Mm Increase In Rainfall Research Articles

The association between climate variables and tuberculosis in Kolaka District, Southeast Sulawesi Province, Indonesia, 2013-2020: a Bayesian autoregressive model.

Tuberculosis is one of the diseases that requires comprehensive treatment. This disease is highly contagious and can be transmitted through the air. Climate factors play a role in the increasing cases of tuberculosis. This study aimed to determine the correlation between climatic variables and TB in Kolaka District, Southeast Sulawesi Province, Indonesia. This research was modeled using an autoregressive (AR) Bayesian model with three possible likelihoods; Gaussian, Poisson and Negative Binomial responses. Minimum temperature and average temperature, a coefficient of 4.234 suggests that for every 1 degree increase in minimum temperature, there is an estimated increase of approximately four cases, assuming other variables remain constant. Maximum temperature, a coefficient of 17.851 suggests that for every 1 degree increase in maximum temperature, there is an estimated increase of around 17-18 cases, assuming other variables remain constant. Humidity, a coefficient of -13.413 suggests that for every 1% increase in humidity, there is an estimated decrease of around 13 cases, assuming other variables remain constant. Rainfall, a coefficient of -0.327 suggests that for every 1 mm increase in rainfall, there is an estimated decrease of around 0.327 cases, assuming other variables remain constant. Light, a coefficient of -4.322 suggests that for every 1-hour increase in light duration, there is an estimated decrease of around four cases, assuming other variables remain constant. Climate change has a significant impact on tuberculosis through temperature-related factors. These factors influence the prevalence, spread, and vulnerability to TB. Addressing these challenges requires a holistic approach involving adaptation planning. Strong public health systems and healthcare infrastructure can help mitigate the risks and impacts of climate change-related tuberculosis.

A comparative study on eDNA-based detection of Siamese bat catfish (Oreoglanissiamensis) in wet and dry conditions

The use of environmental DNA (eDNA) analysis has demonstrated notable efficacy in detecting the existence of freshwater species, including those that are endangered or uncommon. This application holds significant potential for enhancing environmental monitoring and management efforts. However, the efficacy of eDNA-based detection relies on several factors. In this study, we assessed the impact of rainfall on the detection of eDNA for the Siamese bat catfish (Oreoglanis siamensis). Quantitative polymerase chain reaction (qPCR) analysis indicated that samples from days with average rainfall exceeding 35 mm (classified as heavy and very heavy rain) yielded negative results. While eDNA detection remains feasible on light or moderate rainy days, a noteworthy reduction in eDNA concentration and qPCR-positive likelihood was observed. Analysis across 12 sampling sites established a statistically significant negative relationship (p < 0.001) between eDNA detection and rainfall. Specifically, for each 1 mm increase in rainfall, there was an observed drop in eDNA concentration of 0.19 copies/mL (±0.14). The findings of this study provide definitive evidence that precipitation has a significant impact on the detection of eDNA in Siamese bat catfish. However, in the case of adverse weather conditions occurring on the day of sampling, our research indicates that it is acceptable to continue with the task, as long as the rainfall is not heavy or very heavy. To enhance the effectiveness of an eDNA survey, it is crucial to consider many factors related to climatic conditions. The aforementioned factor holds significant importance not only for the specific species under scrutiny but also for the broader dynamics of the climate.

Assessment of Projected Temperature and Precipitation in the Northern Province of Sri Lanka through Statistically Downscaled CMIP6 Projection

&#x0D; &#x0D; &#x0D; The issue of climate change has emerged as a paramount concern for the global community, with several nations grappling with the far-reaching effects thereof on their respective territories. Among these nations, Sri Lanka is ranked as the country facing the greatest potential threat. In the Northern Province of Sri Lanka, which has experienced a rebirth after years of internal conflict with the aid of international agencies, projections regarding climate change indicate a diverse array of potential impacts. To arrive at these projections, a comprehensive analysis was conducted, which leveraged downscaled data derived from the Coupled Model Inter-comparison Projects, Phase 6 (CMIP6), obtained via the grid for the Northern Province. Various models were employed to scrutinize this data, and to validate the findings, an analysis was performed by comparing the model-simulated past climate data to observed data. Multimodal ensembles provided insights into unique temperature and precipitation patterns under varying emis- sion scenarios, including the Shared Socioeconomic Pathways (SSP) 4.5 and 8.5, between 2020 and 2100. Under the SSP2 4.5 scenario, for instance, the temperature increase would total 1.13°C, accompanied by 106.19mm of augmented rainfall. By contrast, under the SSP5-8.5 scenario, temperature would increase by 1.81°C, with a projection of 159.6mm increase in rainfall. Moreover, spatially, the future changes in temperature and rainfall for the Northern Province of Sri Lanka display consequential variations. Specifically, the western part is projected to witness higher rates of temperature and rainfall increase than the eastern part. However, it should be noted that variations exist in the values of the projections of temperature and rainfall across the different models. Regardless, the region must brace itself for elevated temperatures, resulting in heatwaves and an augmented frequency of scorching days, indicating an urgent need for policymakers and communities to incorporate these findings when developing and implementing climate adaptation strategies that aim to mitigate climate change’s adverse impact in the area of study.&#x0D; &#x0D; &#x0D;

The association between climate variables and tuberculosis in Kolaka District, Southeast Sulawesi Province, Indonesia, 2013–2020: a Bayesian autoregressive model

Background Tuberculosis is one of the diseases that requires comprehensive treatment. This disease is highly contagious and can be transmitted through the air. Climate factors play a role in the increasing cases of tuberculosis. This study aimed to determine the correlation between climatic variables and TB in Kolaka District, Southeast Sulawesi Province, Indonesia, Methods This research was modeled using an autoregressive (AR) Bayesian model with three possible likelihoods; Gaussian, Poisson and Negative Binomial responses. Results Minimum temperature, a coefficient of 4.234 suggests that for every 1 degree increase in minimum temperature, there is an estimated increase of approximately four cases, assuming other variables remain constant. Maximum temperature, a coefficient of 17.851 suggests that for every 1 degree increase in maximum temperature, there is an estimated increase of around 17-18 cases, assuming other variables remain constant. Average temperature, a coefficient of 4.234 suggests that for every 1 degree increase in average temperature, there is an estimated increase of approximately four cases, assuming other variables remain constant. Humidity, a coefficient of -13.413 suggests that for every 1% increase in humidity, there is an estimated decrease of around 13 cases, assuming other variables remain constant. Rainfall, a coefficient of -0.327 suggests that for every 1 mm increase in rainfall, there is an estimated decrease of around 0.327 cases, assuming other variables remain constant. Light, a coefficient of -4.322 suggests that for every 1-hour increase in light duration, there is an estimated decrease of around four cases, assuming other variables remain constant. Conclusions Climate change has a significant impact on tuberculosis through temperature-related factors. These factors influence the prevalence, spread, and vulnerability to TB. Addressing these challenges requires a holistic approach involving adaptation planning. Strong public health systems and healthcare infrastructure can help mitigate the risks and impacts of climate change-related tuberculosis.

Investigation of the impacts of climate change and rising temperature on food poisoning cases in Malaysia.

This study is an attempt to investigate climate-induced increases in morbidity rates of food poisoning cases. Monthly food poisoning cases, average monthly meteorological data, and population data from 2004 to 2014 were obtained from the Malaysian Ministry of Health, Malaysian Meteorological Department, and Department of Statistics Malaysia, respectively. Poisson generalised linear models were developed to assess the association between climatic parameters and the number of reported food poisoning cases. The findings revealed that the food poisoning incidence in Malaysia during the 11 years study period was 561 cases per 100 000 population for the whole country. Among the cases, females and the ethnic Malays most frequently experienced food poisoning with incidence rates of 313 cases per 100,000 and 438 cases per 100,000 population over the period of 11 years, respectively. Most of the cases occurred within the active age of 13 to 35 years old. Temperature gave a significant impact on the incidence of food poisoning cases in Selangor (95% CI: 1.033-1.479; p = 0.020), Melaka (95% CI: 1.046-2.080; p = 0.027), Kelantan (95% CI: 1.129-1.958; p = 0.005), and Sabah (95% CI: 1.127-2.690; p = 0.012) while rainfall was a protective factor in Terengganu (95% CI: 0.996-0.999; p = 0.034) at lag 0 month. For a 1.0°C increase in temperature, the excess risk of food poisoning in each state can increase up to 74.1%, whereas for every 50 mm increase in rainfall, the risk of getting food poisoning decreased by almost 10%. The study concludes that climate does affect the distribution of food poisoning cases in Selangor, Melaka, Kelantan, Sabah, and Terengganu. Food poisoning cases in other states are not directly associated with temperature but related to monthly trends and seasonality.

Behavior of dairy cows managed outdoors in winter: Effects of weather and paddock soil conditions.

Dairy cows are motivated to access dry lying surfaces and will seek protection from wind and rain, but winter conditions may limit these opportunities when cows are managed outdoors. The primary aim of this observational study was to determine the effects of weather and paddock soil conditions on lying behavior of dairy cows managed outdoors during winter and fed crop in situ, a practice occurring in New Zealand with year-round grazing of dairy cows. A secondary aim was to characterize eating and ruminating behaviors during winter weather and paddock soil conditions. Four groups (99 nonlactating, pregnant cows each) were managed on 4 outdoor paddock areas on the same farm; the groups were fed pasture silage and grazed either kale (2 groups) or fodder beet (2 groups). Behaviors were recorded using validated leg-based (lying behavior) and ear-based (eating and ruminating time) accelerometers on 30 focal cows in each group over 32 d. Soil depth and wetness were scored daily at 25 points along 4 transects within each paddock area using recognized technical measures (penetrometer, soil volumetric water content), which were compared with practical tools for farmer use (ruler, moisture meter, percentage of sites in paddock scored as dry, wet, sodden, or with surface water pooling). Rainfall occurred most days during the study (mean 1.6 mm/d; maximum 12.2 mm/d), resulting in wet and muddy paddocks (mud depth with ruler: mean 6 cm, maximum 18 cm; paddock sites scored as wet or sodden: mean 34%, maximum 100%; paddock sites with surface water pooling: mean 27%, maximum 100%). Group lying time was 9.6 ± 2.3 h/d (mean ± standard deviation); however, 21% of cows consistently lay less than 8 h/d (to a minimum of 4.9 h/d). A mixed regression model tested the effects of daily weather and paddock soil conditions on daily lying time, with group as the observational unit, day as repeated measure, crop type as a fixed effect, crop type interactions with explanatory variables, and random intercepts of group and paddock within group. Lying time was less on the day of and day after rainfall (24 and 29 min/d less for 1 mm increase in rainfall, respectively). Two days after rainfall, lying time rebounded to about 1 h longer than before the rainfall. On the day after the heaviest rainfall event, group average lying time was only 2.5 ± 1.9 h/d (mean ± standard deviation); in 2 groups, 30% and 38% of cows, respectively, did not lie down at all for 24 h. Lying time decreased with deteriorating paddock soil conditions, especially with increasing surface water pooling, suggesting that this may be a useful measure to estimate the quality of the lying surface. Descriptively, ruminating time appeared to decrease with increased surface water pooling, possibly due to decreased lying time. Our results demonstrated that dairy cows could experience periods of short or no lying time during inclement weather and muddy paddock soil conditions. Prior rainfall and surface water pooling may be useful measures to determine if lying time, and thus animal welfare, are compromised.

The influence of rainfall measurement on the associations between rainfall and acute gastro-intestinal illness in North Carolina

BACKGROUND AND AIM: Climate change and extreme weather are predicted to have a significant impact on diarrheal diseases. However, rainfall depends on local conditions and studies use varied measurements (e.g., absolute rainfall, heavy rainfall, or antecedent conditions) to define exposures. We systematically explored the influence of different rainfall measures on the association between acute gastrointestinal illness (AGI) and rainfall in North Carolina. METHODS: Common measures of rainfall were derived from recent studies (e.g., absolute rainfall (mm); heavy rainfall (90th, 95th, 99th percentile) and constructed from PRISM gridded daily weather data spatially aggregated to the ZIP code level. Rate ratios between rainfall (lagged 0-7 days) and AGI were estimated using quasi-Poisson time series models. All-cause AGI was defined by the daily emergency department (ED) visits per ZIP code using ICD-9 diagnosis codes from North Carolina’s syndromic surveillance system (2008-2015). Unadjusted and adjusted (for mean temperature and relative humidity) model estimates and goodness-of-fit measures were compared across rainfall measures. RESULTS: Between 2008-2015, there were 1.07M ZIP code-days with at least one ED visits for all-cause AGI per ZIP code per day in North Carolina. Adjusting for lagged mean temperature and relative humidity, we observed a 4.2% increase (RR=1.035; 95% CI: 1.029-1.040) and 5.1% (1.051; 1.035-1.067) in AGI ED visits respectively following lagged 90th and 99th percentile rainfall (1 day lag), with similar patterns for lags 2-7. By contrast, a 0.1% change in AGI ED visits was associated with a 1 mm increase in rainfall depending on the lag (e.g., lag 1: 1.001-1.001, 1.002 vs. lag 7: 0.999; 0.999-1.000). CONCLUSIONS: Heavy rainfall was associated with an increase in AGI rates, but absolute rainfall had a much smaller and ambiguous effect. These results suggest the importance of rainfall measurement and adjustment for other weather variables. KEYWORDS: diarrhea, rainfall, weather, climate, time series, exposure measurement

Quantifying effects of meteorological parameters on air pollution in Kathmandu valley through regression models.

Most studies on air pollution have focused on source apportionment aspect but very few have considered meteorological factors responsible for variation in air pollution levels including studies in Nepal. Consequently, the effects of meteorological parameters including effects of seasonality and lag effects are investigated and quantified for Kathmandu valley, Nepal. Daily temporal data of air pollution for 2017-early 2020 monitored by the Department of Environment and US Embassy, Kathmandu, Nepal, and meteorological data monitored by the Department of Hydrology and Meteorology, Kathmandu, Nepal, are used. Regression models namely exponential, Box-Cox transformed and Gamma generalized linear models are used to quantify the effects supported by regression diagnostics. Results depict high proportions of observed air pollution variations (79-85%) explained by the fitted models with varied effects of meteorological parameters. Around 5% reduction in PM10 (96% CI: 0.034-0.069) and PM1 (95% CI: .0.029-0.063) levels per 1°C increase in average temperature and significant increase in surface O3 level (0.177, 95% CI: 0.126-0.228 Box-Cox transformed value) per 1°C increase in average temperature are detected. Similarly, around 0.7% (95% CI: 0.1-1.3) and 2% (96% CI:1.3-2.5) decrease in PM1 and PM10, respectively per 1% increase in relative humidity, 0.032 (95% CI: 0.024-0.040) decrease in transformed value of PM2.5 per 1mm increase in rainfall, and 7.3% (95% CI: 1.3-15.9) decrease in PM10 per 1m/s increase in wind speed are also detected. In conclusion, meteorological conditions are found significant contributing factors in determining air pollution levels in Kathmandu valley. On the long run, atmospheric conditions can play vital roles in air pollution situation shifts mainly due to climate change characterized by changes in meteorological values.

Effect of Climate, Growth and Decomposition Analysis of Maize in India

Maize is third most important cereal crop in India after rice and wheat. Maize can be grown under various agro-climatic zones.&#x0D; Aims: &#x0D; &#x0D; To study the effect of temperature, rainfall and crop are on crop yield in maize in India.&#x0D; To study Compound Annual Growth Rate of area, production and productivity of maize in India.&#x0D; To study percentage contribution of area, yield and their interaction to maize production in India.&#x0D; &#x0D; Study Design: Only secondary data has been considered for the present study. Maize in India is considered for study purpose.&#x0D; Data Considered: All the data required for the study is obtained from Indiastat website. The impact of climatic variables on yield is studied by using data for a period of 28 year i.e., from 1992-93 to 2019-20. For calculating CAGR and for decomposition analysis data from 1950-51 to 2019-20 were considered.&#x0D; Methodology: Ordinary Least Squares method, Compound Annual Growth Rate(CAGR) and decomposition analysis were tools employed for the study.&#x0D; Results: For every 10c rise in temperature, the yield of maize decreased by 1.6 units. For every 1 mm increase in rainfall, the yield increased by 0.05 units and for every thousand hectares increase in temperature, the crop yield increased by nearly 1.37 units. Temperature, Rainfall, crop area and time explain 89 per cent of variation in maize yield. Highest and significant growth rate of 3.39 per cent per annum was observed in area during the period II i.e., from 1960-61 to 1969-70. Similarly, in the case of production and yield a highest and significant growth rate of 7.32 and 4.03 per cent respectively were observed during the period I i.e., 1950-51 to 1959-60. For the entire study period i.e., from 1950-51 to 2019-20 significant and positive growth rates of 1.26, 3.34, and 2.05 per cent per annum were observed in area, production and yield respectively. For the entire period, the contribution of interaction effect is high (58.30%) followed by yield (28.73%) and area effects(12.97%). The results revealed that area and yield effect contributes more to production than their interaction when data is considered for a shorter period (10 years) whereas the interaction effect contributes more to production if the period considered is of longer duration(70 years).&#x0D; Conclusion: Care should be taken by the government in the supply of hybrid maize seeds to farmers. Farmers should be educated about the effect of climatic variables on maize yield and they are also trained in following an improved package of practices.

Impact of Climate Change on Paddy Production: Evidence from Nepal

This study assesses the long-run relationship and short-run dynamics between paddy yields and climate variables, particularly maximum and minimum temperature and rainfall, using time-series data from 1971 to 2014 in Nepal. Applying Autoregressive-Distributed Lag Regression or ARDL bounds testing approach for analysis of co-integration between the variables, we confirm that there is a long-run relationship among the variables. Furthermore, we employ Granger non-causality tests for robustness. The findings reveal that rainfall has substantial effects on the rice yield. Specifically, a positive and significant relationship exists between rice yields and rainfall and that this relationship is unidirectional. Rainfall impacts on rice yield and holding all things constant, a 1 mm increase in rainfall increases rice yields by 0.65 percent. Given the effects of temperature on rice crops and increasing climate change vulnerabilities, agricultural scientists should focus on research and development of temperature tolerant rice varieties in the production of rice yields.

Impact of Climate Change on Paddy Production: Evidence from Nepal

This study assesses the long-run relationship and short-run dynamics between paddy yields and climate variables, particularly maximum and minimum temperature and rainfall, using time-series data from 1971 to 2014 in Nepal. Applying Autoregressive-Distributed Lag Regression or ARDL bounds testing approach for analysis of co-integration between the variables, we confirm that there is a long-run relationship among the variables. Furthermore, we employ Granger non-causality tests for robustness. The findings reveal that rainfall has substantial effects on the rice yield. Specifically, a positive and significant relationship exists between rice yields and rainfall and that this relationship is unidirectional. Rainfall impacts on rice yield and holding all things constant, a 1 mm increase in rainfall increases rice yields by 0.65 percent. Given the effects of temperature on rice crops and increasing climate change vulnerabilities, agricultural scientists should focus on research and development of temperature tolerant rice varieties in the production of rice yields.

Are meteorological factors enhancing COVID-19 transmission in Bangladesh? Novel findings from a compound Poisson generalized linear modeling approach.

Novel coronavirus (SARS-CoV-2) causing COVID-19 disease has arisen to be a pandemic. Since there is a close association between other viral infection cases by epidemics and environmental factors, this study intends to unveil meteorological effects on the outbreak of COVID-19 across eight divisions of Bangladesh from March to April 2020. A compound Poisson generalized linear modeling (CPGLM), along with a Monte-Carlo method and random forest (RF) model, was employed to explore how meteorological factors affecting the COVID-19 transmission in Bangladesh. Results showed that subtropical climate (mean temperature about 26.6 °C, mean relative humidity (MRH) 64%, and rainfall approximately 3 mm) enhanced COVD-19 onset. The CPGLM model revealed that every 1 mm increase in rainfall elevated by 30.99% (95% CI 77.18%, − 15.20%) COVID-19 cases, while an increase of 1 °C of diurnal temperature (TDN) declined the confirmed cases by − 14.2% (95% CI 9.73%, − 38.13%) on the lag 1 and lag 2, respectively. In addition, NRH and MRH had the highest increase (17.98% (95% CI 22.5%, 13.42%) and 19.92% (95% CI: 25.71%, 14.13%)) of COVID-19 cased in lag 4. The results of the RF model indicated that TDN and AH (absolute humidity) influence the COVID-19 cases most. In the Dhaka division, MRH is the most vital meteorological factor that affects COVID-19 deaths. This study indicates the humidity and rainfall are crucial factors affecting the COVID-19 case, which is contrary to many previous studies in other countries. These outcomes can have policy formulation for the suppression of the COVID-19 outbreak in Bangladesh.

The abundance of Culex mosquito vectors for West Nile Virus and other flaviviruses: A time-series analysis of rainfall and temperature dependence in Singapore

Culex mosquitoes are important vectors of West Nile Virus (WNV), St. Louis Encephalitis Virus (SLEV) and Japanese Encephalitis Virus (JEV). Climate change is expected to alter their ability to spread diseases in human populations. Studies examining the influence of climate variability on Culex mosquitoes in South East Asia are scarce.We examined the influence of climate variability on reported Culex mosquito larval habitats from 2009 to 2018 in Singapore. We analysed the non-linear immediate and lagged weather dependence of Culex habitats over 5 weeks in negative binomial regression models using nationally representative data. We adjusted for the effects of long-term trend, seasonality, public holidays and autocorrelation.There were 41,170 reported Culex larval habitats over the study period. Non-residential premises were associated with more reports of habitats compared to residential premises [Rate Ratio (RR): 113.9, 95% CI: 110.9, 116.9]. Larvae in more than 90% of these habitats were entomologically identified as Culex quinquefasciatus. In residences, every 10 mm increase in rainfall above a 90 mm threshold was associated with a 10.1% [Incidence Rate Ratio (IRR): 0.899, 95% CI: 0.836, 0.968] cumulative decline in larval habitats. Public holidays were not significantly included in the model analysing larval habitats in residences. In non-residences, a 1 °C increase in the ambient air temperature with respect to the mean was associated with a 36.0% (IRR: 1.360, 95% CI: 1.057, 1.749) cumulative increase in Culex larval habitats. Public holidays were associated with a decline in Culex larval habitats in the same week.Our study provides evidence of how ambient air temperature and rainfall variability influences the abundance of Culex mosquito larval habitats. Our findings support the utility of using weather data in predictive models to inform the timing of vector control measures aimed at reducing the risk of WNV and other Culex-borne flavivirus transmission in urban areas.

Exploring the association between childhood diarrhea and meteorological factors in Southwestern Ethiopia

Childhood diarrhea has been one of the major public health concerns in countries that have limited resources like Ethiopia. Understanding the association between childhood diarrhea and meteorological factors would contribute to safeguard children from adverse health effects through early warning mechanisms. Thus, this study aimed at exploring the association between childhood diarrhea and meteorological factors to enable reducing health risks. A retrospective study design was used to explore the association between meteorological factors and childhood diarrhea in southwestern Ethiopia from 2010 to 2017. Mann Kendall trend test and Spearman's correlation were computed to test the association of childhood diarrhea and meteorological factors. The space-time permutation model was used to identify the risky periods, seasons with most likely clusters, and high childhood diarrhea. Similarly, a negative binomial regression model was fitted to determine the predictability of meteorological factors for childhood diarrhea. The highest childhood diarrhea morbidity was 92.60 per 1000 per under five children. The risk of childhood diarrhea increased by 16.66% (RR: 1.1666; 95% CI: 1.164–1.168) per increase in 1 °C temperature. Furthermore, rainfall was found to be a significant risk factor of childhood diarrhea, with 0.16% (RR: 1.00167; 95% CI: 1.001306–1.001928) per 1 mm increase in rainfall. The temperature was positively correlated with the occurrence of childhood diarrhea. But the association with rainfall showed spatial variability. The space-time permutation model revealed that dry season was found to be a high-risk period with excess childhood diarrhea. The results showed that the observed association between meteorological factors and childhood diarrhea could be used as evidence for early warning systems for the prevention of childhood diarrhea.

A spatio-temporal analysis to identify the drivers of malaria transmission in Bhutan

At a time when Bhutan is on the verge of malaria elimination, the aim of this study was to identify malaria clusters at high geographical resolution and to determine its association with local environmental characteristics. Malaria cases from 2006–2014 were obtained from the Vector-borne Disease Control Program under the Ministry of Health, Bhutan. A Zero-Inflated Poisson multivariable regression model with a conditional autoregressive (CAR) prior structure was developed. Bayesian Markov chain Monte Carlo (MCMC) simulation with Gibbs sampling was used to estimate posterior parameters. A total of 2,062 Plasmodium falciparum and 2,284 Plasmodium vivax cases were reported during the study period. Both species of malaria showed seasonal peaks with decreasing trend. Gender and age were not associated with the transmission of either species of malaria. P. falciparum increased by 0.7% (95% CrI: 0.3%, 0.9%) for a one mm increase in rainfall, while climatic variables (temperature and rainfall) were not associated with P. vivax. Insecticide treated bed net use and residual indoor insecticide coverage were unaccounted for in this study. Hot spots and clusters of both species were isolated in the central southern part of Bhutan bordering India. There was significant residual spatial clustering after accounting for climate and demographic variables.

Predicting the periodic risk of anthrax in livestock in Victoria, Australia, using meteorological data.

Cases of anthrax in livestock are infrequently and irregularly reported in the state of Victoria, Australia; however, their impact on individual livestock, farming communities and the government agencies tasked with containing these outbreaks is high. This infrequency has been anecdotally associated with differences in annual and local weather patterns. In this study, we used historical anthrax cases and meteorological data from weather stations throughout Victoria to train a generalized linear mixed effects model to predict the daily odds of a case of anthrax occurring in each shire in the coming 30days. Meteorological variables were transformed to deviations from the mean values for temperature or cumulative values for rainfall in the shire across all years. Shire was incorporated as a random effect to account for meteorological variation between shires. The model incorporated a post hoc weighting for the frequency of historic cases within each shire and the spatial contribution of each shire to the recently redefined Australian Anthrax Belt. Our model reveals that anthrax cases were associated with drier summer conditions (OR 0.96 (95% CI 0.95-0.97) and OR 0.98 (95% CI 0.97-0.99) for every mm increase in rainfall during September and December, respectively) and cooler than average spring (OR 0.20 (95% CI 0.11-0.52) for every °C increase in minimum daily temperature during November and warmer than average summer temperatures (OR 1.45 (95% CI 1.29-1.61) for every °C increase in maximum daily temperature during January. Cases were also preceded by a 40-day period of cooler, drier temperatures (OR 0.5 (95% CI 0.27-0.74) for every °C increase in maximum daily temperature and OR 0.96 (95% CI 0.95-0.97) for every mm increase in rainfall followed by a warmer than average minimum (or nightly) temperature 10days immediately before the case (OR 1.46 (95% CI 1.35-1.58) for every °C increase in maximum daily temperature). These coefficients of this training model were then applied daily to meteorological data for each shire, and output of these models was presented as a choropleth and timeline plot in a Shiny web application. The application builds on previous spatial modelling and provides Victorian agencies with a tool to engage at-risk farmers and guide discussions towards anthrax control. This application can contribute to the wider rejuvenation of anthrax knowledge and control in Victoria and corroborates the anecdote that increased odds of disease can be linked to meteorological events.

Modeling the effect of climate variability on childhood diarrhea in Kathmandu, Nepal

PDS 72: Environmental health issues in LMIC, Exhibition Hall (PDS), Ground floor, August 28, 2019, 10:30 AM - 12:00 PM Background: Evidence suggests that diarrhea onset can be influenced by change in climate parameters like precipitation and temperature. Hence, this study was undertaken to assess the effect of climate variability on diarrhea burden among children below 5 years of age living in Kathmandu, Nepal. Methods: We conducted a time-series study using the monthly count of diarrhea (2003- 2013), among children below 5 years of age living in Kathmandu Nepal. Generalized linear model assuming quasi Poisson distribution was fitted to estimate the effect of monthly maximum temperature and precipitation on childhood diarrhea. Seasonality and long term effects were adjusted using natural cubic splines with 4 degree of freedom per year and the lag effect was modeled using distributed lag linear model using, DLNM package in software R. Results: In total, 219,774 cases of diarrhea were recorded during the study period with a range between 100 to 4323 cases per month. The results of regression model revealed monthly count of diarrhea to increase by 8.1% (RR: 1.081; 95% CI: 1.02-1.14) per 1 degree Celsius increase in maximum temperature above the monthly average, within the same month. Likewise, rainfall was found to have significant effect on diarrhea with a 0.9% (RR: 1.009; 95% CI: 1.004 -1.015) increase in cases per 10 mm increase in rainfall above the monthly average, within the same month. Conclusion: The results of our analysis demonstrated a strong association between childhood diarrhea and increase in maximum temperature as well as precipitation for Kathmandu, Nepal.

Assessing the effect of climate factors on childhood diarrhoea burden in Kathmandu, Nepal.

This study was undertaken to assess the effect of climate variability on diarrhoeal disease burden among children under 5 years of age living in Kathmandu, Nepal. The researchers sought to predict future risk of childhood diarrhoea under different climate change scenarios to advance the evidence base available to public health decision-makers, and the Nepalese infection control division, in planning for climate impacts. A time series study was conducted using the monthly case count of diarrhoeal disease (2003-2013) among children under 5 years of age living in Kathmandu, Nepal. A quasi Poisson generalised linear equation with distributed lag linear model was fitted to estimate the lagged effect of monthly maximum temperature and rainfall on childhood diarrhoea. The environmental framework of comparative risk assessment was used to assess the environmental burden of diarrhoea within this population. A total of 219,774 cases of diarrhoeal disease were recorded during the study period with a median value of 1286 cases per month. The results of a regression model revealed that the monthly count of diarrhoea cases increased by 8.1% (RR: 1.081; 95% CI: 1.02-1.14) per 1 °C increase in maximum temperature above the monthly average recorded within that month. Similarly, rainfall was found to have significant effect on the monthly diarrhoea count, with a 0.9% (RR; 1.009; 95% CI: 1.004-1.015) increase in cases for every 10 mm increase in rainfall above the monthly cumulative value recorded within that month. It was estimated that 7.5% (95% CI: 2.2%-12.5%) of the current burden of diarrhoea among children under 5 years of age could be attributed to climatic factors (maximum temperature), and projected that 1357 (UI: 410-2274) additional cases of childhood diarrhoea could be climate attributable by the year 2050 under low-risk scenario (0.9 °C increase in maximum temperature). It is estimated that there exists a significant association (p < 0.05) between childhood diarrhoea and an increase in maximum temperature and rainfall in Kathmandu, Nepal. The findings of this study may inform the conceptualization and design of early warning systems for the prediction and control of childhood diarrhoea, based upon the observed pattern of climate change in Kathmandu.

Effects of future climate change on summer maize growth in Shijin Irrigation District

The paper selected Shijin Irrigation District as the study area. Considering the future climate change, the paper studied the effects of climate change on summer maize growth. The effects of climate change on summer maize yield were quantitatively analyzed by combining the climate scenario Representative Concentration Pathway (RCP)4.5 and AquaCrop model. The results showed that under the RCP4.5 scenario, the future yield of summer maize in Shijin Irrigation District was mainly reduced, but the yield change showed an upward trend. When the direct impact of CO2 is not considered, the yield change ranged from − 6.72~− 3.04%, and the deforestation rate of summer maize is 10.795 kg for every 1 °C increase in growth period. The increase of summer maize is 3.35 kg for every 1 mm increase in rainfall. When considering the direct impact of CO2, the yield change ranged from − 4.04~+ 0.11%. It can be seen that CO2 can compensate for the production reduction effect caused by rising temperature.

Quantifying increased groundwater demand from prolonged drought in the East African Rift Valley

Millions of people in the arid regions of Kenya and Ethiopia face water scarcity and frequent drought. Water resource forecasting and reliable operation of groundwater distribution systems may improve drought resilience. In this study, we examined three remote sensing data sets against in-situ sensor-collected groundwater extraction data from 221 water points serving over 1.34 million people across northern Kenya and Afar, Ethiopia between January 1, 2017 and August 31, 2018. In models containing rainfall as a binary variable, we observed an overall 23% increase in borehole runtime following weeks with no rainfall compared to weeks preceded by some rainfall. Further, a 1 mm increase in rainfall was associated with a 1% decrease in borehole use the following week. When surface water availability is reduced during the dry seasons, groundwater demand increases. Our findings emphasize the imperative to maintain functionality of groundwater boreholes in these regions which often suffer drought related emergencies. Funding provided by the United States Agency for International Development, the World Bank, the National Science Foundation, and the Cisco Foundation. The views expressed in this article do not necessarily reflect the views of the United States Agency for International Development or the United States Government.