Impact of global change on transmission of human infectious diseases

Modeling pH and Temperature Effects as Climatic Hazards in Vibrio Vulnificus and Vibrio Parahaemolyticus Planktonic Growth and Biofilm Formation

Climate-induced stressors, such as changes in temperature, salinity, and pH, contribute to the emergence of infectious diseases. These changes alter geographical constraint, resulting in increased Vibrio spread, exposure, and infection rates, thus facilitating greater Vibrio-human interactions. Multiple efforts have been developed to predict Vibrio exposure and raise awareness of health risks, but most models only use temperature and salinity as prediction factors. This study aimed to better understand the potential effects of temperature and pH on V. vulnificus and V. parahaemolyticus planktonic and biofilm growth. Vibrio strains were grown in triplicate at 25°, 30°, and 37°C in 96 well plates containing Modified Seawater Yeast Extract modified with CaCl₂ at pH's ranging from 5 to 9.6. AMiGA software was used to model growth curves using Gaussian process regression. The effects of temperature and pH were evaluated using randomized complete block analysis of variance, and the growth rates of V. parahaemolyticus and V. vulnificus were modeled using the interpolation fit on the MatLab Curve Fitting Toolbox. Different optimal conditions involving temperature and pH were observed for planktonic and biofilm Vibrio growth within- and between-species. This study showed that temperature and pH factors significantly affect Vibrio planktonic growth rates and V. parahaemolyticus biofilm formation. Therefore, pH effects must be added to the Vibrio growth modeling efforts to better predict Vibrio risk in estuarine and coastal zones that can potentially experience the cooccurrence of Vibrio and harmful algal bloom outbreak events.

Epidemiological characteristics of severe fever with thrombocytopenia syndrome and its relationship with meteorological factors in Liaoning Province, China

Background

Severe fever with thrombocytopenia syndrome (SFTS), one kind of tick-borne acute infectious disease, is caused by a novel bunyavirus. The relationship between meteorological factors and infectious diseases is a hot topic of current research. Liaoning Province has reported a high incidence of SFTS in recent years. However, the epidemiological characteristics of SFTS and its relationship with meteorological factors in the province remain largely unexplored.

Methods

Data on reported SFTS cases were collected from 2011 to 2019. Epidemiological characteristics of SFTS were analyzed. Spearman’s correlation test and generalized linear models (GLM) were used to identify the relationship between meteorological factors and the number of SFTS cases.

Results

From 2011 to 2019, the incidence showed an overall upward trend in Liaoning Province, with the highest incidence in 2019 (0.35/100,000). The incidence was slightly higher in males (55.9%, 438/783), and there were more SFTS patients in the 60–69 age group (31.29%, 245/783). Dalian City and Dandong City had the largest number of cases of SFTS (87.99%, 689/783). The median duration from the date of illness onset to the date of diagnosis was 8 days [interquartile range (IQR): 4–13 days]. Spearman correlation analysis and GLM showed that the number of SFTS cases was positively correlated with monthly average rainfall (r_s = 0.750, P < 0.001; β = 0.285, P < 0.001), monthly average relative humidity (r_s = 0.683, P < 0.001; β = 0.096, P < 0.001), monthly average temperature (r_s = 0.822, P < 0.001; β = 0.154, P < 0.001), and monthly average ground temperature (r_s = 0.810, P < 0.001; β = 0.134, P < 0.001), while negatively correlated with monthly average air pressure (r_s = −0.728, P < 0.001; β = −0.145, P < 0.001), and monthly average wind speed (r_s = −0.272, P < 0.05; β = −1.048, P < 0.001). By comparing both correlation coefficients and regression coefficients between the number of SFTS cases (dependent variable) and meteorological factors (independent variables), no significant differences were observed when considering immediate cases and cases with lags of 1 to 5 weeks for dependent variables. Based on the forward and backward stepwise GLM regression, the monthly average air pressure, monthly average temperature, monthly average wind speed, and time sequence were selected as relevant influences on the number of SFTS cases.

Conclusion

The annual incidence of SFTS increased year on year in Liaoning Province. Incidence of SFTS was affected by several meteorological factors, including monthly average air pressure, monthly average temperature, and monthly average wind speed.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05395-4.

The impact of geo-environmental factors on global COVID-19 transmission: A review of evidence and methodology

Studies on Coronavirus Disease 2019 (COVID-19) transmission indicate that geo-environmental factors have played a significant role in the global pandemic. However, there has not been a systematic review on the impact of geo-environmental factors on global COVID-19 transmission in the context of geography. As such, we reviewed 49 well-chosen studies to reveal the impact of geo-environmental factors (including the natural environment and human activity) on global COVID-19 transmission, and to inform critical intervention strategies that could mitigate the worldwide effects of the pandemic. Existing studies frequently mention the impact of climate factors (e.g., temperature and humidity); in contrast, a more decisive influence can be achieved by human activity, including human mobility, health factors, and non-pharmaceutical interventions (NPIs). The above results exhibit distinct spatiotemporal heterogeneity. The related analytical methodology consists of sensitivity analysis, mathematical modeling, and risk analysis. For future studies, we recommend highlighting geo-environmental interactions, developing geographically statistical models for multiple waves of the pandemic, and investigating NPIs and care patterns. We also propose four implications for practice to combat global COVID-19 transmission.

Several major issues concerning the environmental transmission and risk prevention of SARS-CoV-2

Coronavirus disease 2019 (COVID-19) is the most serious infectious disease pandemic in the world in a century, and has had a serious impact on the health, safety, and social and economic development of all mankind. Since the earth entered the “Anthropocene”, human activities have become the most important driving force of the evolution of the earth system. At the same time, the epidemic frequency of major human infectious diseases worldwide has been increasing, with more than 70% of novel diseases having zoonotic origins. The review of several major epidemics in human history shows that there is a common rule, i.e., changes in the natural environment have an important and profound impact on the occurrence and development of epidemics. Therefore, the impact of the natural environment on the current COVID-19 pandemic and its mechanisms have become scientific issues that need to be resolved urgently. From the perspective of the natural environment, this study systematically investigated several major issues concerning the environmental transmission and risk prevention of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). From a macroscopic temporal and spatial scale, the research focus on understand the impact of the destruction of the natural environment and global changes on the outbreak of infectious diseases; the threat of zoonotic diseases to human health; the regularity for virus diffusion, migration and mutation in environmental media; the mechanisms of virus transmission from animals and environmental media to humans; and environmental safety, secondary risk prevention and control of major epidemics. Suggestions were made for future key research directions and issues that need attention, with a view to providing a reference for the prevention and control of the global coronavirus disease 2019, and to improving the ability of response to major public health emergencies.

Addressing External Shock in Urban Agglomeration: Implications From the Transmission Pattern of COVID-19 in the Beijing-Tianjin-Hebei Area

Properly addressing external shocks in urban agglomeration is critical to sustaining the complex regional system. The COVID-19 pandemic has been widely acknowledged as an unintended external shock, but the temporal and spatial transmission patterns are largely ignored. This study analyzed the temporal and spatial transmission patterns of COVID-19 at the macro, meso, and micro levels, and proposes a conceptual model for regional comprehensive risk calculation, taking the Beijing-Tianjin-Hebei (BTH) area as the focus region. Our results showed that 1) at the temporal scale, the epidemic in the BTH area experienced stages of rapid increase, gradual decrease, and stabilization, and the first wave of the epidemic was under control from 23 February 2020; 2) at the spatial scale, confirmed cases were largely distributed at the terminal of the migration network, with closely interconnected cities in the BTH area, including Beijing, Tianjin, Tangshan, and Langfang, holding the highest comprehensive epidemic risk, thus requiring special attention for epidemic prevention and control. Finally, a “two-wheels” conceptual framework was built to discuss implications for future policies for addressing external shocks. Our proposed framework consists of an isolation wheel, which involves information sharing from the holistic perspective, and a circulation wheel, which emphasizes stakeholder involvement from the individual perspective. The findings of this study provide a knowledge basis for epidemic prevention and control as well as useful implications for addressing external shocks in the future.

Over half of known human pathogenic diseases can be aggravated by climate change

It is relatively well accepted that climate change can affect human pathogenic diseases; however, the full extent of this risk remains poorly quantified. Here we carried out a systematic search for empirical examples about the impacts of ten climatic hazards sensitive to greenhouse gas (GHG) emissions on each known human pathogenic disease. We found that 58% (that is, 218 out of 375) of infectious diseases confronted by humanity worldwide have been at some point aggravated by climatic hazards; 16% were at times diminished. Empirical cases revealed 1,006 unique pathways in which climatic hazards, via different transmission types, led to pathogenic diseases. The human pathogenic diseases and transmission pathways aggravated by climatic hazards are too numerous for comprehensive societal adaptations, highlighting the urgent need to work at the source of the problem: reducing GHG emissions.

Using internet-based query and climate data to predict climate-sensitive infectious disease risks: a systematic review of epidemiological evidence

The use of internet-based query data offers a novel approach to improve disease surveillance and provides timely disease information. This paper systematically reviewed the literature on infectious disease predictions using internet-based query data and climate factors, discussed the current research progress and challenges, and provided some recommendations for future studies. We searched the relevant articles in the PubMed, Scopus, and Web of Science databases between January 2000 and December 2019. We initially included studies that used internet-based query data to predict infectious disease epidemics, then we further filtered and appraised the studies that used both internet-based query data and climate factors. In total, 129 relevant papers were included in the review. The results showed that most studies used a simple descriptive approach (n=80; 62%) to detect epidemics of influenza (including influenza-like illness (ILI)) (n=88; 68%) and dengue (n=9; 7%). Most studies (n=61; 47%) purely used internet search metrics to predict the epidemics of infectious diseases, while only 3 out of the 129 papers included both climate variables and internet-based query data. Our research shows that including internet-based query data and climate variables could better predict climate-sensitive infectious disease epidemics; however, this method has not been widely used to date. Moreover, previous studies did not sufficiently consider the spatiotemporal uncertainty of infectious diseases. Our review suggests that further research should use both internet-based query and climate data to develop predictive models for climate-sensitive infectious diseases based on spatiotemporal models.

The effect of climate variables on the incidence of cutaneous leishmaniasis in Isfahan, Central Iran

In recent years, there have been considerable changes in the distribution of diseases that are potentially tied to ongoing climate variability. The aim of this study was to investigate the association between the incidence of cutaneous leishmaniasis (CL) and climatic factors in an Iranian city (Isfahan), which had the highest incidence of CL in the country. CL incidence and meteorological data were acquired from April 2010 to March 2017 (108 months) for Isfahan City. Univariate and multivariate seasonal autoregressive integrated moving average (SARIMA), generalized additive models (GAM), and generalized additive mixed models (GAMM) were used to identify the association between CL cases and meteorological variables, and forecast CL incidence. AIC, BIC, and residual tests were used to test the goodness of fit of SARIMA models; and R² was used for GAM/GAMM. 6798 CL cases were recorded during this time. The incidence had a seasonal pattern and the highest number of cases was recorded from August to October. In univariate SARIMA, (1,0,1) (0,1,1)₁₂ was the best fit for predicting CL incidence (AIC=8.09, BIC=8.32). Time series regression (1,0,1) (0,1,1)₁₂ showed that monthly mean humidity after 4-month lag was inversely related to CL incidence (AIC=8.53, BIC=8.66). GAMM results showed that average temperature with 2-month lag, average relative humidity with 3-month lag, monthly cumulative rainfall with 1-month lag, and monthly sunshine hours with 1-month lag were related to CL incidence (R²=0.94). The impact of meteorological variables on the incidence of CL is not linear and GAM models that include non-linear structures are a better fit for prediction. In Isfahan, Iran, meteorological variables can greatly predict the incidence of CL, and these variables can be used for predicting outbreaks.

The COVID-19 pandemic and its implications on the environment

The emerging threat posed by COVID-19 pandemic has strongly modified our lifestyle, making urgent to re-consider the humans-environment relationships and stimulating towards more sustainable choices in our daily behavior. Scientific evidences showed that the onset of new viral pathogens with a high epidemic-pandemic potential is often the result of complex interactions between animals, humans and environment. In this context, the interest of the scientific community has also been attracted towards the potential interactions of SARS-CoV-2 with environmental compartments. Many issues, ranging from the epidemiology and persistence of SARS-CoV-2 in water bodies to the potential implications of lockdown measures on environmental quality status are here reviewed, with a special reference to marine ecosystems. Due to current sanitary emergence, the relevance of pilot studies regarding the interactions between SARS-CoV-2 spread and the direct and indirect environmental impacts of the COVID-19 pandemic, that are still a matter of scientific debate, is underlined.

Population density and basic reproductive number of COVID-19 across United States counties

The basic reproductive number (R₀) is a function of contact rates among individuals, transmission probability, and duration of infectiousness. We sought to determine the association between population density and R₀ of SARS-CoV-2 across U.S. counties. We conducted a cross-sectional analysis using linear mixed models with random intercept and fixed slopes to assess the association of population density and R₀, and controlled for state-level effects using random intercepts. We also assessed whether the association was differential across county-level main mode of transportation percentage as a proxy for transportation accessibility, and adjusted for median household income. The median R₀ among the United States counties was 1.66 (IQR: 1.35–2.11). A population density threshold of 22 people/km² was needed to sustain an outbreak. Counties with greater population density have greater rates of transmission of SARS-CoV-2, likely due to increased contact rates in areas with greater density. An increase in one unit of log population density increased R₀ by 0.16 (95% CI: 0.13 to 0.19). This association remained when adjusted for main mode of transportation and household income. The effect of population density on R₀ was not modified by transportation mode. Our findings suggest that dense areas increase contact rates necessary for disease transmission. SARS-CoV-2 R₀ estimates need to consider this geographic variability for proper planning and resource allocation, particularly as epidemics newly emerge and old outbreaks resurge.

Natural and human environment interactively drive spread pattern of COVID-19: A city-level modeling study in China

A novel Coronavirus COVID-19 has caused high morbidity and mortality in China and worldwide. A few studies have explored the impact of climate change or human activity on the disease incidence in China or a city. The integrated study concerning environment impact on the emerging disease is rarely reported. Therefore, based on the two-stage modeling study, we investigate the effect of both natural and human environment on COVID-19 incidence at a city level. Besides, the interactive effect of different factors on COVID-19 incidence is analyzed using Geodetector; the impact of effective factors and interaction terms on COVID-19 is simulated with Geographically Weighted Regression (GWR) models. The results find that mean temperature (MeanT), destination proportion in population flow from Wuhan (WH), migration scale (MS), and WH*MeanT, are generally promoting for COVID-19 incidence before Wuhan's shutdown (T1); the WH and MeanT play a determinant role in the disease spread in T1. The effect of environment on COVID-19 incidence after Wuhan's shutdown (T2) includes more factors (including mean DEM, relative humidity, precipitation (Pre), travel intensity within a city (TC), and their interactive terms) than T1, and their effect shows distinct spatial heterogeneity. Interestingly, the dividing line of positive-negative effect of MeanT and Pre on COVID-19 incidence is 8.5°C and 1 mm, respectively. In T2, WH has weak impact, but the MS has the strongest effect. The COVID-19 incidence in T2 without quarantine is also modeled using the developed GWR model, and the modeled incidence shows an obvious increase for 75.6% cities compared with reported incidence in T2 especially for some mega cities. This evidences national quarantine and traffic control take determinant role in controlling the disease spread. The study indicates that both natural environment and human factors integratedly affect the spread pattern of COVID-19 in China.

Spatial and Temporal Characteristics of Hand-Foot-and-Mouth Disease and Its Response to Climate Factors in the Ili River Valley Region of China

Background: As the global climate changes, the number of cases of hand-foot-and-mouth disease (HFMD) is increasing year by year. This study comprehensively considers the association of time and space by analyzing the temporal and spatial distribution changes of HFMD in the Ili River Valley in terms of what climate factors could affect HFMD and in what way. Methods: HFMD cases were obtained from the National Public Health Science Data Center from 2013 to 2018. Monthly climate data, including average temperature (MAT), average relative humidity (MARH), average wind speed (MAWS), cumulative precipitation (MCP), and average air pressure (MAAP), were obtained from the National Meteorological Information Center. The temporal and spatial distribution characteristics of HFMD from 2013 to 2018 were obtained using kernel density estimation (KDE) and spatiotemporal scan statistics. A regression model of the incidence of HFMD and climate factors was established based on a geographically and temporally weighted regression (GTWR) model and a generalized additive model (GAM). Results: The KDE results show that the highest density was from north to south of the central region, gradually spreading to the whole region throughout the study period. Spatiotemporal cluster analysis revealed that clusters were distributed along the Ili and Gongnaisi river basins. The fitted curves of MAT and MARH were an inverted V-shape from February to August, and the fitted curves of MAAP and MAWS showed a U-shaped change and negative correlation from February to May. Among the individual climate factors, MCP coefficient values varied the most while MAWS values varied less from place to place. There was a partial similarity in the spatial distribution of coefficients for MARH and MAT, as evidenced by a significant degree of fit performance in the whole region. MCP showed a significant positive correlation in the range of 15–35 mm, and MAAP showed a positive correlation in the range of 925–945 hPa. HFMD incidence increased with MAT in the range of 15–23 °C, and the effective value of MAWS was in the range of 1.3–1.7 m/s, which was positively correlated with incidences of HFMD. Conclusions: HFMD incidence and climate factors were found to be spatiotemporally associated, and climate factors are mostly non-linearly associated with HFMD incidence.

A review on prediction of seasonal diseases based on climate change using big data

Big Data occupies an important place in the prediction of diseases that happen due to climate change. In each aspect of human life, the weather plays a major role. It directly affects human society or human life. Because of an extreme weather condition creates various diseases among humans. Such as Vector-borne diseases (Malaria, dengue and chikungunya fever), Water-borne diseases (Cholera, Typhoid), Air-borne diseases (Chicken Pox, influenza and small Pox) and Food-borne diseases (Diarrhoea and Salmonella) etc. This survey presents an overview for a climate variable such as extreme temperature, precipitation, humidity and how unexpected climate conditions can affect the disease and living organism.

Population density and basic reproductive number of COVID-19 across United States counties

The basic reproductive number (R0) is a function of contact rates among individuals, transmission probability, and duration of infectiousness. We sought to determine the association between population density and R0 of SARS-CoV-2 across U.S. counties, and whether population density could be used as a proxy for contact rates. We conducted a cross-sectional analysis using linear mixed models with random intercept and fixed slopes to assess the association of population density and R0. We also assessed whether this association was differential across county-level main mode of transportation-to-work percentage. Counties with greater population density have greater rates of transmission of SARS-CoV-2, likely due to increased contact rates in areas with greater density. The effect of population density and R0 was not modified by private transportation use. Differential R0 by population density can assist in more accurate predictions of the rate of spread of SARS-CoV-2 in areas that do not yet have active cases.

Influence of socio-ecological factors on COVID-19 risk: a cross-sectional study based on 178 countries/regions worldwide

Background

The initial outbreak of COVID-19 caused by SARS-CoV-2 in China in 2019 has been severely tested in other countries worldwide. We aimed to describe the spatial distribution of the COVID-19 pandemic worldwide and assess the effects of various socio-ecological factors on COVID-19 risk.

Methods

We collected COVID-19 pandemic infection data and social-ecological data of 178 countries/regions worldwide from three database. We used spatial econometrics method to assess the global and local correlation of COVID-19 risk indicators for COVID-19. To estimate the adjusted incidence rate ratio (IRR), we modelled negative binomial regression analysis with spatial information and socio-ecological factors.

Findings

The study indicated that 37, 29 and 39 countries/regions were strongly opposite from the IR, CMR and DCI index "spatial autocorrelation hypothesis", respectively. The IRs were significantly positively associated with GDP per capita, the use of at least basic sanitation services and social insurance program coverage, and were significantly negatively associated with the proportion of the population spending more than 25% of household consumption or income on out-of-pocket health care expenses and the poverty headcount ratio at the national poverty lines. The CMR was significantly positively associated with urban populations, GDP per capita and current health expenditure, and was significantly negatively associated with the number of hospital beds, number of nurses and midwives, and poverty headcount ratio at the national poverty lines. The DCI was significantly positively associated with urban populations, population density and researchers in R&D, and was significantly negatively associated with the number of hospital beds, number of nurses and midwives and poverty headcount ratio at the national poverty lines. We also found that climatic factors were not significantly associated with COVID-19 risk.

Conclusion

Countries/regions should pay more attention to controlling population flow, improving diagnosis and treatment capacity, and improving public welfare policies.

Impact of climate change on dysentery: Scientific evidences, uncertainty, modeling and projections

Dysentery is water-borne and food-borne infectious disease and its incidence is sensitive to climate change. Although the impact of climate change on dysentery is being studied in specific areas, a systematic review is lacking. We searched the worldwide literature using three sets of keywords and six databases. We identified and selected 98 studies during 1866-2019 and reviewed the relevant findings. Climate change, including long-term variations in factors, such as temperature, precipitation, and humidity, and short-term variations in extreme weather events, such as floods and drought, mostly had a harmful impact on dysentery incidence. However, some uncertainty over the exact effects of climate factors exists, specifically in the different indexes for the same climate factor, various determinant indexes for different dysentery burdens, and divergent effects for different population groups. These complicate the accurate quantification of such impacts. We generalized two types of methods: sensitivity analysis, used to detect the sensitivity of dysentery to climate change, including Pearson's and Spearman's correlations; and mathematical models, which quantify the impact of climate on dysentery, and include models that examine the associations (including negative binomial regression models) and quantify correlations (including single generalized additive models and mixed models). Projection studies mostly predict disease risks, and some predict disease incidence based on climate models under RCP 4.5. Since some geographic heterogeneity exists in the climate-dysentery relationship, modeling and projection of dysentery incidence on a national or global scale remain challenging. The reviewed results have implications for the present and future. Current research should be extended to select appropriate and robust climate-dysentery models, reasonable disease burden measure, and appropriate climate models and scenarios. We recommend future studies focus on qualitative investigation of the mechanism involved in the impact of climate on dysentery, and accurate projection of dysentery incidence, aided by advancing accuracy of extreme weather forecasting.

Modelling the effects of physicochemical variables and anthropogenic activities as ecological drivers of Plesiomonas shigelloides distribution and freshwaters quality

Spatio-temporal distribution of pathogens in freshwater is driven by environmental variables (EVs), natural, and human-induced activities and the spread of infections and disease outbreaks are triggered significantly by these processes. The role of EVs and anthropogenic activities on the distribution of Plesiomonas shigelloides is not well understood; hence this study aimed at modelling the effects of EVs and anthropogenic gradients on the densities of Plesiomonas in freshwaters and freshwater quality. Three freshwaters were sampled from February to December 2017. The EVs and Plesiomonas densities of the freshwaters were determined using standard techniques, while partial least square path modelling and correlation analysis were performed on the data collected. Factors underpinning the quality of the freshwaters were identified through principal component analysis (PCA). Most EVs fell within the bounds of recommended permissible limits except turbidity, TSS, salinity and TDS. Results revealed a significant increase of Plesiomonas densities with an increase in the magnitude of path coefficients and intensities of anthropogenic activities along the freshwaters. The distribution of Plesiomonas correlated with temperature (r = 0.69, p < 0.01), TSS (r = 0.30, p < 0.01), TBS (r = 0.28, p < 0.01), and BOD (r = 0.39, p < 0.01). Similarly, a significant correlation existed between conductivity and TDS (r = 0.97, p < 0.01) and salinity (r = 0.99, p < 0.01). Network analysis of EVs identified three closed networks essential for freshwaters quality and Plesiomonas distribution with nodes of variables under synergistic latent influences. Overall, PCA identified four drivers of the freshwater quality and in part, Plesiomonas density; namely, nutrient loading; thermal and organic pollutions, aesthetic pollution, and pH modulators. Higher component score indicated a greater impact of nutrient loading on the freshwater quality. The study concluded that Plesiomonas distribution is largely shaped by anthropogenic gradients and EVs in rivers, and these may play a major role in its dissemination along freshwater milieus.

Aggravation of Human Diseases and Climate Change Nexus

For decades, researchers have debated whether climate change has an adverse impact on diseases, especially infectious diseases. They have identified a strong relationship between climate variables and vector's growth, mortality rate, reproduction, and spatiotemporal distribution. Epidemiological data further indicates the emergence and re-emergence of infectious diseases post every single extreme weather event. Based on studies conducted mostly between 1990-2018, three aspects that resemble the impact of climate change impact on diseases are: (a) emergence and re-emergence of vector-borne diseases, (b) impact of extreme weather events, and (c) social upliftment with education and adaptation. This review mainly examines and discusses the impact of climate change based on scientific evidences in published literature. Humans are highly vulnerable to diseases and other post-catastrophic effects of extreme events, as evidenced in literature. It is high time that human beings understand the adverse impacts of climate change and take proper and sustainable control measures. There is also the important requirement for allocation of effective technologies, maintenance of healthy lifestyles, and public education.

Effects of Socio-Environmental Factors on Malaria Infection in Pakistan: A Bayesian Spatial Analysis

The role of socio-environmental factors in shaping malaria dynamics is complex and inconsistent. Effects of socio-environmental factors on malaria in Pakistan at district level were examined. Annual malaria cases data were obtained from Directorate of Malaria Control Program, Pakistan. Meteorological data were supplied by Pakistan Meteorological Department. A major limitation was the use of yearly, rather than monthly/weekly malaria data in this study. Population data, socio-economic data and education score data were downloaded from internet. Bayesian conditional autoregressive model was used to find the statistical association of socio-environmental factors with malaria in Pakistan. From 136/146 districts in Pakistan, >750,000 confirmed malaria cases were included, over a three years' period (2013-2015). Socioeconomic status ((posterior mean value -3.965, (2.5% quintile, -6.297%), (97.5% quintile, -1.754%)) and human population density (-7.41 × 10-4, -0.001406%, -1.05 × 10-4 %) were inversely related, while minimum temperature (0.1398, 0.05275%, 0.2145%) was directly proportional to malaria in Pakistan during the study period. Spatial random effect maps presented that moderate relative risk (RR, 0.75 to 1.24) and high RR (1.25 to 1.99) clusters were scattered throughout the country, outnumbering the ones' with low RR (0.23 to 0.74). Socio-environmental variables influence annual malaria incidence in Pakistan and needs further evaluation.

Sociodemographic, climatic variability and lower respiratory tract infections: a systematic literature review

Pneumonia is the leading cause of mortality and morbidity in developing countries, particularly for children and elderly. The main objective of this review paper is to review the epidemiological evidence about the effects of sociodemographic and climatic variability on pneumonia and other lower respiratory tract infections. A detailed literature search was conducted in PubMed and Scopus following PRISMA guidelines. The articles, which considered the effect of only climatic or both climatic and sociodemographic factors on pneumonia and other lower respiratory tract infections, included in this review. A total thirty-four relevant articles were reviewed. Of 34 studies, only 14 articles (41%) examined the joint effects of sociodemographic and climate factors on pneumonia and other lower respiratory infections while most of them (59%) assessed climate factors separately. Among these fourteen, only three articles (8.8%) considered detailed sociodemographic factors. All of the reviewed articles suggested different degrees of positive or negative relationship of temperature with pneumonia or other lower respiratory tract infections. Fifteen (44%) articles suggested an association with relative humidity and 13 (38%) with rainfall. Only 3 articles (8.8%) found a relationship with wind speed. Three articles (8.8%) considered other risk factors such as particulate matter 2.5 (PM_2.5) and particulate matter 10 (PM₁₀). One study among the reviewed articles used spatial analysis methods but this study did not examine the joint effects. Among the reviewed articles, 18 (53%) articles used different time series models, one article (3%) used spatiotemporal time series model, 8 (23%) studies used other models and rest 7 (21%) studies used simple descriptive analysis. A total of 18 studies (53%) were conducted in Asia, most of them in China. There were 6 studies (17%) in Europe and 8 studies (23%) in America (South, North and Central). In Africa and Oceania, only one study was found for each region. The joint effect of climate and sociodemographic factors on pneumonia and other lower respiratory tract infections remain to be determined and further research is highly recommended for future prevention of this important and common disease.

Decline in malaria incidence in a typical county of China: Role of climate variance and anti-malaria intervention measures

Malaria is an important vector-borne disease which is widespread in tropical and subtropical areas worldwide as well as in south China. Previous research has separately focused on the association between malaria incidence and meteorological variables or between malaria incidence and anti-malaria intervention measures in China, especially in Yunnan Province. Therefore, a typical county, Tengchong County, in Yunnan Province with high malaria incidence was selected as the study area to investigate the integrated influence of climate variance and anti-malaria intervention measures. Malaria incidence and meteorological variables were analyzed with a 2-month lag. The variables include average monthly temperature, minimum temperature, maximum temperature, cumulative precipitation, wind speed, maximum wind speed, relative humidity and minimum relative humidity. First, the principal component analysis was introduced to investigate the relationship between malaria incidence and meteorological variables; classification and regression trees were used to clarify contributions of key meteorological variables to malaria incidence afterwards. Second, based on existing anti-malaria intervention measures and above results, the integrated impact of climate variance and anti-malaria interventions on interannual trends of malaria incidence was analyzed. High malaria incidence occurred under one of the two meteorological conditions: 1) high minimum temperature combined with high minimum relative humidity or both precipitation and minimum relative humidity above middle level; 2) middle minimum temperature combined with both precipitation and minimum relative humidity below middle levels. Moreover, the steep interannual decline of malaria incidence in Tengchong was determined by slight climate variance and persistent anti-malaria intervention measures during malaria epidemics, predominantly by the latter. These findings will provide evidence data for developing malaria surveillance strategies in China.

A systematic evidence review of the effect of climate change on malaria in Iran

Climate is an effective factor in the ecological structure which plays an important role in control and outbreak of the diseases caused by biological factors like malaria. With regard to the occurring climatic change, this study aimed to review the effects of climate change on malaria in Iran. In this systematic review, Cochrane, PubMed and ScienceDirect (as international databases), SID and Magiran as Persian databases were investigated through MESH keywords including climate change, global warming, malaria, Anopheles, and Iran. The related articles were screened and finally their results were extracted using data extraction sheets. Totally 41 papers were resulted through databases searching process. Finally 14 papers which met inclusion criteria were included in data extraction stage. The findings indicated that Anopheles mosquitoes are present at least in 115 places in Iran; they are compatible with climatic zones of Iran. Malaria and it's vectors are affected by climate change. Temperature, precipitation, relative humidity, wind intensity and direction are the most important climatic factors affecting the growth and proliferation of Anopheles, Plasmodium and the prevalence of malaria. The transmission of malaria in Iran is associated with the climatic factors of temperature, rainfall, and humidity. Therefore, with regard to the occurring climatic change, the incidence of the disease may also change which needs to be taken into consideration while planning of malaria control.

The effects of socioecological factors on variation of communicable diseases: A multiple-disease study at the national scale of Vietnam

OBJECTIVE

To examine the effects of socioecological factors on multiple communicable diseases across Vietnam.

METHODS

We used the Moran's I tests to evaluate spatial clusters of diseases and applied multilevel negative binomial regression models using the Bayesian framework to analyse the association between socioecological factors and the diseases queried by oral, airborne, vector-borne, and animal transmission diseases.

RESULTS AND SIGNIFICANCE

The study found that oral-transmission diseases were spatially distributed across the country; whereas, the airborne-transmission diseases were more clustered in the Northwest and vector-borne transmission diseases were more clustered in the South. Most of diseases were sensitive with climatic factors. For instance, a 1°C increase in average temperature is significantly associated with 0.4% (95CI, 0.3-0.5), 2.5% (95%CI, 1.4-3.6), 0.9% (95%CI, 0.6-1.4), 1.1% (95%CI), 5% (95%CI, 3-.7.4), 0.4% (95%CI, 0.2-0.7), and 2% (95%CI, 1.5-2.8) increase in risk of diarrhoea, shigellosis, mumps, influenza, dengue, malaria, and rabies respectively. The influences of socio-economic factors on risk of communicable diseases are varied by factors with the biggest influence of population density. The research findings reflect an important implication for the climate change adaptation strategies of health sectors. A development of weather-based early warning systems should be considered to strengthen communicable disease prevention in Vietnam.

Advanced nanotechnologies in avian influenza: Current status and future trends - A review

In the last decade, the control of avian influenza virus has experienced many difficulties, which have caused major global agricultural problems that have also led to public health consequences. Conventional biochemical methods are not sufficient to detect and control agricultural pathogens in the field due to the growing demand for food and subsidiary products; thus, studies aiming to develop potent alternatives to conventional biochemical methods are urgently needed. In this review, emerging detection systems, their applicability to diagnostics, and their therapeutic possibilities in view of nanotechnology are discussed. Nanotechnology-based sensors are used for rapid, sensitive and cost-effective diagnostics of agricultural pathogens. The application of different nanomaterials promotes interactions between these materials and the virus, which enables researchers to construct portable electroanalytical biosensing analyser that should effectively detect the influenza virus. The present review will provide insights into the guidelines for future experiments to develop better techniques to detect and control influenza viruses.

Identification of weather variables sensitive to dysentery in disease-affected county of China

Climate change mainly refers to long-term change in weather variables, and it has significant impact on sustainability and spread of infectious diseases. Among three leading infectious diseases in China, dysentery is exclusively sensitive to climate change. Previous researches on weather variables and dysentery mainly focus on determining correlation between dysentery incidence and weather variables. However, the contribution of each variable to dysentery incidence has been rarely clarified. Therefore, we chose a typical county in epidemic of dysentery as the study area. Based on data of dysentery incidence, weather variables (monthly mean temperature, precipitation, wind speed, relative humidity, absolute humidity, maximum temperature, and minimum temperature) and lagged analysis, we used principal component analysis (PCA) and classification and regression trees (CART) to examine the relationships between the incidence of dysentery and weather variables. Principal component analysis showed that temperature, precipitation, and humidity played a key role in determining transmission of dysentery. We further selected weather variables including minimum temperature, precipitation, and relative humidity based on results of PCA, and used CART to clarify contributions of these three weather variables to dysentery incidence. We found when minimum temperature was at a high level, the high incidence of dysentery occurred if relative humidity or precipitation was at a high level. We compared our results with other studies on dysentery incidence and meteorological factors in areas both in China and abroad, and good agreement has been achieved. Yet, some differences remain for three reasons: not identifying all key weather variables, climate condition difference caused by local factors, and human factors that also affect dysentery incidence. This study hopes to shed light on potential early warnings for dysentery transmission as climate change occurs, and provide a theoretical basis for the control and prevention of dysentery.

Preparation of Polyethylene Composites Containing Silver(I) Acylpyrazolonato Additives and SAR Investigation of their Antibacterial Activity

Novel composite materials PEn (n = 1-9) have been prepared by an easily up-scalable embedding procedure of three different families of Ag(I) acylpyrazolonato complexes in polyethylene (PE) matrix. In details, PE1-PE3 composites contain polynuclear [Ag(Q^R)]_n complexes, PE4-PE6 contain mononuclear [Ag(Q^R)(L)_m] complexes and PE7-PE9 are loaded with mononuclear [Ag(Q^R) (PPh₃)₂] complexes, respectively (where L = 1-methylimidazole or 2-ethylimidazole, m = 1 or 2, and HQ^R = 1-phenyl-3-methyl-4-RC(═O)-5-pyrazolone, where in detail HQ^fb, R = -CF₂CF₂CF₃; HQ^cy, R = -cyclo-C₆H₁₁; HQ^be, R = -C(H)═C(CH₃)₂). The PEn composites, prepared by using a 1:1000 w/w silver additive/polyethylene ratio, have been characterized in bulk by IR spectroscopy and TGA analyses, which confirmed that the properties of polyethylene matrix are essentially unchanged. AFM, SEM, and EDX surface techniques show that silver additives form agglomerates with dimensions 10-100 μm on the polyethylene surface, with a slight increment of surface roughness of pristine plastic within 50 nm. However, the elastic properties of the composites are essentially the same of PE. The antibacterial activity of all composites has been tested against three bacterial strains (E. coli, P. aeruginosa and S. aureus) and results show that two classes of composites, PE1-PE3 and PE4-PE6, display high and persistent bactericidal and bacteriostatic activity, comparable to PE embedded with AgNO₃. By contrast, composites PE7-PE9 exhibit a reduced antibacterial action. Contact and release tests in several conditions for specific migration of Ag⁺ from plastics, indicate a very limited but time persistent release of silver ions from PE1-PE6 composites, thus suggesting that they are potential antibacterial materials for future applications. Instead, PE7-PE9 almost do not release silver, only trace levels of silver ions being detected, in accordance with their reduced antibacterial action. None of the composites is toxic against higher organisms, as confirmed by D. magna test of ecotoxicity.

Construction of a Seasonal Difference-Geographically and Temporally Weighted Regression (SD-GTWR) Model and Comparative Analysis with GWR-Based Models for Hemorrhagic Fever with Renal Syndrome (HFRS) in Hubei Province (China)

Hemorrhagic fever with renal syndrome (HFRS) is considered a globally distributed infectious disease which results in many deaths annually in Hubei Province, China. In order to conduct a better analysis and accurately predict HFRS incidence in Hubei Province, a new model named Seasonal Difference-Geographically and Temporally Weighted Regression (SD-GTWR) was constructed. The SD-GTWR model, which integrates the analysis and relationship of seasonal difference, spatial and temporal characteristics of HFRS (HFRS was characterized by spatiotemporal heterogeneity and it is seasonally distributed), was designed to illustrate the latent relationships between the spatio-temporal pattern of the HFRS epidemic and its influencing factors. Experiments from the study demonstrated that SD-GTWR model is superior to traditional models such as GWR- based models in terms of the efficiency and the ability of providing influencing factor analysis.

Impact of climate change on human infectious diseases: Empirical evidence and human adaptation

Climate change refers to long-term shifts in weather conditions and patterns of extreme weather events. It may lead to changes in health threat to human beings, multiplying existing health problems. This review examines the scientific evidences on the impact of climate change on human infectious diseases. It identifies research progress and gaps on how human society may respond to, adapt to, and prepare for the related changes. Based on a survey of related publications between 1990 and 2015, the terms used for literature selection reflect three aspects--the components of infectious diseases, climate variables, and selected infectious diseases. Humans' vulnerability to the potential health impacts by climate change is evident in literature. As an active agent, human beings may control the related health effects that may be effectively controlled through adopting proactive measures, including better understanding of the climate change patterns and of the compound disease-specific health effects, and effective allocation of technologies and resources to promote healthy lifestyles and public awareness. The following adaptation measures are recommended: 1) to go beyond empirical observations of the association between climate change and infectious diseases and develop more scientific explanations, 2) to improve the prediction of spatial-temporal process of climate change and the associated shifts in infectious diseases at various spatial and temporal scales, and 3) to establish locally effective early warning systems for the health effects of predicated climate change.

How environmental conditions impact mosquito ecology and Japanese encephalitis: an eco-epidemiological approach

Japanese encephalitis (JE) is one of the major vector-borne diseases in Southeast Asia and the Western Pacific region, posing a threat to human health. In rural and suburban areas, traditional rice farming and intensive pig breeding provide an ideal environment for both mosquito development and the transmission of JEV among human beings. Combining surveillance data for mosquito vectors, human JE cases, and environmental conditions in Changsha, China, 2004-2009, generalized threshold models were constructed to project the mosquito and JE dynamics. Temperature and rainfall were found to be closely associated with mosquito density at 1, and 4month lag, respectively. The two thresholds, maximum temperature of 22-23°C for mosquito development and minimum temperature of 25-26°C for JEV transmission, play key roles in the ecology of JEV. The model predicts that, in the upper regime, a 1g/m(3) increase in absolute humidity would on average increase human cases by 68-84%. A shift in mosquito species composition in 2007 was observed, and possibly caused by a drought. Effective predictive models could be used in risk management to provide early warnings for potential JE transmission.

Spatial, temporal and genetic dynamics of highly pathogenic avian influenza A (H5N1) virus in China

BACKGROUND

The spatial spread of H5N1 avian influenza, significant ongoing mutations, and long-term persistence of the virus in some geographic regions has had an enormous impact on the poultry industry and presents a serious threat to human health.

METHODS

We applied phylogenetic analysis, geospatial techniques, and time series models to investigate the spatiotemporal pattern of H5N1 outbreaks in China and the effect of vaccination on virus evolution.

RESULTS

Results showed obvious spatial and temporal clusters of H5N1 outbreaks on different scales, which may have been associated with poultry and wild-bird transmission modes of H5N1 viruses. Lead-lag relationships were found among poultry and wild-bird outbreaks and human cases. Human cases were preceded by poultry outbreaks, and wild-bird outbreaks were led by human cases. Each clade has gained its own unique spatiotemporal and genetic dominance. Genetic diversity of the H5N1 virus decreased significantly between 1996 and 2011; presumably under strong selective pressure of vaccination. Mean evolutionary rates of H5N1 virus increased after vaccination was adopted in China. A clear signature of positively selected sites in the clade 2.3.2 virus was discovered and this may have resulted in the emergence of clade 2.3.2.1.

CONCLUSIONS

Our study revealed two different transmission modes of H5N1 viruses in China, and indicated a significant role of poultry in virus dissemination. Furthermore, selective pressure posed by vaccination was found in virus evolution in the country.

A method for screening climate change-sensitive infectious diseases

Climate change is a significant and emerging threat to human health, especially where infectious diseases are involved. Because of the complex interactions between climate variables and infectious disease components (i.e., pathogen, host and transmission environment), systematically and quantitatively screening for infectious diseases that are sensitive to climate change is still a challenge. To address this challenge, we propose a new statistical indicator, Relative Sensitivity, to identify the difference between the sensitivity of the infectious disease to climate variables for two different climate statuses (i.e., historical climate and present climate) in non-exposure and exposure groups. The case study in Anhui Province, China has demonstrated the effectiveness of this Relative Sensitivity indicator. The application results indicate significant sensitivity of many epidemic infectious diseases to climate change in the form of changing climatic variables, such as temperature, precipitation and absolute humidity. As novel evidence, this research shows that absolute humidity has a critical influence on many observed infectious diseases in Anhui Province, including dysentery, hand, foot and mouth disease, hepatitis A, hemorrhagic fever, typhoid fever, malaria, meningitis, influenza and schistosomiasis. Moreover, some infectious diseases are more sensitive to climate change in rural areas than in urban areas. This insight provides guidance for future health inputs that consider spatial variability in response to climate change.

Identifying risk factors of avian infectious diseases at household level in Poyang Lake region, China

Poultry kept in backyard farms are susceptible to acquiring and spreading infectious diseases because of free ranging and poor biosecurity measures. Since some of these diseases are zoonoses, this is also a significant health concern to breeders and their families. Backyard farms are common in rural regions of China. However, there is lack of knowledge of backyard poultry in the country. To obtain first-hand information of backyard poultry and identify risk factors of avian infectious diseases, a cross-sectional study was carried out at household level in rural regions around Poyang Lake. A door-to-door survey was conducted to collect data on husbandry practices, trading practices of backyard farmers, and surrounding environments of backyard farms. Farms were categorized into cases and controls based on their history of poultry death. Data were collected for 137 farms, and the association with occurrence of poultry death event was explored by chi-square tests. Results showed that vaccination implementation was a protective factor (odds ratio OR=0.40, 95% confidence interval CI: 0.20-0.80, p=0.01), while contact with other backyard flocks increased risk (OR=1.72, 95% CI: 0.79-3.74, p=0.16). A concept of "farm connectivity" characterized by the density of particular land-use types in the vicinity of the farm was proposed to characterize the degree of contact between poultry in one household farm and those in other household farms. It was found that housing density in a 20-m buffer zone of the farmhouse was most significantly associated with poultry death occurrence (OR=1.08, 95% CI: 1.02-1.17, p=0.03), and was in agreement with observation of villagers. Binary logistic regression was applied to evaluate the relationship between poultry death event and density of land-use types in all buffer zones. When integrated with vaccination implementation for poultry, prediction accuracy of poultry death event reached 72.0%. Results combining questionnaire survey with geographical approaches indicated that occurrence of poultry death event among backyard farms within a village was heavily impacted by farm connectivity. This study provides new insight for the study and help to develop more targeted prevention and countermeasure in a typical rural environment of China.