Solar radiation and water vapor pressure to forecast chickenpox epidemics

Associations Between Air Temperature and Daily Varicella Cases - Jinan City, Shandong Province, China, 2019-2021

What is already known about this topic?

The impact of air temperature on varicella has been studied, but there is limited research exploring its effect on varicella by gender and age group.

What is added by this report?

We conducted a time series analysis to examine the differential effects of air temperature on varicella infection across different demographic groups. Our findings indicate that lower temperatures have a more pronounced influence on varicella incidence among males and children compared to females and adults.

What are the implications for public health practice?

These findings can assist in identifying populations that are vulnerable to temperature-related varicella and in guiding the implementation of effective measures for varicella control.

Association between meteorological factors and varicella incidence: a multicity study in Yunnan Province, China

This multicenter study aimed to investigate the relationship between varicella incidence and meteorological factors including mean temperature, relative humidity, sunshine duration, diurnal temperature difference, wind speed, and rainfall, as previous studies have produced varying results. Our study also sought to identify potential sources of heterogeneity. Data on reported daily varicella numbers and meteorological factors were collected for 14 cities in Yunnan Province from 2017 to 2021. A distribution-lagged nonlinear model was constructed to explore the relationship between meteorological conditions and varicella incidence in each included city. We then used multiple meta-regression to explore sources of heterogeneity using demographic economics indicators, air pollutants, and geographic location as potential modifiers. The cumulative hazard effect plot showed an inverted S-shape for the relationship between temperature and varicella, with the smallest RR (relative risk) (0.533, 95% CI: 0.401-0.708) at temperatures up to 27.2 °C. The maximum RR (1.171, 95% CI: 1.001-1.371) was obtained when the relative humidity was equal to 98.5%. The RR (1.164, 95% CI: 1.002-1.352) was greatest at a diurnal temperature range of 2 °C (1.164, 95% CI: 1.002-1.352) and least (0.913, 95% CI: 0.834-0.999) at a diurnal temperature range of 16.1 °C. The maximum RR (1.214, 95% CI: 1.089-1.354) was obtained at 0 h of sunshine, and the minimum RR (0.808, 95% CI: 0.675-0.968) was obtained at 12.4 h of sunshine. The RR (0.792, 95% CI: 0.633-0.992) was minimum at a wind velocity of 4.8 m/s. Residual heterogeneity ranged from 1 to 42.7%, with PM10 (particles with an aerodynamic diameter less than 10 μm), GDP (gross domestic product), and population density explaining some of this heterogeneity. The temperature has a dual effect on varicella incidence. Varicella cases are negatively correlated with diurnal temperature range, sunshine duration, and wind speed, and positively correlated with relative humidity. GDP and PM10 may have a significant role in altering the association between temperature and varicella, while PM10 and population density may alter the association between wind velocity and varicella.

Effects of meteorological factors on the incidence of varicella in Lu'an, Eastern China, 2015-2020

Varicella (chickenpox) is a serious public health problem in China, with the most reported cases among childhood vaccine-preventable infectious diseases, and its reported incidence has increased over 20-fold since 2005. Few previous studies have explored the association of multiple meteorological factors with varicella and considered the potential confounding effects of air pollutants. It is the first study to investigate and analyze the effects of multiple meteorological factors on varicella incidence, controlling for the confounding effects of various air pollutants. Daily meteorological and air pollution data and varicella cases were collected from January 1, 2015, to December 31, 2020, in Lu'an, Eastern China. A combination of the quasi-Poisson generalized additive model (GAM) and distributed lag nonlinear model (DLNM) was used to evaluate the meteorological factor-lag-varicella relationship, and the risk of varicella in extreme meteorological conditions. The maximum single-day lag effects of varicella were 1.288 (95%CI, 1.201-1.381, lag 16 day), 1.475 (95%CI, 1.152-1.889, lag 0 day), 1.307 (95%CI, 1.196-1.427, lag 16 day), 1.271 (95%CI, 0.981-1.647, lag 4 day), and 1.266 (95%CI, 1.162-1.378, lag 21 day), when mean temperature, diurnal temperature range (DTR), mean air pressure, wind speed, and sunshine hours were -5.8°C, 13.5°C, 1035.5 hPa, 6 m/s, and 0 h, respectively. At the maximum lag period, the overall effects of mean temperature and pressure on varicella showed W-shaped curves, peaked at 17.5°C (RR=2.085, 95%CI: 1.480-2.937) and 1035.5 hPa (RR=5.481, 95%CI: 1.813-16.577), while DTR showed an M-shaped curve and peaked at 4.4°C (RR=6.131, 95%CI: 1.120-33.570). Sunshine hours were positively correlated with varicella cases at the lag of 0-8 days and 0-9 days when sunshine duration exceeded 10 h. Furthermore, the lag effects of extreme meteorological factors on varicella cases were statistically significant, except for the extremely high wind speed. We found that mean temperature, mean air pressure, DTR, and sunshine hours had significant nonlinear effects on varicella incidence, which may be important predictors of varicella early warning.

Climate influence the human leptospirosis cases in Brazil, 2007-2019: a time series analysis

BACKGROUND

Human leptospirosis is responsible for great losses and deaths, especially in developing countries, which can be mitigated by knowing the correct health indicators and climate influence on the disease.

METHODS

Leptospirosis cases and deaths, population and precipitation were recovered from different databases (2007-2019). Annual incidence, mortality and case fatality rates (CFRs) of human leptospirosis and average precipitation were calculated for Brazil and its regions. Time series analysis using an moving average with external variable (ARMAX) model was used to analyse the monthly contribution and precipitation influence over leptospirosis cases for each Brazilian region and for the whole country. A forecast model to predict cases for 2020 was created for Brazil.

RESULTS

Human leptospirosis exhibited heterogeneous distribution among Brazilian regions, with most cases occurring during the rainy season and precipitation influenced the disease occurrence in all regions but the South. The forecast model predicted 3276.99 cases for 2020 (mean absolute percentage error 14.680 and root mean square error 53.013). Considering the annual average for the period, the leptospirosis incidence was 1913 cases per 100 000 inhabitants, mortality was 0.168 deaths per 100 000 inhabitants and the CFR was 8.83%.

CONCLUSIONS

The models built can be useful for planning leptospirosis surveillance and control actions for the whole country and its regions and, together with the health indicators, revealed no uniform epidemiological situation of leptospirosis in Brazil.

Association between climatic factors and varicella incidence in Guangzhou, Southern China, 2006-2018

OBJECTIVE

To analyze the correlation between climatic factors and the incidence of varicella in Guangzhou, and improve the prevention measures about public health.

METHODS

Data for daily climatic variables and varicella incidence from 2006 to 2018 in Guangzhou were collected from the Guangzhou Meteorological Bureau and the National Notifiable Disease Report System. Distributed lag nonlinear models were applied to evaluate the association between climatic factors and varicella incidence.

RESULTS

The nonlinear effects of meteorological factors were observed. At lag day21，when the mean temperature was 31.8 °C, the relative risk was the highest as 1.11 (95% CI: 1.07-1.16). When the diurnal temperature range was 24.0 °C at lag day 20, the highest RR was 1.11 (95% CI: 1.05-1.17). For rainfall, the highest RR was 1.09 (95% CI: 1.01-1.19) at lag day 21，when the aggregate rainfall was 160 mm. When air pressure was 1028 hPa, the highest RR was 1.08 (95% CI: 1.04-1.13) at lag day 21. When wind speed was 0.7 m/s, the highest RR was 1.07 (95% CI: 1.04-1.11) at lag day 7. When the hours of sunshine were 9.0 h at lag day 21, the RR was highest as 1.04 (95% CI: 1.02-1.05). Aggregate rainfall, air pressure, and sunshine hours were positively correlated with the incidence of varicella, which was inconsistent with the wind velocity. Mean temperature showed a reverse U-shape curve relationship with varicella, while the diurnal temperature range showed a binomial distribution curve. The extreme effect of climatic factors on the varicella cases was statistically significant, apart from the extremely low effect of rainfall.

CONCLUSION

Our preliminary results offered fundamental knowledge which might be benefit to give an insight into epidemic trends of varicella and develop an early warning system. We could use our findings about influential factors to strengthen the intervention and prevention of varicella.

Seasonal dynamics of varicella incidence in Bulgaria

Aim: Spatial and temporal distribution of varicella is heterogenic and insufficiently studied in Europe. The present study tries to fill the gap that exists about the seasonality of the infection in Bulgaria. Materials & methods: A 4-year retrospective study of the monthly and seasonal varicella epidemiology was performed at both national and district level. Results: In Bulgaria, varicella incidence peaked during winter (37% of the 2015–2018 cases), followed by spring (33%) and autumn (23%). Highly populated districts were more likely to follow this pattern, while less inhabited districts with smaller urbanized areas showed different periodicity of the infection. Conclusion: Winter peak in varicella incidence is positively associated with high accumulation of people in the large cities (>75,000 inhabitants).

Real-time predictive seasonal influenza model in Catalonia, Spain

Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010–2011 to 2013–2014) was created. A pilot test was conducted during the 2014–2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015–2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015–2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included.

Effects of meteorologic factors and schooling on the seasonality of group A streptococcal pharyngitis

The objective of this study was to determine the seasonal pattern of group A streptococcal pharyngitis in children attended at a hospital emergency department in the Mediterranean island of Mallorca (Spain), and its association with meteorologic factors and schooling. We conducted a retrospective review of the medical records of children aged 1-15 years with a diagnosis of Streptococcus pyogenes pharyngitis between January 2006 and December 2011. The number of S. pyogenes pharyngitis was correlated to temperature, humidity, rainfall, atmospheric pressure, wind speed, solar radiation, and schooling, using regression and time series techniques. A total of 906 patients (median, 4 years old) with S. pyogenes pharyngitis, confirmed by throat culture, were attended during the study period. A seasonal pattern was observed with a peak activity in June and a minimum in September. Mean temperature, solar radiation, and school holidays were the best predicting variables (R(2) = 0.68; p < 0.001). S. pyogenes activity increased with the decrease of mean temperature (z = -2.4; p < 0.05), the increase of solar radiation (z = 4.2; p < 0.001), and/or the decrease in school holidays (z = -2.4; p < 0.05). In conclusion, S. pyogenes pharyngitis had a clear seasonality predominating in springtime, and an association with mean temperature, solar radiation, and schooling was observed. The resulting model predicted 68 % of S. pyogenes pharyngitis.

Association between the incidence of varicella and meteorological conditions in Jinan, Eastern China, 2012-2014

BACKGROUND

Varicella remains an important public health issue in China. In this study we explored the effect of weather conditions on the incidence of varicella in the temperate city of Jinan, Eastern China during 2012-2014 to inform public health prevention and control measures.

METHODS

Data on reported cases of varicella were obtained from National Notifiable Disease Report System. Meteorological data for the same time period were obtained from the Jinan Meteorological Bureau. A negative binomial regression model was used to assess the relationships between meteorological variables and the incidence of varicella. Given collinearity between average temperature and atmospheric pressure, separate models were constructed: one including average temperature without atmospheric pressure, the other including atmospheric pressure but without average temperature. Both models included relative humidity, wind velocity, rainfall, sunshine, and year as independent variables.

RESULTS

Annual incidence rates of varicella were 44.47, 53.69, and 46.81 per 100,000 for 2012, 2013, and 2014, respectively. Each increase of 100 Pa (hPa) in atmospheric pressure was estimated to be associated with an increase in weekly incidence of 3.35 % (95 % CI = 2.94-3.67 %), while a 1 °C rise in temperature was associated with a decrease of 3.44 % (95 % CI = -3.73-3.15 %) in the weekly incidence of varicella. Similarly, a 1 % rise in relative humidity corresponded to a decrease of 0.50 % or 1.00 %, a 1 h rise in sunshine corresponded to an increase of 1.10 % or 0.50 %, and a 1 mm rise in rainfall corresponded to an increase of 0.20 % or 0.30 %, in the weekly incidence of varicella cases, depending on the variable considered in the model.

CONCLUSION

Our findings show that weather factors have a significant influence on the incidence of varicella. Meteorological conditions should be considered as important predictors of varicella incidence in Jinan, Eastern China.