scholarly journals Evaluating the impact of the weather conditions on the influenza propagation

2019 ◽  
Author(s):  
David E. Singh ◽  
Maria-cristina Marinescu ◽  
Jesus Carretero ◽  
Concepcion Delgado-Sanz ◽  
Diana Gomez-Barroso ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Meteorological Data ◽  
Personal Characteristics ◽  
Effect Of Temperature ◽  
Urban Region ◽  
Influenza Surveillance ◽  
Interaction Patterns ◽  
Meteorological Model ◽  
Simulation Results ◽  
The Impact

Abstract Background: Predicting the details of how an epidemic evolves is highly valuable as health institutions need to better plan towards limiting the infection propagation effects and optimizing their prediction and response capabilities. Simulation is a cost- and time-effective way of predicting the evolution of the infection as the joint influence of many different factors: interaction patterns, personal characteristics, travel patterns, meteorological conditions, previous vaccination, etc. The work presented in this paper extends EpiGraph, our influenza epidemic simulator, by introducing a meteorological model as a modular component that interacts with the rest of EpiGraph's modules to refine our previous simulation results. Our goal is to estimate the effects of changes in temperature and relative humidity on the patterns of epidemic influenza based on data provided by the Spanish Influenza Sentinel Surveillance System (SISSS) and the Spanish Meteorological Agency (AEMET). Methods: Our meteorological model is based on the regression model developed by Barreca and Shimshack, and it is tuned with influenza surveillance data obtained from SISSS. After pre-processing this data to clean it and reconstruct missing samples, we obtain new values for the reproduction number of each urban region in Spain, every 10 minutes during 2011. We simulate the propagation of the influenza by setting the date of the epidemic onset and the initial influenza-illness rates for each urban region. Results: We show that the simulation results have the same propagation shape as the weekly influenza rates as recorded by SISSS. We perform experiments for a realistic scenario based on actual meteorological data from 2010-2011, and for synthetic values assumed under simplified predicted climate change conditions. Results show that a diminishing relative humidity of 10\% produces an increment of about 1.6\% in the final infection rate. The effect of temperature changes on the infection spread is also noticeable, with a decrease of 1.1\% per extra degree. Conclusions: Using a tool like ours could help predict the shape of developing epidemics and its peaks, and would permit to quickly run scenarios to determine the evolution of the epidemic under different conditions. We make EpiGraph source code and epidemic data publicly available.

scholarly journals Evaluating the impact of the weather conditions on the influenza propagation

2020 ◽  
Author(s):  
David E. Singh ◽  
maria-cristina marinescu ◽  
Jesus Carretero ◽  
Concepcion Delgado-Sanz ◽  
Diana Gomez-Barroso ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Meteorological Data ◽  
Personal Characteristics ◽  
Effect Of Temperature ◽  
Urban Region ◽  
Influenza Surveillance ◽  
Interaction Patterns ◽  
Meteorological Model ◽  
Simulation Results ◽  
The Impact

Abstract Background: Predicting the details of how an epidemic evolves is highly valuable as health institutions need to better plan towards limiting the infection propagation effects and optimizing their prediction and response capabilities. Simulation is a cost- and time-effective way of predicting the evolution of the infection as the joint influence of many different factors: interaction patterns, personal characteristics, travel patterns, meteorological conditions, previous vaccination, etc. The work presented in this paper extends EpiGraph, our influenza epidemic simulator, by introducing a meteorological model as a modular component that interacts with the rest of EpiGraph's modules to refine our previous simulation results. Our goal is to estimate the effects of changes in temperature and relative humidity on the patterns of epidemic influenza based on data provided by the Spanish Influenza Sentinel Surveillance System (SISSS) and the Spanish Meteorological Agency (AEMET). Methods: Our meteorological model is based on the regression model developed by Barreca and Shimshack, and it is tuned with influenza surveillance data obtained from SISSS. After pre-processing this data to clean it and reconstruct missing samples, we obtain new values for the reproduction number of each urban region in Spain, every 10 minutes during 2011. We simulate the propagation of the influenza by setting the date of the epidemic onset and the initial influenza-illness rates for each urban region. Results: We show that the simulation results have the same propagation shape as the weekly influenza rates as recorded by SISSS. We perform experiments for a realistic scenario based on actual meteorological data from 2010-2011, and for synthetic values assumed under simplified predicted climate change conditions. Results show that a diminishing relative humidity of 10\% produces an increment of about 1.6\% in the final infection rate. The effect of temperature changes on the infection spread is also noticeable, with a decrease of 1.1\% per extra degree. Conclusions: Using a tool like ours could help predict the shape of developing epidemics and its peaks, and would permit to quickly run scenarios to determine the evolution of the epidemic under different conditions. We make EpiGraph source code and epidemic data publicly available.

scholarly journals Evaluating the impact of the weather conditions on the influenza propagation

2020 ◽  
Author(s):  
David E. Singh ◽  
Maria-Cristina Marinescu ◽  
Jesus Carretero ◽  
Concepcion Delgado-Sanz ◽  
Diana Gomez-Barroso ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Meteorological Data ◽  
Personal Characteristics ◽  
Effect Of Temperature ◽  
Urban Region ◽  
Influenza Surveillance ◽  
Interaction Patterns ◽  
Meteorological Model ◽  
Simulation Results ◽  
The Impact

Abstract Background: Predicting the details of how an epidemic evolves is highly valuable as health institutions need to better plan towards limiting the infection propagation effects and optimizing their prediction and response capabilities. Simulation is a cost- and time-effective way of predicting the evolution of the infection as the joint influence of many different factors: interaction patterns, personal characteristics, travel patterns, meteorological conditions, previous vaccination, etc. The work presented in this paper extends EpiGraph, our influenza epidemic simulator, by introducing a meteorological model as a modular component that interacts with the rest of EpiGraph's modules to refine our previous simulation results. Our goal is to estimate the effects of changes in temperature and relative humidity on the patterns of epidemic influenza based on data provided by the Spanish Influenza Sentinel Surveillance System (SISSS) and the Spanish Meteorological Agency (AEMET). Methods: Our meteorological model is based on the regression model developed by Barreca and Shimshack, and it is tuned with influenza surveillance data obtained from SISSS. After pre-processing this data to clean it and reconstruct missing samples, we obtain new values for the reproduction number of each urban region in Spain, every 10 minutes during 2011. We simulate the propagation of the influenza by setting the date of the epidemic onset and the initial influenza-illness rates for each urban region. Results: We show that the simulation results have the same propagation shape as the weekly influenza rates as recorded by SISSS. We perform experiments for a realistic scenario based on actual meteorological data from 2010-2011, and for synthetic values assumed under simplified predicted climate change conditions. Results show that a diminishing relative humidity of 10\% produces an increment of about 1.6\% in the final infection rate. The effect of temperature changes on the infection spread is also noticeable, with a decrease of 1.1\% per extra degree. Conclusions: Using a tool like ours could help predict the shape of developing epidemics and its peaks, and would permit to quickly run scenarios to determine the evolution of the epidemic under different conditions. We make EpiGraph source code and epidemic data publicly available.

scholarly journals Evaluating the impact of the weather conditions on the influenza propagation

2020 ◽  
Author(s):  
David E. Singh ◽  
maria-cristina marinescu ◽  
Jesus Carretero ◽  
Concepcion Delgado-Sanz ◽  
Diana Gomez-Barroso ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Meteorological Data ◽  
Personal Characteristics ◽  
Effect Of Temperature ◽  
Urban Region ◽  
Influenza Surveillance ◽  
Interaction Patterns ◽  
Meteorological Model ◽  
Simulation Results ◽  
The Impact

Abstract Background: Predicting the details of how an epidemic evolves is highly valuable as health institutions need to better plan towards limiting the infection propagation effects and optimizing their prediction and response capabilities. Simulation is a cost- and time-effective way of predicting the evolution of the infection as the joint influence of many different factors: interaction patterns, personal characteristics, travel patterns, meteorological conditions, previous vaccination, etc. The work presented in this paper extends EpiGraph, our influenza epidemic simulator, by introducing a meteorological model as a modular component that interacts with the rest of EpiGraph's modules to refine our previous simulation results. Our goal is to estimate the effects of changes in temperature and relative humidity on the patterns of epidemic influenza based on data provided by the Spanish Influenza Sentinel Surveillance System (SISSS) and the Spanish Meteorological Agency (AEMET). Methods: Our meteorological model is based on the regression model developed by Barreca and Shimshack, and it is tuned with influenza surveillance data obtained from SISSS. After pre-processing this data to clean it and reconstruct missing samples, we obtain new values for the reproduction number of each urban region in Spain, every 10 minutes during 2011. We simulate the propagation of the influenza by setting the date of the epidemic onset and the initial influenza-illness rates for each urban region. Results: We show that the simulation results have the same propagation shape as the weekly influenza rates as recorded by SISSS. We perform experiments for a realistic scenario based on actual meteorological data from 2010-2011, and for synthetic values assumed under simplified predicted climate change conditions. Results show that a diminishing relative humidity of 10\% produces an increment of about 1.6\% in the final infection rate. The effect of temperature changes on the infection spread is also noticeable, with a decrease of 1.1\% per extra degree. Conclusions: Using a tool like ours could help predict the shape of developing epidemics and its peaks, and would permit to quickly run scenarios to determine the evolution of the epidemic under different conditions. We make EpiGraph source code and epidemic data publicly available.

scholarly journals The modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rui Zhang ◽  
Yujie Meng ◽  
Hejia Song ◽  
Ran Niu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Air Pollution ◽  
High Temperature ◽  
Air Pollutants ◽  
Meteorological Data ◽  
Effect Of Temperature ◽  
Distributed Lag ◽  
Modification Effect ◽  
Incidence Risk ◽  
The Impact ◽  
The Relationship

Abstract Background Although exposure to air pollution has been linked to many health issues, few studies have quantified the modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China. Methods The data of daily incidence of influenza and the relevant meteorological data and air pollution data in Ningbo from 2014 to 2017 were retrieved. Low, medium and high temperature layers were stratified by the daily mean temperature with 25th and 75th percentiles. The potential modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo was investigated through analyzing the effects of air pollutants stratified by temperature stratum using distributed lag non-linear model (DLNM). Stratified analysis by sex and age were also conducted. Results Overall, a 10 μg/m3 increment of O3, PM2.5, PM10 and NO2 could increase the incidence risk of influenza with the cumulative relative risk of 1.028 (95% CI 1.007, 1.050), 1.061 (95% CI 1.004, 1.122), 1.043 (95% CI 1.003, 1.085), and 1.118 (95% CI 1.028, 1.216), respectively. Male and aged 7–17 years were more sensitive to air pollutants. Through the temperature stratification analysis, we found that temperature could modify the impacts of air pollution on daily incidence of influenza with high temperature exacerbating the impact of air pollutants. At high temperature layer, male and the groups aged 0–6 years and 18–64 years were more sensitive to air pollution. Conclusion Temperature modified the relationship between air pollution and daily incidence of influenza and high temperature would exacerbate the effects of air pollutants in Ningbo.

scholarly journals Effects and interaction of meteorological factors on hemorrhagic fever with renal syndrome incidence in Huludao City, northeastern China, 2007–2018

2021 ◽  
Vol 15 (3) ◽  
pp. e0009217
Author(s):  
Wanwan Sun ◽  
Xiaobo Liu ◽  
Wen Li ◽  
Zhiyuan Mao ◽  
Jimin Sun ◽  
...  
Keyword(s):  
Public Health ◽  
High Temperature ◽  
Relative Humidity ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Additive Model ◽  
Hemorrhagic Fever ◽  
Epidemiological Data ◽  
Effect Of Temperature ◽  
Delayed Effects

Background Hemorrhagic fever with renal syndrome (HFRS), a rodent-borne disease, is a severe public health threat. Previous studies have discovered the influence of meteorological factors on HFRS incidence, while few studies have concentrated on the stratified analysis of delayed effects and interaction effects of meteorological factors on HFRS. Objective Huludao City is a representative area in north China that suffers from HFRS with primary transmission by Rattus norvegicus. This study aimed to evaluate the climate factors of lag, interaction, and stratified effects of meteorological factors on HFRS incidence in Huludao City. Methods Our researchers collected meteorological data and epidemiological data of HFRS cases in Huludao City during 2007–2018. First, a distributed lag nonlinear model (DLNM) for a maximum lag of 16 weeks was developed to assess the respective lag effect of temperature, precipitation, and humidity on HFRS incidence. We then constructed a generalized additive model (GAM) to explore the interaction effect between temperature and the other two meteorological factors on HFRS incidence and the stratified effect of meteorological factors. Results During the study period, 2751 cases of HFRS were reported in Huludao City. The incidence of HFRS showed a seasonal trend and peak times from February to May. Using the median WAT, median WTP, and median WARH as the reference, the results of DLNM showed that extremely high temperature (97.5th percentile of WAT) had significant associations with HFRS at lag week 15 (RR = 1.68, 95% CI: 1.04–2.74) and lag week 16 (RR = 2.80, 95% CI: 1.31–5.95). Under the extremely low temperature (2.5th percentile of WAT), the RRs of HFRS infection were significant at lag week 5 (RR = 1.28, 95% CI: 1.01–1.67) and lag 6 weeks (RR = 1.24, 95% CI: 1.01–1.57). The RRs of relative humidity were statistically significant at lag week 10 (RR = 1.19, 95% CI: 1.00–1.43) and lag week 11 (RR = 1.24, 95% CI: 1.02–1.50) under extremely high relative humidity (97.5th percentile of WARH); however, no statistically significance was observed under extremely low relative humidity (2.5th percentile of WARH). The RRs were significantly high when WAT was -10 degrees Celsius (RR = 1.34, 95% CI: 1.02–1.76), -9 degrees Celsius (1.37, 95% CI: 1.04–1.79), and -8 degrees Celsius (RR = 1.34, 95% CI: 1.03–1.75) at lag week 5 and more than 23 degrees Celsius after 15 weeks. Interaction and stratified analyses showed that the risk of HFRS infection reached its highest when both temperature and precipitation were at a high level. Conclusions Our study indicates that meteorological factors, including temperature and humidity, have delayed effects on the occurrence of HFRS in the study area, and the effect of temperature can be modified by humidity and precipitation. Public health professionals should pay more attention to HFRS control when the weather conditions of high temperature with more substantial precipitation and 15 weeks after the temperature is higher than 23 degrees Celsius.

scholarly journals Evaluation of nature-based solutions implementation scenarios, using urban surface modelling

2021 ◽  
pp. 1-42
Author(s):  
Emmanuel Panagiotakis ◽  
Dionysia Kolokotsa ◽  
Nektarios Chrysoulakis
Keyword(s):  
Heat Flux ◽  
Relative Humidity ◽  
Urban Environment ◽  
Air Temperature ◽  
Green Infrastructure ◽  
Model Simulation ◽  
Meteorological Data ◽  
Sensible Heat Flux ◽  
The Impact ◽  
The City

The present paper aims to study the impact of Nature Based Solutions (NBS) on the urban environment. The Surface Urban Energy and Water balance Scheme (SUEWS) is used to quantify the impact of NBS in the city of Heraklion, Crete, Greece, a densely built urban area. Local meteorological data and data from an Eddy Covariance flux tower installed in the city center are used for the model simulation and evaluation. Five different scenarios are tested by replacing the city’s roofs and pavements with green infrastructure, i.e., trees and grass, and water bodies. The NBS impact evaluation is based on the changes of air temperature above 2m from the ground, relative humidity and energy fluxes. A decrease of the air temperature is revealed with the highest reduction (2.3%) occurring when the pavements are replaced with grass for all scenarios. The reduction of the air temperature is followed by a decrease in turbulent sensible heat flux. For almost all cases, an increase of the relative humidity is noticed, accompanied by a considerable increase of the turbulent latent heat flux. Therefore, NBS in cities change the energy balance significantly and modify the urban environment for the citizens' benefit.

scholarly journals Effect of meteorological factors on the activity of influenza in Chongqing, China, 2012–2019

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246023
Author(s):  
Li Qi ◽  
Tian Liu ◽  
Yuan Gao ◽  
Dechao Tian ◽  
Wenge Tang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Warning System ◽  
Absolute Humidity ◽  
Influenza Surveillance ◽  
Average Wind Speed ◽  
Mean Temperature ◽  
Influenza Activity

Background The effects of multiple meteorological factors on influenza activity remain unclear in Chongqing, the largest municipality in China. We aimed to fix this gap in this study. Methods Weekly meteorological data and influenza surveillance data in Chongqing were collected from 2012 to 2019. Distributed lag nonlinear models (DLNMs) were conducted to estimate the effects of multiple meteorological factors on influenza activity. Results Inverted J-shaped nonlinear associations between mean temperature, absolute humidity, wind speed, sunshine and influenza activity were found. The relative risks (RRs) of influenza activity increased as weekly average mean temperature fell below 18.18°C, average absolute humidity fell below 12.66 g/m3, average wind speed fell below 1.55 m/s and average sunshine fell below 2.36 hours. Taking the median values as the references, lower temperature, lower absolute humidity and windless could significantly increase the risks of influenza activity and last for 4 weeks. A J-shaped nonlinear association was observed between relative humidity and influenza activity; the risk of influenza activity increased with rising relative humidity with 78.26% as the break point. Taking the median value as the reference, high relative humidity could increase the risk of influenza activity and last for 3 weeks. In addition, we found the relationship between aggregate rainfall and influenza activity could be described with a U-shaped curve. Rainfall effect has significantly higher RR than rainless effect. Conclusions Our study shows that multiple meteorological factors have strong associations with influenza activity in Chongqing, providing evidence for developing a meteorology-based early warning system for influenza to facilitate timely response to upsurge of influenza activity.

Effect of temperature, relative humidity and rainfall on rotavirus infections in Kolkata, India

2012 ◽  
Vol 141 (8) ◽  
pp. 1652-1661 ◽  
Author(s):  
A. SUMI ◽  
K. RAJENDRAN ◽  
T. RAMAMURTHY ◽  
T. KRISHNAN ◽  
G. B. NAIR ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Least Squares Method ◽  
Monsoon Season ◽  
Meteorological Data ◽  
Meteorological Condition ◽  
Meteorological Factor ◽  
Effect Of Temperature ◽  
Isolation Rate ◽  
Quantitative Correlation ◽  
Rotavirus Infections

SUMMARYRotavirus is a common viral cause of severe diarrhoea. For the underlying cause of rotavirus seasonality, the meteorological factor has been suspected, whereas quantitative correlation between seasonality and meteorological factor has not been fully investigated. In this study, we investigated the correlation of temporal patterns of the isolation rate of rotavirus with meteorological condition (temperature, relative humidity, rainfall) in Kolkata, India. We used time-series analysis combined with spectral analysis and least squares method. A 1-year cycle explained underlying variations of rotavirus and meteorological data. The 1-year cycle for rotavirus data was correlated with an opposite phase to that for meteorological data. Relatively high temperature could be associated with a low value of isolation rate of rotavirus in the monsoon season. Quantifying a correlation of rotavirus infections with meteorological conditions might prove useful in predicting rotavirus epidemics and health services could plan accordingly.

scholarly journals Correlation Analysis of Malaria Cases and Meteorological Factors in Lagos State, Nigeria

2020 ◽  
Author(s):  
Oluwatosin Omobolaji Onasanya ◽  
Muhammad Sani Ibrahim ◽  
Ayo Stephen Adebowale ◽  
Adefisoye Oluwaseun Adewole ◽  
Muhammad Shakir Balogun ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Correlation Analysis ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Meteorological Factor ◽  
Health Facilities ◽  
Free Access ◽  
District Health ◽  
Lagos State ◽  
The Impact

Abstract BackgroundMalaria transmission affects malaria infection rates. There are several potential drivers of malaria transmission. A suitable meteorological factor such as rainfall, temperature, and relative humidity encourages the breeding of the vector. This improves the survival of the parasite in the host. The female Plasmodium falciparum plays a crucial role in the variability of malaria prevalence. Lagos State is a coastal malaria-endemic area in Nigeria. This study presents a correlation analysis of malaria cases and meteorological factors between the periods of January 2015 to April 2018 in Lagos state.MethodsThe study was a secondary data analysis of meteorological variables and records of malaria cases reported by health facilities in Lagos state. We accessed weather variables through free access “weather underground.com” a meteorological data sharing service system (MDSSS). The MDSSS provides real-time online weather information from four meteorological monitoring stations in Lagos state. We accessed the malaria cases through the district health information system 2 databases. It is used to report cases of malaria by all the private and public health facilities in the state. We performed the correlation analyses to show the relationship between temperature, humidity, rainfall, and malaria cases at a 5% level of significance. We analysed data using the statistical package for social sciences version 25.ResultsMalaria cases peaked between the period of July to November 2016 and the period of April to May 2017 and declined between March to May 2017. The temperature, relative humidity, and rainfall showed a positive correlation with malaria cases. The temperature is most correlated with the occurrence of malaria cases (r = 0.65, p< 0.02).ConclusionThis correlation analysis provides an approach for studying the impact of meteorological variability on the prevalence of malaria cases. This can help to forecast the malaria epidemic while preparing for the elimination of malaria in Lagos state.

scholarly journals Effect of ambient temperature on outpatient admission for osteoarthritis and rheumatoid arthritis in a subtropical Chinese city

2020 ◽  
Author(s):  
Desheng Zhao ◽  
Jian Cheng ◽  
Ping Bao ◽  
Yanwu Zhang ◽  
Fengjuan Liang ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Ambient Temperature ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Individual Characteristics ◽  
Effect Of Temperature ◽  
Temperature Decrease ◽  
Distributed Lag ◽  
The Impact ◽  
The Relationship

Abstract Background Current findings on the impact of weather conditions on osteoarthritis (OA) and rheumatoid arthritis (RA) are sparse and not conclusive. This study aimed to investigate the relationship between temperature change and OA/RA admission. Methods Daily OA/RA admission and meteorological data from 1 January 2014 to 31 December 2017 in Hefei, China, were collected. We quantified the relationship between ambient temperature and OA/RA admission using a distributed lag nonlinear model (DLNM). The effect modifications by gender and age were also examined. Results Sudden temperature decrease was significantly associated with RA admission (25th percentile of temperature versus 50th percentile of temperature), with the acute and largest effect at current days lag (RR: 1.063, 95%CI: 1.010–1.118). However, no association between temperature and OA admission was observed. When conducting subgroup analyses by individual characteristics, we found that females and patients aged 41–65 years were more vulnerable to temperature decrease than males, patients aged 0–40 and ≧ 66 years, respectively. Conclusions This study suggested that sudden temperature decrease was a risk factor for increase RA admission. Females and patients aged 41–65 years were particularly vulnerable to the effect of temperature decrease.

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