pandemic Influenza: Understanding Patterns of Emergence and Transmission\u003c/strong\u003e. \u003cem\u003ePathogens (Basel, Switzerland) \u003c/em\u003e2016, \u003cstrong\u003e5\u003c/strong\u003e(4).\u003c/li\u003e\n\u003cli\u003ePappas C, Aguilar PV, Basler CF, Sol\u0026oacute;rzano A, Zeng H, Perrone LA, Palese P, Garc\u0026iacute;a-Sastre A, Katz JM, Tumpey TM: \u003cstrong\u003eSingle gene reassortants identify a critical role for PB1, HA, and NA in the high virulence of the 1918 pandemic influenza virus\u003c/strong\u003e. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America \u003c/em\u003e2008, \u003cstrong\u003e105\u003c/strong\u003e(8):3064-3069.\u003c/li\u003e\n\u003cli\u003eTaubenberger JK, Morens DM: \u003cstrong\u003e1918 Influenza: the mother of all pandemics\u003c/strong\u003e. \u003cem\u003eEmerging infectious diseases \u003c/em\u003e2006, \u003cstrong\u003e12\u003c/strong\u003e(1):15-22.\u003c/li\u003e\n\u003cli\u003eViboud C, Simonsen L, Fuentes R, Flores J, Miller MA, Chowell G: \u003cstrong\u003eGlobal Mortality Impact of the 1957-1959 Influenza Pandemic\u003c/strong\u003e. \u003cem\u003eThe Journal of infectious diseases \u003c/em\u003e2016, \u003cstrong\u003e213\u003c/strong\u003e(5):738-745.\u003c/li\u003e\n\u003cli\u003eSimonsen L, Spreeuwenberg P, Lustig R, Taylor RJ, Fleming DM, Kroneman M, Van Kerkhove MD, Mounts AW, Paget WJ: \u003cstrong\u003eGlobal mortality estimates for the 2009 Influenza Pandemic from the GLaMOR project: a modeling study\u003c/strong\u003e. \u003cem\u003ePLoS medicine \u003c/em\u003e2013, \u003cstrong\u003e10\u003c/strong\u003e(11):e1001558.\u003c/li\u003e\n\u003cli\u003eChitnis N: \u003cstrong\u003eIntroduction to SEIR Models. 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illness from 2012 to 2015 in Huludao, a northeastern city in China\u003c/strong\u003e. \u003cem\u003ePeerJ \u003c/em\u003e2019, \u003cstrong\u003e7\u003c/strong\u003e:e6919.\u003c/li\u003e\n\u003cli\u003eIanevski A, Zusinaite E, Shtaida N, Kallio-Kokko H, Valkonen M, Kantele A, Telling K, Lutsar I, Letjuka P, Metelitsa N\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eLow Temperature and Low UV Indexes Correlated with Peaks of Influenza Virus Activity in Northern Europe during 2010⁻2018\u003c/strong\u003e. \u003cem\u003eViruses \u003c/em\u003e2019, \u003cstrong\u003e11\u003c/strong\u003e(3).\u003c/li\u003e\n\u003cli\u003eGuo Q, Dong Z, Zeng W, Ma W, Zhao D, Sun X, Gong S, Xiao J, Li T, Hu W: \u003cstrong\u003eThe effects of meteorological factors on influenza among children in Guangzhou, China\u003c/strong\u003e. \u003cem\u003eInfluenza and other respiratory viruses \u003c/em\u003e2019, \u003cstrong\u003e13\u003c/strong\u003e(2):166-175.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Statistics of influenza incidence in different years in Fujian Province\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"93%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.375%\"\u003e\n \u003cp\u003eWeek\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e25\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e75\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.5%\"\u003e\n \u003cp\u003eWilcox\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"14.583333333333334%\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.714285714285714%\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.095238095238095%\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.714285714285714%\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e5.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.095238095238095%\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.714285714285714%\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e8.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.095238095238095%\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.714285714285714%\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.904761904761905%\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.095238095238095%\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 2 Correlation coefficient between temperature and influenza incidence rate\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.276729559748425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"32.075471698113205%\"\u003e\n \u003cp\u003eIncidence rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"33.64779874213836%\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.276729559748425%\"\u003e\n \u003cp\u003eIncidence rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.075471698113205%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"33.64779874213836%\"\u003e\n \u003cp\u003e-0.45\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.276729559748425%\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.075471698113205%\"\u003e\n \u003cp\u003e-0.45\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"33.64779874213836%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e*：P\u0026lt;0.05；**：P\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 ANN mean square error MSE with different variables and different numbers of neurons in the hidden layer\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"17.429577464788732%\"\u003e\n \u003cp\u003eVar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" valign=\"top\" width=\"82.57042253521126%\"\u003e\n \u003cp\u003enumber of neurons\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.102564102564102%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.316239316239317%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.316239316239317%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.316239316239317%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.316239316239317%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.316239316239317%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.316239316239317%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.640211640211641%\"\u003e\n \u003cp\u003e1.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e1.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e1.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e1.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e1.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e1.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e1.989\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.640211640211641%\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.46031746031746%\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.640211640211641%\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.81657848324515%\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Table 4 \u0026nbsp; DNN mean square error MSE\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.425992779783392%\"\u003e\n \u003cp\u003e3-layer\u003c/p\u003e\n \u003cp\u003edropout rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" width=\"83.5740072202166%\"\u003e\n \u003cp\u003enumber of neurons\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.425992779783392%\"\u003e\n \u003cp\u003e5-layer\u003c/p\u003e\n \u003cp\u003edropout rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" width=\"83.5740072202166%\"\u003e\n \u003cp\u003enumber of neurons\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.425992779783392%\"\u003e\n \u003cp\u003e8-layer\u003c/p\u003e\n \u003cp\u003edropout rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" width=\"83.5740072202166%\"\u003e\n \u003cp\u003enumber of neurons\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.425992779783392%\"\u003e\n \u003cp\u003e11-layer\u003c/p\u003e\n \u003cp\u003edropout rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" width=\"83.5740072202166%\"\u003e\n \u003cp\u003enumber of neurons\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Table 5 \u0026nbsp; RNN mean square error MSE\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.425992779783392%\"\u003e\n \u003cp\u003e3-layer\u003c/p\u003e\n \u003cp\u003edropout rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" width=\"83.5740072202166%\"\u003e\n \u003cp\u003enumber of neurons\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.425992779783392%\"\u003e\n \u003cp\u003e5-layer\u003c/p\u003e\n \u003cp\u003edropout rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" width=\"83.5740072202166%\"\u003e\n \u003cp\u003enumber of neurons\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.425992779783392%\"\u003e\n \u003cp\u003e8-layer\u003c/p\u003e\n \u003cp\u003edropout rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" width=\"83.5740072202166%\"\u003e\n \u003cp\u003enumber of neurons\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.425992779783392%\"\u003e\n \u003cp\u003e11-layer\u003c/p\u003e\n \u003cp\u003edropout rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" width=\"83.5740072202166%\"\u003e\n \u003cp\u003enumber of neurons\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e1.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Table 6 \u0026nbsp; GRU mean square error MSE\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.425992779783392%\"\u003e\n \u003cp\u003e3-layer\u003c/p\u003e\n \u003cp\u003edropout rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" width=\"83.5740072202166%\"\u003e\n \u003cp\u003enumber of neurons\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.425992779783392%\"\u003e\n \u003cp\u003e5-layer\u003c/p\u003e\n \u003cp\u003edropout rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" width=\"83.5740072202166%\"\u003e\n \u003cp\u003enumber of neurons\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.425992779783392%\"\u003e\n \u003cp\u003e8-layer\u003c/p\u003e\n \u003cp\u003edropout rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" width=\"83.5740072202166%\"\u003e\n \u003cp\u003enumber of neurons\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.425992779783392%\"\u003e\n \u003cp\u003e11-layer\u003c/p\u003e\n \u003cp\u003edropout rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" width=\"83.5740072202166%\"\u003e\n \u003cp\u003enumber of neurons\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e1.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e1.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e1.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.934900542495479%\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Influenza, neural network model, weather, temperature, lag","lastPublishedDoi":"10.21203/rs.3.rs-1891828/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1891828/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: This study aimed to assess and compare the predictive effects of meteorological factors on the incidence of influenza in Fujian Province, China,using four different deep learning network models.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: From 2016 to 2020,weekly meteorological and influenza surveillance data in Fujian Province were collected. Using four different deep learning network models, including ordinary neural network (ANN), deep neural network (DNN), recurrent neural network (RNN), and gated recurrent unit (GRU), the prediction model of the weekly average temperature, influenza lag and influenza incidence were determined, and the predictive effects from each different models were compared.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The incidence of influenza in Fujian Province showed obvious seasonality, with a high incidence in winter, especially from November to March, during which influenza incidence reached the highest value each year. A non-linear negative correlation between temperature and incidence of influenza was obtained. Compared with the prediction model that only considers “temperature” as a factor, the model that includes both temperature and lag had a better predictive effect. Overall, the GRU model, with three hidden layers (constructed from temperature, influenza lag of one week and two weeks), had the best prediction ability, followed by RNN, DNN, and ANN, respectively.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Temperature and influenza incidence showed a non-linear negative correlation. Furthermore, the GRU model provides a better prediction of the influenza incidence and, therefore, can be used to develop an influenza risk early warning system based on temperature and influenza lag, to prevent the incidence and spread of influenza.\u003c/p\u003e","manuscriptTitle":"Analysis of meteorological factors influencing the incidence of influenza in Fujian Province based on a neural network model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-09 16:02:28","doi":"10.21203/rs.3.rs-1891828/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e0d1a728-3d5b-4532-8229-03700426cf44","owner":[],"postedDate":"August 9th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-02-01T05:59:32+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-09 16:02:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1891828","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1891828","identity":"rs-1891828","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}