Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review

Epidemiologists are adopting new remote sensing techniques to study a variety of vector-borne diseases. Associations between satellite-derived environmental variables such as temperature, humidity, and land cover type and vector density are used to identify and characterize vector habitats. The convergence of factors such as the availability of multi-temporal satellite data and georeferenced epidemiological data, collaboration between remote sensing scientists and biologists, and the availability of sophisticated, statistical geographic information system and image processing algorithms in a desktop environment creates a fertile research environment. The use of remote sensing techniques to map vector-borne diseases has evolved significantly over the past 25 years. In this paper, we review the status of remote sensing studies of arthropod vector-borne diseases due to mosquitoes, ticks, blackflies, tsetse flies, and sandflies, which are responsible for the majority of vector-borne diseases in the world. Examples of simple image classification techniques that associate land use and land cover types with vector habitats, as well as complex statistical models that link satellite-derived multi-temporal meteorological observations with vector biology and abundance, are discussed here. Future improvements in remote sensing applications in epidemiology are also discussed.     

Remotely Sensed Environmental Conditions and Malaria Mortality in Three Malaria Endemic Regions in Western Kenya

BackgroundMalaria is an important cause of morbidity and mortality in malaria endemic countries. The malaria mosquito vectors depend on environmental conditions, such as temperature and rainfall, for reproduction and survival. To investigate the potential for weather driven early warning systems to prevent disease occurrence, the disease relationship to weather conditions need to be carefully investigated. Where meteorological observations are scarce, satellite derived products provide new opportunities to study the disease patterns depending on remotely sensed variables. In this study, we explored the lagged association of Normalized Difference Vegetation Index (NVDI), day Land Surface Temperature (LST) and precipitation on malaria mortality in three areas in Western Kenya.ConclusionThis study identified lag patterns and association of remote- sensing environmental factors and malaria mortality in three malaria endemic regions in Western Kenya. Our results show that rainfall has the most consistent predictive pattern to malaria transmission in the endemic study area. Results highlight a potential for development of locally based early warning forecasts that could potentially reduce the disease burden by enabling timely control actions.     

Identification of the Key Weather Factors Affecting Overwintering Success of Apolygus lucorum Eggs in Dead Host Tree Branches

Understanding the effects of weather on insect population dynamics is crucial to simulate and forecast pest outbreaks, which is becoming increasingly important with the effects of climate change. The mirid bug Apolygus lucorum is an important pest on cotton, fruit trees and other crops in China, and primarily lays its eggs on dead parts of tree branches in the fall for subsequent overwintering. As such, the eggs that hatch the following spring are most strongly affected by ambient weather factors, rather than by host plant biology. In this study, we investigated the effects of three major weather factors: temperature, relative humidity and rainfall, on the hatching rate of A. lucorum eggs overwintering on dead branches of Chinese date tree (Ziziphus jujuba). Under laboratory conditions, rainfall (simulated via soaking) was necessary for the hatching of overwintering A. lucorum eggs. In the absence of rainfall (unsoaked branches), very few nymphs successfully emerged under any of the tested combinations of temperature and relative humidity. In contrast, following simulated rainfall, the hatching rate of the overwintering eggs increased dramatically. Hatching rate and developmental rate were positively correlated with relative humidity and temperature, respectively. Under field conditions, the abundance of nymphs derived from overwintering eggs was positively correlated with rainfall amount during the spring seasons of 2009–2013, while the same was not true for temperature and relative humidity. Overall, our findings indicate that rainfall is the most important factor affecting the hatching rate of overwintering A. lucorum eggs on dead plant parts and nymph population levels during the spring season. It provides the basic information for precisely forecasting the emergence of A. lucorum and subsequently timely managing its population in spring, which will make it possible to regional control of this insect pest widely occurring in multiple crops in summer.     

Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: a review

As evaporation of water is an energy-demanding process, increasing evapotranspiration rates decrease the surface temperature (T(s)) of leaves and plants. Based on this principle, ground-based thermal remote sensing has become one of the most important methods for estimating evapotranspiration and drought stress and for irrigation. This paper reviews its application in agriculture. The review consists of four parts. First, the basics of thermal remote sensing are briefly reviewed. Second, the theoretical relation between T(s) and the sensible and latent heat flux is elaborated. A modelling approach was used to evaluate the effect of weather conditions and leaf or vegetation properties on leaf and canopy temperature. T(s) increases with increasing air temperature and incoming radiation and with decreasing wind speed and relative humidity. At the leaf level, the leaf angle and leaf dimension have a large influence on T(s); at the vegetation level, T(s) is strongly impacted by the roughness length; hence, by canopy height and structure. In the third part, an overview of the different ground-based thermal remote sensing techniques and approaches used to estimate drought stress or evapotranspiration in agriculture is provided. Among other methods, stress time, stress degree day, crop water stress index (CWSI), and stomatal conductance index are discussed. The theoretical models are used to evaluate the performance and sensitivity of the most important methods, corroborating the literature data. In the fourth and final part, a critical view on the future and remaining challenges of ground-based thermal remote sensing is presented.     

The use of ZIP and CART to model cryptosporidiosis in relation to climatic variables

This research assesses the potential impact of weekly weather variability on the incidence of cryptosporidiosis disease using time series zero-inflated Poisson (ZIP) and classification and regression tree (CART) models. Data on weather variables, notified cryptosporidiosis cases and population size in Brisbane were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Both time series ZIP and CART models show a clear association between weather variables (maximum temperature, relative humidity, rainfall and wind speed) and cryptosporidiosis disease. The time series CART models indicated that, when weekly maximum temperature exceeded 31°C and relative humidity was less than 63%, the relative risk of cryptosporidiosis rose by 13.64 (expected morbidity: 39.4; 95% confidence interval: 30.9–47.9). These findings may have applications as a decision support tool in planning disease control and risk-management programs for cryptosporidiosis disease.     

Improvement of Disease Prediction and Modeling through the Use of Meteorological Ensembles: Human Plague in Uganda

Climate and weather influence the occurrence, distribution, and incidence of infectious diseases, particularly those caused by vector-borne or zoonotic pathogens. Thus, models based on meteorological data have helped predict when and where human cases are most likely to occur. Such knowledge aids in targeting limited prevention and control resources and may ultimately reduce the burden of diseases. Paradoxically, localities where such models could yield the greatest benefits, such as tropical regions where morbidity and mortality caused by vector-borne diseases is greatest, often lack high-quality in situ local meteorological data. Satellite- and model-based gridded climate datasets can be used to approximate local meteorological conditions in data-sparse regions, however their accuracy varies. Here we investigate how the selection of a particular dataset can influence the outcomes of disease forecasting models. Our model system focuses on plague (Yersinia pestis infection) in the West Nile region of Uganda. The majority of recent human cases have been reported from East Africa and Madagascar, where meteorological observations are sparse and topography yields complex weather patterns. Using an ensemble of meteorological datasets and model-averaging techniques we find that the number of suspected cases in the West Nile region was negatively associated with dry season rainfall (December-February) and positively with rainfall prior to the plague season. We demonstrate that ensembles of available meteorological datasets can be used to quantify climatic uncertainty and minimize its impacts on infectious disease models. These methods are particularly valuable in regions with sparse observational networks and high morbidity and mortality from vector-borne diseases.     

Forecasting model of bacterial leaf spot disease of mulberry caused by Xanthomonas campestris pv. mori

In order to develop weather-based forecasting model of bacterial leaf spot (BLS) disease of mulberry caused by Xanthomonas campestris pv. mori, weekly disease severity data were recorded for three years on the ruling cultivar S-1. Daily meteorological data viz. maximum temperature, minimum temperature, maximum relative humidity, minimum relative humidity, rainfall and number of rainy days were also recorded. It was observed that BLS appeared in April/May and continued up to November with maximum severity in July. The correlation coefficient between disease severity and meteorological parameters revealed that the BLS disease severity has significant positive correlation with minimum temperatures, maximum and minimum relative humidity, rainfall and number of rainy days and negative correlation with maximum temperature. Multiple regressions analysis revealed that average of maximum temperature, minimum temperature and rainfall of preceding seven days and maximum relative humidity, minimum relative humidity of previous 9–15 days was found to maximally influence BLS disease severity. The contribution of the meteorological factors was found to be highest of minimum temperature (40.65%) followed by maximum temperature (24.20%), maximum relative humidity (16.41%), minimum relative humidity (8.07%), rainfall (5.29%) and number of rainy days (5.38%).     

Prediction of cotton leaf curl virus disease and its management through resistant germplasm and bio-products

Cotton leaf curl virus disease reduces the cotton yield significantly every year and is transmitted by Bemisia tabaci. The study was designed to evaluate 15 varieties/lines against the disease. Multiple regression analysis was performed based on a-biotic environmental variables (maximum air temperature, minimum air temperature, relative humidity and rainfall) to predict disease incidence and its vector (Bemisia tabaci). Two bio-products were evaluated against the whitefly population to control the disease. Out of 15 cotton varieties/lines, no one was found highly resistant against the disease. Five varieties/lines (BT BT-980, BT-457, KIRAN, BT-666 and SLH-BT-6) exhibited moderately resistant response. Maximum air temperature (34–35.5 °C), minimum temperature (25.75–26.25 °C), relative humidity (64.14–66%), rainfall (1–2 mm) and wind speed (5.50–5.75 Kmh^?1) favoured the disease development. Maximum whitefly population was favoured by maximum air temperature from 34–35.5 °C, 25.8–26.2 °C minimum air temperature, 64.14–66% relative humidity, 1–2 mm from rainfall and 5.50–5.75 Kmh^?1 wind speed. Datura stramonium was found more effective as compared to Aviara (Homoeopathic) but not from the positive control (Acetamiprid).     

Spectral index-based time series analysis of canopy resistance and resilience in a watershed under intermittent weather changes

Understanding the long-term impact of intermittent weather events on diverse forest and plant communities at the canopy level has become a key issue in sustainability science. Unlike a single event, intermittent weather events may exert more physiological and biological pressure on terrestrial vegetative surfaces. The amalgamation of spectral index-based satellite remote sensing information to assess intermittent weather changes in canopy dynamics may help identify phenological shifts of biota in response to external and consecutive disturbances. We conducted this study to assess such impacts of alternating weather changes on grassland, agricultural, deciduous forested, and evergreen forested land uses via remote sensing and to compare the resilience and resistance of canopy under intermittent weather changes in a subtropical watershed. The research findings indicate that deciduous forested and evergreen forested land showed a strong correlation between canopy-level biophysical and biochemical properties driven by land surface temperature. The variation of energy driven by land surface temperature changes the sensitivity levels of resilience in forest land uses, affecting the gross primary productivity.     

城市绿地的生态环境效应研究进展

工业化的快速发展和城市化进程的加剧,使得城市生态环境的破坏和污染愈来愈严重,加速了城市的“生态环境危机”.城市绿地能够通过植被的光合作用、吸收作用、隔离阻挡作用、蒸腾以及蒸散作用改善城市的生态环境.目前研究主要集中在降温、增湿、固碳释氧、降噪、抗污染、生物多样性保护等6个方面.关于城区绿地对6种生态环境要素的改善程度、影响范围、机理问题以及不同植被种类生态环境效应的差异情况等,国内外学者都进行了大量详细的研究.基于传统实地观测数据进行城市绿地的生态环境效应研究是目前主要使用的研究方法,部分学者开始使用遥感技术手段研究城区绿地的降温增湿效应.在总结和分析前人研究成果的基础上,从研究内容的不同着手,主要总结并系统评估了目前关于城市绿地6种生态环境效应的研究,为今后相关研究提供参考.     

Climate change impacts on West Nile virus transmission in a global context

West Nile virus (WNV), the most widely distributed virus of the encephalitic flaviviruses, is a vector-borne pathogen of global importance. The transmission cycle exists in rural and urban areas where the virus infects birds, humans, horses and other mammals. Multiple factors impact the transmission and distribution of WNV, related to the dynamics and interactions between pathogen, vector, vertebrate hosts and environment. Hence, among other drivers, weather conditions have direct and indirect influences on vector competence (the ability to acquire, maintain and transmit the virus), on the vector population dynamic and on the virus replication rate within the mosquito, which are mostly weather dependent. The importance of climatic factors (temperature, precipitation, relative humidity and winds) as drivers in WNV epidemiology is increasing under conditions of climate change. Indeed, recent changes in climatic conditions, particularly increased ambient temperature and fluctuations in rainfall amounts, contributed to the maintenance (endemization process) of WNV in various locations in southern Europe, western Asia, the eastern Mediterranean, the Canadian Prairies, parts of the USA and Australia. As predictions show that the current trends are expected to continue, for better preparedness, any assessment of future transmission of WNV should take into consideration the impacts of climate change.     

Epidemiology and prediction of brown leaf rust of mulberry caused by Peridiopsora mori

Brown leaf rust (BLR) caused by Peridiopsora mori is one of the major foliar diseases of mulberry (Morus sp.) in the subtropical hills of eastern India. The disease appeared in first week of August and continued up to September with maximum severity in second and third week of September. The disease symptoms appeared at atmospheric temperature (27.00–20.07°C), relative humidity (92.14–82.43%), rainfall (11.20 cm) and rainy days (7) of the preceding week. Disease severity (>50 PDI) was observed at temperature (26.29–19.29°C), relative humidity (94.14–80.14%), rainfall (4.12 cm) and number of rainy days (2–3 days). Apparent rate of infection was found high at temperature (27.00–19.83°C), relative humidity (94.67–85.00%), rainfall (4.6 cm) and rainy days (2) of the preceding week. The correlation coefficient between disease severity and average meteorological factors of the preceding 7 days revealed that BLR disease severity showed significant negative correlation with minimum temperature. It was also revealed that contribution of maximum and minimum temperature 42.23% and 35.21%, maximum and minimum relative humidity (RH) 11.23% and 10.69% and rainfall and number of rainy days 0.11% and 0.50%, respectively towards development of BLR disease severity. Multiple regression analysis revealed that average of maximum and minimum temperatures and minimum RH of preceding 7 days were found to maximally influence BLR disease severity.     

Day-to-day variation in nitrogenase activity of Alnus incana explained by weather variables: a multivariate time series analysis

A modelling system is described that indicates the extent to which day-to-day variations in nitrogenase activity in young Alnus incana (L.) Moench, grown in defined conditions in the field, may be affected by weather conditions both during and prior to the day of measurement. Nitrogenase activity (acetylene reduction activity, ARA) was measured weekly on intact field-grown grey alder (A. incana) plants, 0.15–0.42 m tall at planting, nodulated with Frankia. The measurements were done at noon on two groups of plants in 1987 and on two other groups in 1988. Each group was made up of five or six plants. Seven weather variables: daily sunshine hours, daily mean, maximum and minimum air temperature, daily mean and 1300 h relative humidity, and daily rainfall were used. The relation between log(ARA/leaf area) and the weather variables were analysed using a PLS model (partial least squares projection to latent structures). The advantage of PLS is that it can handle x-variables that are correlated. Data from 1987 were chosen as a training set. Multivariate PLS time series analysis was made by adding, in a stepwise manner, the weather data up to 5 d before the day of measurement. This procedure gave six models with n * 7 x-variables (n= 1–6). With the models from the time series analysis of 1987 data, true predictions of ARA per leaf area were made from weather data 1988 (test set 1) and from ‘early-season’ weather data from 1987 and 1988 (test set 2). The variation in ARA/leaf area could be predicted from the weather conditions. The predictions of the two test sets improved when the weather conditions one and two days before the day of measurements were added to the model. The further addition of weather data from 3 to 5 d before the day of measurement did not improve the model. The good predictions of ARA/leaf area show that the alders responded to the variable weather conditions in the same way in 1988 as in 1987, despite the ten-fold difference in size (leaf area) at the end of the growing season. Among the weather variables, air temperature and the daily sunshine hours were positively correlated to ARA, while relative air humidity and rainfall were negatively correlated to ARA. The daily minimum temperature and rainfall appeared to have least impact on ARA. By use of PLS, we could extract information out of a data set containing highly correlated x-variables, information that is non-accessible with conventional statistical tools such as multiple regression. When making measurements of nitrogenase activities under field conditions, we propose that attention should be paid to the weather conditions on the days preceding the day of measurement. The day-to-day variation in nitrogenase activity is discussed with reference to known effects of stress factors under controlled conditions.     

Climatic implications of δ13C and δ18O ratios from C3 and C4 plants growing in a tropical montane habitat in southern India

The stable carbon and oxygen isotope ratios in cellulose of C3 and C4 plants growing on the surface of a montane peat bog in the Nilgiri hills, southern India, were measured. The mean monthly δ¹³C values in cellulose of both C3 and C4 plants are found to be significantly related to rainfall, while the δ¹⁸O values are sensitive to changes in maximum temperature and relative humidity of the region. Further, higher δ¹⁸O values were observed in C4 plants compared to C3 plants, suggesting that C4 plants are probably less sensitive to relative humidity as compared to C3 plants and are able to photosynthesize even during drier conditions. The plant isotope-climate correlations thus established can be used for reconstructing the past temperature and rainfall conditions of the tropics from the isotopic ratios of peat deposits, derived from a mixture of C3 and C4 plants in the region.     

Disease forecasting model for newly emerging bacterial seed and boll rot of cotton disease and its vector (Dysdercus cingulatus)

Bacterial seed and boll rot disease is a newly emerging threat to the cotton growers. Disease prediction model was devised to predict the disease progression impacted by the vector (Dysdercus cingulatus) and environmental variables (maximum air temperature, minimum air temperature, relative humidity and rainfall) on four varieties to minimise its losses and disease management cost. Impact of a-biotic environmental variables (maximum and minimum air temperature, relative humidity and rainfall) was assessed on bacterial seed and rot of cotton disease and its vector (D. cingulatus) on FH-941, FH-942, MNH-886 and FH-114 cotton varieties. Maximum red cotton bug population was assessed at 29–31 °C maximum temperature and at 15–17 °C minimum temperature. Disease severity was noticed maximum when maximum and minimum temperature was measured at 28–29 °C and 13–14.5 °C, respectively. Vector population was maximum when relative humidity and rainfall were 63–66% and 1.50–2.5 mm, respectively. Relative humidity at 66–68% and 0.5–1.5 mm rainfall favoured disease development. With increase in number of bugs, increase in disease severity was noted, maximum disease severity 45–48% noticed when 7–8 bugs were recorded. Red cotton bug (Dysdercus cingulatus) population prediction model was devised based on a-biotic factors (maximum and minimum air temperature, relative humidity and rainfall) on four cotton varieties. Disease forecasting model was developed based on biotic (D. cingulatus) and a-biotic factors. A close resemblance between observed and the predicted red cotton bugs and disease severity was seen.     

Nectar secretion dynamics of Ziziphus nummularia: A melliferous species of dry land ecosystems

Nectar is used as raw material for the production of honey and as significant reward in the relationship between bees and plants during pollination. Therefore, it is important to investigate its abundance, dynamics and associated governing factors. Weather conditions are known to influence nectar production, and predicted climate changes may be responsible for future declining in total yield from beekeeping activities. We investigated nectar production as total soluble solids (TSS) of well-known species for honey production, Ziziphus nummularia in a hot-arid environment of Saudi Arabia. Data on nectar samples from bagged flowers of different stages during two blooming seasons, 2013 and 2015 were collected on weekly bases, and the data were correlated with weather conditions (temperature, relative humidity, and wind). A significant difference in TSS amount has been obtained, with 1-day old flowers displaying the higher content. TSS production was varied along the different day intervals, for both years, with a peak of production in the afternoon. In our results, nectar production was not correlated to temperature and wind, but was significantly negatively correlated with relative humidity. According to the current and future weather forecasting conditions, understanding of the relationship between weather conditions and nectar availability turned out to be important predictive information that may be interpreted into an economic projection of incomes from beekeeping activities.     

基于遥感的湿地景观格局季相分析

以中国东北地区三江平原北部为研究区域,利用2012年多季相遥感影像作为数据源,结合野外调查数据,应用面向对象的分类方法,根据影像的物候、时相等特征,提取不同月份的湿地信息,进行景观格局季相分析。结果表明:(1)研究区湿地面积、类型格局在同一年不同季节不同月份会有不同幅度的变化,总体呈现缓增骤减的态势。湿地主要分布在低洼地区,主要湿地类型为草本沼泽,其次为河流,其他湿地占总面积比例较小。(2)研究区各阶段湿地都有转化,主要发生在湿地和非湿地之间,多数表现在草本沼泽和草地之间的转化。(3)湿地分布和湿地转化面积主要集中在低海拔区域和低坡度区域,其中海拔100 m和坡度5°以下范围内的湿地分布面积和湿地转化面积占湿地总面积及湿地转化面积的绝大部分。(4)年内季节性湿地转化与降水、温度和湿地植被物候关系密切。     

阔叶红松混交林林隙大小和掘根微立地对小气候的影响

在小兴安岭阔叶红松混交林2.55 hm²样地内,选取由掘根倒木形成且具有坑和丘微立地的3个代表性林隙,并以空旷地和郁闭林分为对照,利用多通道HOBO自动气象站于2011年7-9月测定了不同大小林隙中心和丘顶部的光合有效辐射(PAR)、气温、相对湿度以及林隙中心的总辐射和降水量,比较了不同月份不同大小林隙中心和丘顶部微气候因子的差异,分析了不同大小林隙中心微气候因子的月变化以及不同大小林隙在典型天气条件下林隙中心和丘顶部微气候因子的日变化.结果表明: 3个不同大小林隙的月均PAR和月均气温排序是大林隙>中林隙>小林隙,月均相对湿度排序是小林隙>中林隙>大林隙;同一林隙中,丘顶部月均PAR和月均气温大于林隙中心,月均相对湿度为林隙中心>丘顶部;不同大小林隙和对照月均总辐射与月均气温均为7月>8月>9月,空旷地>大林隙>中林隙>小林隙>郁闭林分,月均相对湿度为郁闭林分>小林隙>中林隙>大林隙>空旷地.郁闭林分与各林隙以及与空旷地之间的月均相对湿度差异均显著;7-9月总降水量按照空旷地、大林隙、中林隙、小林隙、郁闭林分的次序依次递减;无论晴天与阴天,丘顶部日均PAR和日均气温都大于林隙中心,日均相对湿度则相反;无论丘顶部还是林隙中心,晴天日均PAR和日均气温都大于阴天,日均相对湿度则为阴天>晴天.     

Imported Dengue Cases,Weather Variation and Autochthonous Dengue Incidence in Cairns,Australia

Background

Dengue fever (DF) outbreaks often arise from imported DF cases in Cairns, Australia. Few studies have incorporated imported DF cases in the estimation of the relationship between weather variability and incidence of autochthonous DF. The study aimed to examine the impact of weather variability on autochthonous DF infection after accounting for imported DF cases and then to explore the possibility of developing an empirical forecast system.

Methodology/principal finds

Data on weather variables, notified DF cases (including those acquired locally and overseas), and population size in Cairns were supplied by the Australian Bureau of Meteorology, Queensland Health, and Australian Bureau of Statistics. A time-series negative-binomial hurdle model was used to assess the effects of imported DF cases and weather variability on autochthonous DF incidence. Our results showed that monthly autochthonous DF incidences were significantly associated with monthly imported DF cases (Relative Risk (RR):1.52; 95% confidence interval (CI): 1.01–2.28), monthly minimum temperature (^oC) (RR: 2.28; 95% CI: 1.77–2.93), monthly relative humidity (%) (RR: 1.21; 95% CI: 1.06–1.37), monthly rainfall (mm) (RR: 0.50; 95% CI: 0.31–0.81) and monthly standard deviation of daily relative humidity (%) (RR: 1.27; 95% CI: 1.08–1.50). In the zero hurdle component, the occurrence of monthly autochthonous DF cases was significantly associated with monthly minimum temperature (Odds Ratio (OR): 1.64; 95% CI: 1.01–2.67).

Conclusions/significance

Our research suggested that incidences of monthly autochthonous DF were strongly positively associated with monthly imported DF cases, local minimum temperature and inter-month relative humidity variability in Cairns. Moreover, DF outbreak in Cairns was driven by imported DF cases only under favourable seasons and weather conditions in the study.     

气象因素对心力衰竭的影响

目的:相关性分析近两年上海的气象因素及心力衰竭发病患者之间的关系,探讨影响心力衰竭高发的天气气候条件,为开展疾病预防和干预提供了理论和实践依据。方法:前瞻性统计2011年1月-2012年12月上海市胸科医院急诊的心力衰竭人数,并与同期气温,气压,湿度等气象资料进行相关性分析。结果:温度、气压、湿度对心衰的发生具有显著的影响。心力衰竭数与平均气温、最高气温、最低气温呈显著负相关,而与日平均气压呈显著正相关。前期相对湿度变化与心衰数显著相关。温度对心力衰竭数的影响具有滞后效应。结论:气温、气压及湿度与心力衰竭发病有明显相关,揭示了急性呼吸系统感染高发的天气气候条件,为开展疾病预防和干预提供了理论和实践依据。