The influence of anti-COVID-19 measures on the incidence of hand-foot-mouth disease in Zhanggong district of Ganzhou city in China

Abstract

Objectives

In order to understand the efficacy of control measures and their impact on the prevention of Hand-foot-mouth disease, this study focuses on assessing the repercussions of the COVID-19 pandemic outbreak on the hand-foot-mouth disease prevalence.

Methods

Descriptive epidemiological methods and an autoregressive integrated moving average model were employed to analyze the hand-foot-mouth disease incidence data in Zhanggong district of Ganzhou city in China from 2010 to 2019. Predictions for year 2020 and 2021 were extrapolated based on incidence series from 2010 to 2019. Subsequently, a comparative evaluation was undertaken between the predicted and actual values of the incidence of hand-foot-mouth disease during 2020 and 2021.

Results

The annual average number of reported cases of hand-foot-mouth disease during the period from 2010 to 2021 was 2,027, and the annual incidence was 321.28 per 100,000 citizens in Zhanggong district of Ganzhou city. The autoregressive integrated moving average model (1, 1, 0) with smallest Bayesian information criteria value as 151.33 was chose for prediction. And the values of RMSE and MAPE were 775.54 and 32.7, respectively. The autoregressive integrated moving average model predicted 2640 cases above the observed count of 756 cases in 2020, and 463 cases above the observed count of 2140 cases in 2021.

Conclusion

The disparity between the predicted values and the actual values for 2020 is larger than that for 2021. It is suggested that the COVID-19 prevention and control measures have a significant impact on reducing the incidence of hand-foot-mouth disease.

1. Introduction

Hand-foot-mouth disease (HFMD) is a kind of acute infectious disease caused by various pathogenic enterovirus, such as Coxsackie virus (A16), human enterovirus (EV71) or other enteroviruses including Coxsackie virus A4, 5, 9, 10, B2 and 5. According to the statistics in December 2021, the incidence of HFMD ranks the top three among class C infectious diseases in China [1]. HFMD is mainly transmitted among children under five years old due to their immature immune systems and lack of self-protection consciousness, but it may also infect adults [2,3]. EV71 virus can survive in the host body for an extended period under the right conditions, and even 75 % alcohol cannot destroy the virus [4], which is transmitted through the respiratory tract, close contact with infected individuals, inhalation of infectious droplets, and others [5]. Despite the widespread prevalence of HFMD, reliable preventative and therapeutic measures are still lacking. The EV71 vaccination, which was found to be safe and efficacious for all children, was intended to provide a dependable, affordable means of preventing HFMD [4]. However, monovalent vaccines, such as the EV71 vaccine, are less effective when faced with changes in the spectrum of transmitted pathogens [6]. And there has been little reduction in the HFMD outbreak in China even after the vaccination was made available in hospitals and communities [7]. Therefore, HFMD epidemic prevention work is challenging, necessitating the implementation and evaluation of effective preventive measures.

COVID-19 is a respiratory syndrome caused by SARS-CoV-2, which is transmitted mainly through respiratory droplets and contact [8,9], encompassing direct contact with infected individuals and surfaces (contaminants), droplet transmission and aerosol transmission [10]. COVID-19 affects all age groups and spreads widely, particularly in crowded and closed indoor spaces with poor or no ventilation [11]. During COVID-19 outbreak, our country implements strict control measures, such as avoiding gathering in large crowds, regular hand washing, mask-wearing, minimizing face-to-face interaction, and maintaining a distance of at least 1 m [12], which has impacted the mass population from several aspects including individual lifestyle and national policy [13]. Research indicates an increase in the reported cases of HFMD before the COVID-19 outbreak. For example, Beijing experienced a severe epidemic of HFMD from 2008 to 2012, with 127,707 cases reported, representing an increase of 108.87 % [8]. However, studies have shown a significant decrease in the incidence of HFMD in Japan in 2020 during and after school suspensions [14], indicating prevention and control policies may affect the spread of HFMD by largely disrupting the transmission route of HFMD and greatly reducing its transmission effectiveness [15,16]. It is worth noting that the epidemiological characteristics, spatial and temporal patterns, and interactions between social and natural environments of HFMD cases require further monitoring and evaluation [17]. Therefore, models for predicting HFMD are still required to investigate whether the strict control measures of COVID-19 have a significant impact on the incidence of HFMD.

ARIMA is a time series time-dependent structure modeling model that can account for change trend, periodic change, and random interference [18]. Several studies have used this model to predict the incidence of many diseases, such as the incidence of pertussis [19], COVID-2019 [20], and Brucellosis [21]. Using mathematical statistics for early detection and response during an epidemic can assist policymakers in developing prevention strategies. The infectious disease prediction model is regarded as an important tool for predicting the occurrence of infectious diseases and developing reasonable short- and long-term preventive measures. In the past, this model has been used to predict climate such as rainfall, supply demand such as energy demand, and machine life in the early stage. Currently, it is also used in medical research fields such as drug consumption prediction and hepatitis prediction. However, the prediction of HFMD is still lacking [19]. This is a valuable opportunity to analyze the correlation between prevention and control strategies and the incidence of HFMD by using ARIMA model.

To investigate whether COVID-19 has a significant impact on the incidence rate of HFMD, we collected incidence rate data of HFMD in Zhanggong district of Ganzhou from 2010 to 2021. Using the years 2010–2019 as a time series, the ARIMA model was employed to predict the incidence of HFMD in 2020 and 2021 and compare it with the actual values. This analysis contributes to gathering information and establishing a comprehensive database to aid in the formulation and improvement of HFMD prevention and control strategies, providing an in-depth quantitative analysis and exploration of the correlation between the implementation of COVID-19 prevention and control measures and the transmission and prevalence of HFMD. It also offers an opportunity to monitor the prevalence of HFMD and evaluate the effectiveness of preventive measures.

2. Methods

2.1. Data collection

Surveillance data for HFMD were obtained from the Center for Disease Prevention and Control (CDC) Information System for Zhanggong district of Ganzhou City, Jiangxi Province, China from January 1, 2010 to December 31, 2021. Population data were obtained from the statistical yearbook of a region from 2010 to 2021, including the average annual population categorized by gender and age. All medical institutions had trained and qualified registrars to register new HFMD cases for registration, which included general information, name of diagnosis, basis of diagnosis, and date of diagnosis. The basis of diagnosis was referred to the health industry standard of the People's Republic of China, Diagnostic Standards for Hand, Foot, and Mouth Disease (WS588-2018). According to the relevant provisions of the Prevention and CDC Law of the People's Republic of China, the reported data were reviewed and evaluated according to the reporting system of HFMD.

2.2. Data processing

The overall incidence, gender, and age distribution of HFMD in Zhanggong district of Ganzhou City, Jiangxi Province, China from 2010 to 2021 were systematically analyzed using retrospective epidemiological methods, and the data were organized and statistically analyzed using Excel 2010 and SPSS 20.0 with the number of incidences and incidence rates as the main analysis indicators.

2.3. ARIMA model prediction

ARIMA model was used for the prediction study. ARIMA is a statistical model which is mainly employed for the analysis and prediction of time series data. It consists of three components: Auto-Regressive, Integrated and Moving Average. The proposed research used ARIMA model to capture long-term trends and cyclical changes in time series data during 2010–2019 while eliminating seasonality and trends through differential operations, which facilitates better fit to the data and reduction of forecasting errors. The stability of the data was examined first to see whether preprocess is needed. After the data stabilized, white noise test was carried out. White noise refers to a random stationary sequence with zero mean and constant variance. If the data is a stationary non-white noise series, the appropriate ARIMA model can be determined by looking at the autocorrelation coefficient (ACF) and partial autocorrelation coefficient (PACF).

The research focused on the ACF and PACF of the number of HFMD cases, 2010–2019 can reveal dependencies and lag effects between the data. Then the model is fitted to the lower and higher order parameters one by one by using the nonlinear least square method. After the parameters were determined, white noise detection was performed on the residual sequence. The best model was determined afterwards to predict the incidence of HFMD in a certain district in 2020 and 2021.

In this research, Prediction conditions has been focused on the time series as the prediction object is a smooth random series of one with zero means. If the data is not smooth, it can be pre-processed by differential smoothing. Secondly, it was been followed up the prediction steps. First determine whether the original data is a smooth series, if not smooth to be pre-processed, and then the next step of analysis after processing. Draw the time series diagram of the original data and the ACF curve and PACF curve to initially identify the model and assign an order to the model. The model is then fitted to the lower and higher order parameters one by one using the nonlinear least squares method [4]. After determining the parameters, the residual series were tested for white noise, and the best model was determined after passing the test, and the incidence of HFMD in a district from 2020 to 2021 was predicted and compared with the actual incidence.

3. Results

3.1. Cases distribution and demographic characteristics of HFMD during 2010–2019

The major routes of COVID-19 transmission were through respiratory droplets and contact, encompassing direct contact with infected individuals and surfaces (contaminants), droplet transmission and aerosol transmission (Fig. 1). So when COVID-19 breakout, the implementation of COVID-19 prevention and control measures are extremely strict. We then investigated whether these control measures would influence the incidence of HFMD. The average annual number of reported cases of HFMD in Zhanggong district of Ganzhou, China during the period from 2010 to 2021 was 2,027, and the average annual incidence rate was 321.28 per 100,000 citizens, with the overall fluctuation in the reported incidence rate.

Fig. 1.

COVID-19 transmission routes.

The gender distribution of Zhanggong district of Ganzhou City can be seen in Fig. 2. From 2010 to 2019, the male-to-female sex ratio of reported HFMD cases in Zhanggong district of Ganzhou City increased from 2.00:1 (1287/645) in 2010 to 1.43:1 (1523/1064) in 2019. The highest incidence rate for both genders during the 10 years period was in 2016, with a male incidence rate of 649.50/100,000 and a female incidence rate of 428.69/100,000.

Fig. 2.

Incidence rates by gender group in a district from 2010 to 2019 in Zhanggong district of Ganzhou City, Jiangxi Province in China.

As shown in Fig. 3, the age distribution of the district in Ganzhou City shows that the reported incidence of HFMD in Zhanggong district of Ganzhou City varied widely by age group from 2010 to 2019, with the highest incidence reported in the 1 to 2-year-old group (average annual incidence rate of 87.204/100,000) followed by the 3 to 4-year-old group (average annual incidence rate of 80.083/100,000) and the 2 to 3-year-old group (average annual incidence rate of 76.236/100,000) during each year.

Fig. 3.

Morbidity of HMFD in different age groups in Zhanggong district of Ganzhou City, Jiangxi Province, China from 2010 to 2019.

3.2. The results of HFMD cases prediction in 2020-2021 using the ARIMA model

The result of Augmented Dickey-Fuller (ADF) test shows the p-value is 0.7044 for the original data, which is a non-stationary series. Therefore, the first-order difference is performed, and the p-value is 0.03411, which is less than 0.05, indicating the data was smooth series after the first-order difference. According to the ACF plot and PACF plot, both of them have the first-order truncated tail phenomenon, so the fixed order is (1, 1, 1). See Fig. 4 for details. According to the data result, model equation: Xt = −0.6971Xt-1+∈t-0.0115∈t-1, {∈t}A ∼ N(0.668115). After attempting several parameter combinations, we identified the ARIMA (1, 1, 0) was the optimal model with the smallest Bayesian information criteria (BIC) value as 151.33. The coefficient of AR (1) was −0.7022, and the standard error was 0.1945, their quotient was 3.61, which was greater than the 5 % critical value with the T statistic (1.96), indicating the test result of ARIMA (1, 1, 0) model was significant. The Residual test showed that p-value = 0.6686 > 0.05, indicating that the ARIMA model residuals are white noise and pass the test, and indicating that the model extracts good information. And the values of RMSE and MAPE were 775.54 and 32.7, respectively. The predicted number of incidences in 2020 in 2640.216 cases with 95%-Confidence interval of [1038.1745, 4242.257]. The predicted number of incidences in 2021 is 2603.122 cases with 95%-Confidence interval of [934.4192, 4271.824]

Fig. 4.

First-order difference ACF plot and PACF plot of the number of HFMD cases,2010–2019.

4. Discussion

In this paper, we modeled the incidence series from 2010 to 2019 to predict the number of incidences in Zhanggong district of Ganzhou, China in 2020 and 2021. Through a comparison between the predicted and observed values, the actual values in 2020 is significantly smaller than the predicted values, while the actual values in 2021 closely aligns with the predicted values. See Fig. 5.

Fig. 5.

Forecast data of HFMD incidence in Zhanggong district of Ganzhou from 2010 to 2019.

The comparison between the predicted values in 2020 and the actual values better reflects that under the influence of COVID-19, a series of prevention and control measures have effectively impeded the spread of HFMD and reduced its incidence. During the pandemic, classes were suspended, public gatherings were canceled and traffic control measures was imposed to limit congregations. Concurrently, individuals adopted personal protective measures such as regular hand washing, mask-wearing and enhanced ventilation, creating an “immune barrier” [22]. Moreover, heightened concerns about nosocomial infections promoted individuals to buy over-the-counter medication from pharmacies for minor symptoms, resulting in a decrease in outpatient reporting and influencing the epidemiological trend [23]. Concurrently, certain disruptions in the infectious disease reporting systems or delayed reporting, or stalled reporting in some regions during the COVID-19 epidemic introduced instability into the case statistics, including those of HFMD. In addition, the cyclical nature of transmission and outbreak of infectious diseases introduces an element of uncertainty. It remains plausible that the transmission of HFMD is experiencing a decline or stability amidst the ongoing COVID-19 epidemic. These uncertainties will be worthy of follow-up and continuous observation and research [16].

In 2021, the gradual improvement of the COVID-19 situation facilitated the resumption of work and increased population movements, elevating the potential transmissibility of the virus. At the same time, the return of students to school and the enrollment of young children in nursery institutions contributed to an upsurge in cases, given their underdeveloped immune systems and nascent hygiene habits. Despite the ongoing implementation of preventative measures in the context of the COVID-19 epidemic in Ganzhou, certain factors have contributed to the escalation of HFMD incidence. During the early isolation and control period, the population's insufficient physical activity and limited exposure to fresh air, coupled with the adoption of unhealthy habits such as staying up late, have resulted in compromised human immunity and other adverse health issues. These factors have increased the vulnerability to infection, resulting to a rebound in the incidence of HFMD beyond the observed level in 2020. Furthermore, public perception of the new coronavirus epidemic may undergo changes, with some individuals believing that the COVID-19 epidemic at this stage is not harmful to the human body. Hence, there has been a decrease in their awareness of prevention and control, leading to an increase in the incidence of the disease. Under epidemic control, the difference between the predicted and actual values of HFMD in 2021 is not significant, indicating that the relaxation of epidemic control has an impact on the incidence of HFMD [2].

HFMD has now become a very important infectious disease [24]. The incidence of HFMD in Zhanggong district of Ganzhou, China exceeded the national average in 2018 and 2019, and the incidence of HFMD is not optimistic with prevalence of HFMD higher in children aged 1–3 years old. On the one hand, the seasonal influence, coupled with various bacteria, viruses and other pathogenic microorganisms' growth and reproduction accelerated in spring, leads to a reduction in children's immunity. This seasonal vulnerability contributes to an increased risk of transmission [25]. On the other hand, the characteristics of children in this period are active and cannot accurately distinguish the dirty environment, elevate the risk of transmission. Inadequate parental supervision during frequent visits to gathering places further compounds the risk [26].

The comparable transmission routes of COVID-19 and HFMD highlights the importance of interrupting the transmission routes to mitigate the spread of infectious disease epidemics. In this stage of the epidemic, we should implement a regular prevention and control mechanism, intensify the promotion and communication efforts of EV71 vaccine, and increase the vaccination rate. Concurrently, promoting the adoption of good self-hygiene habits. Health departments at all levels should strengthen the monitoring and management of HFMD and actively respond to critically ill patients. Implementation of a comprehensive regional health and isolation system, coupled with a stringent enforcement and emphasis on the training of primary health care workers to raise awareness of the disease is of great importance. Additionally, strengthening the management of early childhood education-related institutions to ensure thorough disinfection and ventilation of densely populated public areas and activity spaces is recommended.

5. Conclusions

The predicted number of HFMD in Zhanggong district of Ganzhou, China in 2020 is higher than the actual value, and the prevention and control of COVID-19 has a certain effect on reducing the prevalence of HFMD; the predicted value of HFMD in Zhanggong district of Ganzhou, China in 2021 is close to the actual value, indicating that the relaxation of epidemic prevention and control has an impact on the incidence of HFMD, and the prevention and control of COVID-19 should be strengthened to interrupt the spread of COVID-19, which can also reduce the incidence of HFMD. The actual value of the epidemic control is close to the actual value of the epidemic control.

CRediT authorship contribution statement

Yixing Guo: Writing – review & editing, Writing – original draft, Software, Resources, Methodology, Formal analysis, Data curation. Yuhan Zhu: Writing – review & editing, Resources. Yihan Wang: Writing – original draft, Validation. Hui Yin: Writing – original draft, Validation, Investigation, Formal analysis. Laiye Han: Validation. Tian Zhong: Writing – review & editing. Ying Xiao: Writing – review & editing. Lawrence T. Lam: Writing – review & editing. Xi Yu: Visualization, Supervision, Project administration, Funding acquisition, Conceptualization.

Ethics declarations

This study was conducted with the approval of the Ethics Committee of Gannan Healthcare Vocational College on April 4, 2023 (NO:2024001).

Data availability statement

All data were available on request from the corresponding author.

Funding source

This work was supported by the Science and Technology Development Fund, Macau SAR (Grant 0065/2023/ITP2) and the Crossing Research Project of Gannan Medical University (HX202410).

Declaration of competing interest

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.