The relationship between meteorological factors and mumps based on Boosted regression tree model

The impact of temperature, humidity and closing school on the mumps epidemic: a case study in the mainland of China

BACKGROUND

To control resurging infectious diseases like mumps, it is necessary to resort to effective control and preventive measures. These measures include increasing vaccine coverage, providing the community with advice on how to reduce exposure, and closing schools. To justify such intervention, it is important to understand how well each of these measures helps to limit transmission.

METHODS

In this paper, we propose a simple SEILR (susceptible-exposed-symptomatically infectious-asymptomatically infectious-recovered) model by using a novel transmission rate function to incorporate temperature, humidity, and closing school factors. This new transmission rate function allows us to verify the impact of each factor either separately or combined. Using reported mumps cases from 2004 to 2018 in the mainland of China, we perform data fitting and parameter estimation to evaluate the basic reproduction number  R 0 . As a wide range of one-dose measles, mumps, and rubella (MMR) vaccine programs in China started only in 2008, we use different vaccination proportions for the first Stage I period (from 2004 to 2008) and the second Stage II period (from 2009 to 2018). This allows us to verify the importance of higher vaccine coverage with a possible second dose of MMR vaccine.

RESULTS

We find that the basic reproduction number  R 0  is generally between 1 and 3. We then use the Akaike Information Criteria to assess the extent to which each of the three factors contributed to the spread of mumps. The findings suggest that the impact of all three factors is substantial, with temperature having the most significant impact, followed by school opening and closing, and finally humidity.

CONCLUSION

We conclude that the strategy of increasing vaccine coverage, changing micro-climate (temperature and humidity), and closing schools can greatly reduce mumps transmission.

Spatio-temporal pattern and associate meteorological factors of airborne diseases in Bangladesh using geospatial mapping and spatial regression model

Background and Aims

Airborne diseases due to climate change pose significant public health challenges in Bangladesh. Little was known about the spatio-temporal pattern of airborne diseases at the district level in the country. Therefore, this study aimed to investigate the spatio-temporal pattern and associated meteorological factors of airborne diseases in Bangladesh using exploratory analysis and spatial regression models.

Methods

This study used district-level reported cases of airborne diseases (meningococcal, measles, mumps, influenza, tuberculosis, and encephalitis) and meteorological data (temperature, relative humidity, wind speed, and precipitation) from 2017 to 2020. Geospatial mapping and spatial error regression models were utilized to analyze the data.

Results

From 2017 to 2020, a total of 315 meningococcal, 5159 measles, 1341 mumps, 346 influenza, 4664 tuberculosis, and 229 encephalitis cases were reported in Bangladesh. Among airborne diseases, measles demonstrated the highest prevalence, featuring a higher incidence rate in the coastal Bangladeshi districts of Lakshmipur, Patuakhali, and Cox's Bazar, as well as in Maulvibazar and Bandarban districts from 2017 to 2020. In contrast, tuberculosis (TB) emerged as the second most prevalent disease, with a higher incidence rate observed in districts such as Khagrachhari, Rajshahi, Tangail, Bogra, and Sherpur. The spatial error regression model revealed that among climate variables, mean (β = 9.56, standard error [SE]: 3.48) and maximum temperature (β = 1.19, SE: 0.40) were significant risk factors for airborne diseases in Bangladesh. Maximum temperature positively influenced measles (β = 2.74, SE: 1.39), whereas mean temperature positively influenced both meningococcal (β = 5.57, SE: 2.50) and mumps (β = 11.99, SE: 3.13) diseases.

Conclusion

The findings from the study provide insights for planning early warning, prevention, and control strategies to combat airborne diseases in Bangladesh and similar endemic countries. Preventive measures and enhanced monitoring should be taken in some high-risk districts for airborne diseases in the country.

The effects of meteorological factors and air pollutants on the incidence of tuberculosis in people living with HIV/AIDS in subtropical Guangxi, China

BACKGROUND

Previous studies have shown the association between tuberculosis (TB) and meteorological factors/air pollutants. However, little information is available for people living with HIV/AIDS (PLWHA), who are highly susceptible to TB.

METHOD

Data regarding TB cases in PLWHA from 2014 to2020 were collected from the HIV antiviral therapy cohort in Guangxi, China. Meteorological and air pollutants data for the same period were obtained from the China Meteorological Science Data Sharing Service Network and Department of Ecology and Environment of Guangxi. A distribution lag non-linear model (DLNM) was used to evaluate the effects of meteorological factors and air pollutant exposure on the risk of TB in PLWHA.

RESULTS

A total of 2087 new or re-active TB cases were collected, which had a significant seasonal and periodic distribution. Compared with the median values, the maximum cumulative relative risk (RR) for TB in PLWHA was 0.663 (95% confidence interval [CI]: 0.507-0.866, lag 4 weeks) for a 5-unit increase in temperature, and 1.478 (95% CI: 1.116-1.957, lag 4 weeks) for a 2-unit increase in precipitation. However, neither wind speed nor PM10 had a significant cumulative lag effect. Extreme analysis demonstrated that the hot effect (RR = 0.638, 95%CI: 0.425-0.958, lag 4 weeks), the rainy effect (RR = 0.285, 95%CI: 0.135-0.599, lag 4 weeks), and the rainless effect (RR = 0.552, 95%CI: 0.322-0.947, lag 4 weeks) reduced the risk of TB. Furthermore, in the CD4(+) T cells < 200 cells/µL subgroup, temperature, precipitation, and PM10 had a significant hysteretic effect on TB incidence, while temperature and precipitation had a significant cumulative lag effect. However, these effects were not observed in the CD4(+) T cells ≥ 200 cells/µL subgroup.

CONCLUSION

For PLWHA in subtropical Guangxi, temperature and precipitation had a significant cumulative effect on TB incidence among PLWHA, while air pollutants had little effect. Moreover, the influence of meteorological factors on the incidence of TB also depends on the immune status of PLWHA.

Effectiveness and safety of a mumps containing vaccine in preventing laboratory-confirmed mumps cases from 2002 to 2017: A meta-analysis

Emerging evidence has figured that serum conversion rate of mumps is a crucial link of mumps disease. Nevertheless, a rising number of mumps outbreaks caused our attention and studies examining the serum conversion cases were conducted in small samples previously; this meta-analysis was conducted to assess the immunogenicity and safety of a mumps containing vaccine (MuCV) before 2019. We identified a total of 17 studies from the year of 2002-2017. In the case-control studies, the vaccine effectiveness (VE) of MuCV in preventing laboratory-confirmed mumps was 68% (odds risk: 0.32; 95% confidence interval [CI], 0.14-0.70) while in the cohort studies and randomised control trials, 58% (relative risk [RR]: 0.42; 95% CI, 0.26-0.69). Similar intervals of effectiveness rates were found during non-outbreak periods compared with outbreak periods (VE: 66%; RR: 0.34; 95% CI, 0.18-0.68 versus VE: 49%; RR: 0.51; 95% CI, 0.21-1.27). In addition, the MuCV group with two and three doses did not show enhanced laboratory-confirmed mumps than one dose (VE: 58%; RR: 0.42; 95% CI, 0.20-0.88 versus VE: 65%, RR: 0.35; 95% CI, 0.20-0.61) for the reason of the overlap of 95% CI. MuCV had comparable effectiveness comparing non-outbreak and outbreak period, one dose, and two or three doses. MuCV displayed acceptable adverse event profiles.

Associations between temperature variations and tourist arrivals: analysis based on Baidu Index of hot-spring tourism in 44 cities in China

Hot-spring tourism refers to entertainment, health preservation, commercial conferences, and other leisure activities at hot-spring locations. This tourism often shows periodic variability, which may be due to seasonal temperature variations. However, studies on the effects of temperature variations on tourist arrivals at hot springs are limited. Therefore, this study aimed to evaluate this relationship in 31 provincial capital cities and 13 s-tier cities in China. Using the Baidu Index, we obtained data for tourist arrivals to hot springs in each city and constructed a generalised additive model to explore the associations between temperature variations and tourist arrivals. We also analysed the statistical significance of the estimated effects during different seasons to explore potential effect modification. A 1 °C increase in temperature was associated with a 1.81% (95% confidence interval (CI): 1.69-1.93) decrease in daily tourist arrivals for hot-spring tourism. Significant positive associations between the abovementioned factors were observed in summer (2.18% change, 95% CI: 1.32-3.04). The effect of temperature on the volume of tourist arrivals may last for approximately 2 months. Robustness analysis confirmed the data reliability. The results indicate that significant relationships exist between temperature variations and hot-spring tourism arrivals, which vary seasonally. This study has significant implications for travel agencies to effectively manage tourist visits to hot spring locations.

A Multi-Age-Group Interrupted Time-Series Study for Evaluating the Effectiveness of National Expanded Program on Immunization on Mumps

The national Expanded Program on Immunization (EPI) in China has covered vaccines for measles, mumps, and rubella, among children aged 18–24 months since September 2008. However, no previous studies have quantified the effectiveness of the EPI on mumps incidence. There are methodological challenges in assessing the effect of an intervention that targets a subpopulation but finally influences the whole population. In this study, monthly data on mumps incidence were collected in Guangzhou, China, during 2005–2019. We proposed a multi-age-group interrupted time-series design, setting the starting time of exerting effect separately for 14 different age groups. A mixed-effects quasi-Poisson regression was applied to analyze the effectiveness of the EPI on mumps incidence, after controlling for long-term and seasonal trends, and meteorological factors. The model also accounted for the first-order autocorrelation within each age group. Between-age-group correlations were expressed using the contact matrix of age groups. We found that 70,682 mumps cases were reported during 2005–2019, with an annual incidence rate of 37.91 cases per 100,000 population. The effect of EPI strengthened over time, resulting in a decrease in the incidence of mumps by 16.6% (EPI-associated excess risk% = −16.6%, 95% CI: −27.0% to −4.7%) in September 2009 to 40.1% (EPI-associated excess risk% = −40.1%, 95% CI: −46.1% to −33.3%) in September 2019. A reverse U-shape pattern was found in age-specific effect estimates, with the largest reduction of 129 cases per 100,000 population (95% CI: 14 to 1173) in those aged 4–5 years. The EPI is effective in reducing the mumps incidence in Guangzhou. The proposed modeling strategy can be applied for simultaneous assessment of the effectiveness of public health interventions across different age groups, with adequate adjustment for within- and between-group correlations.

Association between Meteorological Factors and Mumps and Models for Prediction in Chongqing, China

(1) Background: To explore whether meteorological factors have an impact on the prevalence of mumps, and to make a short−term prediction of the case number of mumps in Chongqing. (2) Methods: K−means clustering algorithm was used to divide the monthly mumps cases of each year into the high and low case number clusters, and Student t−test was applied for difference analysis. The cross−correlation function (CCF) was used to evaluate the correlation between the meteorological factors and mumps, and an ARIMAX model was constructed by additionally incorporating meteorological factors as exogenous variables in the ARIMA model, and a short−term prediction was conducted for mumps in Chongqing, evaluated by MAE, RMSE. (3) Results: All the meteorological factors were significantly different (p < 0.05), except for the relative humidity between the high and low case number clusters. The CCF and ARIMAX model showed that monthly precipitation, temperature, relative humidity and wind velocity were associated with mumps, and there were significant lag effects. The ARIMAX model could accurately predict mumps in the short term, and the prediction errors (MAE, RMSE) were lower than those of the ARIMA model. (4) Conclusions: Meteorological factors can affect the occurrence of mumps, and the ARIMAX model can effectively predict the incidence trend of mumps in Chongqing, which can provide an early warning for relevant departments.

Correlation between mumps and meteorological factors in Xiamen City, China: A modelling study

Objective

Mumps is a seasonal infectious disease, always occurring in winter and spring. In this study, we aim to analyze its epidemiological characteristics, transmissibility, and its correlation with meteorological variables.

Method

A seasonal Susceptible–Exposed–Infectious/Asymptomatic–Recovered model and a next-generation matrix method were applied to estimate the time-dependent reproduction number (R_t).

Results

The seasonal double peak of annual incidence was mainly in May to July and November to December. There was high transmission at the median of R_t = 1.091 (ranged: 0 to 4.393). R_t was seasonally distributed mainly from February to April and from September to November. Correlations were found between temperature (Pearson correlation coefficient [r] ranged: from 0.101 to 0.115), average relative humidity (r = 0.070), average local pressure (r = -0.066), and the number of new cases. In addition, average local pressure (r = 0.188), average wind speed (r = 0.111), air temperature (r ranged: -0.128 to -0.150), average relative humidity (r = -0.203) and sunshine duration (r = -0.075) were all correlated with R_t.

Conclusion

A relatively high level of transmissibility has been found in Xiamen City, leading to a continuous epidemic of mumps. Meteorological factors, especially air temperature and relative humidity, may be more closely associated with mumps than other factors.

Comparison of different predictive models on HFMD based on weather factors in Zibo city, Shandong Province, China

The early identification and prediction of hand-foot-and-mouth disease (HFMD) play an important role in the disease prevention and control. However, suitable models are different in regions due to the differences in geography, social economy factors. We collected data associated with daily reported HFMD cases and weather factors of Zibo city in 2010~2019 and used the generalised additive model (GAM) to evaluate the effects of weather factors on HFMD cases. Then, GAM, support vectors regression (SVR) and random forest regression (RFR) models are used to compare predictive results. The annual average incidence was 129.72/100 000 from 2010 to 2019. Its distribution showed a unimodal trend, with incidence increasing from March, peaking from May to September. Our study revealed the nonlinear relationship between temperature, rainfall and relative humidity and HFMD cases and based on the predictive result, the performances of three models constructed ranked in descending order are: SVR > GAM> RFR, and SVR has the smallest prediction errors. These findings provide quantitative evidence for the prediction of HFMD for special high-risk regions and can help public health agencies implement prevention and control measures in advance.

Exploring the Relationship between Mumps and Meteorological Factors in Shandong Province, China Based on a Two-Stage Model

Background: Small-scale studies have identified temperature and other meteorological factors as risk factors for human health. However, only a few have quantified the specific impact of meteorological factors on mumps. A quantitative examination of the exposure–response relationship between meteorological factors and mumps is needed to provide new insights for multi-city analysis. Methods: The daily recorded number of mumps cases and meteorological data in 17 cities of Shandong Province from 2009 to 2017 were collected. A two-stage model was built to explore the relationship between meteorological factors and mumps. Results: A total of 104,685 cases of mumps were recorded from 2009 to 2017. After controlling for seasonality and long-term trends, the effect of low temperature on mumps was significant at the provincial level, with a cumulative RR of 1.035 (95%CI: 1.002–1.069) with a 1-day lagged effect. The proportion of primary and middle school students was determined as an effect modifier, which had a significant impact on mumps (Stat = 8.374, p = 0.039). There was heterogeneity in the combined effect of temperature on mumps (Q = 95.447, p = 0.000), and its size was I² = 49.7%. Conclusions: We have identified a non-linear relationship between mumps and temperature in Shandong Province. In particular, low temperatures could bring more cases of mumps, with certain lagged effects. More public health measures should be taken to reduce the risks when temperatures are low, especially for cities with a high proportion of primary and secondary school students.

Epidemiological Characteristics and Regional Risk Prediction of Hemorrhagic Fever with Renal Syndrome in Shandong Province, China

BACKGROUND

Hemorrhagic fever with renal syndrome (HFRS), a rodent-borne disease caused by different species of hantaviruses, is widely endemic in China. Shandong Province is one of the most affected areas. This study aims to analyze the epidemiological characteristics of HFRS, and to predict the regional risk in Shandong Province.

METHODS

Descriptive statistics were used to elucidate the epidemiological characteristics of HFRS cases in Shandong Province from 2010 to 2018. Based on environmental and socioeconomic data, the boosted regression tree (BRT) model was applied to identify important influencing factors, as well as predict the infection risk zones of HFRS.

RESULTS

A total of 11,432 HFRS cases were reported from 2010 to 2018 in Shandong, with groups aged 31-70 years (81.04%), and farmers (84.44%) being the majority. Most cases were from central and southeast Shandong. There were two incidence peak periods in April to June and October to December, respectively. According to the BRT model, we found that population density (a relative contribution of 15.90%), elevation (12.02%), grassland (11.06%), cultivated land (9.98%), rural settlement (9.25%), woodland (8.71%), and water body (8.63%) were relatively important influencing factors for HFRS epidemics, and the predicted high infection risk areas were concentrated in central and eastern areas of Shandong Province. The BRT model provided an overall prediction accuracy, with an area under the receiver operating characteristic curve of 0.91 (range: 0.83-0.95).

CONCLUSIONS

HFRS in Shandong Province has shown seasonal and spatial clustering characteristics. Middle-aged and elderly farmers are a high-risk population. The BRT model has satisfactory predictive capability in stratifying the regional risk of HFRS at a county level in Shandong Province, which could serve as an important tool for risk assessment of HFRS to deploy prevention and control measures.

The association between extreme temperature and pulmonary tuberculosis in Shandong Province, China, 2005-2016: a mixed method evaluation

BACKGROUND

The effects of extreme temperature on infectious diseases are complex and far-reaching. There are few studies to access the relationship of pulmonary tuberculosis (PTB) with extreme temperature. The study aimed to identify whether there was association between extreme temperature and the reported morbidity of PTB in Shandong Province, China, from 2005 to 2016.

METHODS

A generalized additive model (GAM) was firstly conducted to evaluate the relationship between daily reported incidence rate of PTB and extreme temperature events in the prefecture-level cities. Then, the effect estimates were pooled using meta-analysis at the provincial level. The fixed-effect model or random-effect model was selected based on the result of heterogeneity test.

RESULTS

Among the 446,016 PTB reported cases, the majority of reported cases occurred in spring. The higher reported incidence rate areas were located in Liaocheng, Taian, Linyi and Heze. Extreme low temperature had an impact on the reported incidence of PTB in only one prefecture-level city, i.e., Binzhou (RR = 0.903, 95% CI: 0.817-0.999). While, extreme high temperature was found to have a positive effect on reported morbidity of PTB in Binzhou (RR = 0.924, 95% CI: 0.856-0.997) and Weihai (RR = 0.910, 95% CI: 0.843-0.982). Meta-analysis showed that extreme high temperature was associated with a decreased risk of PTB (RR = 0.982, 95% CI: 0.966-0.998). However, extreme low temperature was no relationship with the reported incidence of PTB.

CONCLUSION

Our findings are suggested that extreme high temperature has significantly decreased the risk of PTB at the provincial levels. The findings have implications for developing strategies to response to climate change.

Non-linear relationships and interactions of meteorological factors on mumps in Jinan, China

Although vaccination is available, mumps remains a public health concern in many countries including China. Previous studies have indicated the impact of meteorological factors and mumps, but findings vary across different regions with limited evidence to inform local public health responses. We aim to examine the impacts of meteorological variables on mumps in Jinan, a temperate city of China, and explore the interactions of temperature with humidity or wind speed. Weekly meteorological data and notified cases of mumps in Jinan were collected for 2014-2018. Regression analyses using the generalized additive model were performed with considerations of multicollinearity, lag effects, school holidays, long-term trend, and seasonality. A stratification model was applied to investigate the interaction. We found a non-linear relationship between weekly mean temperature and the number of cases. Between 1.2 and 24.5 °C, the excess risk (ER) of mumps for a 1 °C increase in weekly mean temperature was 3.08% (95% CI 1.32 to 4.87%) at 0-week lag. The lagged effects could last for 3 weeks. There were interactions between mean temperature and relative humidity or wind speed. The effect of mean temperature was enhanced in days with low relative humidity or high wind speed. This study suggests that temperature is positively associated with mumps cases with thresholds in the temperate city of China, and the effect can be modified by relative humidity and wind speed and is independent of vaccine coverage. Findings could be integrated into current early warning systems of mumps in order to protect people's health from the risk of changing climate.

Real-time forecasting and early warning of bacillary dysentery activity in four meteorological and geographic divisions in China

BACKGROUND

Accurate and timely forecasts of bacillary dysentery (BD) incidence can be used to inform public health decision-making and response preparedness. However, our ability to detect BD dynamics and outbreaks remains limited in China.

OBJECTIVES

This study aims to explore the impacts of meteorological factors on BD transmission in four representative regions in China and to forecast weekly number of BD cases and outbreaks.

METHODS

Weekly BD and meteorological data from 2014 to 2016 were collected for Beijing (Northern China), Shenyang (Northeast China), Chongqing (Southwest China) and Shenzhen (Southern China). A boosted regression tree (BRT) model was conducted to assess the impacts of meteorological factors on BD transmission. Then a real-time forecast and early warning model based on BRT was developed to track the dynamics of BD and detect the outbreaks. The forecasting methodology was compared with generalized additive model (GAM) and seasonal autoregressive integrated moving average model (SARIMA) that have been used to model the BD case data previously.

RESULTS

Ambient temperature was the most important meteorological factor contributing to the transmission of BD (80.81%-92.60%). A positive effect of temperature was observed when weekly mean temperature exceeded 4 °C, -3 °C, 9 °C and 16 °C in Beijing (Northern China), Shenyang (Northeast China), Chongqing (Southwest China) and Shenzhen (Southern China), respectively. BD incidence (Beijing and Shenyang) in temperate cities was more sensitive to high temperature than that in subtropical cities (Chongqing and Shenzhen). The dynamics and outbreaks of BD can be accurately forecasted and detected by the BRT model. Compared to GAM and SARIMA, BRT model showed more accurate forecasting for 1-, 2-, 3-weeks ahead forecasts in Beijing, Shenyang and Shenzhen.

CONCLUSIONS

Temperature plays the most important role in weather-attributable BD transmission. The BRT model achieved a better performance in comparison with GAM and SARIMA in most study cities, which could be used as a more accurate tool for forecasting and outbreak alert of BD in China.

Use of meteorological parameters for forecasting scarlet fever morbidity in Tianjin, Northern China

The scarlet fever incidence has increased drastically in recent years in China. However, the long-term relationship between climate variation and scarlet fever remains contradictory, and an early detection system is lacking. In this study, we aim to explore the potential long-term effects of variations in monthly climatic parameters on scarlet fever and to develop an early scarlet-fever detection tool. Data comprising monthly scarlet fever cases and monthly average climatic variables from 2004 to 2017 were retrieved from the Notifiable Infectious Disease Surveillance System and National Meteorological Science Center, respectively. We used a negative binomial multivariable regression to assess the long-term impacts of weather parameters on scarlet fever and then built a novel forecasting technique by integrating an autoregressive distributed lag (ARDL) method with a nonlinear autoregressive neural network (NARNN) based on the significant meteorological drivers. Scarlet fever was a seasonal disease that predominantly peaked in spring and winter. The regression results indicated that a 1 °C increment in the monthly average temperature and a 1-h increment in the monthly aggregate sunshine hours were associated with 17.578% (95% CI 7.674 to 28.393%) and 0.529% (95% CI 0.035 to 1.025%) increases in scarlet fever cases, respectively; a 1-hPa increase in the average atmospheric pressure at a 1-month lag was associated with 12.996% (95% CI 9.972 to 15.919%) decrements in scarlet fever cases. Based on the model evaluation criteria, the best-performing basic and combined approaches were ARDL(1,0,0,1) and ARDL(1,0,0,1)-NARNN(5, 22), respectively, and this hybrid approach comprised smaller performance measures in both the training and testing stages than those of the basic model. Climate variability has a significant long-term influence on scarlet fever. The ARDL-NARNN technique with the incorporation of meteorological drivers can be used to forecast the future epidemic trends of scarlet fever. These findings may be of great help for the prevention and control of scarlet fever.

The long-term effects of meteorological parameters on pertussis infections in Chongqing, China, 2004-2018

Evidence on the long-term influence of climatic variables on pertussis is limited. This study aims to explore the long-term quantitative relationship between weather variability and pertussis. Data on the monthly number of pertussis cases and weather parameters in Chongqing in the period of 2004-2018 were collected. Then, we used a negative binomial multivariable regression model and cointegration testing to examine the association of variations in monthly meteorological parameters and pertussis. Descriptive statistics exhibited that the pertussis incidence rose from 0.251 per 100,000 people in 2004 to 3.661 per 100,000 persons in 2018, and pertussis was a seasonal illness, peaked in spring and summer. The results from the regression model that allowed for the long-term trends, seasonality, autoregression, and delayed effects after correcting for overdispersion showed that a 1 hPa increment in the delayed one-month air pressure contributed to a 3.559% (95% CI 0.746-6.293%) reduction in the monthly number of pertussis cases; a 10 mm increment in the monthly aggregate precipitation, a 1 °C increment in the monthly average temperature, and a 1 m/s increment in the monthly average wind velocity resulted in 3.641% (95% CI 0.960-6.330%), 19.496% (95% CI 2.368-39.490%), and 3.812 (95% CI 1.243-11.690)-fold increases in the monthly number of pertussis cases, respectively. The roles of the mentioned weather parameters in the transmission of pertussis were also evidenced by a sensitivity analysis. The cointegration testing suggested a significant value among variables. Climatic factors, particularly monthly temperature, precipitation, air pressure, and wind velocity, play a role in the transmission of pertussis. This finding will be of great help in understanding the epidemic trends of pertussis in the future, and weather variability should be taken into account in the prevention and control of pertussis.

SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA

Background

The early warning model of infectious diseases plays a key role in prevention and control. This study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect.

Methods

Data on notified HFRS cases in Weifang city, Shandong Province were collected from the official website and Shandong Center for Disease Control and Prevention between January 1, 2005 and December 31, 2018. The SARFIMA model considering both the short memory and long memory was performed to fit and predict the HFRS series. Besides, we compared accuracy of fit and prediction between SARFIMA and SARIMA which was used widely in infectious diseases.

Results

Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA (1, 0.11, 2)(1, 0, 1)₁₂: Akaike information criterion (AIC):-631.31; SARIMA (1, 0, 2)(1, 1, 1)₁₂: AIC: − 227.32) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE):0.058; SARIMA: RMSE: 0.090).

Conclusions

The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help to improve the forecast of monthly HFRS incidence based on a long-range dataset.

The role of meteorological factors on mumps incidence among children in Guangzhou, Southern China

Mumps, a common childhood disease, has a high incidence in Guangzhou city, China. It has been proven that mumps is influenced by seasonality. However, the role of meteorological factors among children is yet to be fully ascertained. This study explored the association between meteorological factors and the incidence of mumps among children in Guangzhou. Distributed lag nonlinear models were used to evaluate the correlation between meteorological factors and the incidence of mumps among children from 2014–2018. The nonlinear lag effects of some meteorological factors were detected. Mean temperature, atmospheric pressure, and relative humidity were positively correlated with mumps incidence, contrary to that of wind speed. Extreme effects of temperature, wind speed, atmospheric pressure, and relative humidity on the incidence of mumps among children in Guangzhou were evaluated in a subgroup analysis according to gender and age. Our preliminary results offered fundamental information to better understand the epidemic trends of mumps among children to develop an early warning system, and strengthen the intervention and prevention of mumps.