scholarly journals A positive association between cryptosporidiosis notifications and ambient temperature, Victoria, Australia, 2001–2009

2015 ◽  
Vol 13 (4) ◽  
pp. 1039-1047 ◽  
Author(s):  
Lillian Kent ◽  
Michelle McPherson ◽  
Nasra Higgins
Keyword(s):  
Climate Change ◽  
Ambient Temperature ◽  
Rural Areas ◽  
Minimum Temperature ◽  
Negative Binomial ◽  
Negative Binomial Regression Model ◽  
Public Health Response ◽  
Monthly Average ◽  
Average Maximum ◽  
Average Minimum Temperature

Increased temperatures provide optimal conditions for pathogen survival, virulence and replication as well as increased opportunities for human–pathogen interaction. This paper examined the relationship between notifications of cryptosporidiosis and temperature in metropolitan and rural areas of Victoria, Australia between 2001 and 2009. A negative binomial regression model was used to analyse monthly average maximum and minimum temperatures, rainfall and the monthly count of cryptosporidiosis notifications. In the metropolitan area, a 1 °C increase in monthly average minimum temperature of the current month was associated with a 22% increase in cryptosporidiosis notifications (incident rate ratio (IRR) 1.22; 95% confidence interval (CI) 1.13–1.31). In the rural area, a 1 °C increase in monthly average minimum temperature, lagged by 3 months, was associated with a 9% decrease in cryptosporidiosis notifications (IRR 0.91; 95% CI 0.86–0.97). Rainfall was not associated with notifications in either area. These relationships should be considered when planning public health response to ecological risks as well as when developing policies involving climate change. Rising ambient temperature may be an early warning signal for intensifying prevention efforts, including appropriate education for pool users about cryptosporidiosis infection and management, which might become more important as temperatures are projected to increase as a result of climate change.

scholarly journals Evaluation of Climate Change on Streamflow, Sediment, and Nutrient Load at Watershed Scale

Climate ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 165
Author(s):  
Prem B. Parajuli ◽  
Avay Risal
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
Minimum Temperature ◽  
Future Climate ◽  
Maximum Temperature ◽  
Nutrient Load ◽  
Climate Scenarios ◽  
Monthly Average ◽  
Average Maximum ◽  
Monthly Streamflow

This study evaluated changes in climatic variable impacts on hydrology and water quality in Big Sunflower River Watershed (BSRW), Mississippi. Site-specific future time-series precipitation, temperature, and solar radiation data were generated using a stochastic weather generator LARS-WG model. For the generation of climate scenarios, Representative Concentration Pathways (RCPs), 4.5 and 8.5 of Global Circulation Models (GCMs): Hadley Center Global Environmental Model (HadGEM) and EC-EARTH, for three (2021–2040, 2041–2060 and 2061–2080) future climate periods. Analysis of future climate data based on six ground weather stations located within BSRW showed that the minimum temperature ranged from 11.9 °C to 15.9 °C and the maximum temperature ranged from 23.2 °C to 28.3 °C. Similarly, the average daily rainfall ranged from 3.6 mm to 4.3 mm. Analysis of changes in monthly average maximum/minimum temperature showed that January had the maximum increment and July/August had a minimum increment in monthly average temperature. Similarly, maximum increase in monthly average rainfall was observed during May and maximum decrease was observed during September. The average monthly streamflow, sediment, TN, and TP loads under different climate scenarios varied significantly. The change in average TN and TP loads due to climate change were observed to be very high compared to the change in streamflow and sediment load. The monthly average nutrient load under two different RCP scenarios varied greatly from as low as 63% to as high as 184%, compared to the current monthly nutrient load. The change in hydrology and water quality was mainly attributed to changes in surface temperature, precipitation, and stream flow. This study can be useful in the development and implementation of climate change smart management of agricultural watersheds.

scholarly journals Association between weather factors and dengue in Dhaka: a time-series analysis

2021 ◽  
Vol 39 (1) ◽  
pp. 43-49
Author(s):  
Shafia Shaheen
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Incidence Rate ◽  
Negative Binomial ◽  
Weather Data ◽  
Negative Binomial Regression Model ◽  
Weather Factors ◽  
Average Temperature ◽  
Series Analysis ◽  
Average Maximum

Background: There was an epidemic of dengue fever that happened  in Bangladesh  in the year of 2019. Temperature of this country has been raising which leads to changing in rainfall pattern. This study was aimed to investigate the relationship of weather factors and dengue incidence in Dhaka. Methods: A time series analysis was carried out by using 10 years weather data as average , maximum and minimum monthly temperature, average monthly humidity and average and cumulative monthly rainfall. Reported number of dengue cases was extracted from January 2009 to July 2019. Firstly, dengue incidence rate was  calculated. Correlation analysis and negative binomial regression model was developed. Results: Dengue incidence rate had sharp upward trend. Dengue incidence and mean, maximum and minimum average temperature showed statistically significant negative correlation at 3 months' lag. Highest incidence Rate Ratio (IRR) of dengue was found at minimum average temperature at 0 and I-month lag. Average humidity showed positive and significant correlation with dengue incidence at 0-month lag. Average and cumulative rainfall also showed negative and significant correlation only at 3-months lag period. Conclusion: Weather variability influences dengue incidence and the association between the weather factors are non­ linear and not consistent. So the study findings should be evaluated area basis with other local factors to develop early warning for dengue epidemic prediction. JOPSOM 2020; 39(1): 43-49

scholarly journals Factors Associated with Seasonal Food Insecurity among Small-Scale Subsistence Farming Households in Rural Honduras

2020 ◽  
Vol 17 (3) ◽  
pp. 706
Author(s):  
Warren Dodd ◽  
Marvin Gómez Cerna ◽  
Paola Orellena ◽  
Sally Humphries ◽  
Margaux L. Sadoine ◽  
...  
Keyword(s):  
Climate Change ◽  
Food Insecurity ◽  
Agricultural Production ◽  
Negative Binomial ◽  
Extreme Weather Events ◽  
Small Scale ◽  
Negative Binomial Regression Model ◽  
Subsistence Agriculture ◽  
Production Factors ◽  
The Relationship

In the context of climate change, a nutritional transition, and increased pressures to migrate internally and internationally, this study examined the relationship between seasonal food insecurity and demographic, socioeconomic, and agricultural production factors among small-scale subsistence farmers in rural northern Honduras. Anchored by a partnership with the Fundación para la Investigación Participativa con Agricultores de Honduras (FIPAH) and the Yorito Municipal Health Centre, a cross-sectional household survey was administered in Yorito, Honduras, in July 2014. The study population included 1263 individuals from 248 households across 22 rural communities. A multivariate mixed effects negative binomial regression model was built to investigate the relationship between the self-reported number of months without food availability and access from subsistence agriculture in the previous year (August 2013–July 2014) and demographic, socioeconomic, and agricultural production variables. This study found a lengthier ‘lean season’ among surveyed household than previously documented in Honduras. Overall, 62.2% (95% confidence interval (CI): [59.52, 64.87]) of individuals experienced at least four months of insufficient food in the previous year. Individuals from poorer and larger households were more likely to experience insufficient food compared to individuals from wealthier and smaller households. Additionally, individuals from households that produced both maize and beans were less likely to have insufficient food compared to individuals from households that did not grow these staple crops (prevalence ratio (PR) = 0.83; 95% CI: [0.69, 0.99]). Receiving remittances from a migrant family member did not significantly reduce the prevalence of having insufficient food. As unpredictable crop yields linked to climate change and extreme weather events are projected to negatively influence the food security and nutrition outcomes of rural populations, it is important to understand how demographic, socioeconomic, and agricultural production factors may modify the ability of individuals and households engaged in small-scale subsistence agriculture to respond to adverse shocks.

scholarly journals Ecological, Social and Other Environmental Determinants of Dengue Vector Abundance in Urban and Rural Areas of Northeastern Thailand

2021 ◽  
Vol 18 (11) ◽  
pp. 5971
Author(s):  
Md. Siddikur Rahman ◽  
Tipaya Ekalaksananan ◽  
Sumaira Zafar ◽  
Petchaboon Poolphol ◽  
Oleg Shipin ◽  
...  
Keyword(s):  
Climate Change ◽  
Risk Factors ◽  
Disease Prevention ◽  
Adult Female ◽  
Rural Areas ◽  
Negative Binomial ◽  
Early Warning Systems ◽  
Vector Abundance ◽  
Urban And Rural Areas ◽  
Northeastern Thailand

Aedes aegypti is the main vector of dengue globally. The variables that influence the abundance of dengue vectors are numerous and complex. This has generated a need to focus on areas at risk of disease transmission, the spatial-temporal distribution of vectors, and the factors that modulate vector abundance. To help guide and improve vector-control efforts, this study identified the ecological, social, and other environmental risk factors that affect the abundance of adult female and immature Ae. aegypti in households in urban and rural areas of northeastern Thailand. A one-year entomological study was conducted in four villages of northeastern Thailand between January and December, 2019. Socio-demographic; self-reported prior dengue infections; housing conditions; durable asset ownership; water management; characteristics of water containers; knowledge, attitudes, and practices (KAP) regarding climate change and dengue; and climate data were collected. Household crowding index (HCI), premise condition index (PCI), socio-economic status (SES), and entomological indices (HI, CI, BI, and PI) were calculated. Negative binomial generalized linear models (GLMs) were fitted to identify the risk factors associated with the abundance of adult females and immature Ae. aegypti. Urban sites had higher entomological indices and numbers of adult Ae. aegypti mosquitoes than rural sites. Overall, participants’ KAP about climate change and dengue were low in both settings. The fitted GLM showed that a higher abundance of adult female Ae. aegypti was significantly (p < 0.05) associated with many factors, such as a low education level of household respondents, crowded households, poor premise conditions, surrounding house density, bathrooms located indoors, unscreened windows, high numbers of wet containers, a lack of adult control, prior dengue infections, poor climate change adaptation, dengue, and vector-related practices. Many of the above were also significantly associated with a high abundance of immature mosquito stages. The GLM model also showed that maximum and mean temperature with four-and one-to-two weeks of lag were significant predictors (p < 0.05) of the abundance of adult and immature mosquitoes, respectively, in northeastern Thailand. The low KAP regarding climate change and dengue highlights the engagement needs for vector-borne disease prevention in this region. The identified risk factors are important for the critical first step toward developing routine Aedes surveillance and reliable early warning systems for effective dengue and other mosquito-borne disease prevention and control strategies at the household and community levels in this region and similar settings elsewhere.

scholarly journals Heat health risk assessment analysing heatstroke patients in Fukuoka City, Japan

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253011
Author(s):  
Nishat Tasnim Toosty ◽  
Aya Hagishima ◽  
Ken-Ichi Tanaka
Keyword(s):  
Climate Change ◽  
Risk Factors ◽  
Negative Binomial ◽  
Significant Risk ◽  
Transport Systems ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Negative Binomial Regression Model ◽  
Current Time ◽  
Emergency Transport

Background Climate change, as a defining issue of the current time, is causing severe heat-related illness in the context of extremely hot weather conditions. In Japan, the remarkable temperature increase in summer caused by an urban heat island and climate change has become a threat to public health in recent years. Methods This study aimed to determine the potential risk factors for heatstroke by analysing data extracted from the records of emergency transport to the hospital due to heatstroke in Fukuoka City, Japan. In this regard, a negative binomial regression model was used to account for overdispersion in the data. Age-structure analyses of heatstroke patients were also embodied to identify the sub-population of Fukuoka City with the highest susceptibility. Results The daily maximum temperature and wet-bulb globe temperature (WBGT), along with differences in both the mean temperature and time-weighted temperature from those of the consecutive past days were detected as significant risk factors for heatstroke. Results indicated that there was a positive association between the resulting risk factors and the probability of heatstroke occurrence. The elderly of Fukuoka City aged 70 years or older were found to be the most vulnerable to heatstroke. Most of the aforementioned risk factors also encountered significant and positive associations with the risk of heatstroke occurrence for the group with highest susceptibility. Conclusion These results can provide insights for health professionals and stakeholders in designing their strategies to reduce heatstroke patients and to secure the emergency transport systems in summer.

scholarly journals Impacts of ambient temperature on the burden of bacillary dysentery in urban and rural Hefei, China

2017 ◽  
Vol 145 (8) ◽  
pp. 1567-1576 ◽  
Author(s):  
J. CHENG ◽  
M. Y. XIE ◽  
K. F. ZHAO ◽  
J. J. WU ◽  
Z. W. XU ◽  
...  
Keyword(s):  
Climate Change ◽  
Ambient Temperature ◽  
Urban Area ◽  
Rural Areas ◽  
Bacillary Dysentery ◽  
Climate Change Scenarios ◽  
Apparent Difference ◽  
Distributed Lag ◽  
Mitigation And Adaptation ◽  
Urban And Rural Areas

SUMMARYBacillary dysentery continues to be a major health issue in developing countries and ambient temperature is a possible environmental determinant. However, evidence about the risk of bacillary dysentery attributable to ambient temperature under climate change scenarios is scarce. We examined the attributable fraction (AF) of temperature-related bacillary dysentery in urban and rural Hefei, China during 2006–2012 and projected its shifting pattern under climate change scenarios using a distributed lag non-linear model. The risk of bacillary dysentery increased with the temperature rise above a threshold (18·4 °C), and the temperature effects appeared to be acute. The proportion of bacillary dysentery attributable to hot temperatures was 18·74% (95 empirical confidence interval (eCI): 8·36–27·44%). Apparent difference of AF was observed between urban and rural areas, with AF varying from 26·87% (95% eCI 16·21–36·68%) in urban area to −1·90% (95 eCI −25·03 to 16·05%) in rural area. Under the climate change scenarios alone (1–4 °C rise), the AF from extreme hot temperatures (>31·2 °C) would rise greatly accompanied by the relatively stable AF from moderate hot temperatures (18·4–31·2 °C). If climate change proceeds, urban area may be more likely to suffer from rapidly increasing burden of disease from extreme hot temperatures in the absence of effective mitigation and adaptation strategies.

scholarly journals SARIMA modeling of the monthly average maximum and minimum temperatures in the eastern plateau region of India

MAUSAM ◽  
2021 ◽  
Vol 67 (4) ◽  
pp. 841-848
Author(s):  
ENAKSHI SAHA ◽  
ARNAB HAZRA ◽  
PABITRA BANIK
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Minimum Temperature ◽  
Information Criteria ◽  
Temperature Series ◽  
Plateau Region ◽  
Akaike Information Criteria ◽  
Monthly Average ◽  
Standardized Residuals ◽  
Average Maximum

The SARIMA time series model is fitted to the monthly average maximum and minimum temperature data sets collected at Giridih, India for the years 1990-2011. From the time-series  plots, we observe that the patterns of both the series are quite different; maximum temperature series contain sharp peaks in almost all the years while it is not true for the minimum temperature series and hence both the series are modeled separately (also for the sake of simplicity). SARIMA models are selected based on observing autocorrelation function (ACF) and partial autocorrelation function (PACF) of the monthly temperature series. The model parameters are obtained by using maximum likelihood method with the help of three tests [i.e., standard error, ACF and PACF of residuals and Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC) and corrected Akaike Information Criteria (AICc)]. Adequacy of the selected models is determined using diagnostic checking with the standardized residuals, ACF of residuals, normal Q-Q plot of the standardized residuals and p-values of the Ljung-Box statistic. The models ARIMA (1; 0; 2) × (0; 1; 1)12  and ARIMA (0; 1; 1) × (1; 1; 1)12  are finally selected for forecasting of monthly average maximum and minimum temperature values respectively for the eastern plateau region of India.  

scholarly journals Influence of prevailing weather parameters on population dynamics of spotted stem borer, Chilo partellus (Swinhoe) and its natural enemies on maize in Haryana

2021 ◽  
Vol 22 (3) ◽  
pp. 295-304
Author(s):  
GAURAV SINGH ◽  
MAHA SINGH JAGLAN ◽  
TARUN VERMA ◽  
SHIVANI KHOKHAR
Keyword(s):  
Population Dynamics ◽  
Chilo Partellus ◽  
Natural Enemies ◽  
Minimum Temperature ◽  
Significant Positive Correlation ◽  
Maximum Temperature ◽  
Positive Correlation ◽  
Average Maximum ◽  
Weather Parameters ◽  
Average Minimum Temperature

The experiment was conducted at CCS Haryana Agricultural University Regional Research Station, Karnal to ascertain the influence of prevailing meteorological parameters on population dynamics of Chilo partellus and its natural enemies on maize during Kharif, 2017. Maximum oviposition (0.75 egg masses per plant) was recorded during 28th standard meteorological week (SMW) whereas larval population was at peak during 31st SMW (3.8 larvae per plant). Cumulative (47.5%) and fresh plant infestation (11.5%) were maximum during 34th and 28th SMW, respectively. Maximum egg parasitisation (6.53%) by Trichogramma sp. and larval parasitisation (31.64%) by Cotesia flavipes was recorded during 28th and 33rd SMW, respectively. Changes in pest population were correlated and regressed with weather parameters. Egg and larval populations of C. partellus and parasitisation by Trichogramma sp. exhibited significant positive correlation with average minimum temperature whereas C. flavipes exhibited significant negative correlation with average maximum temperature (r = -0.741) and highly significant positive correlation with evening relative humidity (r = 0.695). Plant infestation and dead heart formation were significantly correlated with average minimum temperature and non-significantly correlated with all other weather parameters. The multiple linear regression analysis explained the variability due to various weather parameters. This information can be utilised while formulating integrated management tactics against this pest.

Stochastic modelling of the monthly average maximum and minimum temperature patterns in India 1981–2015

2018 ◽  
Vol 131 (4) ◽  
pp. 775-787 ◽  
Author(s):  
K. V. Narasimha Murthy ◽  
R. Saravana ◽  
K. Vijaya Kumar
Keyword(s):  
Minimum Temperature ◽  
Stochastic Modelling ◽  
Monthly Average ◽  
Average Maximum

Unobservable Components Modelling of Monthly Average Maximum and Minimum Temperature Patterns in India 1981–2015

2018 ◽  
Vol 176 (1) ◽  
pp. 463-482 ◽  
Author(s):  
Narasimha Murthy Kaipa Viswanath ◽  
Saravana Ramachandran
Keyword(s):  
Minimum Temperature ◽  
Monthly Average ◽  
Average Maximum
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